Identifying loci under positive selection in complex population histories

Abstract

Detailed modeling of a species' history is of prime importance for understanding how natural selection operates over time. Most methods designed to detect positive selection along sequenced genomes, however, use simplified representations of past histories as null models of genetic drift. Here, we present the first method that can detect signatures of strong local adaptation across the genome using arbitrarily complex admixture graphs, which are typically used to describe the history of past divergence and admixture events among any number of populations. The method-called Graph-aware Retrieval of Selective Sweeps (GRoSS )-has good power to detect loci in the genome with strong evidence for past selective sweeps and can also identify which branch of the graph was most affected by the sweep. As evidence of its utility, we apply the method to bovine, codfish and human population genomic data containing multiple population panels related in complex ways. We find new candidate genes for important adaptive functions, including immunity and metabolism in under-studied human populations, as well as muscle mass, milk production and tameness in specific bovine breeds. We are also able to pinpoint the emergence of large regions of differentiation due to inversions in the history of Atlantic codfish.

The timing of human adaptation from Neanderthal introgression

“…Our method focuses on inferring the history of selection from haplotypes alone, and does not incorporate the distribution of the selected allele frequency across populations (as in Racimo et al, 2018; Refoyo-Martínez et al, 2019). We could incorporate into our composite likelihoods the conditional probability of x s across populations under a given parameterization of the selection model, but here our focus is on the information about the timing of selection contained in haplotype patterns.…”

Population genomics perspectives on convergent adaptation

“…One possibility to formalize a test of convergence is to condition on the observed allele frequency change in one subset of the species range, using the relatedness matrix, while testing for non-neutral allele frequency change elsewhere. Additionally, frameworks that use admixture graphs, parameterized versions of the relatedness matrix [62], to determine whether multiple instances of selection on distinct branches are being developed to examine allele frequency change across populations [63]. Therefore, we are now in position to move towards formally testing for non-neutral convergence in allele frequency changes among populations.…”

Identifying signatures of natural selection in Indian populations

“…To incorporate the genetic history of the Indian populations in the inference of natural selection events, we applied the Graph-aware Retrieval of Selective Sweeps (GRoSS) software [ 34 ]. This method uses complex admixture graphs to infer signatures of natural selection along the branch of the graph, based on the statistics developed by Racimo et al, 2018 [ 46 ].…”

“…Here, we present the results of a genome-wide scan for signatures of positive selection using data from four tribal groups (Kokana, Warli, Bhil, and Pawara) and two caste groups (Deshastha Brahmin and Kunbi Maratha) from West Maharashtra, as well as two samples of South Asian ancestry from the 1000 Genome Project (Gujarati Indian from Houston, Texas and Indian Telugu from UK) ( S1 Fig ). In order to identify putative genomic regions under positive selection, we used tests of positive selection based on different statistics, including Population Branch Statistic (PBS), Cross-population Extended Haplotype Homozygosity (xpEHH), Integrated Haplotype Score (iHS), Composite Likelihood Ratio (CLR), Tajima’s D, as well as two recently developed methods: Graph-aware Retrieval of Selective Sweeps (GRoSS)—that uses admixture graphs to infer signatures of selection in specific branches of the graphs [ 34 ] and Ascertained Sequentially Markovian Coalescent (ASMC)—a coalescence-based method [ 35 ].…”