We do work in several different areas, all of which fall under the broader topic of population genetics. We are interested in developing tools for the analysis of large genomic data sets as well as efficient simulation tools to enable exploration of complex population genetic models. The overal goals are to (1) understand the contribution of structural changes to genome evolution and function, (2) understand the genetics of adaptation, and (3) explore the interfact of traditional population- and quantitative- genetics theory with an eye towards understanding adaptation and the genetics of heritable traits, especially human disease.
We have been exploring the implications of explicit models of allelic heterogeneity on the study of human heritable diseases. A major result from the cloning of single-gene disorders was that there are many different disease-causing mutations in the population (allelic heterogeneity). We have been exploring the implications of such heterogeneity for complex diseases via explicit simulations of the heterogeneity models (1). Recent results (2) suggest that such models are broadly compatible with a wide range of observations from human GWAS. In fact, we show that the heterogeneity model in fact reconciles seemingly contradictory observations regarding the contribution of non-additive (dominance) variance to human traits.
Related work includes modeling the adaptation of quantitative traits to changing environments (“optimum shifts”). We are seeking to understand the dynamics of phenotype adaptation using a framework that doesn’t rely on restrictive a priori assumptions about the number of potential “soft” or “hard” selective sweeps. This work is directly tied in to research on effective methods for forward-time simulation (3) and the development of a Python package for running and analyzing such simulations.
The lab has active and ongoing collaborations with: