Project Summary / Abstract Variation in DNA sequences contributes to variation in gene expression, splicing, and protein function, which may in turn impact evolutionary fitness. Using humans as a model, my lab seeks to understand the mechanisms linking DNA variation to cellular and organismal phenotypes and fitness. To this end, we propose to develop and apply computational and statistical methods that leverage genomic, functional genomic, and phenotypic data to achieve a more complete view of germline and somatic evolution within our species. One goal of our work is to overcome limitations of previous studies to better understand functional genomic diversity and evolution. To date, gene expression studies in humans have been strongly biased toward populations of European ancestries, limiting broader understanding of gene expression evolution. To fill this gap, my lab has generated RNA-seq data from diverse populations of the 1000 Genomes Project. These data will allow us to quantify the distribution of variation in gene expression and splicing within and between human populations and link these patterns to demographic and selective events by which they originated. By intersecting these data with published DNA sequencing data from the same samples, we propose to leverage the haplotype structure of the sample to home in on variants with causal effects on gene expression and splicing. We will supplement these data with single-molecule long-read sequencing of RNA from the same samples, offering unprecedented resolution of isoform diversity within and between human populations. A second goal of our work is to leverage the tools of population genetics to understand evolutionary forces that operate on developmental timescales, shaping genetic diversity among cells within individuals. Genetic variation among gametes traces to meiotic recombination, whereby parental genomes are shuffled to produce diverse haploid sperm or eggs. Recombination is also essential for accurate chromosome segregation, as abnormal numbers or locations of meiotic crossovers predispose to aneuploidy. However, recombination is also mutagenic, evidenced by the enrichment of point mutations and structural variation at recombination hotspots. We propose to develop a quantitative genetic model to understand these inherent fitness tradeoffs and test its predictions on data from preimplantation embryos. We are also extending our work to understand mechanisms of somatic evolution shaping other developmental systems such as clonal hematopoiesis—a phenomenon whereby a subset of blood stem cells disproportionately contributes to the descendant population. Through simulation and analysis of published sequencing data from single blood cells, we will develop and test phylogenetic methods toward a better understanding of the evolutionary forces shaping clonal hematopoiesis. In the coming years, my lab will continue to integrate diverse datasets and novel quantitative methods to understand re...