Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations

Pybus, Marc; Luisi, Pierre; Dall’Olio, Giovanni Marco; Uzkudun, Manu; Laayouni, Hafid; Bertranpetit, Jaume; Engelken, Johannes

doi:10.1093/bioinformatics/btv493

Cited by 86 publications

(103 citation statements)

References 48 publications

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“…We suspect that the abundance of selection signals in this population is partly a consequence of longer LD blocks in East Asian populations relative to European and West African populations, and partly a consequence of the fact that the simulation software cosi 9 precludes modeling demographic events during the duration of a sweep, and so very recent population expansions are not modeled in our simulations; Supplementary Figure 20 shows that the distributions of component statistics across populations differs more in observed 1000 Genomes data than in our demographic simulations. We also note that this abundance of signal in East Asian populations has been previously observed 21 . Chromosome 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 COL11A1 ZNF695, ZNF670 ADAM17, YWHAQ, IAH1, CPSF3, ITGB1BP1 SMC6, VSNL1, GEN1, MSGN1, RAD51AP2, KCNS3 CYP26B1, EXOC6B EDAR, RANBP2, CCDC138, LIMS1 LINC01116 CPS1, TTLL4, CYP27A1, PRKAG3, WNT6 LINC00629 BBX, CCDC54, LINC00635, LINC00636 EPHB1, COPB2, RBP2, MRPS22 CRIMP1, JAKMIP1 GAB1, SMARCA5 LINC01020, ADAMS16 ACOT12, SSBP2 LINC00992, DTWD2 TRIM52 PREP ZNF804B LUZP6, MTPN XKR6, BLK, LINC00208, FAM167A, DAFB136, CTSB SPIN1, NXNL2 OTUD1, KIAA1217, GPR158, LINC00836 PCDH15 MYOF TRUB1 LUZP2 LINC00457, RFC3 LINC00448 TRPM1, MTMR10, SQRDL, SLC30A4 LINC00922 BCAS3 Genes of interest:…”

Section: Supplementary Figure 19supporting

confidence: 82%

“…These differences may be a consequence of the fact that our simulations did not include very recent population expansions, because of limitations of the simulation software cosi regarding the overlap of selective sweeps and demographic events 9 . Most of these differences likely have the effect of leading to an increase in the number of predicted sweep sites in East Asia, and a decrease in West Africa, as we observe in our scan using the 1000 Genomes data, and as has been observed previously 21 . Note that x-and y-axis limits vary for each panel.…”

Section: Supplementary Figure 20supporting

confidence: 79%

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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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Section: Supplementary Figure 19supporting

confidence: 82%

Section: Supplementary Figure 20supporting

confidence: 79%

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

Sugden

Atkinson

Fischer

et al. 2017

Preprint

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“…As such, it provides a broad picture of the influence of natural selection in each genomic region and Hominidae species. All the information is available as an interactive browser at webpage: http://tinyurl.com/nf8qmzh following the criteria and configuration of a recently published human dataset (Pybus et al 2014, 2015). The UCSC-style format facilitates the integration with the rich UCSC browser tracks, a search mask allows easy access to results for specific genes or genomic regions, and the raw scores (test statistic value and rank score/empirical P value) can be conveniently downloaded using the UCSC Table function.…”

Section: Resultsmentioning

confidence: 99%

Natural Selection in the Great Apes

et al. 2016

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Natural selection is crucial for the adaptation of populations to their environments. Here, we present the first global study of natural selection in the Hominidae (humans and great apes) based on genome-wide information from population samples representing all extant species (including most subspecies). Combining several neutrality tests we create a multi-species map of signatures of natural selection covering all major types of natural selection. We find that the estimated efficiency of both purifying and positive selection varies between species and is significantly correlated with their long-term effective population size. Thus, even the modest differences in population size among the closely related Hominidae lineages have resulted in differences in their ability to remove deleterious alleles and to adapt to changing environments. Most signatures of balancing and positive selection are species-specific, with signatures of balancing selection more often being shared among species. We also identify loci with evidence of positive selection across several lineages. Notably, we detect signatures of positive selection in several genes related to brain function, anatomy, diet and immune processes. Our results contribute to a better understanding of human evolution by putting the evidence of natural selection in humans within its larger evolutionary context. The global map of natural selection in our closest living relatives is available as an interactive browser at http://tinyurl.com/nf8qmzh.

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“…These characteristics are illustrated in Fig. and have been extensively reviewed elsewhere , so we do not devote space to considering their basis here, but do discuss the evidence for selection for each gold standard.…”

Section: Gold Standard Examples Of Classic Selective Sweeps In Humansmentioning

confidence: 99%

How well do we understand the basis of classic selective sweeps in humans?

et al. 2019

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Classic selective sweeps occur when positive selection increases a variant's frequency from low to high in a population, and underlie some long‐studied human characteristics such as variation in skin, hair or eye colour. In such well‐studied ‘gold standard’ examples, a known variant has been associated with a plausible phenotype and underlying selective force. Signatures of classic sweeps have more recently been detected in population‐genetic data independently of any prior information about the corresponding phenotype or selective force, and usually without suggesting any insights into these. Motivated by the need to understand such candidates, we first review the gold standards and show that our understanding of them is often incomplete or unconvincing; only two of the examples we consider are compellingly explained. We assess approaches for large‐scale association of classic sweep candidate variants to phenotypes and selective forces, test these on the gold standards, and discuss the standards of evidence needed to adequately understand a selective sweep.

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Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations

Abstract: The genome-wide results for three human populations from The 1000 Genomes Project and an R-package implementing the 'Hierarchical Boosting' framework are available at http://hsb.upf.edu/.

Cited by 86 publications

References 48 publications

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

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

Natural Selection in the Great Apes

How well do we understand the basis of classic selective sweeps in humans?

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