. The PI's research program in population genetics focuses on the coalescent-based inference of population his- tories from whole genomes, and the determination of genetic basis of adaptation and disease at multiple biolog- ical scales?from mutations to genes to gene subnetworks. In the genomic era, computational and statistical methods are essential for identifying candidate adaptive and disease-associated mutations in humans, in whom mapping via linkage studies is challenging and costly. State-of-the-art approaches that scan genome-wide for signatures of selection or association with phenotype state are routinely applied to samples from one homoge- neous ancestry, rely on arbitrary thresholds for interpreting results, and produce results at genomic scales that can be difficult to connect to biological mechanism (for example, analyzing linkage blocks or sliding genomic windows). Thus, despite the enormous investments made by the NIH and biobanks around the world to generate large-scale genomic datasets from diverse individuals, methods for analyzing such datasets are lagging behind. This application describes a series of projects motivated by answering three fundamental questions in human population genetics: (1) what role has balancing selection played in human adaptation? (2) to what extent has adaptive evolution versus non-adaptive processes shaped human genomes? (3) to what extent do the genetic architectures of human traits vary by ancestry? The overall strategy for future research plans draws on the PI's expertise in coalescent theory, Bayesian inference, population genetics, and statistical genetics to produce new frameworks for analyzing patterns in and evolutionary processes underlying multiethnic genomic datasets. The outcomes of the research described in this MIRA application will give new insight into the interaction between selection and dynamic population histories in generating human genetic diversity, while determining the different modes of selection shaping human phenotypes and diseases.
. A fundamental goal of evolutionary and biomedical studies of human genomes is to determine the genetic basis of human traits and diseases. The proposed research is relevant to public health because the new methods developed here will be applied to large-scale genomic datasets to infer the evolutionary processes, mutations and genetic pathways that underly human adaptation and disease. Thus, the proposed research is relevant to the part of NIH's mission pertaining to developing fundamental knowledge that will enhance health.
