Project Summary/Abstract There 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 comprehensive approaches for identifying selection in natural populations and understanding its functional consequences. The objectives of this application are to develop and apply novel approaches for inferring correlated selection between genomic sites and between natural populations. The rationale for the proposed research is that the approaches developed will be broadly applicable, providing a foundation understanding adaptation in pathogens and the genetic architecture of human disease. In Aim 1, the PI proposes to leverage his recently developed approach for calculating the statistics of pairs of linked genetic loci to quantify several aspects of natural selection in hu- mans and Drosophila melanogaster. He will first focus on individual known adaptive loci, quantifying the strength, timing, and mode of selection. He will then infer the distribution of fitness effects of new nonsynonymous mutations. Lastly, we will infer the joint distribution of fitness 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 inferring joint distributions of mutation fitness effects and apply it to genes sets of differing molecular function and populations of differing divergence. The proposed research is innovative both methodologically and conceptually. The methods to be developed are novel, as are the concepts of joint distributions of fitness effects be- tween sites and populations. The expected outcomes of the proposed research are new population genetic inference methods and inferences of natural selection in humans and two model organisms. These outcomes are expected to have important positive impact on the field of population genetics. The methods will be widely applicable and well-supported, and the inferences will feed into approaches for inferring the evolutionary past and predicting the evolutionary future. Project Summary/Abstract