Biology subjects

Andrew D Kern

Publications and source records attributed to Andrew D Kern.

3 recordsLinked to original sources

bioRxiv2016

Effects of linked selective sweeps on demographic inference and model selection

The availability of large-scale population genomic sequence data has resulted in an explosion in efforts to infer the demographic histories of natural populations across a broad range of organisms. As demographic events alter coalescent genealogies they leave detectable signatures in patterns of genetic variation within and between populations. Accordingly, a variety of approaches have been designed to leverage population genetic data to uncover the footprints of demographic change in the genome. The vast majority of these methods make the simplifying assumption that the measures of genetic variation used as their input are unaffected by natural selection. However, natural selection can dramatically skew patterns of variation not only at selected sites, but at linked, neutral loci as well. Here we assess the impact of recent positive selection on demographic inference by characterizing the performance of three popular methods through extensive simulation of datasets with varying numbers of linked selective sweeps. In particular, we examined three different demographic models relevant to a number of species, finding that positive selection can bias parameter estimates of each of these models--often severely. Moreover, we find that selection can lead to incorrect inferences of population size changes when none have occurred. We argue that the amount of recent positive selection required to skew inferences may often be acting in natural populations. These results suggest that demographic studies conducted in many species to date may have exaggerated the extent and frequency of population size changes.

Evolutionary Biology↗

bioRxiv2015

S/HIC: Robust identification of soft and hard sweeps using machine learning

Detecting the targets of adaptive natural selection from whole genome sequencing data is a central problem for population genetics. However, to date most methods have shown sub-optimal performance under realistic demographic scenarios. Moreover, over the past decade there has been a renewed interest in determining the importance of selection from standing variation in adaptation of natural populations, yet very few methods for inferring this model of adaptation at the genome scale have been introduced. Here we introduce a new method, S/HIC, which uses supervised machine learning to precisely infer the location of both hard and soft selective sweeps. We show that S/HIC has unrivaled accuracy for detecting sweeps under demographic histories that are relevant to human populations, and distinguishing sweeps from linked as well as neutrally evolving regions. Moreover we show that S/HIC is uniquely robust among its competitors to model misspecification. Thus even if the true demographic model of a population differs catastrophically from that specified by the user, S/HIC still retains impressive discriminatory power. Finally we apply S/HIC to the case of resequencing data from human chromosome 18 in a European population sample and demonstrate that we can reliably recover selective sweeps that have been identified earlier using less specific and sensitive methods.

Evolutionary Biology↗

bioRxiv2014

Behavioral variation in Drosophila melanogaster: no evidence for common alleles of large- effect at the foraging gene in a population from North Carolina, USA

Thirty four years ago, it was postulated that natural populations of Drosophila melanogaster are comprised of two behavioral morphs termed \"rover\" and \"sitter\", and that this variation is caused mainly by large-effect alleles at a single locus. Since that time, considerable data has been amassed that compares the behavior and physiology of these morphs. Contrary to common assertions, however, published support for the existence of common large effect alleles in nature is quite limited. To further investigate, we quantified the foraging behavior of 36 natural strains, performed a genome-wide association study, and described patterns of molecular evolution at the foraging locus. Though there was significant variation in foraging behavior among genotypes, this variation was continuously distributed and not significantly associated with genetic variation at the foraging gene. Patterns of molecular population genetic variation at this gene also provide no support for the hypothesis that for is a target of long term balancing selection We propose that additional data is required to support a hypothesis of common alleles of large effect on foraging behavior in nature. Genome-wide association does support a role for natural variation at several other loci, including the sulfateless gene, though these associations should be considered preliminary until validated with a larger sample size.

Evolutionary Biology↗