Project Summary My research program aims to make sense of modern genomic data through the lens of molecular evolution. Drawing from ideas and techniques in statistics, artificial intelligence, and population genetics, we seek both to understand the evolutionary forces that have shaped present-day genome sequences, and to use evolutionary patterns to gain insight into the phenotypic importance of particular genomic sequences, with broad implications for human health. Our work focuses in particular on three major areas: (1) evolutionary reconstruction based on genome sequences; (2) inference of the fitness consequences of human mutations; and (3) the study of transcriptional regulation and its evolution in mammals. In the last funding period (2018–2022), we achieved major advances in each of these areas, including both methodological advances and applications of new methods to important and timely scientific questions. For example, we recently developed innovative new methods for the inference of ancestral recombination graphs (ARGs) from multi-population sequence data; for the detection of selective sweeps using ARGs and deep neural networks; for the identification of essential genes from CRISPR-Cas9 screens; for the estimation of relative RNA half-lives based on widely available RNA-sequencing data types; and for the detection of gains and losses of cis-regulatory elements along the branches of a phylogeny based on epigenomic data. These methods have all been implemented as publicly available software tools. Based on these and other methods, we published a variety of novel scientific findings, including the discovery and characterization of previously unknown introgression events between modern and archaic hominins; a genomic analysis of South American birds indicating their radiation was primarily driven by recent selective sweeps; an analysis indicating extensive evidence of rapid evolution in immunity- and cancer-related genes in bats; and an analysis indicating that enhancers are gained and lost at about twice the rate of promoters in mammalian evolution. These findings were described in a total of 17 original papers and preprints. For this renewal application, we propose to continue our research within each of these three key areas. Specific goals include developing new statistical sampling methods that scale to very large ARGs; using domain adaptation to reduce training bias in population genomics; measuring extreme levels of purifying selection from patterns of rare variation in human populations; characterizing the distributions of fitness effects for polygenic traits; identifying and characterizing deleterious variants linked to advantageous alleles; and developing a unified biophysical modeling framework for nascent RNA sequencing data with applications to comparative genomics and elongation-rate estimation. A renewal of R35 funding will enable us to remain highly productive contributors to this critically important research area.