Tests of Selection in Pooled Case-Control Data: An Empirical Study

Abstract

For smaller organisms with faster breeding cycles, artificial selection can be used to create sub-populations with different phenotypic traits. Genetic tests can be employed to identify the causal markers for the phenotypes, as a precursor to engineering strains with a combination of traits. Traditional approaches involve analyzing crosses of inbred strains to test for co-segregation with genetic markers. Here we take advantage of cheaper next generation sequencing techniques to identify genetic signatures of adaptation to the selection constraints. Obtaining individual sequencing data is often unrealistic due to cost and sample issues, so we focus on pooled genomic data. We explore a series of statistical tests for selection using pooled case (under selection) and control populations. The tests generally capture skews in the scaled frequency spectrum of alleles in a region, which are indicative of a selective sweep. Extensive simulations are used to show that these approaches work well for a wide range of population divergence times and strong selective pressures. Control vs control simulations are used to determine an empirical False Positive Rate, and regions under selection are determined using a 1% FPR level. We show that pooling does not have a significant impact on statistical power. The tests are also robust to reasonable variations in several different parameters, including window size, base-calling error rate, and sequencing coverage. We then demonstrate the viability (and the challenges) of one of these methods in two independent Drosophila populations (Drosophila melanogaster) bred under selection for hypoxia and accelerated development, respectively. Testing for extreme hypoxia tolerance showed clear signals of selection, pointing to loci that are important for hypoxia adaptation. Overall, we outline a strategy for finding regions under selection using pooled sequences, then devise optimal tests for that strategy. The approaches show promise for detecting selection, even several generations after fixation of the beneficial allele has occurred.

Whole genome sequencing of Ethiopian highlanders reveals conserved hypoxia tolerance genes

“…These tests are based on two common estimators of θ: the summed non-fixed frequencies estimator, denoted θ f , and the average pairwise heterozygosity estimator, denoted θ π [ 47 ]. For a given region, a high log ratio of θ π (θ f ) in the control relative to the study population is indicative of selection [ 48 ]. We label these log ratio statistics as S π for the average heterozygosity estimator and S f for the summed frequency estimator, such that:…”

Whole-Genome Sequencing Uncovers the Genetic Basis of Chronic Mountain Sickness in Andean Highlanders

“…21,22 For a given region, observing a high log ratio of q p (q f ) in the control relative to the case population indicates selection. 23 We label these log ratio statistics as S p for the average heterozygosity estimator and S f for the summed frequency estimator, where…”

“…However, the SFS (and thus the performance of different tests of selection) is significantly affected by many parameters, including the selective pressure(s) affecting the beneficial allele, as well as the length of time (t) for which the allele has been under selection. 23 For a complex phenotype such as hypoxia tolerance, we expect that multiple loci throughout the genome may simultaneously undergo selective sweeps, each under a distinct selection pressure and for a distinct time period. For this reason, we apply several tests of selection: S f , S p , F ST , and PBS.…”

Learning Natural Selection from the Site Frequency Spectrum

“…This process, known as a selective sweep, is mitigated by recombination and can therefore be observed mostly in the vicinity of the beneficial allele. Improving our ability to detect the genomic signatures of selection is crucial for shedding light on genes responsible for adaptation to environmental stress, including disease.Many tests of neutrality have been proposed based on the site frequency spectrum (Tajima 1989;Fay and Wu 2000;Zeng et al 2006;Chen et al 2010;Udpa et al 2011). We start by describing these tests in a common framework delineated by Achaz (2009).…”

“…Many tests of neutrality have been proposed based on the site frequency spectrum (Tajima 1989;Fay and Wu 2000;Zeng et al 2006;Chen et al 2010;Udpa et al 2011). We start by describing these tests in a common framework delineated by Achaz (2009).…”