[Reminder] Detecting interactions in association mapping studies @ Thu Jan 31 4pm - 5pm (Bio Events)

Matthew MacManes, this is a reminder for

Detecting interactions in association mapping studies

Thu Jan 31 4pm – 5pm
(Pacific Time)
177 Stanley Hall (map)
Calendar: Bio Events

Detecting interactions in association mapping studies and the prospects of using evolutionary inferences to inform such studies
Professor Rasmus Nielsen


For most common diseases with heritable components, not a single or a few single-nucleotide polymorphisms (SNPs) explain most of the variance for these disorders. Instead, much of the variance may be caused by interactions (epistasis) among multiple SNPs or interactions with environmental conditions. I will discuss a new statistical model for analyzing and interpreting genomic data that influence multifactorial phenotypic traits with a complex and likely polygenic inheritance. The new method is based on Markov chain Monte Carlo (MCMC) and allows for identification of sets of SNPs and environmental factors that when combined increase disease risk or change the distribution of a quantitative trait. Using simulations, we show that the MCMC method can detect disease association when multiple, interacting SNPs are present in the data. When applying the method on real large-scale data from a Danish population-based cohort, multiple interactions are identified that severely affect serum triglyceride levels in the study individuals. The method is designed for quantitative traits but can also be applied on qualitative traits. It is computationally feasible even for a large number of possible interactions and differs fundamentally from most previous approaches by entertaining nonlinear interactions and by directly addressing the multiple-testing problem. I will also discuss an approach for using evolutionary information to inform association mapping studies. We have developed a Bayesian approach which combines structural, population genetic and comparative genomic data to quantify the probability that a particular mutation is deleterious. We validate the method on real data and show that has better frequentist properties than previous method for predicting the fitness effects of mutations.
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