Project Summary/AbstractThe growing abundance of population genomic data creates a critical need for inference approaches that canreveal evolutionary history. The PI's long-term goal is to understand how natural selection shapes the evolutionand function of the molecular networks that comprise life. Toward that goal the PI's group develops and appliesmethods for inferring the evolutionary past from population genomic data. The objectives of this application are tounderstand how context affects mutation tness effects to develop improved inference methods and to supportthe population genomics research community. The rationale is that this research program will both reveal newinsights into evolution and enhance the ability of colleagues to reveal complementary insights.The PI's research group has expanded the concept of a distribution of tness effects to multiple dimensionsfocusing on differences in mutation tness effects among populations. The PI proposes to apply this approach tonumerous systems to elucidate the relative roles of genetic and environmental context in creating differences intness effects. The group will also extend this approach to consider differences in tness effects over time.The PI developed and maintains the software dadi among the most popular approaches for tting populationgenomic models to data. The PI will continue to support and enhance dadi while developing complementaryinference approaches. These will include new diffusion methods based on pairs of loci and the linkage amongthem and a novel deep learning approach for inferring the distribution of tness effects.The PI helped found the PopSim consortium which aims to expand the rigor and transparency of population ge-nomic models for the scientic community. The PI's group will continue to be active in the consortium particularlyleading a new initiative to facilitate rigorous testing of population genomic methods via open competition.The proposed research program is innovative both conceptually and methodologically. The novel concept of amultidimensional distribution of tness effects has many applications and the group will develop novel method-ology for several population genomics inferences. The expected outcomes of the proposed research are newinsights into the ecology and biology of mutation tness effects new population genomic inference tools and aframework for blinded evaluation of such tools. These outcomes are expected to have important positive impacton the led of population genomics. The methods will be widely applicable and well-supported and the inferenceswill feed into approaches for inferring the evolutionary past and predicting the evolutionary future.