Epistasis Blog

From the Computational Genetics Laboratory at Dartmouth Medical School (www.epistasis.org)

Sunday, December 27, 2009

On the Classification of Epistatic Interactions

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.

Thursday, December 24, 2009

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

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.

Wednesday, December 23, 2009

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

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.

Tuesday, December 22, 2009

Epistasis in a quantitative trait captured by a molecular model of transcription factor interactions

Another interesting paper.

Gertz J, Gerke JP, Cohen BA. Epistasis in a quantitative trait captured by a molecular model of transcription factor interactions. Theor Popul Biol. 2009 Oct [PubMed]

Abstract

With technological advances in genetic mapping studies more of the genes and polymorphisms that underlie Quantitative Trait Loci (QTL) are now being identified. As the identities of these genes become known there is a growing need for an analysis framework that incorporates the molecular interactions affected by natural polymorphisms. As a step towards such a framework we present a molecular model of genetic variation in sporulation efficiency between natural isolates of the yeast, Saccharomyces cerevisiae. The model is based on the structure of the regulatory pathway that controls sporulation. The model captures the phenotypic variation between strains carrying different combinations of alleles at known QTL. Compared to a standard linear model the molecular model requires fewer free parameters, and has the advantage of generating quantitative hypotheses about the affinity of specific molecular interactions in different genetic backgrounds. Our analyses provide a concrete example of how the thermodynamic properties of protein-protein and protein-DNA interactions naturally give rise to epistasis, the non-linear relationship between genotype and phenotype. As more causative genes and polymorphisms underlying QTL are identified, thermodynamic analyses of quantitative traits may provide a useful framework for unraveling the complex relationship between genotype and phenotype.

Monday, December 21, 2009

Competition between recombination and epistasis can cause a transition from allele to genotype selection

This is an interesting new paper.

Neher RA, Shraiman BI. Competition between recombination and epistasis can cause a transition from allele to genotype selection. Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6866-71. [PubMed]

Abstract

Biochemical and regulatory interactions central to biological networks are expected to cause extensive genetic interactions or epistasis affecting the heritability of complex traits and the distribution of genotypes in populations. However, the inference of epistasis from the observed phenotype-genotype correlation is impeded by statistical difficulties, while the theoretical understanding of the effects of epistasis remains limited, in turn limiting our ability to interpret data. Of particular interest is the biologically relevant situation of numerous interacting genetic loci with small individual contributions to fitness. Here, we present a computational model of selection dynamics involving many epistatic loci in a recombining population. We demonstrate that a large number of polymorphic interacting loci can, despite frequent recombination, exhibit cooperative behavior that locks alleles into favorable genotypes leading to a population consisting of a set of competing clones. When the recombination rate exceeds a certain critical value that depends on the strength of epistasis, this "genotype selection" regime disappears in an abrupt transition, giving way to "allele selection"--the regime where different loci are only weakly correlated as expected in sexually reproducing populations. We show that large populations attain highest fitness at a recombination rate just below critical. Clustering of interacting sets of genes on a chromosome leads to the emergence of an intermediate regime, where blocks of cooperating alleles lock into genetic modules. These haplotype blocks disappear in a second transition to pure allele selection. Our results demonstrate that the collective effect of many weak epistatic interactions can have dramatic effects on the population structure.

Sunday, December 06, 2009

A New Age of Genetic Testing - New Hampshire Public Radio

I will be a guest Monday at 9am on the New Hampshire Public Radio show 'The Exchange' hosted by Laura Knoy. The topic is 'A New Age of Genetic Testing'. I will be discussing our latest paper on genome-wide association studies and personal genetic testing that appeared in the American Journal of Human Genetics. See my Sept. 11, 2009 blog post on Epistasis and its Implications for Personal Genetics.

Listen to the replay on Windows Media or MP3.

Thursday, December 03, 2009

Orange Data Mining Software

A new version of the Orange data mining software package is available. This is one of my favorites. Don't forget to also install graphviz so you can use some of the graphing features.