Localization of adaptive variants in human genomes using averaged one-dependence estimation

Abstract

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios.

Learning the properties of adaptive regions with functional data analysis

“…In addition, SURFDAWave is currently designed to detect and analyze putative selected regions using information from a single population. However, incorporating multiple populations would likely provide greater power to not only detect selection, but predict selection parameters as well [77,78]. Including other populations allows the use of statistics such as XP-EHH that can identify selected loci by looking at population differentiation [79].…”

A community-maintained standard library of population genetic models

“…While population genetics has always used statistical methods to make inferences from data, the degree of sophistication of the questions, models, data, and computational approaches used have all increased over the past two decades. Currently, there exist a myriad of computational methods that can infer the histories of populations ( Gutenkunst et al, 2009 ; Li and Durbin, 2011 ; Excoffier et al, 2013 ; Schiffels and Durbin, 2014 ; Terhorst et al, 2017 ; Ragsdale and Gravel, 2019 ), the distribution of fitness effects ( Boyko et al, 2008 ; Kim et al, 2017 ; Tataru et al, 2017 ; Fortier et al, 2019 ; Huang and Siepel, 2019 ; Vecchyo et al, 2019 ), recombination rates ( McVean et al, 2004 ; Chan et al, 2012 ; Lin et al, 2013 ; Adrion et al, 2020 ; V Barroso et al, 2019 ), and the extent of positive selection in genome sequence data ( Kim and Stephan, 2002 ; Eyre-Walker and Keightley, 2009 ; Alachiotis et al, 2012 ; Garud et al, 2015 ; DeGiorgio et al, 2016 ; Kern and Schrider, 2018 ; Sugden et al, 2018 ). While these methods have undoubtedly increased our understanding of genetic and evolutionary processes, very little has been done to systematically benchmark the quality of these inferences or their robustness to deviations from their underlying assumptions.…”

Discovery of ongoing selective sweeps withinAnophelesmosquito populations using deep learning

“…Classically, methods for finding sweeps have focused on a particular aspect of genetic variation, for instance observing the site frequency spectrum (SFS) at a locus and comparing it to expectations under neutrality and selective sweeps [19]. More recently, the field has made excellent progress in combining signals across multiple features of genetic variation through supervised machine learning (SML) [20–27], which has substantially improved power, accuracy, and robustness in what have been stubbornly difficult inference problems within population genetics [28]. While much attention has been paid to applying SML for the identification and classification of completed selective sweeps in the genome [24,26], less effort has been made for using SML to identify sweeps that are incomplete within a population, sometimes called partial sweeps (although see Sugden et al .…”

“…While much attention has been paid to applying SML for the identification and classification of completed selective sweeps in the genome [24,26], less effort has been made for using SML to identify sweeps that are incomplete within a population, sometimes called partial sweeps (although see Sugden et al . [27] for a recent example). In these cases, the beneficial allele is not currently fixed within the population, thereby creating a weaker hitchhiking effect in comparison to a completed sweep, and accordingly a more subtle perturbation of patterns of genetic variation [29].…”