Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

Knowledge of mutation rates is crucial for calibrating population genetics models of demographic history in units of years. However, mutation rates remain challenging to estimate because of the need to identify extremely rare events. We estimated the nuclear mutation rate in wolves by identifying de novo mutations in a pedigree of seven wolves. Putative de novo mutations were discovered by whole-genome sequencing and were verified by Sanger sequencing of parents and offspring. Using stringent filters and an estimate of the false negative rate in the remaining observable genome, we obtain an estimate of ∼4.5 × 10−9 per base pair per generation and provide conservative bounds between 2.6 × 10−9 and 7.1 × 10−9. Although our estimate is consistent with recent mutation rate estimates from ancient DNA (4.0 × 10−9 and 3.0–4.5 × 10−9), it suggests a wider possible range. We also examined the consequences of our rate and the accompanying interval for dating several critical events in canid demographic history. For example, applying our full range of rates to coalescent models of dog and wolf demographic history implies a wide set of possible divergence times between the ancestral populations of dogs and extant Eurasian wolves (16,000–64,000 years ago) although our point estimate indicates a date between 25,000 and 33,000 years ago. Aside from one study in mice, ours provides the only direct mammalian mutation rate outside of primates and is likely to be vital to future investigations of mutation rate evolution.

Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that resolve asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of LINE repeats. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.