Epistasis Blog

Lower-order effects adjustment in quantitative traits model-based multifactor

2012-01-17T12:25:00.001-05:00

Here is a new MDR paper from Kristel Van Steen.

Mahachie John JM, Cattaert T, Van Lishout F, Gusareva ES, Van Steen K. Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction. PLoS One. 2012;7(1):e29594. [PubMed]

Abstract

Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2-way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use automatic SNP selection procedures, SNP selection based on "top" findings, or SNP selection based on p-value criterion for interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality Reduction and involve using residuals as the new trait. We advocate using "on-the-fly" lower-order effects adjusting when screening for SNP-SNP interactions using Model-Based Multifactor Dimensionality Reduction analysis.

Imaging Genetics

2012-01-14T10:24:00.003-05:00

I am collaborating with Drs. Andy Saykin and Li Shen at IUPUI to develop and apply novel methods for the genetic analysis of neuroimaging phenotypes. This is a really hot new area. I will be giving a talk on some of our recent results at the 8th International Imaging Genetics Conference, to be held on January 16th and 17th, 2012 at the Beckman Center of the National Academy of Sciences in Irvine, CA. We have applied some of our recent network science methods (see paper below) to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data.

Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH. Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinformatics. 2011 Sep 12;12:364. [PubMed]

List of Epistasis Blog Posts from 2011

2012-01-01T12:47:00.003-05:00

January, 2011

Yeast genetics is complex. What about humans?

The Meaning of Interaction

Model-based multifactor dimensionality reduction for detecting epistasis

Application of the Explicit Test of Epistasis to Colon Cancer

Real-world comparison of CPU and GPU implementations of SNPrank

NIH/NIGMS Funding by Priority Score

Layers of Epistasis

February, 2011

Gene-Gene Interaction Analysis Using ReliefF and MDR

A genome-wide screen of gene-gene interactions for rheumatoid arthritis susceptibility

Epistatic Interactions in Genetic Regulation of t-PA and PAI-1 Levels in a Ghanaian Population

Dissecting genetic networks underlying complex phenotypes: the theoretical framework

A Comparison of Multifactor Dimensionality Reduction and Penalized Regression

March, 2011

Interactome Networks and Human Disease

Gene–Environment Interactions in Human Disease

Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data

April, 2011

Genetic analysis of complex traits in the emerging collaborative cross

Travelling the world of gene-gene interactions

May, 2011

Detecting genetic interactions for quantitative traits with U-statistics

Transcriptional robustness and protein interactions are associated in yeast

The effects of linkage disequilibrium in large scale SNP datasets for MDR

Computational Intelligence Using Genetic Programming

Microbiome Studies at the 2012 Pacific Symposium on Biocomputing

June, 2011

Pathway of distinction analysis

Molecular mechanisms of epistasis

Two Epistasis Papers in Science

July, 2011

Generating data with complex genotype-phenotype relationships

Powerful SNP-set analysis for case-control genome-wide association studies

August, 2011

People are inherently biased against creative ideas

New Center Grant on Gene-Environment Interactions

September, 2011

Gene-environment interaction in psychiatric research

Characterizing Genetic Interactions in Human Disease Association Studies Using Statistical Epistasis Networks

HyperCube Rule Mining

An R Package Implementation of Multifactor Dimensionality Reduction

The 24/7 Lab - Does Creativity Suffer?

November, 2011

Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions

December, 2011

The Causes of Epistasis

The Causes of Epistasis

2011-12-03T14:38:00.002-05:00

A must read for those of you interested in epistasis.

de Visser JA, Cooper TF, Elena SF. The causes of epistasis. Proc Biol Sci. 2011 Dec 22;278(1725):3617-24. [PubMed]

Abstract

Since Bateson's discovery that genes can suppress the phenotypic effects of other genes, gene interactions-called epistasis-have been the topic of a vast research effort. Systems and developmental biologists study epistasis to understand the genotype-phenotype map, whereas evolutionary biologists recognize the fundamental importance of epistasis for evolution. Depending on its form, epistasis may lead to divergence and speciation, provide evolutionary benefits to sex and affect the robustness and evolvability of organisms. That epistasis can itself be shaped by evolution has only recently been realized. Here, we review the empirical pattern of epistasis, and some of the factors that may affect the form and extent of epistasis. Based on their divergent consequences, we distinguish between interactions with or without mean effect, and those affecting the magnitude of fitness effects or their sign. Empirical work has begun to quantify epistasis in multiple dimensions in the context of metabolic and fitness landscape models. We discuss possible proximate causes (such as protein function and metabolic networks) and ultimate factors (including mutation, recombination, and the importance of natural selection and genetic drift). We conclude that, in general, pleiotropy is an important prerequisite for epistasis, and that epistasis may evolve as an adaptive or intrinsic consequence of changes in genetic robustness and evolvability.

Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions

2011-11-12T10:49:00.002-05:00

This is a nice paper we just published with Dr. Ruzong Fan of the NIH. Entropy is a great way to parameterize gene-gene interactions.

Fan R, Zhong M, Wang S, Zhang Y, Andrew A, Karagas M, Chen H, Amos CI, Xiong M, Moore JH. Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases. Genet Epidemiol. 2011 Nov;35(7):706-21. [PubMed]

Abstract

For complex diseases, the relationship between genotypes, environment factors, and phenotype is usually complex and nonlinear. Our understanding of the genetic architecture of diseases has considerably increased over the last years. However, both conceptually and methodologically, detecting gene-gene and gene-environment interactions remains a challenge, despite the existence of a number of efficient methods. One method that offers great promises but has not yet been widely applied to genomic data is the entropy-based approach of information theory. In this article, we first develop entropy-based test statistics to identify two-way and higher order gene-gene and gene-environment interactions. We then apply these methods to a bladder cancer data set and thereby test their power and identify strengths and weaknesses. For two-way interactions, we propose an information gain (IG) approach based on mutual information. For three-ways and higher order interactions, an interaction IG approach is used. In both cases, we develop one-dimensional test statistics to analyze sparse data. Compared to the naive chi-square test, the test statistics we develop have similar or higher power and is robust. Applying it to the bladder cancer data set allowed to investigate the complex interactions between DNA repair gene single nucleotide polymorphisms, smoking status, and bladder cancer susceptibility. Although not yet widely applied, entropy-based approaches appear as a useful tool for detecting gene-gene and gene-environment interactions. The test statistics we develop add to a growing body methodologies that will gradually shed light on the complex architecture of common diseases.

Gene-environment interaction in psychiatric research

2011-09-30T14:03:00.002-04:00

This is a nice new critical review exploring issues of power and replication for detecting gene-envrionment interactions in psychiatric genetics. This paper makes a number of very nice points and is worth reading. A few things to keep in mind. First, the power issues discussed make the important assumption that the effect size of gene-environment interactions will be as small or smaller than main effects. This may not be true in some circumstances. Second, I don't expect gene-environment interactions, or most genetic effects for that matter, to replicate under a complex systems model. There are many good biological reasons why an interaction effect detected in one population might not replicate in a second independent population. See our 2009 PLoS One paper by Greene et al. for an explanation. Also, I find their distinction of candidate gene-environment interaction (cGxE) to be a bit strange.

Duncan LE, Keller MC. A Critical Review of the First 10 Years of Candidate Gene-by-Environment Interaction Research in Psychiatry. Am J Psychiatry, in press (2011). [PubMed]

Abstract

Objective: Gene-by-environment interaction (G×E) studies in psychiatry have typically been conducted using a candidate G×E (cG×E) approach, analogous to the candidate gene association approach used to test genetic main effects. Such cG×E research has received widespread attention and acclaim, yet cG×E findings remain controversial. The authors examined whether the many positive cG×E findings reported in the psychiatric literature were robust or if, in aggregate, cG×E findings were consistent with the existence of publication bias, low statistical power, and a high false discovery rate. Method: The authors conducted analyses on data extracted from all published studies (103 studies) from the first decade (2000-2009) of cG×E research in psychiatry. Results: Ninety-six percent of novel cG×E studies were significant compared with 27% of replication attempts. These findings are consistent with the existence of publication bias among novel cG×E studies, making cG×E hypotheses appear more robust than they actually are. There also appears to be publication bias among replication attempts because positive replication attempts had smaller average sample sizes than negative ones. Power calculations using observed sample sizes suggest that cG×E studies are underpowered. Low power along with the likely low prior probability of a given cG×E hypothesis being true suggests that most or even all positive cG×E findings represent type I errors. Conclusions: In this new era of big data and small effects, a recalibration of views about groundbreaking findings is necessary. Well-powered direct replications deserve more attention than novel cG×E findings and indirect replications.

Characterizing Genetic Interactions in Human Disease Association Studies Using Statistical Epistasis Networks

2011-09-15T07:42:00.002-04:00

Our paper on using network science to study the genetic architecture of disease susceptibility has been published.

Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH. Characterizing Genetic Interactions in Human Disease Association Studies Using Statistical Epistasis Networks. BMC Bioinformatics. 2011 Sep 12;12(1):364.[BMC]

ABSTRACT

Background: Epistasis is recognized ubiquitous in the genetic architecture of complex traits such as disease susceptibility. Experimental studies in model organisms have revealed extensive evidence of biological interactions among genes. Meanwhile, statistical and computational studies in human populations have suggested non-additive effects of genetic variation on complex traits. Although these studies form a baseline for understanding the genetic architecture of complex traits, to date they have only considered interactions among a small number of genetic variants. Our goal here is to use network science to determine the extent to which non-additive interactions exist beyond small subsets of genetic variants. We infer statistical epistasis networks to characterize the global space of pairwise interactions among approximately 1500 Single Nucleotide Polymorphisms (SNPs) spanning nearly 500 cancer susceptibility genes in a large population-based study of bladder cancer.

Results: The statistical epistasis network was built by linking pairs of SNPs if their pairwise interactions were stronger than a systematically derived threshold. Its topology clearly differentiated this real-data network from networks obtained from permutations of the same data under the null hypothesis that no association exists between genotype and phenotype. The network had a signiffcantly higher number of hub SNPs and, interestingly, these hub SNPs were not necessarily with high main effects. The network had a largest connected component of 39 SNPs that was absent in any other permuted-data networks. In addition, the vertex degrees of this network were distinctively found following an approximate power-law distribution and its topology appeared scale-free.

Conclusions: In contrast to many existing techniques focusing on high main-effect SNPs or models of several interating SNPs, our network approach characterized a global picture of gene-gene interactions in a population-based genetic data. The network was built using pairwise interactions, and its distinctive network topology and large connected components indicated joint effects in a large set of SNPs. Our observations suggested that this particular statistical epistasis network captured important features of the genetic architecture of bladder cancer that have not been described previously.

HyperCube Rule Mining

2011-09-13T08:48:00.002-04:00

This looks like a neat rule-based machine learning method for association studies. Let me know if you try it.

Loucoubar C, Paul R, Bar-Hen A, Huret A, Tall A, et al. (2011) An Exhaustive, Non-Euclidean, Non-Parametric Data Mining Tool for Unraveling the Complexity of Biological Systems – Novel Insights into Malaria. PLoS ONE 6(9): e24085. [PLoS]

Abstract

Complex, high-dimensional data sets pose significant analytical challenges in the post-genomic era. Such data sets are not exclusive to genetic analyses and are also pertinent to epidemiology. There has been considerable effort to develop hypothesis-free data mining and machine learning methodologies. However, current methodologies lack exhaustivity and general applicability. Here we use a novel non-parametric, non-euclidean data mining tool, HyperCubeH, to explore exhaustively a complex epidemiological malaria data set by searching for over density of events in m-dimensional space. Hotspots of over density correspond to strings of variables, rules, that determine, in this case, the occurrence of Plasmodium falciparum clinical malaria episodes. The data set contained 46,837 outcome events from 1,653 individuals and 34 explanatory variables. The best predictive rule contained 1,689 events from 148 individuals and was defined as: individuals present during 1992–2003, aged 1–5 years old, having hemoglobin AA, and having had previous Plasmodium malariae malaria parasite infection #10 times. These individuals had 3.71 times more P. falciparum clinical malaria episodes than the general population. We validated the rule in two different cohorts. We compared and contrasted the HyperCubeH rule with the rules using variables identified by both traditional statistical methods and non-parametric regression tree methods. In addition, we tried all possible sub-stratified quantitative variables. No other model with equal or greater representativity gave a higher Relative Risk. Although three of the four variables in the rule were intuitive, the effect of number of P. malariae episodes was not. HyperCubeH efficiently sub-stratified quantitative variables to optimize the rule and was able to identify interactions among the variables, tasks not easy to perform using standard data mining methods. Search of local over density in m-dimensional space, explained by easily interpretable rules, is thus seemingly ideal for generating hypotheses for large datasets to unravel the complexity inherent in biological systems.

An R Package Implementation of Multifactor Dimensionality Reduction

2011-09-05T08:36:00.001-04:00

A new R package for our Multifactor Dimensionality Reduction (MDR) method is available.

Winham SJ, Motsinger-Reif AA. An R Package Implementation of Multifactor Dimensionality Reduction. BioData Min. 2011 Aug 16;4(1):24. [PubMed]

ABSTRACT:

BACKGROUND:

A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users.

RESULTS:

We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package.

CONCLUSIONS:

MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users.

The 24/7 Lab - Does Creativity Suffer?

2011-09-01T09:18:00.006-04:00

There was an interesting piece in Nature recently about Dr. Quiñones-Hinojosa and his promotion of the 24/7 lab. He selects people for his lab that he can motivate to work around the clock. He claims that this intense work ethic yields results. Here is a quote:

>>>Quiñones-Hinojosa credits his professional rise to his resilience and a seemingly limitless capacity for hard work. "When you go that extra step, you are training your brain like an athlete," he says. And the fact that his group has published 113 articles in the past six years and holds 13 funding grants is not, he says, because he is brighter or better connected than colleagues. "It's just a matter of volume," he says. "The key is we submit a couple of dozen grant applications a year, and we learn from our mistakes."<<<

I certainly credit hard work and long hours to my own success. However, I have a very different approach to running my lab. I believe that successful research is about much more than productivity. Productivity must not be achieved at the expense of creativity. I am willing to bet that the intense pressure that Quiñones-Hinojosa inflicts on his staff and students stifles their ability to think creatively. Instead of trying some new crazy idea they are intensely focused on getting the next experiment done so Quiñones-Hinojosa doesn't think they are slacking. I firmly believe that hard work must be balanced with fun and time for creative thought. The role of the PI is to set a good example by working hard, but at the same time to establish a relaxed work environment where innovation can flourish and staff and students aren't afraid to try new things. I have always liked the Google work philosophy and programs such as their 'day to play'. My experience has been that good staff and students work harder naturally when they are allowed to express themselves creatively. Some of our best work has come from people in my lab trying crazy ideas.

Here is a followup comment by Overbaugh posted in Nature.

People are inherently biased against creative ideas

2011-08-28T12:33:00.001-04:00

Physorg.com reports on a study to be published in Psychological Science that suggests people are inherently biased against creative ideas. If true, this could have rather significant implications for how we conduct the peer review and practice of science.

According to Physorg.com, the study finds:

• Creative ideas are by definition novel, and novelty can trigger feelings of uncertainty that make most people uncomfortable.

• People dismiss creative ideas in favor of ideas that are purely practical -- tried and true.

• Objective evidence shoring up the validity of a creative proposal does not motivate people to accept it.

• Anti-creativity bias is so subtle that people are unaware of it, which can interfere with their ability to recognize a creative idea.

New Center Grant on Gene-Environment Interactions

2011-08-16T17:22:00.002-04:00

I have been awarded a five-year $11M NIH/NCRR Center of Biomedical Research Excellence (COBRE) grant to mentor junior investigators, fund junior investigator research projects, hire faculty and build bioinformatics infrastructure for the analysis of gene-environment interactions. This new center grant will complement the institutional funding I received to establish the Institute for Quantitative Biomedical Sciences (iQBS) at Dartmouth College.

Abstract

Modern biomedical research relies on interdisciplinary approaches such as bioinformatics that synthesize knowledge and methods from other disciplines to provide an integrated framework for solving biomedical problems. The rapid advancement of high-throughput technologies for measuring biological systems has generated a significant demand at Dartmouth College and other research institutions across Northern New England for interdisciplinary approaches in the quantitative sciences (e.g. bioinformatics, biostatistics, genomics, mathematical biology, proteomics, and systems biology). Integrating high-dimensional research databases with clinical databases from medical schools and hospitals across the region will be needed for translational medicine to become a reality. Unfortunately, the research institutions in Maine, New Hampshire, and Vermont are in a largely rural setting have not kept pace those in larger metropolitan areas such as nearby Boston or New York. The goal of this COBRE program is to establish an Institute for Quantitative Biomedical Sciences (iQBS) that will support and enhance quantitative research across the region and facilitate its integration and synergy with experimental and observational biology. This will be accomplished by 1) establishing a new Institute focused on developing, supporting, and enhancing quantitative research in Maine, New Hampshire, and Vermont that will become nationally and internationally recognized, free standing, and will foster meaningful collaborations with experimental biologists thus improving the ability of investigators in the region to compete for NIH funding, 2) recruiting talented tenure track quantitative scientists to Maine, New Hampshire, and Vermont, 3) mentoring the development of four junior quantitative scientists across the region and 4) promoting synergistic collaborations between quantitative scientists and experimental biologists through four research projects, an Administrative Core and an Integrative Biology Core. The scientific focus of the four research projects is gene-environment interaction within the context of environmental health and toxicology. This provides an important unifying and synergistic theme for the COBRE.

Generating data with complex genotype-phenotype relationships

2011-07-22T09:28:00.002-04:00

This is a new paper from my group on a novel approach for generating complex data for testing machine learning algorithms in human genetics. The interesting thing about this approach is that it can generate data with complex interaction patterns in the absence of a pre-defined model.

Himmelstein DS, Greene CS, Moore JH. Evolving hard problems: Generating human genetics datasets with a complex etiology. BioData Min. 2011 Jul 7;4(1):21. [PubMed]

ABSTRACT:

BACKGROUND: A goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models.

RESULTS: Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate eight-hundred pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variants have been minimized, while the predictiveness of third, fourth, or fifth order combinations is maximized. Two hundred runs of the algorithm are further dedicated to creating datasets with predictive four or five order interactions and minimized lower-level effects.

CONCLUSIONS: This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This allows researchers to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 76,600 datasets are available from http://discovery.dartmouth.edu/model free data/.

Powerful SNP-set analysis for case-control genome-wide association studies

2011-07-19T09:49:00.000-04:00

Moving in the right direction!

Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, Lin X. Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet. 2010 Jun 11;86(6):929-42. [PubMed]

Abstract

GWAS have emerged as popular tools for identifying genetic variants that are associated with disease risk. Standard analysis of a case-control GWAS involves assessing the association between each individual genotyped SNP and disease risk. However, this approach suffers from limited reproducibility and difficulties in detecting multi-SNP and epistatic effects. As an alternative analytical strategy, we propose grouping SNPs together into SNP sets on the basis of proximity to genomic features such as genes or haplotype blocks, then testing the joint effect of each SNP set. Testing of each SNP set proceeds via the logistic kernel-machine-based test, which is based on a statistical framework that allows for flexible modeling of epistatic and nonlinear SNP effects. This flexibility and the ability to naturally adjust for covariate effects are important features of our test that make it appealing in comparison to individual SNP tests and existing multimarker tests. Using simulated data based on the International HapMap Project, we show that SNP-set testing can have improved power over standard individual-SNP analysis under a wide range of settings. In particular, we find that our approach has higher power than individual-SNP analysis when the median correlation between the disease-susceptibility variant and the genotyped SNPs is moderate to high. When the correlation is low, both individual-SNP analysis and the SNP-set analysis tend to have low power. We apply SNP-set analysis to analyze the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer GWAS discovery-phase data.

Pathway of distinction analysis

2011-06-28T07:46:00.002-04:00

A very nice paper on pathway analysis of genetic association data. I like this paper because it doesn't make any assumptions about there being main effects.

Braun R, Buetow K. Pathways of Distinction Analysis: A New Technique for Multi-SNP Analysis of GWAS Data. PLoS Genet. 2011 Jun;7(6):e1002101. [PubMed]

Abstract

Genome-wide association studies (GWAS) have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases (cancers, diabetes, and Alzheimers, amongst others) are unlikely to have a single causative gene. Thus, there is a pressing need for multi-SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi-SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). The method uses GWAS data and known pathway-gene and gene-SNP associations to identify pathways that permit, ideally, the distinction of cases from controls. The technique is based upon the hypothesis that, if a pathway is related to disease risk, cases will appear more similar to other cases than to controls (or vice versa) for the SNPs associated with that pathway. By systematically applying the method to all pathways of potential interest, we can identify those for which the hypothesis holds true, i.e., pathways containing SNPs for which the samples exhibit greater within-class similarity than across classes. Importantly, PoDA improves on existing single-SNP and SNP-set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects. This permits PoDA to reveal pathways in which epistatic interactions drive risk. In this paper, we detail the PoDA method and apply it to two GWAS: one of breast cancer and the other of liver cancer. The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility. PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.

Molecular mechanisms of epistasis

2011-06-27T14:47:00.004-04:00

This is an interesting new paper that discusses how molecular interactions might give rise to epistasis. The connection between biological and statistical epistasis is a very important question.

Lehner B. Molecular mechanisms of epistasis within and between genes. Trends Genet. 2011 [PubMed]

Abstract

'Disease-causing' mutations do not cause disease in all individuals. One possible important reason for this is that the outcome of a mutation can depend upon other genetic variants in a genome. These epistatic interactions between mutations occur both within and between molecules, and studies in model organisms show that they are extremely prevalent. However, epistatic interactions are still poorly understood at the molecular level, and consequently difficult to predict de novo. Here I provide an overview of our current understanding of the molecular mechanisms that can cause epistasis, and areas where more research is needed. A more complete understanding of epistasis will be vital for making accurate predictions about the phenotypes of individuals.

Two Epistasis Papers in Science

2011-06-04T14:22:00.004-04:00

There are two papers on epistasis in the June 3rd issue of Science.

Khan AI, Dinh DM, Schneider D, Lenski RE, Cooper TF. Negative epistasis between beneficial mutations in an evolving bacterial population. Science. 2011 Jun 3;332(6034):1193-6. PubMed PMID: 21636772. [PubMed]

Chou HH, Chiu HC, Delaney NF, Segrè D, Marx CJ. Diminishing returns epistasis among beneficial mutations decelerates adaptation. Science. 2011 Jun 3;332(6034):1190-2. PubMed PMID: 21636771. [PubMed]

Detecting genetic interactions for quantitative traits with U-statistics

2011-05-31T09:44:00.003-04:00

This is an interesting paper that addresses an important topic. We need more methods that focus on epistasis contributing to interindividual variation in quantitative traits. U statistics seem promising.

Li M, Ye C, Fu W, Elston RC, Lu Q. Detecting genetic interactions for
quantitative traits with U-statistics. Genet Epidemiol. 2011 May 26. doi:
10.1002/gepi.20594. [Epub ahead of print] [PubMed] PMID: 21618602.

Abstract

The genetic etiology of complex human diseases has been commonly viewed as a process that involves multiple genetic variants, environmental factors, as well as their interactions. Statistical approaches, such as the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR), have recently been proposed to test the joint association of multiple genetic variants with either dichotomous or continuous traits. In this study, we propose a novel Forward U-Test to evaluate the combined effect of multiple loci on quantitative traits with consideration of gene-gene/gene-environment interactions. In this new approach, a U-Statistic-based forward algorithm is first used to select potential disease-susceptibility loci and then a weighted U-statistic is used to test the joint association of the selected loci with the disease. Through a simulation study, we found the Forward U-Test outperformed GMDR in terms of greater power. Aside from that, our approach is less computationally intensive, making it feasible for high-dimensional gene-gene/gene-environment research. We illustrate our method with a real data application to nicotine dependence (ND), using three independent datasets from the Study of Addiction: Genetics and Environment. Our gene-gene interaction analysis of 155 SNPs in 67 candidate genes identified two SNPs, rs16969968 within gene CHRNA5 and rs1122530 within gene NTRK2, jointly associated with the level of ND (P-value = 5.31e-7). The association, which involves essential interaction, is replicated in two independent datasets with P-values of 1.08e-5 and 0.02, respectively. Our finding suggests that joint action may exist between the two gene products.

Transcriptional robustness and protein interactions are associated in yeast

2011-05-30T10:48:00.001-04:00

This result is entirely consistent with epistasis due to canalization.

Bekaert M, Conant GC. Transcriptional robustness and protein interactions are associated in yeast. BMC Syst Biol. 2011 May 5;5(1):62. [PubMed]

ABSTRACT:

BACKGROUND: Robustness to insults, both external and internal, is a characteristic feature of life. One level of biological organization for which noise and robustness have been extensively studied is gene expression. Cells have a variety of mechanisms for buffering noise in gene expression, but it is not completely clear what rules govern whether or not a given gene uses such tools to maintain appropriate expression.

RESULTS: Here, we show a general association between the degree to which yeast cells have evolved mechanisms to buffer changes in gene expression and whether they possess protein-protein interactions. We argue that this effect bears a resemblance to epistasis, because yeast appears to have evolved regulatory mechanisms such that distant changes in gene copy number for a protein-protein interaction partner gene can alter a gene's expression. This association is not unexpected given recent work linking epistasis and the deleterious effects of changes in gene dosage (i.e., the dosage balance hypothesis). Using gene expression data from artificial aneuploid strains of bakers' yeast, we found that genes coding for proteins that physically interact with other proteins show less expression variation in response to aneuploidy than do other genes. This effect is even more pronounced for genes whose products interact with proteins encoded on aneuploid chromosomes. We further found that genes targeted by transcription factors encoded on aneuploid chromosomes were more likely to change in expression after aneuploidy.

CONCLUSIONS: We suggest that these observations can be best understood as resulting from the higher fitness cost of misexpression in epistatic genes and a commensurate greater regulatory control of them.

The effects of linkage disequilibrium in large scale SNP datasets for MDR

2011-05-21T12:58:00.003-04:00

This is a nice new open-access paper from Dr. Marylyn Ritchie's lab on the effects of LD on MDR models.

Grady BJ, Torstenson ES, Ritchie MD. The effects of linkage disequilibrium in large scale SNP datasets for MDR. BioData Min. 2011 May 5;4(1):11. [PubMed] [BioData Mining]

ABSTRACT:

BACKGROUND: In the analysis of large-scale genomic datasets, an important consideration is the power of analytical methods to identify accurate predictive models of disease. When trying to assess sensitivity from such analytical methods, a confounding factor up to this point has been the presence of linkage disequilibrium (LD). In this study, we examined the effect of LD on the sensitivity of the Multifactor Dimensionality Reduction (MDR) software package.

RESULTS: Four relative amounts of LD were simulated in multiple one- and two-locus scenarios for which the position of the functional SNP(s) within LD blocks varied. Simulated data was analyzed with MDR to determine the sensitivity of the method in different contexts, where the sensitivity of the method was gauged as the number of times out of 100 that the method identifies the correct one- or two-locus model as the best overall model. As the amount of LD increases, the sensitivity of MDR to detect the correct functional SNP drops but the sensitivity to detect the disease signal and find an indirect association increases.

CONCLUSIONS: Higher levels of LD begin to confound the MDR algorithm and lead to a drop in sensitivity with respect to the identification of a direct association; it does not, however, affect the ability to detect indirect association. However, careful examination of the solution models generated by MDR reveals that MDR can identify loci in the correct LD block; though it is not always the functional SNP. As such, the results of MDR analysis in datasets with LD should be carefully examined to consider the underlying LD structure of the dataset.

Computational Intelligence Using Genetic Programming

2011-05-16T14:02:00.004-04:00

I just returned from the IXth Genetic Programming Theory and Practice Workshop held by the Center for the Study of Complex Systems at the University of Michigan. This is an invitation only workshop that brings together theorists and practitioners interested in the development and application of computer systems that can solve complex problems by developing their own programs (i.e. automatic programming). This group focuses on the use of genetic programming or GP to discover useful computer programs using the principles of evolution by natural selection. The proceedings from this workshop are published each year in a book that can be found on Amazon. The proceedings from this year will be published in late 2011 or early 2012.

The real value of this workshop is the large amount of time dedicted to open-ended discussion about how solve complex problems in medicine, industry, finance, etc. My own motivation for working with GP is to teach the computer how to solve a complex human genetics problem as I would. I do not believe that naive computer programs or analysis strategies such as those used in the agnostics genome-wide association study (GWAS) paradigm will be successful in addressing the complexity of the genotype-phenotype relationship. We, as human analysis engines, don't ignore the pathobiology of disease when we look at data. Why should we instruct the computer to do the same? Given infinite time, each of us would tinker and try new and different things with the data until we found a good answer that made biological sense. We would use our knoweldge of biochemistry, genomics, molecular biology, pathology and physiology to both frame the analysis and interpret the results. Our series of papers published as part of GPTP since 2006 have focused on adaptive computer programs that harness this kind of biological and biomedical knowledge to explore the space of computer programs that can build models of genetic architecture.

One of the more interesting and extended discussions at GPTP this year was about novelty-seeking. Ken Stanley gave a great talk about rewarding computer programs that explore new and different solutions to a problem (read more). His Picbreeder program is a nice example of novelty search in the sense that you can discover and develop interesting pictures without a clear initial objective in mind (e.g. evolve a picture of a car). An analogy in human genetics would be to reward computer program that generate genetic models of disease by exploring new biochemical pathways. I am working on approaches to try this within our own genetic analysis system. I like Ken's quote: "To achieve your highest goals, you must be willing to abandon them."

It is very clear that GP has been used to solve problems that humans or other computer programs haven't been able to. For example, Moshe Sipper has developed computer game players that rival human players (read more). Some of the participants (e.g. Michael Korns) even invest and make money using GP. This is a powerful way to do automatic programming and should be part of the broader toolbox of any complex problem-solver. I would be happy to send you a pre-print of our current GPTP paper.

Microbiome Studies at the 2012 Pacific Symposium on Biocomputing

2011-05-04T18:26:00.003-04:00

I will be co-chairing again next year the Microbiome Studies session at PSB. Here is the call for papers: http://psb.stanford.edu/cfp-ms. Papers are due July 11, 2011. The conference will be held January 3-7, 2012 on the Big Island of Hawaii. Let me know if you have any questions.

Genetic analysis of complex traits in the emerging collaborative cross

2011-04-19T13:37:00.004-04:00

It will be interesting to see how useful the collaborative cross is for studying epistasis in mice.

Aylor DL, Valdar W, Foulds-Mathes W, Buus RJ, Verdugo RA, Baric RS, Ferris MT, Frelinger JA, Heise M, Frieman MB, Gralinski LE, Bell TA, Didion JD, Hua K, Nehrenberg DL, Powell CL, Steigerwalt J, Xie Y, Kelada SN, Collins FS, Yang IV, Schwartz DA, Branstetter LA, Chesler EJ, Miller DR, Spence J, Liu EY, McMillan L, Sarkar A, Wang J, Wang W, Zhang Q, Broman KW, Korstanje R, Durrant C, Mott R, Iraqi FA, Pomp D, Threadgill D, Pardo-Manuel de Villena F, Churchill GA. Genetic analysis of complex traits in the emerging collaborative cross. Genome Res. 2011 Mar 15.[PubMed]

Abstract

The Collaborative Cross (CC) is a mouse recombinant inbred strain panel that is being developed as a resource for mammalian systems genetics. Here we describe an experiment that uses partially inbred CC lines to evaluate the genetic properties and utility of this emerging resource. Genome-wide analysis of the incipient strains reveals high genetic diversity, balanced allele frequencies, and dense, evenly distributed recombination sites-all ideal qualities for a systems genetics resource. We map discrete, complex, and biomolecular traits and contrast two quantitative trait locus (QTL) mapping approaches. Analysis based on inferred haplotypes improves power, reduces false discovery, and provides information to identify and prioritize candidate genes that is unique to multifounder crosses like the CC. The number of expression QTLs discovered here exceeds all previous efforts at eQTL mapping in mice, and we map local eQTL at 1-Mb resolution. We demonstrate that the genetic diversity of the CC, which derives from random mixing of eight founder strains, results in high phenotypic diversity and enhances our ability to map causative loci underlying complex disease-related traits.

Travelling the world of gene-gene interactions

2011-04-02T14:46:00.000-04:00

A nice new review on gene-gene interaction analysis.

Van Steen K. Travelling the world of gene-gene interactions. Brief Bioinform.
2011 Mar 26. [PubMed]

Abstract

Over the last few years, main effect genetic association analysis has proven to be a successful tool to unravel genetic risk components to a variety of complex diseases. In the quest for disease susceptibility factors and the search for the 'missing heritability', supplementary and complementary efforts have been undertaken. These include the inclusion of several genetic inheritance assumptions in model development, the consideration of different sources of information, and the acknowledgement of disease underlying pathways of networks. The search for epistasis or gene-gene interaction effects on traits of interest is marked by an exponential growth, not only in terms of methodological development, but also in terms of practical applications, translation of statistical epistasis to biological epistasis and integration of omics information sources. The current popularity of the field, as well as its attraction to interdisciplinary teams, each making valuable contributions with sometimes rather unique viewpoints, renders it impossible to give an exhaustive review of to-date available approaches for epistasis screening. The purpose of this work is to give a perspective view on a selection of currently active analysis strategies and concerns in the context of epistasis detection, and to provide an eye to the future of gene-gene interaction analysis.

Interactome Networks and Human Disease

2011-03-24T09:27:00.003-04:00

Consider whether we should be doing genetic analysis one SNP at a time after reading this paper.

Vidal M, Cusick ME, Barabási AL. Interactome networks and human disease. Cell. 2011 Mar 18;144(6):986-98. [PubMed]

Abstract

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Gene–Environment Interactions in Human Disease

2011-03-23T10:19:00.002-04:00

A nice current review in TiG on the importance of assessing GxE.

Ober C, Vercelli D. Gene-environment interactions in human disease: nuisance or opportunity? Trends Genet. 2011 Mar;27(3):107-15. [PubMed]

Abstract

Many environmental risk factors for common, complex human diseases have been revealed by epidemiologic studies, but how genotypes at specific loci modulate individual responses to environmental risk factors is largely unknown. Gene-environment interactions will be missed in genome-wide association studies and could account for some of the 'missing heritability' for these diseases. In this review, we focus on asthma as a model disease for studying gene-environment interactions because of relatively large numbers of candidate gene-environment interactions with asthma risk in the literature. Identifying these interactions using genome-wide approaches poses formidable methodological problems, and elucidating molecular mechanisms for these interactions has been challenging. We suggest that studying gene-environment interactions in animal models, although more tractable, might not be sufficient to shed light on the genetic architecture of human diseases. Lastly, we propose avenues for future studies to find gene-environment interactions.

Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data

2011-03-18T08:46:00.001-04:00

A new model-based MDR paper.

Mahachie John JM, Van Lishout F, Van Steen K. Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data. Eur J Hum Genet. 2011, in press [PubMed]

Abstract

Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the power is lowest in the presence of phenotypic mixtures or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical power estimates reduce even further with main effects corrections, but at the same time, false-positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis.

Gene-Gene Interaction Analysis Using ReliefF and MDR

2011-02-22T06:55:00.003-05:00

Here are two conference papers exploring the properties of ReliefF and MDR for detecting gene-gene interactions. These are both a bit difficult to read but there are some useful ideas presented. Both build on our previous work with the ReliefF family of algorithms. I am not sure whether the first one is relevant, however, given our recent work with spatially uniform ReliefF (SURF) that takes all neighbors within a certain distance. For background reading, see our 2010 Bioinformatics paper that reviews this work.

Pengyi Yang, Joshua WK Ho, Yee Hwa Yang, Bing B Zhou. Gene-gene interaction filtering with ensemble of filters. BMC Bioinformatics 2011, 12(Suppl 1):S10 [PDF]

Abstract

Background. Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the ‘unstable’ results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance.

Results. We propose a simple and effective ensemble approach in which the results from multiple runs of an unstable filter are aggregated based on the general theory of ensemble learning. The ensemble versions of the ReliefF and TuRF algorithms, referred to as ReliefF-E and TuRF-E, are robust to sample order dependency and enable a more informative investigation of data characteristics. Using simulated and real datasets, we demonstrate that both the ensemble of ReliefF and the ensemble of TuRF can generate a much more stable SNP ranking than the original algorithms. Furthermore, the ensemble of TuRF achieved the highest success rate in comparison to many state-of-the-art algorithms as well as traditional χ2-test and odds ratio methods in terms of retaining gene-gene interactions.

Can Yang, Xiang Wan, Zengyou He, Qiang Yang, Hong Xue, Weichuan Yu. The choice of null distributions for detecting gene-gene interactions in genome-wide association studies. BMC Bioinformatics 2011, 12(Suppl 1):S26 [PDF]

Abstract

Background. In genome-wide association studies (GWAS), the number of single-nucleotide polymorphisms (SNPs) typically ranges between 500,000 and 1,000,000. Accordingly, detecting gene-gene interactions in GWAS is computationally challenging because it involves hundreds of billions of SNP pairs. Stage-wise strategies are often used to overcome the computational difficulty. In the first stage, fast screening methods (e.g. Tuning ReliefF) are applied to reduce the whole SNP set to a small subset. In the second stage, sophisticated modeling methods (e.g., multifactor-dimensionality reduction (MDR)) are applied to the subset of SNPs to identify interesting interaction models and the corresponding interaction patterns. In the third stage, the significance of the identified interaction patterns is evaluated by hypothesis testing.

Results. In this paper, we show that this stage-wise strategy could be problematic in controlling the false positive rate if the null distribution is not appropriately chosen. This is because screening and modeling may change the null distribution used in hypothesis testing. In our simulation study, we use some popular screening methods and the popular modeling method MDR as examples to show the effect of the inappropriate choice of null distributions. To choose appropriate null distributions, we suggest to use the permutation test or testing on the independent data set. We demonstrate their performance using synthetic data and a real genome wide data set from an Aged-related Macular Degeneration (AMD) study.

Conclusions. The permutation test or testing on the independent data set can help choosing appropriate null distributions in hypothesis testing, which provides more reliable results in practice.

A genome-wide screen of gene-gene interactions for rheumatoid arthritis susceptibility

2011-02-18T13:36:00.002-05:00

This is a nice example of a genome-wide epistasis analysis. Nice that their interactions replicate. It would interesting to know how many of their interactions that didn't replicate are real. There are many very good resons for why an nteraction effect would not replicate in an indendent sample, especially if it is from a different study or population.

Liu C, Ackerman HH, Carulli JP. A genome-wide screen of gene-gene interactions for rheumatoid arthritis susceptibility. Hum Genet. 2011 Jan 6. [Epub ahead of print] PubMed PMID: 21210282. [PubMed]

Abstract

The objective of the study was to identify interacting genes contributing to rheumatoid arthritis (RA) susceptibility and identify SNPs that discriminate between RA patients who were anti-cyclic citrullinated protein positive and healthy controls. We analyzed two independent cohorts from the North American Rheumatoid Arthritis Consortium. A cohort of 908 RA cases and 1,260 controls was used to discover pairwise interactions among SNPs and to identify a set of single nucleotide polymorphisms (SNPs) that predict RA status, and a second cohort of 952 cases and 1,760 controls was used to validate the findings. After adjusting for HLA-shared epitope alleles, we identified and replicated seven SNP pairs within the HLA class II locus with significant interaction effects. We failed to replicate significant pairwise interactions among non-HLA SNPs. The machine learning approach "random forest" applied to a set of SNPs selected from single-SNP and pairwise interaction tests identified 93 SNPs that distinguish RA cases from controls with 70% accuracy. HLA SNPs provide the most classification information, and inclusion of non-HLA SNPs improved classification. While specific gene-gene interactions are difficult to validate using genome-wide SNP data, a stepwise approach combining association and classification methods identifies candidate interacting SNPs that distinguish RA cases from healthy controls.

Epistatic Interactions in Genetic Regulation of t-PA and PAI-1 Levels in a Ghanaian Population

2011-02-11T13:52:00.003-05:00

A new paper from our lab on epistasis analysis for QTLs.

Penrod NM, Poku KA, Vaughn DE, Asselbergs FW, Brown NJ, Moore JH, Williams SM. Epistatic Interactions in Genetic Regulation of t-PA and PAI-1 Levels in a Ghanaian Population. PLoS One. 2011 Jan 31;6(1):e16639. [PubMed] [PLoS]

Abstract

The proteins, tissue plasminogen activator (t-PA) and plasminogen activator inhibitor 1 (PAI-1), act in concert to balance thrombus formation and degradation, thereby modulating the development of arterial thrombosis and excessive bleeding. PAI-1 is upregulated by the renin-angiotensin system (RAS), specifically by angiotensin II, the product of angiotensin converting enzyme (ACE) cleavage of angiotensin I, which is produced by the cleavage of angiotensinogen (AGT) by renin (REN). ACE indirectly stimulates the release of t-PA which, in turn, activates the corresponding fibrinolytic system. Single polymorphisms in these pathways have been shown to significantly impact plasma levels of t-PA and PAI-1 differently in Ghanaian males and females. Here we explore the involvement of epistatic interactions between the same polymorphisms in central genes of the RAS and fibrinolytic systems on plasma t-PA and PAI-1 levels within the same population (n = 992). Statistical modeling of pairwise interactions was done using two-way ANOVA between polymorphisms in the ETNK2, RENIN, ACE, PAI-1, t-PA, and AGT genes. The most significant interactions that associated with t-PA levels were between the ETNK2 A6135G and the REN T9435C polymorphisms in females (p = 0.006) and the REN T9435C and the TPA I/D polymorphisms (p = 0.005) in males. The most significant interactions for PAI-1 levels were with REN T9435C and the TPA I/D polymorphisms (p = 0.001) in females, and the association of REN G6567T with the TPA I/D polymorphisms (p = 0.032) in males. Our results provide evidence for multiple genetic effects that may not be detected using single SNP analysis. Because t-PA and PAI-1 have been implicated in cardiovascular disease these results support the idea that the genetic architecture of cardiovascular disease is complex. Therefore, it is necessary to consider the relationship between interacting polymorphisms of pathway specific genes that predict t-PA and PAI-1 levels.

Dissecting genetic networks underlying complex phenotypes: the theoretical framework

2011-02-08T11:47:00.002-05:00

I really like the concepts presented in this paper. Right on target. Love Figure 1.

Zhang F, Zhai HQ, Paterson AH, Xu JL, Gao YM, Zheng TQ, Wu RL, Fu BY, Ali J, Li ZK. Dissecting genetic networks underlying complex phenotypes: the theoretical framework. PLoS One. 2011 Jan 20;6(1):e14541. [PLoS]

Abstract

Great progress has been made in genetic dissection of quantitative trait variation during the past two decades, but many studies still reveal only a small fraction of quantitative trait loci (QTLs), and epistasis remains elusive. We integrate contemporary knowledge of signal transduction pathways with principles of quantitative and population genetics to characterize genetic networks underlying complex traits, using a model founded upon one-way functional dependency of downstream genes on upstream regulators (the principle of hierarchy) and mutual functional dependency among related genes (functional genetic units, FGU). Both simulated and real data suggest that complementary epistasis contributes greatly to quantitative trait variation, and obscures the phenotypic effects of many 'downstream' loci in pathways. The mathematical relationships between the main effects and epistatic effects of genes acting at different levels of signaling pathways were established using the quantitative and population genetic parameters. Both loss of function and "co-adapted" gene complexes formed by multiple alleles with differentiated functions (effects) are predicted to be frequent types of allelic diversity at loci that contribute to the genetic variation of complex traits in populations. Downstream FGUs appear to be more vulnerable to loss of function than their upstream regulators, but this vulnerability is apparently compensated by different FGUs of similar functions. Other predictions from the model may account for puzzling results regarding responses to selection, genotype by environment interaction, and the genetic basis of heterosis.

A Comparison of Multifactor Dimensionality Reduction and Penalized Regression

2011-02-07T14:31:00.001-05:00

Winham S, Wang C, Motsinger-Reif AA. A Comparison of Multifactor Dimensionality Reduction and l-Penalized Regression to Identify Gene-Gene Interactions in Genetic Association Studies. Stat Appl Genet Mol Biol. 2011;10(1):Article4. [PubMed]

Abstract

Recently, the amount of high-dimensional data has exploded, creating new analytical challenges for human genetics. Furthermore, much evidence suggests that common complex diseases may be due to complex etiologies such as gene-gene interactions, which are difficult to identify in high-dimensional data using traditional statistical approaches. Data-mining approaches are gaining popularity for variable selection in association studies, and one of the most commonly used methods to evaluate potential gene-gene interactions is Multifactor Dimensionality Reduction (MDR). Additionally, a number of penalized regression techniques, such as Lasso, are gaining popularity within the statistical community and are now being applied to association studies, including extensions for interactions. In this study, we compare the performance of MDR, the traditional lasso with L1 penalty (TL1), and the group lasso for categorical data with group-wise L1 penalty (GL1) to detect gene-gene interactions through a broad range of simulations. We find that each method has both advantages and disadvantages, and relative performance is context dependent. TL1 frequently over-fits, identifying false positive as well as true positive loci. MDR has higher power for epistatic models that exhibit independent main effects; for both Lasso methods, main effects tend to dominate. For purely epistatic models, GL1 has the best performance for lower minor allele frequencies, but MDR performs best for higher frequencies. These results provide guidance of when each approach might be best suited for detecting and characterizing interactions with different mechanisms.

Yeast genetics is complex. What about humans?

2011-01-26T11:22:00.002-05:00

This is a nice new paper documenting the genetic complexity of yeast. This adds to the growing body of literature highlighting the importance of gene-gene and gene-environment interactions in model organisms. I continue to raise the question as to why the field of human genetics continues to downplay such effects in humans. Do we really expect human have simpler genetic archtectures that yeast and other lower organisms?

Cubillos FA, Billi E, Zörgö E, Parts L, Fargier P, Omholt S, Blomberg A, Warringer J, Louis EJ, Liti G. Assessing the complex architecture of polygenic traits in diverged yeast populations. Mol Ecol. 2011 Jan 25. [Epub ahead of print] PubMed PMID: 21261765. [PubMed]

Abstract

Phenotypic variation arising from populations adapting to different niches has a complex underlying genetic architecture. A major challenge in modern biology is to identify the causative variants driving phenotypic variation. Recently, the baker's yeast, Saccharomyces cerevisiae has emerged as a powerful model for dissecting complex traits. However, past studies using a laboratory strain were unable to reveal the complete architecture of polygenic traits. Here, we present a linkage study using 576 recombinant strains obtained from crosses of isolates representative of the major lineages. The meiotic recombinational landscape appears largely conserved between populations; however, strain-specific hotspots were also detected. Quantitative measurements of growth in 23 distinct ecologically relevant environments show that our recombinant population recapitulates most of the standing phenotypic variation described in the species. Linkage analysis detected an average of 6.3 distinct QTLs for each condition tested in all crosses, explaining on average 39% of the phenotypic variation. The QTLs detected are not constrained to a small number of loci, and the majority are specific to a single cross-combination and to a specific environment. Moreover, crosses between strains of similar phenotypes generate greater variation in the offspring, suggesting the presence of many antagonistic alleles and epistatic interactions. We found that subtelomeric regions play a key role in defining individual quantitative variation, emphasizing the importance of the adaptive nature of these regions in natural populations. This set of recombinant strains is a powerful tool for investigating the complex architecture of polygenic traits.

The Meaning of Interaction

2011-01-20T14:47:00.002-05:00

The following paper is a useful discussion of interaction from a model-based parametric statistical point of view. The discussion of biological vs. statistical epistasis is poorly cited, however.

Wang X, Elston RC, Zhu X. The Meaning of Interaction. Hum Hered. 2010 Dec 8;70(4):269-277. [PubMed]

Abstract

Although recent studies have attempted to dispel the confusion that exists in regard to the definition, analysis and interpretation of interaction in genetics, there still remain aspects that are poorly understood by non-statisticians. After a brief discussion of the definition of gene-gene interaction, the main part of this study addresses the fundamental meaning of statistical interaction and its relationship to measurement scale, disproportionate sample sizes in the cells of a two-way table and gametic phase disequilibrium.

Model-based multifactor dimensionality reduction for detecting epistasis

2011-01-19T14:54:00.001-05:00

A new MDR paper.

Cattaert T, Calle ML, Dudek SM, Mahachie John JM, Van Lishout F, Urrea V, Ritchie MD, Van Steen K. Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise. Ann Hum Genet.2011 Jan;75(1):78-89. [PubMed]

Abstract

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.

Application of the Explicit Test of Epistasis to Colon Cancer

2011-01-17T09:44:00.002-05:00

The paper below by Leroy et al. is a nice example of how the explicit test of epistasis by Greene et al. can be used with MDR to identify and confirm interactions that are independent of marginal effects.

Greene CS, Himmelstein DS, Nelson HH, Kelsey KT, Williams SM, Andrew AS, Karagas MR, Moore JH. Enabling personal genomics with an explicit test of epistasis. Pac Symp Biocomput. 2010:327-36. [PubMed]

Leroy EC, Moore JH, Hu C, Martínez ME, Lance P, Duggan D, Thompson PA. Genes in the insulin and insulin-like growth factor pathway and odds of metachronous colorectal neoplasia. Hum Genet. 2011 Jan 11. [Epub ahead of print] PubMed PMID: 21221997. [PubMed]

Abstract

Insulin and insulin-like growth factor (IGF) genes are implicated in colorectal carcinogenesis. Gene-by-gene interactions that influence the insulin/IGF pathways were hypothesized as modifiers of colorectal neoplasia risk. We built a classification tree to detect interactions in 18 IGF and insulin pathway-related genes and metachronous colorectal neoplasia among 1,439 subjects pooled from two chemoprevention trials. The probability of colorectal neoplasia was greatest (71.8%) among carriers of any A allele for rs7166348 (IGF1R) and AA genotype for rs1823023 (PIK3R1). In contrast, carriers of any A at rs7166348 (IGF1R), any G for the PIK3R1 variant, and AA for rs10426094 (INSR) had the lowest probability (14.3%). Logistic regression modeling showed that any A at rs7166348 (IGF1R) with the AA genotype at rs1823023 (PIK3R1) conferred the highest odds of colorectal neoplasia (OR 3.7; 95% CI 2.2-6.5), compared with carriage of GG at rs7166348 (IGF1R). Conversely, any A at rs7166348 (IGFR1), any G allele at rs1823023 (PIK3R1), and the AA genotype at rs10426094 (INSR) conferred the lowest odds (OR 0.22; 95% CI 0.07-0.66). Stratifying the analysis by parent study and intervention arm showed highly consistent trends in direction and magnitude of associations, with preliminary evidence of genotype effects on measured IGF-1 levels in a subgroup of subjects. These results were compared to those from multifactor dimensionality reduction, which identified different single nucleotide polymorphisms in the same genes (INSR and IGF1R) as effect modifiers for colorectal neoplasia. These results support a role for genetic interactions in the insulin/IGF pathway genes in colorectal neoplasia risk.

Real-world comparison of CPU and GPU implementations of SNPrank

2011-01-15T12:46:00.001-05:00

A nice paper on network analysis of GWAS data using high-performance computing.

Davis NA, Pandey A, McKinney BA. Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS. Bioinformatics. 2011 Jan 15;27(2):284-5. [PubMed]

Abstract

MOTIVATION: Bioinformatics researchers have a variety of programming languages and architectures at their disposal, and recent advances in graphics processing unit (GPU) computing have added a promising new option. However, many performance comparisons inflate the actual advantages of GPU technology. In this study, we carry out a realistic performance evaluation of SNPrank, a network centrality algorithm that ranks single nucleotide polymorhisms (SNPs) based on their importance in the context of a phenotype-specific interaction network. Our goal is to identify the best computational engine for the SNPrank web application and to provide a variety of well-tested implementations of SNPrank for Bioinformaticists to integrate into their research.

RESULTS: Using SNP data from the Wellcome Trust Case Control Consortium genome-wide association study of Bipolar Disorder, we compare multiple SNPrank implementations, including Python, Matlab and Java as well as CPU versus GPU implementations. When compared with naïve, single-threaded CPU implementations, the GPU yields a large improvement in the execution time. However, with comparable effort, multi-threaded CPU implementations negate the apparent advantage of GPU implementations.

AVAILABILITY: The SNPrank code is open source and available at http://insilico.utulsa.edu/snprank.

CONTACT: brett.mckinney@gmail.com.

NIH/NIGMS Funding by Priority Score

2011-01-07T12:55:00.004-05:00

The following is a figure put together by the National Institute of General Medical Sciences showing the number of grants reviewed and and the number funded by their priority score. Note that a score of 30 or better was needed to have a good chance of getting funded. I assume this looks similar at other institutes.

Layers of Epistasis

2011-01-06T14:28:00.001-05:00

Our new paper on "'Layers of epistasis: genome-wide regulatory networks and network approaches to genome-wide association studies' has been published online.

Cowper-Sal Lari R, Cole MD, Karagas MR, Lupien M, Moore JH. Layers of epistasis: genome-wide regulatory networks and network approaches to genome-wide association studies. Wiley Interdiscip Rev Syst Biol Med. 2010 Dec 31. [Epub ahead of print] PubMed PMID: 21197657. [PubMed]

Abstract

The conceptual foundation of the genome-wide association study (GWAS) has advanced unchecked since its conception. A revision might seem premature as the potential of GWAS has not been fully realized. Multiple technical and practical limitations need to be overcome before GWAS can be fairly criticized. But with the completion of hundreds of studies and a deeper understanding of the genetic architecture of disease, warnings are being raised. The results compiled to date indicate that risk-associated variants lie predominantly in noncoding regions of the genome. Additionally, alternative methodologies are uncovering large and heterogeneous sets of rare variants underlying disease. The fear is that, even in its fulfillment, the current GWAS paradigm might be incapable of dissecting all kinds of phenotypes. In the following text, we review several initiatives that aim to overcome these limitations. The overarching theme of these studies is the inclusion of biological knowledge to both the analysis and interpretation of genotyping data. GWAS is uninformed of biology by design and although there is some virtue in its simplicity, it is also its most conspicuous deficiency. We propose a framework in which to integrate these novel approaches, both empirical and theoretical, in the form of a genome-wide regulatory network (GWRN). By processing experimental data into networks, emerging data types based on chromatin immunoprecipitation are made computationally tractable. This will give GWAS re-analysis efforts the most current and relevant substrates, and root them firmly on our knowledge of human disease.

Using Biological Knowledge to Uncover the Mystery in the Search for Epistasis in Genome-Wide Association Studies

2010-12-29T13:00:00.002-05:00

A nice new paper from Marylyn Ritchie on using biological knowledge to aid GWAS analysis of epistasis.

Ritchie MD. Using biological knowledge to uncover the mystery in the search for epistasis in genome-wide association studies. Ann Hum Genet. 2011 Jan;75(1):172-82. [PubMed]

Abstract

The search for the missing heritability in genome-wide association studies (GWAS) has become an important focus for the human genetics community. One suspected location of these genetic effects is in gene-gene interactions, or epistasis. The computational burden of exploring gene-gene interactions in the wealth of data generated in GWAS, along with small to moderate sample sizes, have led to epistasis being an afterthought, rather than a primary focus of GWAS analyses. In this review, I discuss some potential approaches to filter a GWAS dataset to a smaller, more manageable dataset where searching for epistasis is considerably more feasible. I describe a number of alternative approaches, but primarily focus on the use of prior biological knowledge from databases in the public domain to guide the search for epistasis. The manner in which prior knowledge is incorporated into a GWA study can be many and these data can be extracted from a variety of database sources. I discuss a number of these approaches and propose that a comprehensive approach will likely be most fruitful for searching for epistasis in large-scale genomic studies of the current state-of-the-art and into the future.

Does Collocation Inform the Impact of Collaboration?

2010-12-18T09:49:00.001-05:00

A very interesting paper from Zak Kohane's group at Harvard.

Lee K, Brownstein JS, Mills RG, Kohane IS (2010) Does Collocation Inform the Impact of Collaboration? PLoS ONE 5(12): e14279. [PLoS One]

Abstract

Background: It has been shown that large interdisciplinary teams working across geography are more likely to be impactful. We asked whether the physical proximity of collaborators remained a strong predictor of the scientific impact of their research as measured by citations of the resulting publications.

Methodology/Principal Findings: Articles published by Harvard investigators from 1993 to 2003 with at least two authors were identified in the domain of biomedical science. Each collaboration was geocoded to the precise three-dimensional location of its authors. Physical distances between any two coauthors were calculated and associated with corresponding citations. Relationship between distance of coauthors and citations for four author relationships (first-last, first-middle, last-middle, and middle-middle) were investigated at different spatial scales. At all sizes of collaborations (from two authors to dozens of authors), geographical proximity between first and last author is highly informative of impact at the microscale (i.e. within building) and beyond. The mean citation for first-last author relationship decreased as the distance between them increased in less than one km range as well as in the three categorized ranges (in the same building, same city, or different city). Such a trend was not seen in other three author relationships.

Conclusions/Significance: Despite the positive impact of emerging communication technologies on scientific research, our results provide striking evidence for the role of physical proximity as a predictor of the impact of collaborations.

Our Latest MDR Papers

2010-11-28T10:03:00.004-05:00

Here are a few of our recently published papers on Multifactor Dimensionality Reduction (MDR). The first is a review while the next three report extensions of MDR for survivavl analysis, covariate adjustment and a robust approach that deals with the rare situation of having only a few genotype combinations contributing information. This work was supported by NIH grants R01 LM009012, R01 LM010098 and R01 AI59694.

Moore JH. Detecting, characterizing, and interpreting nonlinear gene-gene interactions using multifactor dimensionality reduction. Adv Genet. 2010;72:101-16. [PubMed]

Gui J, Moore JH, Kelsey KT, Marsit CJ, Karagas MR, Andrew AS. A novel survival multifactor dimensionality reduction method for detecting gene-gene interactions with application to bladder cancer prognosis. Hum Genet. 2010 Oct 28, in press. [PubMed]

Gui J, Andrew AS, Andrews P, Nelson HM, Kelsey KT, Karagas MR, Moore JH. A simple and computationally efficient sampling approach to covariate adjustment for multifactor dimensionality reduction analysis of epistasis. Hum Hered. 2010;70(3):219-25. [PubMed]

Gui J, Andrew AS, Andrews P, Nelson HM, Kelsey KT, Karagas MR, Moore JH. A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility. Ann Hum Genet. 2010 Nov 22., in press. [PubMed]

Pathway-Based GWAS Analysis

2010-11-22T08:58:00.002-05:00

I am a big fan of pathway-based approaches to the analysis of GWAS data. This looks like a nice overview. This area needs more attention and is much more likely to pay off than the one-SNP-at-a-time approach that has dominated the field.

Wang K, Li M, Hakonarson H. Analysing biological pathways in genome-wide association studies. Nat Rev Genet. 2010 Dec;11(12):843-854. [PubMed]

Abstract

Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Here we review the development of pathway-based approaches for GWA studies, discuss their practical use and caveats, and suggest that pathway-based approaches may also be useful for future GWA studies with sequencing data.

New Book: Computational Methods for Genetics of Complex Traits

2010-11-17T16:09:00.003-05:00

My new book on "Computational Methods for Genetics of Complex Traits" has been published as part of the Advances in Genetics series by Academic Press. Here is a summary and outline. Thanks to all the authors that made this possible.

Jay C. Dunlap and Jason H. Moore, Editor(s), Advances in Genetics, Academic Press, 2010, Volume 72, Computational Methods for Genetics of Complex Traits. [Amazon]

Chapter 1

Marylyn D. Ritchie, William S. Bush, Genome Simulation: Approaches for Synthesizing In Silico Datasets for Human Genomics, In: Jay C. Dunlap and Jason H. Moore, Editor(s), Advances in Genetics, Academic Press, 2010, Volume 72, Computational Methods for Genetics of Complex Traits, Pages 1-24.

Abstract: Simulated data is a necessary first step in the evaluation of new analytic methods because in simulated data the true effects are known. To successfully develop novel statistical and computational methods for genetic analysis, it is vital to simulate datasets consisting of single nucleotide polymorphisms (SNPs) spread throughout the genome at a density similar to that observed by new high-throughput molecular genomics studies. In addition, the simulation of environmental data and effects will be essential to properly formulate risk models for complex disorders. Data simulations are often criticized because they are much less noisy than natural biological data, as it is nearly impossible to simulate the multitude of possible sources of natural and experimental variability. However, simulating data in silico is the most straightforward way to test the true potential of new methods during development. Thus, advances that increase the complexity of data simulations will permit investigators to better assess new analytical methods. In this work, we will briefly describe some of the current approaches for the simulation of human genomics data describing the advantages and disadvantages of the various approaches. We will also include details on software packages available for data simulation. Finally, we will expand upon one particular approach for the creation of complex, human genomic datasets that uses a forward-time population simulation algorithm: genomeSIMLA. Many of the hallmark features of biological datasets can be synthesized in silico; still much research is needed to enhance our capabilities to create datasets that capture the natural complexity of biological datasets.

Chapter 2

Holger Schwender, Ingo Ruczinski, Logic Regression and Its Extensions, In: Jay C. Dunlap and Jason H. Moore, Editor(s), Advances in Genetics, Academic Press, 2010, Volume 72, Computational Methods for Genetics of Complex Traits, Pages 25-45.

Abstract: Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature.

Chapter 3

Melanie A. Wilson, James W. Baurley, Duncan C. Thomas, David V. Conti, Complex System Approaches to Genetic Analysis: Bayesian Approaches, In: Jay C. Dunlap and Jason H. Moore, Editor(s), Advances in Genetics, Academic Press, 2010, Volume 72, Computational Methods for Genetics of Complex Traits, Pages 47-71.

Abstract: Genetic epidemiology is increasingly focused on complex diseases involving multiple genes and environmental factors, often interacting in complex ways. Although standard frequentist methods still have a role in hypothesis generation and testing for discovery of novel main effects and interactions, Bayesian methods are particularly well suited to modeling the relationships in an integrated 'systems biology' manner. In this chapter, we provide an overview of the principles of Bayesian analysis and their advantages in this context and describe various approaches to applying them for both model building and discovery in a genome-wide setting. In particular, we highlight the ability of Bayesian methods to construct complex probability models via a hierarchical structure and to account for uncertainty in model specification by averaging over large spaces of alternative models.

Chapter 4

Yan V. Sun, Multigenic Modeling of Complex Disease by Random Forests, In: Jay C. Dunlap and Jason H. Moore, Editor(s), Advances in Genetics, Academic Press, 2010, Volume 72, Computational Methods for Genetics of Complex Traits, Pages 73-99.

Abstract: The genetics and heredity of complex human traits have been studied for over a century. Many genes have been implicated in these complex traits. Genome-wide association studies (GWAS) were designed to investigate the association between common genetic variation and complex human traits using high-throughput platforms that measured hundreds of thousands of common single-nucleotide polymorphisms (SNPs). GWAS have successfully identified many novel genetic loci associated with complex traits using a univariate regression-based approach. Even for traits with a large number of identified variants, only a small fraction of the interindividual variation in risk phenotypes has been explained. In biological systems, protein, DNA, RNA, and metabolites frequently interact to each other to perform their biological functions, and to respond to environmental factors. The complex interactions among genes and between the genes and environment may partially explain the 'missing heritability.' The traditional regression-based methods are limited to address the complex interactions among the hundreds of thousands of SNPs and their environmental context by both the modeling and computational challenge. Random Forests (RF), one of the powerful machine learning methods, is regarded as a useful alternative to capture the complex interaction effects among the GWAS data, and potentially address the genetic heterogeneity underlying these complex traits using a computationally efficient framework. In this chapter, the features of prediction and variable selection, and their applications in genetic association studies are reviewed and discussed. Additional improvements of the original RF method are warranted to make the applications in GWAS to be more successful.

Chapter 5

Jason H. Moore, Detecting, Characterizing, and Interpreting Nonlinear Gene-Gene Interactions Using Multifactor Dimensionality Reduction, In: Jay C. Dunlap and Jason H. Moore, Editor(s), Advances in Genetics, Academic Press, 2010, Volume 72, Computational Methods for Genetics of Complex Traits, Pages 101-116.

Abstract: Human health is a complex process that is dependent on many genes, many environmental factors and chance events that are perhaps not measurable with current technology or are simply unknowable. Success in the design and execution of population-based association studies to identify those genetic and environmental factors that play an important role in human disease will depend on our ability to embrace, rather that ignore, complexity in the genotype to phenotype mapping relationship for any given human ecology. We review here three general computational challenges that must be addressed. First, data mining and machine learning methods are needed to model nonlinear interactions between multiple genetic and environmental factors. Second, filter and wrapper methods are needed to identify attribute interactions in large and complex solution landscapes. Third, visualization methods are needed to help interpret computational models and results. We provide here an overview of the multifactor dimensionality reduction (MDR) method that was developed for addressing each of these challenges.

Chapter 6

Robert Culverhouse, The Restricted Partition Method, In: Jay C. Dunlap and Jason H. Moore, Editor(s), Advances in Genetics, Academic Press, 2010, Volume 72, Computational Methods for Genetics of Complex Traits, Pages 117-139.

Abstract: For many complex traits, the bulk of the phenotypic variation attributable to genetic factors remains unexplained, even after well-powered genome-wide association studies. Among the multiple possible explanations for the 'missing' variance, joint effects of multiple genetic variants are a particularly appealing target for investigation: they are well documented in biology and can often be evaluated using existing data. The first two sections of this chapter discusses these and other concerns that led to the development of the Restricted Partition Method (RPM). The RPM is an exploratory tool designed to investigate, in a model agnostic manner, joint effects of genetic and environmental factors contributing to quantitative or dichotomous phenotypes. The method partitions multilocus genotypes (or genotype-environmental exposure classes) into statistically distinct 'risk' groups, then evaluates the resulting model for phenotypic variance explained. It is sensitive to factors whose effects are apparent only in a joint analysis, and which would therefore be missed by many other methods. The third section of the chapter provides details of the RPM algorithm and walks the reader through an example. The final sections of the chapter discuss practical issues related to the use of the method. Because exhaustive pairwise or higher order analyses of many SNPs are computationally burdensome, much of the discussion focuses on computational issues. The RPM proved to be practical for a large candidate gene analysis, consisting of over 40,000 SNPs, using a desktop computer. Because the algorithm and software lend themselves to distributed processing, larger analyses can easily be split among multiple computers.

Chapter 7

Peter Holmans, Statistical Methods for Pathway Analysis of Genome-Wide Data for Association with Complex Genetic Traits, In: Jay C. Dunlap and Jason H. Moore, Editor(s), Advances in Genetics, Academic Press, 2010, Volume 72, Computational Methods for Genetics of Complex Traits, Pages 141-179.

Abstract: A number of statistical methods have been developed to test for associations between pathways (collections of genes related biologically) and complex genetic traits. Pathway analysis methods were originally developed for analyzing gene expression data, but recently methods have been developed to perform pathway analysis on genome-wide association study (GWAS) data. The purpose of this review is to give an overview of these methods, enabling the reader to gain an understanding of what pathway analysis involves, and to select the method most suited to their purposes. This review describes the various types of statistical methods for pathway analysis, detailing the strengths and weaknesses of each. Factors influencing the power of pathway analyses, such as gene coverage and choice of pathways to analyze, are discussed, as well as various unresolved statistical issues. Finally, a list of computer programs for performing pathway analysis on genome-wide association data is provided.

Chapter 8

Reagan J. Kelly, Jennifer A. Smith, Sharon L.R. Kardia, Providing Context and Interpretability to Genetic Association Analysis Results Using the KGraph, In: Jay C. Dunlap and Jason H. Moore, Editor(s), Advances in Genetics, Academic Press, 2010, Volume 72, Computational Methods for Genetics of Complex Traits, Pages 181-193.

Abstract: The KGraph is a data visualization system that has been developed to display the complex relationships between the univariate and bivariate associations among an outcome of interest, a set of covariates, and a set of genetic variations such as single-nucleotide polymorphisms (SNPs). It allows for easy simultaneous viewing and interpretation of genetic associations, correlations among covariates and SNPs, and information about the replication and cross-validation of these associations. The KGraph allows the user to more easily investigate multicollinearity and confounding through visualization of the multidimensional correlation structure underlying genetic associations. It emphasizes gene-environment interactions, gene-gene interactions, and correlations, all important components of the complex genetic architecture of most human traits. The KGraph was designed for use in gene-centric studies, but can be integrated into association analysis workflows as well. The software is available at http://www.epidkardia.sph.umich.edu/software/kgrapher

The Complex Genetic Architecture of the Metabolome

2010-11-10T06:49:00.002-05:00

Here is yet another organismal study demonstrating the complexity of the mapping relationship between genotype and phenotype. They find that metabolites are canalized and sensitive to environmental perturbation. They recommend gene-environment interaction analysis for GWAS. If Arabidopsis thaliana is this complex, why would we expect Homo sapiens to be any simpler? Further, this kind of complexity exists at the endophenotype level. Now plug all this metabolite variation into many additional layers of biochemistry and physiology for mapping genotype variation to susceptibility of disease.

Chan EKF, Rowe HC, Hansen BG, Kliebenstein DJ (2010) The Complex Genetic Architecture of the Metabolome. PLoS Genet 6(11): e1001198 [PLoS]

Abstract

Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype–metabolite associations distributed non-randomly within the genome. These clusters of genotype–metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype–metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable.

Top 10 Tips for Getting an R01 Funded by the National Library of Medicine

2010-11-06T09:41:00.003-04:00

I just returned from serving on the Biomedical Library and Informatics Review Committee (BLIRC) for the National Library of Medicine (NLM). Here are 10 important things to keep in mind when writing an R01 for the NLM. These are all based on my experience serving on BLIRC over the past year. My bias is bioinformatics and computational biology. A clinical informaticist or library informaticist might have a different perspective. It is always a good idea to talk with your program officer before writing and submitting a grant.

1) Articulate an important and timely informatics question. Be forward-thinking. Know what is hot and what is going to be hot. Make sure that answering your particular scientific question will have an impact on biomedical research or clinical practice.

2) Propose new and novel informatics methods. Innovation very important. Know the literature and where your new method fits in. If you are havng trouble coming up with a truly innovative approach you might try combining existing methods in innovative ways. This is less exciting but much better than an incremental improvement on an existing approach.

3) Avoid purely applied software engineering projects. In other words, don't focus your grant only on building a database, web server or software package. The majority of the grant must be focused on new and novel algorithms or methods. NLM is looking for new informatics methods. They sometimes have separate RFAs for resource development grants (e.g. G08 mechanism).

4) Compare your algorithm or method to state of the art in field. Don't just propose a new algorithm or method. You need to have a baseline approach to compare it to. How do you know that your novel method is going to work better that what people are currently using?

5) A solid plan for how you will evaluate your novel informatics method is critical. How will you know whether your approach is truly working better than the state of the art in the field? Be very specific about how you will evaluate your approach and what the criteria are for concluding it is indeed working.

6) Application to real data is important. Simulation studies are necessary but not sufficient. Describe the biomedical data you will analyze and how you will improve your method based on results. Don't forget the details of how you will actually do the analysis. What significance criteria will you use?

7) Provide as many details as possible about your new and novel informatics algorithm or method given space constraints. Reviewers are unlikely to give you the benefit of the doubt, especially if you are a junior investigator with a poor track record. Tell the reviewers exactly how you are going to develop, extend, modify, apply and evaluate your informatics approach.

8) Be productive! Reviewers want to see a good paper trail from your previous faculty, postdoc and graduate student research. Your reviewers need to be convinced that if you are awarded a grant that you will actually make a contribution to the literature. It is well worth those extra evenings and weekends to get your papers submitted.

9) Innovation and approach have the biggest impact on your final score. The NLM did a factor analysis of scores for significance, innovation, approach, investigator and environment and their relationship with overall impact score. Innovation and approach had the highest correlation with the overall score. I agree with this completely based on my experience serving on BLIRC.

10) Make sure you have good collaborators with real effort budgeted to cover your weaknesses. It is often the case that a junior investigator will add a well-established senior investigator to the grant thinking the name recognition will help. This does not help and is seen as a negative if the senior person does not have real effort budgeted on the grant. Make sure your senior collaborator can contribute at least 5% effort and preferably 10% or more. Otherwise, noone will believe that the senior person will actually do any real work.

Biological validation of increased schizophrenia risk with NRG1, ERBB4, and AKT1 epistasis via functional neuroimaging in healthy controls

2010-10-19T14:20:00.004-04:00

This study biological validates an epistasis model. Nice.

Nicodemus KK, Law AJ, Radulescu E, Luna A, Kolachana B, Vakkalanka R, Rujescu D, Giegling I, Straub RE, McGee K, Gold B, Dean M, Muglia P, Callicott JH, Tan HY, Weinberger DR. Biological validation of increased schizophrenia risk with NRG1, ERBB4, and AKT1 epistasis via functional neuroimaging in healthy controls. Arch Gen Psychiatry. 2010 Oct;67(10):991-1001. [PubMed]

Abstract

CONTEXT: NRG1 is a schizophrenia candidate gene and plays an important role in brain development and neural function. Schizophrenia is a complex disorder, with etiology likely due to epistasis.

OBJECTIVE: To examine epistasis between NRG1 and selected N-methyl-d-aspartate-glutamate pathway partners implicated in its effects, including ERBB4, AKT1, DLG4, NOS1, and NOS1AP.

DESIGN: Schizophrenia case-control sample analyzed using machine learning algorithms and logistic regression with follow-up using neuroimaging on an independent sample of healthy controls.

PARTICIPANTS: A referred sample of schizophrenic patients (n = 296) meeting DSM-IV criteria for schizophrenia spectrum disorder and a volunteer sample of controls for case-control comparison (n = 365) and a separate volunteer sample of controls for neuroimaging (n = 172).

MAIN OUTCOME MEASURES: Epistatic association between single-nucleotide polymorphisms (SNPs) and case-control status; epistatic association between SNPs and the blood oxygen level-dependent physiological response during working memory measured by functional magnetic resonance imaging.

RESULTS: We observed interaction between NRG1 5' and 3' SNPs rs4560751 and rs3802160 (likelihood ratio test P = .00020) and schizophrenia, which was validated using functional magnetic resonance imaging of working memory in healthy controls; carriers of risk-associated genotypes showed inefficient processing in the dorsolateral prefrontal cortex (P = .015, familywise error corrected). We observed epistasis between NRG1 (rs10503929; Thr286/289/294Met) and its receptor ERBB4 (rs1026882; likelihood ratio test P = .035); a 3-way interaction with these 2 SNPs and AKT1 (rs2494734) was also observed (odds ratio, 27.13; 95% confidence interval, 3.30-223.03; likelihood ratio test P = .042). These same 2- and 3-way interactions were further biologically validated via functional magnetic resonance imaging: healthy individuals carrying risk genotypes for NRG1 and ERBB4, or these 2 together with AKT1, were disproportionately less efficient in dorsolateral prefrontal cortex processing. Lower-level interactions were not observed between NRG1 /ERBB4 and AKT1 in association or neuroimaging, consistent with biological evidence that NRG1 × ERBB4 interaction modulates downstream AKT1 signaling.

CONCLUSION: Our data suggest complex epistatic effects implicating an NRG1 molecular pathway in cognitive brain function and the pathogenesis of schizophrenia.

Does heritability hide in epistasis between linked SNPs?

2010-10-18T15:12:00.003-04:00

This is a very short, but highly relevant, note about the possibility of epistasis between linked loci for complex traits such human height. This is indeed a real possibility.

Haig D. Does heritability hide in epistasis between linked SNPs? Eur J Hum Genet. 2010 Oct 6. [PubMed]

A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis

2010-10-15T11:50:00.001-04:00

Our new paper on covariate adjustment for MDR has been published. This approach is included in the latest version of our MDR software.

Gui J, Andrew AS, Andrews P, Nelson HM, Kelsey KT, Karagas MR, Moore JH. A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis. Hum Hered. 2010 Oct 1;70(3):219-225. [PubMed]

Abstract

Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR's ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire.

Random Jungles

2010-10-02T10:34:00.002-04:00

This is the latest in a series of paper developing random forest methods for the analysis of high-dimensional data. Note the software is freely available. http://www.randomjungle.org

Schwarz DF, König IR, Ziegler A. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data. Bioinformatics. 2010 Jul 15;26(14):1752-8. [PubMed]

Abstract

MOTIVATION: Genome-wide association (GWA) studies have proven to be a successful approach for helping unravel the genetic basis of complex genetic diseases. However, the identified associations are not well suited for disease prediction, and only a modest portion of the heritability can be explained for most diseases, such as Type 2 diabetes or Crohn's disease. This may partly be due to the low power of standard statistical approaches to detect gene-gene and gene-environment interactions when small marginal effects are present. A promising alternative is Random Forests, which have already been successfully applied in candidate gene analyses. Important single nucleotide polymorphisms are detected by permutation importance measures. To this day, the application to GWA data was highly cumbersome with existing implementations because of the high computational burden.

RESULTS: Here, we present the new freely available software package Random Jungle (RJ), which facilitates the rapid analysis of GWA data. The program yields valid results and computes up to 159 times faster than the fastest alternative implementation, while still maintaining all options of other programs. Specifically, it offers the different permutation importance measures available. It includes new options such as the backward elimination method. We illustrate the application of RJ to a GWA of Crohn's disease. The most important single nucleotide polymorphisms (SNPs) validate recent findings in the literature and reveal potential interactions.

AVAILABILITY: The RJ software package is freely available at http://www.randomjungle.org

Machine Learning Prediction of Cancer Susceptibility

2010-09-21T18:20:00.002-04:00

Our grant on "Machine Learning Prediction of Cancer Susceptibility" was renewed by the National Library of Medicine at the NIH for another five years of funding (R01 LM009012). This grant supports our work on the development of powerful machine learning and data mining algorithms for the detection and characterization of gene-gene interactions. Here is the project summary:

Susceptibility to sporadic forms of cancer is determined by numerous genetic factors that interact in a nonlinear manner in the context of an individual’s age and environmental exposure. This complex genetic architecture has important implications for the use of genome-wide association studies for identifying susceptibility genes. The assumption of a simple architecture supports a strategy of testing each single-nucleotide polymorphism (SNP) individually using traditional univariate statistics followed by a correction for multiple tests. However, a complex genetic architecture that is characteristic of most types of cancer requires analytical methods that specifically model combinations of SNPs and environmental exposures. While new and novel methods are available for modeling interactions, exhaustive testing of all combinations of SNPs is not feasible on a genome-wide scale because the number of comparisons is effectively infinite. Thus, it is critical that we develop intelligent strategies for selecting subsets of SNPs prior to combinatorial modeling. The objective of this renewal application is to continue the development of a research strategy for the detection, characterization, and interpretation of gene-gene and gene-environment interactions in genome-wide association studies of bladder cancer susceptibility. To accomplish this objective, we will continue developing and evaluating modifications and extensions to the ReliefF family of algorithms for selecting or filtering subsets of single-nucleotide polymorphisms (SNPs) for multifactor dimensionality reduction (MDR) analysis of gene-gene and gene-environment interactions (AIM 1). We will continue developing and evaluating a stochastic wrapper or search strategy for MDR analysis of interactions that utilizes ReliefF values as a heuristic (AIM 2). We will continue to make available ReliefF algorithms as part of our open-source MDR software package (AIM 3). Finally, we will apply the best ReliefF-MDR analysis strategies to the detection, characterization, and interpretation of gene-gene and gene-environment interactions in large genome-wide association studies of bladder cancer susceptibility (AIM 4). We anticipate the proposed machine learning methods will provide powerful new approaches for identifying genetic variations that are predictive of cancer susceptibility.

Towards a complete resolution of the genetic architecture of disease

2010-09-20T15:03:00.002-04:00

How is it possible to discuss the 'complete resolution of genetic architecture' while completely ignoring gene-gene and gene-environment interaction? I am not at all convinced, as these authors are, that a majority of the missing heritability can be explained by rare variants. I also completely disagree with the last sentence of their abstract: "Whereas major challenges undoubtedly remain, particularly regarding data handling and the functional classification of variants, we suggest that these will be largely practical and not conceptual". How is it possible that the major challenges are practical rather that conceptual when we do not yet fully understand the complexity of the human genome?

Singleton AB, Hardy J, Traynor BJ, Houlden H. Towards a complete resolution of the genetic architecture of disease. Trends Genet. 2010 Aug 31. [PubMed]

Abstract

After years of linear gains in the genetic dissection of human disease we are now in a period of exponential discovery. This is particularly apparent for complex disease. Genome-wide association studies (GWAS) have provided myriad associations between common variability and disease, and have shown that common genetic variability is unlikely to explain the entire genetic predisposition to disease. Here we detail how one can expand on this success and systematically identify genetic risks that lead or predispose to disease using next-generation sequencing. Geneticists have had for many years a protocol to identify Mendelian disease. A similar set of tools is now available for the identification of rare moderate-risk loci and common low-risk variants. Whereas major challenges undoubtedly remain, particularly regarding data handling and the functional classification of variants, we suggest that these will be largely practical and not conceptual.

Human Microbiome Visualization Using 3D Technology

2010-09-13T13:53:00.007-04:00

Our paper on visualization of human microbiome data has been accepted for publication as part of the 2011 Pacific Symposium on Biocomputing (PSB). This paper describes our 3D Heatmap application that harnesses the power of 3D video game engines. The 3dheatmap software is freely available from Sourceforge.net. Be sure and buy a 3D mouse!

Moore JH, Cowper Sal.Lari R, Hibberd P, Hill D, Madan JC. Human microbiome visualization using 3D technology. Pacific Symposium on Biocomputing, in press (2011).

Abstract

High-throughput sequencing technology has opened the door to the study of the human microbiome and its relationship with health and disease. This is both an opportunity and a significant biocomputing challenge. We present here a 3D visualization methodology and freely-available software package for facilitating the exploration and analysis of high-dimensional human microbiome data. Our visualization approach harnesses the power of commercial video game development engines to provide an interactive medium in the form of a 3D heat map for exploration of microbial species and their relative abundance in different patients. The advantage of this approach is that the third dimension provides additional layers of information that cannot be visualized using a traditional 2D heat map. We demonstrate the usefulness of this visualization approach using microbiome data collected from a sample of premature babies with and without sepsis.

Tests for Compositional Epistasis under Single Interaction-Parameter Models

2010-08-30T15:51:00.002-04:00

The following is a nice new paper from VanderWeele on statistical tests for compositional epistasis.

Vanderweele TJ, Laird NM. Tests for Compositional Epistasis under Single
Interaction-Parameter Models. Ann Hum Genet. in press (2010) [PubMed]

Abstract

Summary Compositional epistasis is said to be present when the effect of a genetic factor at one locus is masked by a variant at another locus. Although such compositional epistasis is not equivalent to the presence of an interaction in a statistical model, non-standard tests can sometimes be used to detect compositional epistasis. In this paper we consider empirical tests for compositional epistasis under models for the joint effect of two genetic factors which place no restrictions on the main effects of each factor but constrain the interactive effects of the two factors so as to be captured by a single parameter in the model. We describe the implications of these tests for cohort, case-control, case-only and family-based study designs and we illustrate the methods using an example of gene-gene interaction already reported in the literature.

The impact of phenocopy on the genetic analysis of complex traits

2010-08-19T16:46:00.002-04:00

This is an interesting new paper that shows how phenocopy can significantly impact the power to detect gene-gene interactions. Not really sure why they used the random search option of MDR to do the analysis. Doesn't seem like a very powerful approach. They should have done an exhaustive search.

Lescai F, Franceschi C. The impact of phenocopy on the genetic analysis of complex traits. PLoS One. 2010 Jul 29;5(7):e11876. [PubMed]

Abstract

A consistent debate is ongoing on genome-wide association studies (GWAs). A key point is the capability to identify low-penetrance variations across the human genome. Among the phenomena reducing the power of these analyses, phenocopy level (PE) hampers very seriously the investigation of complex diseases, as well known in neurological disorders, cancer, and likely of primary importance in human ageing. PE seems to be the norm, rather than the exception, especially when considering the role of epigenetics and environmental factors towards phenotype. Despite some attempts, no recognized solution has been proposed, particularly to estimate the effects of phenocopies on the study planning or its analysis design. We present a simulation, where we attempt to define more precisely how phenocopy impacts on different analytical methods under different scenarios. With our approach the critical role of phenocopy emerges, and the more the PE level increases the more the initial difficulty in detecting gene-gene interactions is amplified. In particular, our results show that strong main effects are not hampered by the presence of an increasing amount of phenocopy in the study sample, despite progressively reducing the significance of the association, if the study is sufficiently powered. On the opposite, when purely epistatic effects are simulated, the capability of identifying the association depends on several parameters, such as the strength of the interaction between the polymorphic variants, the penetrance of the polymorphism and the alleles (minor or major) which produce the combined effect and their frequency in the population. We conclude that the neglect of the possible presence of phenocopies in complex traits heavily affects the analysis of their genetic data.

Survival dimensionality reduction

2010-08-11T09:55:00.004-04:00

This is a nice new paper on using MDR for survival analysis. Free software is available. We also have a paper in revision on a survival MDR method. I will post information on our paper when it is accepted.

Beretta L, Santaniello A, Vanriel PL, Coenen MJ, Scorza R. Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data. BMC Bioinformatics. 2010 Aug 6;11(1):416. [PubMed]

ABSTRACT: BACKGROUND: Epistasis is recognized as a fundamental part of the genetic architecture of individuals. Several computational approaches have been developed to model gene-gene interactions in case-control studies, however, none of them is suitable for time-dependent analysis. Herein we introduce the Survival Dimensionality Reduction (SDR) algorithm, a non-parametric method specifically designed to detect epistasis in lifetime datasets. RESULTS: The algorithm requires neither specification about the underlying survival distribution nor about the underlying interaction model and proved satisfactorily powerful to detect a set of causative genes in synthetic epistatic lifetime datasets with a limited number of samples and high degree of right-censorship (up to 70%). The SDR method was then applied to a series of 386 Dutch patients with active rheumatoid arthritis that were treated with anti-TNF biological agents. Among a set of 39 candidate genes, none of which showed a detectable marginal effect on anti-TNF responses, the SDR algorithm did find that the rs1801274 SNP in the FcgammaRIIa gene and the rs10954213 SNP in the IRF5 gene non-linearly interact to predict clinical remission after anti-TNF biologicals. CONCLUSIONS: Simulation studies and application in a real-world setting support the capability of the SDR algorithm to model epistatic interactions in candidate-genes studies in presence of right-censored data. AVAILABILITY: http://sourceforge.net/projects/sdrproject/

Exploring the genetic basis of variation in gene predictions with a synthetic association study

2010-08-06T11:24:00.002-04:00

This is an interesting new paper.

Levin TC, Glazer AM, Pachter L, Brem RB, Eisen MB. Exploring the genetic basis of variation in gene predictions with a synthetic association study. PLoS One. 2010 Jul 29;5(7):e11645. [PubMed]

Abstract

Identifying DNA polymorphisms that affect molecular processes like transcription, splicing, or translation typically requires genotyping and experimentally characterizing tissue from large numbers of individuals, which remains expensive and time consuming. Here we introduce an alternative strategy: a "synthetic association study" in which we computationally predict molecular phenotypes on artificial genomes containing randomly sampled combinations of polymorphic alleles, and perform a classical association study to identify genotypes underlying variation in these computationally predicted annotations. We applied this method to characterize the effects on gene structure of 32,792 single-nucleotide polymorphisms between two strains of the antibiotic producing fungus Penicilium chrysogenum. Although these SNPs represent only 0.1 percent of the nucleotides in the genome, they collectively altered 1.8 percent of predicted gene models between these strains. To determine which SNPs or combinations of SNPs were responsible for this variation, we predicted protein-coding genes in 500 intermediate genomes, each identical except for randomly chosen alleles at each SNP position. Of 30,468 gene models in the genome, 557 varied across these 500 genomes. 226 of these polymorphic gene models (40%) were perfectly correlated with individual SNPs, all of which were within or immediately proximal to the affected gene. The genetic architectures of the other 321 were more complex, with several examples of SNP epistasis that would have been difficult to predict a priori. We expect that many of the SNPs that affect computational gene structure reflect a biologically unrealistic sensitivity of the gene prediction algorithm to sequence changes, and we propose that genome annotation algorithms could be improved by minimizing their sensitivity to natural polymorphisms. However, many of the SNPs we identified are likely to affect transcript structure in vivo, and the synthetic association study approach can be easily generalized to any computed genome annotation to uncover relationships between genotype and important molecular phenotypes.

Hints of hidden heritability in GWAS

2010-07-27T09:37:00.003-04:00

This News and Views piece by Greg Gibson summarizes two recent GWAS papers published in Nature Genetics.

Gibson G. Hints of hidden heritability in GWAS. Nat Genet. 2010 Jul;42(7):558-60. [PubMed]

Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010 Jul;42(7):565-9. [PubMed]

Park JH, Wacholder S, Gail MH, Peters U, Jacobs KB, Chanock SJ, Chatterjee N. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat Genet. 2010 Jul;42(7):570-5. [PubMed]

Maternal-Zygotic Epistasis and the Evolution of Genetic Diseases

2010-07-26T16:06:00.001-04:00

Interesting new paper from Nicholas Priest and Mike Wade.

Priest NK, Wade MJ. Maternal-zygotic epistasis and the evolution of genetic diseases. J Biomed Biotechnol. 2010;2010:478732. [PubMed]

Abstract

Many birth defects and genetic diseases are expressed in individuals that do not carry the disease causing alleles. Genetic diseases observed in offspring can be caused by gene expression in mothers and by interactions between gene expression in mothers and offspring. It is not clear whether the underlying pattern of gene expression (maternal versus offspring) affects the incidence of genetic disease. Here we develop a 2-locus population genetic model with epistatic interactions between a maternal gene and a zygotic gene to address this question. We show that maternal effect genes that affect disease susceptibility in offspring persist longer and at higher frequencies in a population than offspring genes with the same effects. We find that specific forms of maternal-zygotic epistasis can maintain disease causing alleles at high frequencies over a range of plausible values. Our findings suggest that the strength and form of epistasis and the underlying pattern of gene expression may greatly influence the prevalence of human genetic diseases.

GWAS: heritability missing in action?

2010-07-24T10:04:00.003-04:00

The missing heritability discussion continues in this letter. They do acknowledge gene-gene interactions and cite a few of my papers.

Clarke AJ, Cooper DN. GWAS: heritability missing in action? Eur J Hum Genet. 2010 [PubMed]

"So, where is this ‘missing heritability’? We respond to this question in two different ways. First, we believe that complex disorders are indeed complex and that genetic studies of complex disorders in humans face a number of challenges including gene–gene and gene–environment interactions and epigenetic modification of the genome. Second, we shall argue that high estimates of heritability have been misinterpreted as showing that a predisposition to such a condition (one with high heritability) must have been transmitted through the family from parent to child. The complexity of these common conditions is apparent from the range of factors that need to be considered as potentially contributing to the ‘missing heritability’. These can be rare variants whose significance is not yet recognised, less uncommon variants of small effect, or common variants of very small effect (very weakly penetrant)."

Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine

2010-07-19T12:45:00.002-04:00

This is a very nice paper. I like the use of the Google Page-Rank style algorithm.

Davis NA, Crowe JE Jr, Pajewski NM, McKinney BA. Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine. Genes Immun. in press (2010). [PubMed]

Abstract

The variation in antibody response to vaccination likely involves small contributions of numerous genetic variants, such as single-nucleotide polymorphisms (SNPs), which interact in gene networks and pathways. To accumulate the bits of genetic information relevant to the phenotype that are distributed throughout the interaction network, we develop a network eigenvector centrality algorithm (SNPrank) that is sensitive to the weak main effects, gene-gene interactions and small higher-order interactions through hub effects. Analogous to Google PageRank, we interpret the algorithm as the simulation of a random SNP surfer (RSS) that accumulates bits of information in the network through a dynamic probabilistic Markov chain. The transition matrix for the RSS is based on a data-driven genetic association interaction network (GAIN), the nodes of which are SNPs weighted by the main-effect strength and edges weighted by the gene-gene interaction strength. We apply SNPrank to a GAIN analysis of a candidate-gene association study on human immune response to smallpox vaccine. SNPrank implicates a SNP in the retinoid X receptor alpha (RXRA) gene through a network interaction effect on antibody response. This vitamin A- and D-signaling mediator has been previously implicated in human immune responses, although it would be neglected in a standard analysis because its significance is unremarkable outside the context of its network centrality. This work suggests SNPrank to be a powerful method for identifying network effects in genetic association data and reveals a potential vitamin regulation network association with antibody response.

Epistasis: A network of interactors

2010-07-17T12:42:00.003-04:00

The following is a brief note from Nature Reviews Genetics highlighting several new papers on epistasis. Both look interesting.

Casci T. Epistasis: A network of interactors. Nat Rev Genet. 2010
Aug;11(8):531. [PubMed]

Deep Epistasis in Human Metabolism

2010-07-13T09:45:00.002-04:00

Interesting new paper. Connecting epistasis with metabolism is very important for understanding how genetic variation impacts complex traits.

Imielinski M, Belta C. Deep epistasis in human metabolism. Chaos. 2010
Jun;20(2):026104. [PubMed]

Abstract

We extend and apply a method that we have developed for deriving high-order epistatic relationships in large biochemical networks to a published genome-scale model of human metabolism. In our analysis we compute 33,328 reaction sets whose knockout synergistically disables one or more of 43 important metabolic functions. We also design minimal knockouts that remove flux through fumarase, an enzyme that has previously been shown to play an important role in human cancer. Most of these knockout sets employ more than eight mutually buffering reactions, spanning multiple cellular compartments and metabolic subsystems. These reaction sets suggest that human metabolic pathways possess a striking degree of parallelism, inducing "deep" epistasis between diversely annotated genes. Our results prompt specific chemical and genetic perturbation follow-up experiments that could be used to query in vivo pathway redundancy. They also suggest directions for future statistical studies of epistasis in genetic variation data sets.

Is too much data shattering our focus and rewriting our brains?

2010-07-05T13:47:00.004-04:00

Wired magazine has a review and essay about a new book called 'The Shallows' by Nicholas Carr. In this book Carr, argues that the internet is rewiring our brains to be good at 'cursory reading, hurried and distracted thinking and superficial learning'. The effect of this this is that very little of what we see on the internet goes in to longterm memory because there isn't time for the brain to make the important connections and establish context. Thus, we don't actually 'learn' very much from the internet. I think the same thing is happening in genetics and epidemiology with the onslaught of data from high-throughput technology. The field is caught up in a perpetual frenzy to adapt to the latest technology being thrown our way. The result is that we spend much of our time in a panic about data cleaning, data management and high-throughput data analysis. We are not spending our valuable time thinking deeply about the questions and the intepretation of research results. Is it possible that the data deluge is resulting in 'cursory reading, hurried and distracted thinking and superficial learning' just as with the internet? What is happening to the students we are training? Are we really training them how to think or are they just learning how to do?

A Statistical Approach to Genetic Epidemiology

2010-06-21T10:40:00.002-04:00

I received today my new copy of "A Statistical Approach to Genetic Epidemiology" by Ziegler and Konig [Amazon]. I very much liked the first edition and the new second edition seems to have been nicely updated. I was particularly happy to see an entire chapter devoted to gene-gene and gene-environment interaction. They even highlight our MDR approach. This is a very clearly written book that provides a nice introduction to statistical genetics. I highly recommend it. It is a bit expensive for a paperback though which might make it inaccessible to students ($129.95 list).

What we aren't teaching our genetics students

2010-06-15T08:45:00.003-04:00

My new copy of Strachan and Read's textbook on Human Molecular Genetics arrived yesterday. I have always thought that this was a clearly written book that provides a nice overview of human genetics from more of a molecular point of view.

The first thing I turned to was the chapter on genetic association studies to see how they present genome-wide association studies (GWAS). The description of what a GWAS is was not bad. However, the context of the discussion was very much the status quo with text on the limitations on GWAS that very much focused on common variants vs. rare variants. There was no discussion whatsoever on genetic arcitecture and the complexity of the genotype-phenotype relationship due to penomena such as epistasis. I immediately went to the index to look for the word epistasis and found one entry on page 71. This turns out to be a very brief mention of epistasis as gene A controlling gene B as a cause of locus heterogeneity. That is it.

It is indeed disappointing to see a modern human genetics textbook fail to rigorously present and discuss the complexity of genetic architecture. Nothing has changed since I was a graduate student in the 1990s. At least the 4th edition lists epistasis in the index. It did not appear at all in the 3rd edition. I guess this is progress.

A Decade Later, Genetic Map Yields Few New Cures

2010-06-14T07:12:00.003-04:00

This NYT article highlights the grand failure of GWAS. However, the writer fails to uncover the real reason this hasn't worked - complexity. As I point in a recent Nature Reviews Genetics viewpoint piece (see May 18, 2010 post below), this grand failure should not be a surprise to anyone that marvels at the complexity of genetic architecture. The NYT needs to interview the researchers in the field that are thinking deeply about the problem and not those that are just throwing more technology at the genome.

A Decade Later, Genetic Map Yields Few New Cures [NYT]

Common variants, rare variants or rare combinations of common variants?

2010-06-04T09:29:00.005-04:00

Terwilliger and Göring claimed in their 2000 paper "that a larger number of less common alleles is likely to be involved in the etiology of complex disease". They now claim that the grand failure of GWAS and the common variant/common disease(CVCD) hypothesis of Reich and Lander (2001) validates this claim. Their new paper in Human Biology is an interesting read and it is certainly fun to see them slam GWAS. However, I am not yet convinced that the 'missing heritability' is largely due to rare variants. I think it is still highly likely that common variants are important through gene-gene and gene-environment interactions. It is important to note that genotype combinations from multiple common variants are inherently rare in the population. The rare variant effects that everyone is now so excited about might very well be right under our noses. I pointed this out in a recent Nature Reviews Genetics viewpoint piece (see May 18, 2010 post below).

Terwilliger JD, Göring HH. Update to Terwilliger and Göring's "Gene mapping in the 20th and 21st centuries" (2000): gene mapping when rare variants are common and common variants are rare. Hum Biol. 2009 Dec;81(5-6):729-33.

Terwilliger JD, Göring HH. Gene mapping in the 20th and 21st centuries: statistical methods, data analysis, and experimental design. Hum Biol. 2000 Feb;72(1):63-132.

Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010 Jun;11(6):446-50.

Reich DE, Lander ES. On the allelic spectrum of human disease. Trends Genet. 2001 Sep;17(9):502-10.

Note added June 15: Here is a recent blog post I ran across on the same paper: Getting Genetics Done

A screening methodology based on Random Forests to improve the detection of gene-gene interactions

2010-05-24T17:10:00.002-04:00

This is an interesting new paper proposing to use random forests to filter SNPs for MDR modeling of gene-gene interactions. We have seen similar results with our ReliefF-based algorithms. Removing noisy SNPs prior to MDR modeling helps cut down the number of combinations that need to be evaluated thus reducing the chances of overfitting. I am guessing RF work well on smaller numbers of SNPs but will not scale to GWAS when there are no marginal effects of the interacting loci.

De Lobel L, Geurts P, Baele G, Castro-Giner F, Kogevinas M, Van Steen K. A screening methodology based on Random Forests to improve the detection of gene-gene interactions. Eur J Hum Genet. 2010 [PubMed]

Abstract

The search for susceptibility loci in gene-gene interactions imposes a methodological and computational challenge for statisticians because of the large dimensionality inherent to the modelling of gene-gene interactions or epistasis. In an era in which genome-wide scans have become relatively common, new powerful methods are required to handle the huge amount of feasible gene-gene interactions and to weed out false positives and negatives from these results. One solution to the dimensionality problem is to reduce data by preliminary screening of markers to select the best candidates for further analysis. Ideally, this screening step is statistically independent of the testing phase. Initially developed for small numbers of markers, the Multifactor Dimensionality Reduction (MDR) method is a nonparametric, model-free data reduction technique to associate sets of markers with optimal predictive properties to disease. In this study, we examine the power of MDR in larger data sets and compare it with other approaches that are able to identify gene-gene interactions. Under various interaction models (purely and not purely epistatic), we use a Random Forest (RF)-based prescreening method, before executing MDR, to improve its performance. We find that the power of MDR increases when noisy SNPs are first removed, by creating a collection of candidate markers with RFs. We validate our technique by extensive simulation studies and by application to asthma data from the European Committee of Respiratory Health Study II.

Missing heritability and strategies for finding the underlying causes of common diseases

2010-05-18T13:52:00.003-04:00

I participated in the following viewpoint piece in Nature Reviews Genetics. A number of good points are made by each author. I was hoping the piece would be a bit more controversial.

Evan E. Eichler, Jonathan Flint, Greg Gibson, Augustine Kong, Suzanne M. Leal, Jason H. Moore and Joseph H. Nadeau. Missing heritability and strategies for finding the underlying causes of common diseases. Nature Reviews Genetics 11, 446:450 (2010). [Nature] [PubMed]

Abstract

Although recent genome-wide studies have provided valuable insights into the genetic basis of human disease, they have explained relatively little of the heritability of most complex traits, and the variants identified through these studies have small effect sizes. This has led to the important and hotly debated issue of where the ‘missing heritability’ of complex diseases might be found. Here, seven leading geneticists offer their opinion about where this heritability is likely to lie, what this could tell us about the underlying genetic architecture of common diseases and how this could inform research strategies for uncovering genetic risk factors.

Postdoctoral Position in Computational Genetics at Vanderbilt

2010-05-11T11:07:00.001-04:00

This looks like a good opportunity.

The Program in Computational Genomics in the CHGR at Vanderbilt University has an immediate opening for a post-doctoral fellow to pursue new and exciting research in human genetics. The successful candidate will have a Ph.D. degree (or equivalent) in genetics, human genetics, epidemiology, computational biology, bioinformatics, biostatistics, or related field. The successful candidate will work as part of an established research team and will have access to several large genome-wide association study (GWAS) datasets and numerous follow-up studies for association and copy number variation. Both established and evolving methods to detect and characterize single and multi-locus effects will be applied, and rich phenotypic data will permit analysis of discrete and quantitative traits. The candidate will integrate data from linkage, association, CNV, and re-sequencing studies along with knowledge of gene networks to identify susceptibility genes. He/She will also have the opportunity to conduct research in methods development in the study of gene-gene and gene-environment interactions for complex disease. In addition, the candidate will have the opportunity to interact with numerous senior investigators in multiple fields.

The CHGR is an interdisciplinary center with over 40 faculty representing numerous clinical and basic science departments. It has a highly interactive research program organized into three thematic programs: Disease Gene Discovery, Computational Genomics, and Translational Genetics. The CHGR has substantial core facilities for family and patient ascertainment; DNA banking, genotyping, and sequencing; and computational genomics, data management, and data analysis. It occupies over 14,000 sf of newly appointed wet and dry lab space. The CHGR faculty and staff enjoy the substantial benefits of the collaborative Vanderbilt atmosphere. More information about the specific CHGR post-doctoral positions can be found at: http://chgr.mc.vanderbilt.edu/chgr-careers/postdoc.

Interested candidates should forward their C.V. a description of their research interests (preferably by email), and three letters of reference by June 30, 2010:

Dr. Marylyn Ritchie, PhD
c/o Maria Comer
Center for Human Genetics Research
Vanderbilt University
519 Light Hall
Nashville, TN 37232-0700
Email: maria.comer@vanderbilt.edu
Tel: 615-322-7909
Fax: 615-343-8619

Bioinformatics, Genomics and Alzheimer's Disease

2010-05-04T10:21:00.002-04:00

This is a nice example of how bioinformatics and genomics can be used together to study a complex problem like Alzheimer's disease. Studies like this one will be useful for providing the kind of biological knowledge we need to guide machine learning analysis of gene-gene interactions in genome-wide association studies.

Gómez Ravetti M, Rosso OA, Berretta R, Moscato P. Uncovering molecular biomarkers that correlate cognitive decline with the changes of hippocampus' gene expression profiles in Alzheimer's disease. PLoS One. 2010 Apr 13;5(4):e10153. [PLoS One]

Abstract

BACKGROUND: Alzheimer's disease (AD) is characterized by a neurodegenerative progression that alters cognition. On a phenotypical level, cognition is evaluated by means of the MiniMental State Examination (MMSE) and the post-mortem examination of Neurofibrillary Tangle count (NFT) helps to confirm an AD diagnostic. The MMSE evaluates different aspects of cognition including orientation, short-term memory (retention and recall), attention and language. As there is a normal cognitive decline with aging, and death is the final state on which NFT can be counted, the identification of brain gene expression biomarkers from these phenotypical measures has been elusive. METHODOLOGY/PRINCIPAL FINDINGS: We have reanalysed a microarray dataset contributed in 2004 by Blalock et al. of 31 samples corresponding to hippocampus gene expression from 22 AD subjects of varying degree of severity and 9 controls. Instead of only relying on correlations of gene expression with the associated MMSE and NFT measures, and by using modern bioinformatics methods based on information theory and combinatorial optimization, we uncovered a 1,372-probe gene expression signature that presents a high-consensus with established markers of progression in AD. The signature reveals alterations in calcium, insulin, phosphatidylinositol and wnt-signalling. Among the most correlated gene probes with AD severity we found those linked to synaptic function, neurofilament bundle assembly and neuronal plasticity. CONCLUSIONS/SIGNIFICANCE: A transcription factors analysis of 1,372-probe signature reveals significant associations with the EGR/KROX family of proteins, MAZ, and E2F1. The gene homologous of EGR1, zif268, Egr-1 or Zenk, together with other members of the EGR family, are consolidating a key role in the neuronal plasticity in the brain. These results indicate a degree of commonality between putative genes involved in AD and prion-induced neurodegenerative processes that warrants further investigation.

FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals

2010-04-30T18:58:00.001-04:00

A new extension of MDR.

Cattaert T, Urrea V, Naj AC, De Lobel L, De Wit V, Fu M, Mahachie John JM, Shen H, Calle ML, Ritchie MD, Edwards TL, Van Steen K. FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. PLoS One. 2010 Apr 22;5(4):e10304. [PubMed]

Abstract

We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.

Integrating pathway analysis and genetics of gene expression for genome-wide association studies

2010-04-26T09:26:00.005-04:00

This is a very nice paper showing the power of pathway-based approach for GWAS. This is one of many new papers appearing casting doubt on the agnostic biostatistical approach to GWAS analysis.

Zhong H, Yang X, Kaplan LM, Molony C, Schadt EE. Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am J Hum Genet. 2010 Apr 9;86(4):581-91. [PubMed]

Abstract

Genome-wide association studies (GWAS) have achieved great success identifying common genetic variants associated with common human diseases. However, to date, the massive amounts of data generated from GWAS have not been maximally leveraged and integrated with other types of data to identify associations beyond those associations that meet the stringent genome-wide significance threshold. Here, we present a novel approach that leverages information from genetics of gene expression studies to identify biological pathways enriched for expression-associated genetic loci associated with disease in publicly available GWAS results. Specifically, we first identify SNPs in population-based human cohorts that associate with the expression of genes (eSNPs) in the metabolically active tissues liver, subcutaneous adipose, and omental adipose. We then use this functionally annotated set of SNPs to investigate pathways enriched for eSNPs associated with disease in publicly available GWAS data. As an example, we tested 110 pathways from the Kyoto Encylopedia of Genes and Genomes (KEGG) database and identified 16 pathways enriched for genes corresponding to eSNPs that show evidence of association with type 2 diabetes (T2D) in the Wellcome Trust Case Control Consortium (WTCCC) T2D GWAS. We then replicated these findings in the Diabetes Genetics Replication and Meta-analysis (DIAGRAM) study. Many of the pathways identified have been proposed as important candidate pathways for T2D, including the calcium signaling pathway, the PPAR signaling pathway, and TGF-beta signaling. Importantly, we identified other pathways not previously associated with T2D, including the tight junction, complement and coagulation pathway, and antigen processing and presentation pathway. The integration of pathways and eSNPs provides putative functional bridges between GWAS and candidate genes or pathways, thus serving as a potential powerful approach to identifying biological mechanisms underlying GWAS findings.

Genotype to Phenotype: A Complex Problem

2010-04-23T09:29:00.002-04:00

This is a great new study that just appeared in Science. If it is so complex in yeast, why would we expect humans to be simpler?

Dowell RD et al. Genotype to Phenotype: A Complex Problem. Science 328, 469 (2010) [PDF]

"Our genome-wide survey of conditionally essential genes demonstrates that in most cases a complex set of background-specific modifiers influence a mutation whose phenotype differs between individuals. These results raise the possibility that similar complex modifiers may largely explain the difficulty in identifying the genetic basis for individual phenotypes.

Microbiome Bioinformatics at PSB - Call for Papers

2010-04-22T10:39:00.006-04:00

Dr. James Foster and I are organizing a session at the 2011 Pacific Symposium on Biocomputing (PSB) on microbiome studies. We invite contributions to microbiome studies that expand our understanding of the composition, structure, and function of microbial ecosystems and their impact on human health and well being. We particularly encourage studies that apply "next generation" sequencing technologies, and reports of tools that support the analysis and sharing of data from such studies. Problems of specific interest may include, but are not limited to:

• Algorithm, tool, and database development for analyzing data from shotgun metagenomics, 16S hypervariable region surveys, and microbial transcriptomics.

• Association studies of microbial consortia with human health, agriculture, bioremediation, climate change, and engineering.

• Theoretical and simulation studies of microbial ecology, function, and evolution.

• The role of microbial consortia in natural ecosystems.

Other topics within the subject area are welcome. Note that all submitted papers should make clear their relevance for the study of Microbiome Studies. If unsure whether your paper fits the session theme, please contact one of the co-chairs. For more information see the PSB call for papers.

The conference will take place on January 3-7, 2011 at the Fairmont Orchid Resort on the Big Island of Hawaii, USA.

All paper submissions are due July 12, 2010.

The genetic landscape of a cell

2010-04-19T15:37:00.001-04:00

In case you missed it, this is a very interesting paper on gene-gene interaction networks in yeast.

Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, et al. The genetic landscape of a cell. Science. 2010 Jan 22;327(5964):425-31. [PubMed]

Abstract

A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

Genetic Epidemiology

2010-04-03T09:05:00.002-04:00

The April, 2010 issue of Genetic Epidemiology is packed with interesting papers that consider multilocus effects. IMHO, Genetic Epidemiology has been publishing more interesting papers than any of the top journals including Science, Nature and Nature Genetics. This is the place to look for research results that are likely to have an impact. Similarly, I find the content of the annual meeting of the International Genetic Epidemiology Society (IGES) to be very interestiing and useful. This is where the real dialogue about solving the missing heritability problem is happening.

Human genome at ten: Life is complicated

2010-04-01T08:56:00.002-04:00

It is nice to see a major journal providing some recognition that life is indeed complex. I would love to see Nature change its editorial policy to only consider publishing genetic epidemiology papers that directly address the complexity of the genome and human biology. For example, no paper should be published without a thoughtful analysis of gene-gene interactions, gene-environment interactions, pleitropy, locus heterogeneity or other complex phenomena. I would be more than happy to work with Nature to help them change their editorial policy away from technology-driven single-SNP-at-a-time type sciencific papers that have not really improved our ability to predict who is at risk for various common human diseases.

Hayden, EC. Human genome at ten: Life is complicated. Nature 464, 664-667 (2010). [Nature]

The more biologists look, the more complexity there seems to be. Erika Check Hayden asks if there's a way to make life simpler.

On the classification of epistatic interactions

2010-03-31T10:49:00.001-04:00

A wonderful new paper on epistasis from Marcus Feldman. I highly recommend reading this one.

Gao H, Granka JM, Feldman MW. On the classification of epistatic interactions. Genetics. 2010 Mar;184(3):827-37. [PubMed]

Abstract

Modern genomewide association studies are characterized by the problem of "missing heritability." Epistasis, or genetic interaction, has been suggested as a possible explanation for the relatively small contribution of single significant associations to the fraction of variance explained. Of particular concern to investigators of genetic interactions is how to best represent and define epistasis. Previous studies have found that the use of different quantitative definitions for genetic interaction can lead to different conclusions when constructing genetic interaction networks and when addressing evolutionary questions. We suggest that instead, multiple representations of epistasis, or epistatic "subtypes," may be valid within a given system. Selecting among these epistatic subtypes may provide additional insight into the biological and functional relationships among pairs of genes. In this study, we propose maximum-likelihood and model selection methods in a hypothesis-testing framework to choose epistatic subtypes that best represent functional relationships for pairs of genes on the basis of fitness data from both single and double mutants in haploid systems. We gauge the performance of our method with extensive simulations under various interaction scenarios. Our approach performs reasonably well in detecting the most likely epistatic subtype for pairs of genes, as well as in reducing bias when estimating the epistatic parameter (epsilon). We apply our approach to two available data sets from yeast (Saccharomyces cerevisiae) and demonstrate through overlap of our identified epistatic pairs with experimentally verified interactions and functional links that our results are likely of biological significance in understanding interaction mechanisms. We anticipate that our method will improve detection of epistatic interactions and will help to unravel the mysteries of complex biological systems.

Replication in genetic studies of complex traits

2010-03-30T11:35:00.003-04:00

This is a nice paper from 2004 that I just read for the first time. Even more important now given the frenzy over replication in association studies.

Sillanpää MJ, Auranen K. Replication in genetic studies of complex traits. Ann Hum Genet. 2004 Nov;68(Pt 6):646-57 [PubMed]

Abstract

Disappointments in replicating initial findings in gene mapping for complex traits are often attributed to small sample sizes and inadequate techniques to determine the threshold value. This is clearly not the whole truth. More fundamental reasons lie in the inherent heterogeneity related to disease, including genetic heterogeneity, differences in allele frequencies, and context-dependency in genetic architecture. There are also other reasons related to the data collection and analysis. Replication may remain a source of frustration unless more emphasis is put on controlling these sources of heterogeneity between studies.

The discriminative accuracy of genomic profiling in the prediction of common complex diseases

2010-03-27T09:07:00.005-04:00

I am enjoying the numerous papers appearing in the literature on the inability of genetic assoication results to accurately predict common complex diseases. The wake up call is here. My advice to current students is to forget everything you have learned over the last five years and go back and read the historical literature from geneticists that think deeply about genetic architecture. I started a reading list last year on Epistasis Blog. See the May 7, 2009 post on 100 papers every graduate student (in genetic epidemiology) should read.

Moonesinghe R, Liu T, Khoury MJ. Evaluation of the discriminative accuracy of genomic profiling in the prediction of common complex diseases. Eur J Hum Genet. 2010 Apr;18(4):485-9. [PubMed]

Abstract

Genetic testing for susceptibility to common diseases based on a combination of genetic markers may be needed because the effect size associated with each genetic marker is small. Whether or not a genome profile based on a combination of markers could yield a useful test can be evaluated by assessing the discriminative accuracy. The authors present a simple method to calculate the clinical discriminative accuracy of a genomic profile when the relative risk and genotype frequency of each genotype are known. In addition, the clinical discriminative accuracy of a genetic test is presented for given values of the heritability and prevalence of the disease and for the population-attributable fraction of the combined genetic markers. For given values of relative risk and genotype frequency, the discriminative accuracy increases with increasing heritability but declines with increasing prevalence of the disease. For a given value of population-attributable fraction, the discriminative accuracy increases with increasing relative risks, but declines with increasing genotype frequency. On the basis of population-attributable fraction and estimates of heritability of disease, the number of risk genotypes required to have a reasonable clinical discriminative accuracy is much higher than the genome profiles available at present.

Disease Cause Is Pinpointed With Genome

2010-03-11T08:42:00.003-05:00

A recent New York Times article discusses the recent successes with using deep sequencing for rare Mendelian diseases. This is clearly a success. However, those pushing this technology significantly overstate the potential for common human diseases. For example, David Goldstein is quoted as saying “We are finally about to turn the corner, and I suspect that in the next few years human genetics will finally begin to systematically deliver clinically meaningful findings”. Have we not learned the lessons from the past? This the same hype that came with the human genome project, the HapMap project and with GWAS. Technology will not alone solve these problems. We need a fundamental shift in how we think about the compexity of the genetic architecture of common human diseases.

The Genetic Interpretation of Area under the ROC Curve

2010-02-26T13:24:00.003-05:00

Two very interesting new papers. These are must read.

Wray NR, Yang J, Goddard ME, Visscher PM. The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling. PLoS Genet. 2010 6(2): e1000864. [PLoS]

Wray NR, Goddard ME. Multi-locus models of genetic risk of disease. Genome Med. 2010 Feb 2;2(2):10. [PubMed]

Maximal conditional chi-square importance in random forests

2010-02-22T14:01:00.002-05:00

Interesting new paper. Nice to see the conditioning on other SNPs.

Wang M, Chen X, Zhang H. Maximal conditional chi-square importance in random forests. Bioinformatics. 2010. [PubMed]

Abstract

MOTIVATION: High-dimensional data are frequently generated in genome-wide association studies (GWAS) and other studies. It is important to identify features such as single nucleotide polymorphisms (SNPs) in GWAS that are associated with a disease. Random forests represent a very useful approach for this purpose, using a variable importance score. This importance score has several shortcomings. We propose an alternative importance measure to overcome those shortcomings. RESULTS: We characterized the effect of multiple SNPs under various models using our proposed importance measure in random forests, which uses maximal conditional chi-square (MCC) as a measure of asso-ciation between a SNP and the trait conditional on other SNPs. Based on this importance measure, we employed a permutation test to estimate empirical p-values of SNPs. Our method was compared to a univariate test and the permutation test using the Gini and per-mutation importance. In simulation, the proposed method performed consistently superior to the other methods in identifying of risk SNPs. In a genome-wide association study of age-related macular degeneration, the proposed method confirmed two significant SNPs (at the genomewide adjusted level of 0.05). Further analysis showed that these two SNPs conformed with a heterogeneity model. Com-pared with the existing importance measures, the MCC importance measure is more sensitive to complex effects of risk SNPs by utiliz-ing conditional information on different SNPs. The permutation test with the MCC importance measure provides an efficient way to iden-tify candidate SNPs in GWAS and facilitates the understanding of the etiology between genetic variants and complex diseases. CONTACT: heping.zhang@yale.edu.

The GenEpi Toolbox

2010-02-03T09:00:00.002-05:00

This looks useful. Has anyone tried it? The GenEpi Toolbox.

Here is a recent paper discussing this new bioinformatics resource for genetic epidemiology.

Coassin S, Brandstätter A, Kronenberg F. Lost in the space of bioinformatic tools: A constantly updated survival guide for genetic epidemiology. The GenEpi Toolbox. Atherosclerosis. 2009 Oct 29. [Epub ahead of print] [PubMed] PMID:19963217.

Abstract

Genome-wide association studies (GWASs) led to impressive advances in the elucidation of genetic factors underlying complex phenotypes and diseases. However, the ability of GWAS to identify new susceptibility loci in a hypothesis-free approach requires tools to quickly retrieve comprehensive information about a genomic region and analyze the potential effects of coding and non-coding SNPs in a candidate gene region. Furthermore, once a candidate region is chosen for resequencing and fine-mapping studies, the identification of several rare mutations is likely and requires strong bioinformatic support to properly evaluate and prioritize the found mutations for further analysis. Due to the variety of regulatory layers that can be affected by a mutation, a comprehensive in-silico evaluation of candidate SNPs can be a demanding and very time-consuming task. Although many bioinformatic tools that significantly simplify this task were made available in the last years, their utility is often still unknown to researches not intensively involved in bioinformatics. We present a comprehensive guide of 64 tools and databases to bioinformatically analyze gene regions of interest to predict SNP effects. In addition, we discuss tools to perform data mining of large genetic regions, predict the presence of regulatory elements, make in-silico evaluations of SNPs effects and address issues ranging from interactome analysis to graphically annotated proteins sequences. Finally, we exemplify the use of these tools by applying them to hits of a recently performed GWAS. Taken together a combination of the discussed tools are summarized and constantly updated in the web-based "GenEpi Toolbox" (http://genepi_toolbox.i-med.ac.at) and can help to get a glimpse at the potential functional relevance of both large genetic regions and single nucleotide mutations which might help to prioritize the next steps.

Genetic Heterogeneity and Cancer

2010-02-02T16:46:00.002-05:00

The following paper raises the important issue of genetic heterogeneity. This is a nice paper because it addresses the complexity of genetic architecture. However, it is very poorly cited. Note how few citations there are before the year 2000. This is not a new idea. It would have been nice if they could have provided the reader with a historical perspective on this important phenomenon.

Galvan A, Ioannidis JP, Dragani TA. Beyond genome-wide association studies: genetic heterogeneity and individual predisposition to cancer. Trends Genet. 2010 Jan 25. [Epub ahead of print] [PubMed] PMID: 20106545.

Abstract

Genome-wide association studies (GWAS) using population-based designs have identified many genetic loci associated with risk of a range of complex diseases including cancer; however, each locus exerts a very small effect and most heritability remains unexplained. Family-based pedigree studies have also suggested tentative loci linked to increased cancer risk, often characterized by pedigree-specificity. However, comparison between the results of population- and family-based studies shows little concordance. Explanations for this unidentified genetic 'dark matter' of cancer include phenotype ascertainment issues, limited power, gene-gene and gene-environment interactions, population heterogeneity, parent-of-origin-specific effects, and rare and unexplored variants. Many of these reasons converge towards the concept of genetic heterogeneity that might implicate hundreds of genetic variants in regulating cancer risk. Dissecting the dark matter is a challenging task. Further insights can be gained from both population association and pedigree studies.

An Open Access Database of Genome-wide Association Results

2010-02-01T16:03:00.002-05:00

Ran across this paper today. Might be useful for those interested in reanalysis of GWAS data.

Johnson AD, O'Donnell CJ. An open access database of genome-wide association results. BMC Med Genet. 2009 Jan 22;10:6. [PubMed] PMID: 19161620; PubMed Central PMCID: PMC2639349.

BACKGROUND: The number of genome-wide association studies (GWAS) is growing rapidly leading to the discovery and replication of many new disease loci. Combining results from multiple GWAS datasets may potentially strengthen previous conclusions and suggest new disease loci, pathways or pleiotropic genes. However, no database or centralized resource currently exists that contains anywhere near the full scope of GWAS results. METHODS: We collected available results from 118 GWAS articles into a database of 56,411 significant SNP-phenotype associations and accompanying information, making this database freely available here. In doing so, we met and describe here a number of challenges to creating an open access database of GWAS results. Through preliminary analyses and characterization of available GWAS, we demonstrate the potential to gain new insights by querying a database across GWAS. RESULTS: Using a genomic bin-based density analysis to search for highly associated regions of the genome, positive control loci (e.g., MHC loci) were detected with high sensitivity. Likewise, an analysis of highly repeated SNPs across GWAS identified replicated loci (e.g., APOE, LPL). At the same time we identified novel, highly suggestive loci for a variety of traits that did not meet genome-wide significant thresholds in prior analyses, in some cases with strong support from the primary medical genetics literature (SLC16A7, CSMD1, OAS1), suggesting these genes merit further study. Additional adjustment for linkage disequilibrium within most regions with a high density of GWAS associations did not materially alter our findings. Having a centralized database with standardized gene annotation also allowed us to examine the representation of functional gene categories (gene ontologies) containing one or more associations among top GWAS results. Genes relating to cell adhesion functions were highly over-represented among significant associations (p < 4.6 x 10(-14)), a finding which was not perturbed by a sensitivity analysis. CONCLUSION: We provide access to a full gene-annotated GWAS database which could be used for further querying, analyses or integration with other genomic information. We make a number of general observations. Of reported associated SNPs, 40% lie within the boundaries of a RefSeq gene and 68% are within 60 kb of one, indicating a bias toward gene-centricity in the findings. We found considerable heterogeneity in information available from GWAS suggesting the wider community could benefit from standardization and centralization of results reporting.

Whole Genome Association Study of Brain-Wide Imaging Phenotypes

2010-01-30T11:21:00.004-05:00

We did a neat cluster analysis in this paper. Combining GWAS data with brain imaging phenotypes is a challenge.

Shen L, Kim S, Risacher SL, Nho K, Swaminathan S, West JD, Foroud T, Pankratz N, Moore JH, Sloan CD, Huentelman MJ, Craig DW, Dechairo BM, Potkin SG, Jack CR Jr, Weiner MW, Saykin AJ; Alzheimer’s Disease Neuroimaging Initiative. Whole Genome Association Study of Brain-Wide Imaging Phenotypes for Identifying Quantitative Trait Loci in MCI and AD: A Study of the ADNI Cohort. Neuroimage. 2010 Jan 22. [PubMed] PubMed PMID: 20100581.

Abstract

A genome-wide, whole brain approach to investigate genetic effects on neuroimaging phenotypes for identifying quantitative trait loci is described. The Alzheimer's Disease Neuroimaging Initiative 1.5T MRI and genetic dataset was investigated using voxel-based morphometry (VBM) and FreeSurfer parcellation followed by genome wide association studies (GWAS). 142 measures of grey matter (GM) density, volume, and cortical thickness were extracted from baseline scans. GWAS, using PLINK, were performed on each phenotype using quality controlled genotype and scan data including 530,992 of 620,903 single nucleotide polymorphisms (SNPs) and 733 of 818 participants (175 AD, 354 amnestic mild cognitive impairment, MCI, and 204 healthy controls, HC). Hierarchical clustering and heat maps were used to analyze the GWAS results and associations are reported at two significance thresholds (p<10(-7) and p<10(-6)). As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. Detailed image analyses of rs6463843 (flanking NXPH1) revealed reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. A genome wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication.

Genetics of diabetes reveals biology but does not improve prediction

2010-01-19T09:24:00.004-05:00

I very much enjoyed this blog posting on www.phgfoundation.org. They discuss a new paper published in the British Medical Journal (below) that shows traditional risk factors do a much better job of predicting Type II Diabetes than 20 published SNPs. A quote from the post: "By assessing the area under the receiver operator characteristic curve (a plot of sensitivity versus 1-specificity, where a value of 1.0 represents a perfect test and 0.5 represents a useless test), the traditional models significantly outperformed the genetic model (around 0.75 versus 0.54), and their performance was not substantially improved by the addition of genetic risk factors." This comes as no surpise to me because the genetic studies that led to this test were all based on single-locus analyses that completely ignore the underlying complexity of this common disease. It is my working hypothesis that we will not be able to use genetic to predict disease risk until we ebrace, rather than ignore, the complexity of the genetic architecture of common human diseases. We commented on this in a 2007 letter to Science (also below).

Talmud PJ, Hingorani AD, Cooper JA, Marmot MG, Brunner EJ, Kumari M, Kivimäki M, Humphries SE. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ. 2010 Jan 14;340:b4838. doi: 10.1136/bmj.b4838. [PubMed] PMID: 20075150.

Williams SM, Canter JA, Crawford DC, Moore JH, Ritchie MD, Haines JL. Problems with genome-wide association studies. Science. 2007 Jun 29;316(5833):1840-2. [PubMed] PMID: 17605173.

Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

2010-01-16T09:54:00.004-05:00

This is a very nice paper. Hint for students: there might be a research project in there.

Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application. Am J Hum Genet. 2010 Jan 8;86(1):6-22. [PubMed]

Abstract

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results. We review and discuss three analytic methods to combine preliminary GWAS statistics to identify genes, alleles, and pathways for deeper investigations. Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable. Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact. Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases. Reviews of published methods with recommendations for their application are provided within the framework for each approach. Copyright © 2010 The American Society of Human Genetics.

Epistatic Interactions

2010-01-13T15:37:00.002-05:00

I don't agree with everything in this paper but it does provide some useful information.

VanderWeele, Tyler J. (2010) "Epistatic Interactions," Statistical Applications in Genetics and Molecular Biology: Vol. 9 : Iss. 1, Article 1. [PDF]

Abstract

The term "epistasis" is sometimes used to describe some form of statistical interaction between genetic factors and is alternatively sometimes used to describe instances in which the effect of a particular genetic variant is masked by a variant at another locus. In general statistical tests for interaction are of limited use in detecting "epistasis" in the sense of masking. It is, however, shown that there are relations between empirical data patterns and epistasis that have not been previously noted. These relations can sometimes be exploited to empirically test for "epistatic interactions" in the sense of the masking of the effect of a particular genetic variant by a variant at another locus.

Multifactor Dimensionality Reduction for Graphics Processing Units Enables Genome-wide Testing of Epistasis in Sporadic ALS

2010-01-12T10:24:00.005-05:00

Our new paper on using MDR on GPUs for GWAS analysis of epistasis has been accepted for publication in Bioinformatics. A preprint will be available soon. The GPU-MDR software is available from our website.

Casey S. Greene, Nicholas A. Sinnott-Armstrong, Daniel S. Himmelstein, Paul J. Park, Jason H. Moore, and Brent T. Harris. Multifactor Dimensionality Reduction for Graphics Processing Units Enables Genome-wide Testing of Epistasis in Sporadic ALS. Bioinformatics, in press (2010).

ABSTRACT

Motivation: Epistasis, the presence of gene-gene interactions, has been hypothesized to be at the root of many common human diseases, but current genome-wide association studies largely ignore its role. Multifactor dimensionality reduction (MDR) is a powerful model-free method for detecting epistatic relationships between genes but computational costs have made its application to genomewide data difficult. Graphics processing units (GPUs), the hardware responsible for rendering computer games, are powerful parallel processors. Using GPUs to run MDR on a genome-wide dataset allows for statistically rigorous testing of epistasis. Results: The implementation of MDR for GPUs (MDRGPU) includes core features of the widely used Java software package, MDR. This GPU implementation allows for large scale analysis of epistasis at a dramatically lower cost than the standard CPU based implementations. As a proof-of-concept, we applied this software to a genome-wide study of sporadic amyotrophic lateral sclerosis (ALS). We discovered a statistically significant two-SNP classifier and subsequently replicated the significance of these two SNPs in an independent study of ALS. MDRGPU makes the large scale analysis of epistasis tractable and opens the door to statistically rigorous testing of interactions in genome-wide datasets. Availability: MDRGPU is open source and available free of charge from http://www.sourceforge.net/projects/mdr.

A novel approach to simulate gene-environment interactions in complex diseases

2010-01-08T16:38:00.002-05:00

This looks interesting and perhaps useful. Let me know if you try it.

Amato R, Pinelli M, D'Andrea D, Miele G, Nicodemi M, Raiconi G, Cocozza S. A novel approach to simulate gene-environment interactions in complex diseases. BMC Bioinformatics. 2010 Jan 5;11(1):8. [PubMed]

Abstract

BACKGROUND: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the most part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite of a large amount of information that have been collected about both genetic and environmental risk factors, there are relatively few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in this data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interaction. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, like for example simulated ones. RESULTS: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main effort has been to allow user to describe characteristics of population by using standard epidemiological measures and to implement constraints to make the simulator behavior biologically meaningful. CONCLUSIONS: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has a full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A Knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study.

Bioinformatics Strategies for Genome-Wide Association Studies (GWAS)

2010-01-07T12:36:00.004-05:00

Our new review on bioinformatics strategies for GWAS analysis has been published in Bioinformatics. We focus in this paper on methods that are designed to embrace, rather than ignore, the complexity of common human diseases.

Moore, J.H., Asselbergs, F.W., Williams, S.M. Bioinformatics strategies for genome-wide association studies. Bioinformatics (2010). [PDF]

ABSTRACT

Motivation: The sequencing of the human genome has made it possible to identify an informative set of more than one million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWAS). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation, and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving healthcare through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype-phenotype relationship that is characterized by significant heterogeneity and gene-gene and gene-environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.

New Papers for EvoBIO'10 and EvoCOMPLEX'10

2010-01-06T21:43:00.007-05:00

We have four new papers that have been accepted for publication and presentation as part of the EvoBIO'10 and EvoCOMPLEX'10 conferences in Istanbul, Turkey. I hope to see you here!

Payne, J.L., Moore, J.H. Sexual Recombination in Self-Organizing Interaction Networks. Lecture Notes in Computer Science, in press (2010). EvoCOMPLEX'10

Abstract

We build on recent advances in the design of self-organizing interaction networks by introducing a sexual variant of an existing asexual, mutation-limited algorithm. Both the asexual and sexual variants are tested on benchmark optimization problems with varying levels of problem difficulty, deception, and epistasis. Specically, we investigate algorithm performance on Massively Multimodal Deceptive Problems and NK Landscapes. In the former case, we nd that sexual recombination improves solution quality for all problem instances considered; in the latter case, sexual recombination only improves solution quality for problem instances with intermediate levels of epistasis. We conclude that sexual recombination in self-organizing interaction networks may improve solution quality in problem domains with deception or a moderate degree of epistatic interactions.

Greene, C.S., Himmelstein, D.S., Moore, J.H. A Model Free Method to Generate Human Genetics Datasets with Complex Gene-Disease Relationships. Lecture Notes in Computer Science, in press (2010). EvoBIO'10

Abstract

A goal of human genetics is to discover genetic factors that influence individuals’ susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variations and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models. Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate six-hundred pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variations have been minimized, while the predictiveness of third, fourth, or fifth order combinations is maximized. This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This could improve our ability to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 56,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/.

Greene, C.S., Himmelstein, D.S., Kiralis, J., Moore, J.H. The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics. Lecture Notes in Computer Science, in press (2010). EvoBIO'10

Abstract

A primary goal of human genetics is the discovery of genetic factors that influence individual susceptibility to common human diseases. This problem is difficult because common diseases are likely the result of joint failure of two or more interacting components instead of single component failures. Efficient algorithms that can detect interacting attributes are needed. The Relief family of machine learning algorithms, which use nearest neighbors to weight attributes, are a promising approach. Recently an improved Relief algorithm called Spatially Uniform
ReliefF (SURF) has been developed that significantly increases the ability of these algorithms to detect interacting attributes. Here we introduce an algorithm called SURF* which uses distant instances along with the usual nearby ones to weight attributes. The weighting depends
on whether the instances are are nearby or distant. We show this new algorithm significantly outperforms both ReliefF and SURF for genetic analysis in the presence of attribute interactions. We make SURF* freely available in the open source MDR software package. MDR is a crossplatform Java application which features a user friendly graphical interface.

Penrod, N.M., Greene, C.S., Granizo-MacKenzie, D., Moore, J.H., Artificial Immune Systems for Epistasis Analysis in Human Genetics. Lecture Notes in Computer Science, in press (2010). EvoBIO'10

Abstract

Modern genotyping techniques have allowed the field of human genetics to generate vast amounts of data, but analysis methodologies have not been able to keep pace with this increase. In order to allow personal genomics to play a vital role in modern health care, analysis
methods capable of discovering high order interactions that contribute to an individual’s risk of disease must be developed. An artificial immune system (AIS) is a method which maps well to this problem and has a number of appealing properties. By considering many attributes simultaneously, it may be able to effectively and efficiently detect epistasis, that is non-additive gene-gene interactions. This situation of interacting genes is currently very difficult to detect without biological insight or statistical heuristics. Even with these approaches, at low heritability, these approaches have trouble distinguishing genetic signal from noise. The AIS also has a compact solution representation which can be rapidly evaluated. Finally the AIS approach, by iteratively developing an antibody which ignores irrelevant genotypes, may be better able to differentiate signal from noise than machine learning approaches like ReliefF which struggle at small heritabilities. Here we develop a basic AIS and evaluate it on very low heritability datasets. We find that the basic AIS is not robust to parameter settings but that, at some parameter settings, it performs very effectively. We use the settings where the strategy succeeds to suggest a path towards a robust AIS for human genetics. Developing an AIS which succeeds across many parameter settings will be critical to prepare this method for widespread use.

Computational Human Genetics and the Dartmouth Neukom Institute

2010-01-05T18:24:00.003-05:00

Our work on computational methods for the genetic analysis of common human diseases is supported by the Neukom Institute for Computational Science at Dartmouth College. The following are videos of me and Ryan Urbanowicz from my lab talking about our research supported by the Neukom Institute.

On the Classification of Epistatic Interactions

2009-12-27T10:16:00.000-05:00

I look forward to reading this next week.

Gao H, Granka JM, Feldman MW. On the Classification of Epistatic Interactions. Genetics. 2009 Dec 21. [PubMed]

Abstract

Modern genome-wide association studies are characterized by the problem of "missing heritability". Epistasis, or genetic interaction, has been suggested as a possible explanation for the relatively small contribution of single significant associations to the fraction of variance explained. Of particular concern to investigators of genetic interactions is how to best represent and define epistasis. Previous studies have found that the use of different quantitative definitions for genetic interaction can lead to different conclusions when constructing genetic interaction networks and when addressing evolutionary questions. We suggest that instead, multiple representations of epistasis, or epistatic "subtypes," may be valid within a given system. Selecting among these epistatic subtypes may provide additional insight into the biological and functional relationships among pairs of genes. In this study, we propose maximum likelihood and model selection methods in a hypothesis-testing framework to choose epistatic subtypes which best represent functional relationships for pairs of genes based on fitness data from both single and double mutants in haploid systems. We gauge the performance of our method with extensive simulations under various interaction scenarios. Our approach performs reasonably well in detecting the most likely epistatic subtype for pairs of genes, as well as in reducing bias when estimating the epistatic parameter (epsilon). We apply our approach to two available datasets from yeast (Saccharomyces cerevisiae), and demonstrate through overlap of our identified epistatic pairs with experimentally-verified interactions and functional links that our results are likely of biological significance in understanding interaction mechanisms. We anticipate that our method will improve detection of epistatic interactions and will help to unravel the mysteries of complex biological systems.

Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes

2009-12-24T08:43:00.002-05:00

This looks like an important paper.

Gregory Hannum, Rohith Srivas, Aude Gunol, Haico van Attikum, Nevan J. Krogan, Richard M. Karp, Trey Ideker. Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes. PLoS Genetics, Dec. 2009.

Abstract

This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex.

Pair-wise multifactor dimensionality reduction method to detect gene-gene interactions in a case-control study

2009-12-23T18:05:00.000-05:00

A new MDR paper.

He H, Oetting WS, Brott MJ, Basu S. Pair-wise multifactor dimensionality reduction method to detect gene-gene interactions in a case-control study. Hum Hered. 2010;69(1):60-70. [PubMed]

Abstract

OBJECTIVE: The identification of gene-gene interactions has been limited by small sample size and large number of potential interactions between genes. To address this issue, Ritchie et al. [2001] have proposed multifactor dimensionality reduction (MDR) method to detect polymorphisms associated with the disease risk. The MDR reduces the dimension of the genetic factors by classifying them into high-risk and low-risk groups. The strong point in favor of MDR is that it can detect interactions in absence of significant main effects. However, it often suffers from the sparseness of the cells in high-dimensional contingency tables, since it cannot classify an empty cell as high risk or low risk. METHOD: We propose a pair-wise multifactor dimensionality reduction (PWMDR) approach to address the issue of MDR in classifying sparse or empty cells. Instead of looking at the higher dimensional contingency table, we score each pair-wise interaction of the genetic factors involved and combine the scores of all such pairwise interactions. RESULTS: Simulation studies showed that the PWMDR generally had greater power than MDR to detect third order interactions for polymorphisms with low allele frequencies. The PWMDR also outperformed the MDR in detecting gene-gene interaction on a kidney transplant dataset. CONCLUSION: The PWMDR outperformed the MDR to detect polymorphisms with low frequencies.