Epistasis Blog

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

Friday, April 30, 2010

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

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]


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.

Monday, April 26, 2010

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

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]


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.

Friday, April 23, 2010

Genotype to Phenotype: A Complex Problem

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.

Thursday, April 22, 2010

Microbiome Bioinformatics at PSB - Call for Papers

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.

Monday, April 19, 2010

The genetic landscape of a cell

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]


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.

Saturday, April 03, 2010

Genetic Epidemiology

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.

Thursday, April 01, 2010

Human genome at ten: Life is complicated

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.