A Likelihood Approach for Uncovering Selective Sweep Signatures from Haplotype Data

Abstract

Selective sweeps are frequent and varied signatures in the genomes of natural populations, and detecting them is consequently important in understanding mechanisms of adaptation by natural selection. Following a selective sweep, haplotypic diversity surrounding the site under selection decreases, and this deviation from the background pattern of variation can be applied to identify sweeps. Multiple methods exist to locate selective sweeps in the genome from haplotype data, but none leverage the power of a model-based approach to make their inference. Here, we propose a likelihood ratio test statistic T to probe whole genome polymorphism datasets for selective sweep signatures. Our framework uses a simple but powerful model of haplotype frequency spectrum distortion to find sweeps and additionally make an inference on the number of presently sweeping haplotypes in a population. We found that the T statistic is suitable for detecting both hard and soft sweeps across a variety of demographic models, selection strengths, and ages of the beneficial allele. Accordingly, we applied the T statistic to variant calls from European and sub-Saharan African human populations, yielding primarily literature-supported candidates, including LCT, RSPH3, and ZNF211 in CEU, SYT1, RGS18, and NNT in YRI, and HLA genes in both populations. We also searched for sweep signatures in Drosophila melanogaster, finding expected candidates at Ace, Uhg1, and Pimet. Finally, we provide open-source software to compute the T statistic and the inferred number of presently sweeping haplotypes from whole-genome data.

Demographic decline and lineage-specific adaptations characterize New Zealand kiwi

“…The number of sweeps varied widely among lineages; this variation does not necessarily imply that some lineages experienced more selection than others, but could reflect our different abilities in each lineage to distinguish sweeps from the effects of bottlenecks, depending on each lineage's specific demographic history. We may have also detected fewer sweeps in lineages with smaller sample size (electronic supplementary material, table S2) where subtle sweep signatures are more difficult to distinguish from the genomic background [47].…”

Learning the properties of adaptive regions with functional data analysis

“…Uncertainty in this distribution make sweeps more difficult to detect as shown by refs. [30] and [31].…”

“…. }, may be difficult to observe due to the hardening of soft sweeps phenomenon [68,31]. For these reasons, we believe that sweeps lie on a continuum of softness, and predicting the initial frequency of a sweep provides value beyond discrete classification of a sweep as hard or soft.…”

“…[29] in both refs. [31] and [30]. Demographic parameters such as population split times and effective population sizes are drawn from posterior distributions of their estimates [Table S1 of ref .…”

“…As in ref. [31], we used the coalescent-simulator ms [108] to generate neutral variation (burn-in) for its speed, and used the output from these simulations to seed the haplotypic variation within SLiM [21], and employed a recombination rate of 5 × 10 −9 per site per generation and mutation rate of 10 −9 per site per generation as in ref. [30].…”