Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (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]


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.


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