The effect of a mutation on fitness may differ between populations depending on environmental and genetic context, but little is known about the factors that underlie such differences. To quantify genome-wide correlations in mutation fitness effects, we developed a novel concept called a joint distribution of fitness effects (DFE) between populations. We then proposed a new statistic w to measure the DFE correlation between populations. Using simulation, we showed that inferring the DFE correlation from the joint allele frequency spectrum is statistically precise and robust. Using population genomic data, we inferred DFE correlations of populations in humans, Drosophila melanogaster, and wild tomatoes. In these species, we found that the overall correlation of the joint DFE was inversely related to genetic differentiation. In humans and D. melanogaster, deleterious mutations had a lower DFE correlation than tolerated mutations, indicating a complex joint DFE. Altogether, the DFE correlation can be reliably inferred, and it offers extensive insight into the genetics of population divergence.

In forensic familial search methods, a query DNA profile is tested against a database to determine if the query profile represents a close relative of a database entrant. One challenge for familial search is that the calculations may require specification of allele frequencies for the unknown population from which the query profile has originated. Allele-frequency misspecification can substantially inflate false-positive rates compared to use of allele frequencies drawn from the same population as the query profile. Here, we use ancestry inference on the query profile to circumvent the high false-positive rates that result from highly misspecified allele frequencies. In particular, we perform ancestry inference on the query profile and make use of allele frequencies based on its inferred genetic ancestry. In a test for sibling matches on profiles that represent unrelated individuals, we demonstrate that false-positive rates for familial search with use of ancestry inference to specify the allele frequencies are similar to those seen when allele frequencies align with the population of origin of a profile. Because ancestry inference is possible to perform on query profiles, the extreme allele-frequency misspecifications that produce the highest false-positive rates can be avoided. We discuss the implications of the results in the context of concerns about the forensic use of familial searching.

The effect of a mutation on fitness may differ between populations, depending on environmental and genetic context. Experimental studies have shown that such differences exist, but little is known about the broad patterns of such differences or the factors that drive them. To quantify genome-wide patterns of differences in mutation fitness effects, we extended the concept of a distribution of fitness effects (DFE) to a joint DFE between populations. To infer the joint DFE, we fit parametric models that included demographic history to genomic data summarized by the joint allele frequency spectrum. Using simulations, we showed that our approach is statistically powerful and robust to many forms of model misspecification. We then applied our approach to populations of Drosophila melanogaster, wild tomatoes, and humans. We found that mutation fitness effects are overall least correlated between populations in tomatoes and most correlated in humans, corresponding to overall genetic differentiation. In D. melanogaster and tomatoes, mutations in genes involved in immunity and stress response showed the lowest correlation of fitness effects, consistent with environmental influence. In D. melanogaster and humans, deleterious mutations showed a lower correlation of fitness effects than tolerated mutations, hinting at the complexity of the joint DFE. Together, our results show that the joint DFE can be reliably inferred and that it offers extensive insight into the genetics of population divergence. 2 mutation's effect on fitness, population genetics theory can predict a great deal; for example, how likely 3 the mutation is to be lost from or fix in the population. But population genetics theory cannot predict 4 how likely a new mutation is to have a given effect on fitness. It is known that typically the majority of 5 mutations are deleterious (reduce fitness) or nearly neutral (negligible effect on fitness), so only a small 6 minority are adaptive (increase fitness). But these three categories encompass a continuum of fitness effects. 7This continuum is quantified by the distribution of fitness effects (DFE) among new mutations (Eyre-Walker 8

Classical genes within the Major Histocompatibility Complex (MHC) are responsible for peptide presentation to T cells, thus playing a central role in immune defense against pathogens. These genes are subject to strong selective pressures including both balancing and directional selection, resulting in exceptional genetic diversity—thousands of alleles per gene. Moreover, some alleles appear to be shared between primate species, a phenomenon known as trans-species polymorphism (TSP) or incomplete lineage sorting, which is rare in the genome overall. However, despite the clinical and evolutionary importance of MHC diversity, we currently lack a full picture of primate MHC evolution. To start addressing this gap, we used Bayesian phylogenetic methods to determine the extent of TSP at six classical MHC genes. We find strong support for TSP in all six genes, including between humans and old-world monkeys in HLA-DRB1 and even—remarkably—between humans and new-world monkeys in HLA-DQB1. Despite the long-term persistence of ancient lineages, we additionally observe rapid evolution at amino acids within the peptide-binding domain. The most rapidly-evolving positions are also strongly enriched for autoimmune and infectious disease associations. Together, these results suggest complex selective forces arising from differential peptide binding, which drive short-term allelic turnover within lineages while also maintaining deeply divergent lineages for at least 45 million years.

In forensic familial search methods, a query DNA profile is tested against a database to determine if the query profile represents a close relative of a database entrant. One challenge for familial search is that the calculations may require specification of allele frequencies for the unknown population from which the query profile has originated. Allele-frequency misspecification can substantially inflate false-positive rates compared to use of allele frequencies drawn from the same population as the query profile. Here, we use ancestry inference on the query profile to circumvent the high false-positive rates that result from highly misspecified allele frequencies. In particular, we perform ancestry inference on the query profile and make use of allele frequencies based on its inferred genetic ancestry. In a test for sibling matches on profiles that represent unrelated individuals, we demonstrate that false-positive rates for familial search with use of ancestry inference to specify the allele frequencies are similar to those seen when allele frequencies align with the population of origin of a profile. Because ancestry inference is possible to perform on query profiles, the extreme allele-frequency misspecifications that produce the highest false-positive rates can be avoided. We discuss the implications of the results in the context of concerns about the forensic use of familial searching.