Project Summary/AbstractThere is a critical need for population genetic inference approaches to quantify natural selec-tion within and between populations. The PI's long-term goal is to develop comprehensiveapproaches for identifying selection in natural populations and understanding its functionalconsequences. The objectives of this application are to develop and apply novel approachesfor inferring correlated selection between genomic sites and between natural populations.The rationale for the proposed research is that the approaches developed will be broadlyapplicable providing a foundation understanding adaptation in pathogens and the geneticarchitecture of human disease.In Aim 1 the PI proposes to leverage his recently developed approach for calculating thestatistics of pairs of linked genetic loci to quantify several aspects of natural selection in hu-mans and Drosophila melanogaster. He will rst focus on individual known adaptive lociquantifying the strength timing and mode of selection. He will then infer the distribution oftness effects of new nonsynonymous mutations. Lastly we will infer the joint distribution oftness effects of nonsynonymous mutations within the same protein.In Aim 2 the PI proposes to quantify divergent natural selection between populations of hu-mans D. melanogaster and Daphnia pulex. To do so he will develop an approach for inferringjoint distributions of mutation tness effects and apply it to genes sets of differing molecularfunction and populations of differing divergence.The proposed research is innovative both methodologically and conceptually. The methodsto be developed are novel as are the concepts of joint distributions of tness effects be-tween sites and populations. The expected outcomes of the proposed research are newpopulation genetic inference methods and inferences of natural selection in humans and twomodel organisms. These outcomes are expected to have important positive impact on theeld of population genetics. The methods will be widely applicable and well-supported andthe inferences will feed into approaches for inferring the evolutionary past and predicting theevolutionary future.Project Summary/Abstract