Population genetics of human evolutionary history and natural selection

Project Summary This proposed research addresses unresolved and fundamental questions in human evolutionary history and the formation of genetic diversity. It contains three related research themes: (1) uncovering key features of human population history and structure, both in the recent and ancient past, (2) determining factors influencing

selection across the genome and the limits to cross-population phenotypic prediction due to population history and natural selection, and (3) modeling and predicting the genetic basis of complex traits under realistic models of human evolutionary history, population structure and admixture. This work combines ambitious but

feasible methodological developments with new data from diverse worldwide populations. Human evolutionary history is imprinted in the DNA of people living today. Learning this history involves the combined efforts of theoretical, statistical, and empirical analyses. As the volume of genome sequencing data

grows, we have the opportunity to infer new details from key periods in our species’ evolution and clarify how population biology and natural selection shape phenotypic and genetic variation. This requires developing population genetic theory and computational methods that can handle data from many populations and large

sample sizes, and that simultaneously incorporate these many different factors that impact genetic diversity. Over the next five years, my group will develop theoretical and methodological innovations that enable new discoveries from human population genomic data, and we will uncover details of human history that resolve

important questions about our species’ evolution. This work builds on my lab’s expertise in population genetic theory and simulation methods. Recent work shows that our newly developed approaches for modeling diversity and linkage disequilibrium statistics are powerful to learn complex multi-population history. Combined

with newly sequenced high-quality genomic data, we will unravel early human evolution and reconstruct recent admixture and demographic histories of Latin American populations. These models build genomic resources for understudied populations and are essential to downstream studies to understand genetic components of

health and disease susceptibility. Our theoretical advances will enable new analyses of the combined effects of selection and recombination in multiple populations, which we will use to understand fundamental limits to the portability of phenotypic prediction across populations and in recently admixed groups. Finally, we will develop

approaches to predict the architecture of polygenic traits in structured and admixed populations, settings that are most relevant for human genomic studies, but current methods fail to address. The mathematical and statistical advances from this work will be developed into open source, maintained computational resources

that will facilitate genomic discoveries in our own research and that of others in the years to come.