Fine-mapping the Favored Mutation in a Positive Selective Sweep

Akbari, Ali; Jj, Vitti; Iranmehr, Arya; Bakhtiari, Mehrdad; Pc, Sabeti; Mirarab, Siavash; Bafna,

doi:10.1101/139055

Cited by 5 publications

(2 citation statements)

References 53 publications

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“…In addition to identifying sweeps from single large values of G12 and G123, we find that the genomic signature of these MLG-based statistics surrounding the site of selection provides a means of distinguishing a sweep from other types of selection (e.g., balancing selection). This additional layer of differentiation motivates the use of MLG identity statistics as a signature in a statistical learning framework, as such approaches have increasing in prominence for genome analysis [Grossman et al, 2010, Lin et al, 2011, Pavlidis et al, 2010, Ronen et al, 2013, Pybus et al, 2015, Ronen et al, 2015, Sheehan and Song, 2016, Schrider and Kern, 2016, Akbari et al, 2017, Kern and Schrider, 2018, Mughal and DeGiorgio, 2018]. We expect that the MLG-based approaches G12 and G123, in conjunction with G2/G1, will be invaluable in localizing and classifying adaptive targets in both model and non-model study systems.…”

Section: Discussionmentioning

confidence: 99%

Detection and classification of hard and soft sweeps from unphased genotypes by multilocus genotype identity

Harris

Garud

DeGiorgio

2018

Preprint

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Positive natural selection can lead to a decrease in genomic diversity at the selected site and at linked sites, producing a characteristic signature of elevated expected haplotype homozygosity. These selective sweeps can be hard or soft. In the case of a hard selective sweep, a single adaptive haplotype rises to high population frequency, whereas multiple adaptive haplotypes sweep through the population simultaneously in a soft sweep, producing distinct patterns of genetic variation in the vicinity of the selected site. Measures of expected haplotype homozygosity have previously been used to detect sweeps in multiple study systems. However, these methods are formulated for phased haplotype data, typically unavailable for nonmodel organisms, and may have reduced power to detect soft sweeps due to their increased genetic diversity relative to hard sweeps. To address these limitations, we applied the H12 and H2/H1 statistics of Garud et al. [2015] to unphased multilocus genotypes, denoting them as G12 and G2/G1. G12 (and the more direct expected homozygosity analogue to H12, denoted G123) has comparable power to H12 for detecting both hard and soft sweeps. G2/G1 can be used to classify hard and soft sweeps analogously to H2/H1, conditional on a genomic region having high G12 or G123 values. The reason for this power is that under random mating, the most frequent haplotypes will yield the most frequent multilocus genotypes. Simulations based on parameters compatible with our recent understanding of human demographic history suggest that expected homozygosity methods are best suited for detecting recent sweeps, and increase in power under recent population expansions. Finally, we find candidates for selective sweeps within the 1000 Genomes CEU, YRI, GIH, and CHB populations, which corroborate and complement existing studies.

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Section: Discussionmentioning

confidence: 99%

Detection and classification of hard and soft sweeps from unphased genotypes by multilocus genotype identity

Harris

Garud

DeGiorgio

2018

Preprint

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“…by the classifier, k% of those are indeed drawn from class A, and (100 k)% are drawn from class B 25,125 . 489…”

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confidence: 99%

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

Sugden

Atkinson

Fischer

et al. 2017

Preprint

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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.

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

et al. 2018

View full text Add to dashboard Cite

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.

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Fine-mapping the Favored Mutation in a Positive Selective Sweep

Cited by 5 publications

References 53 publications

Detection and classification of hard and soft sweeps from unphased genotypes by multilocus genotype identity

Detection and classification of hard and soft sweeps from unphased genotypes by multilocus genotype identity

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

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

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