Identifying the favored mutation in a positive selective sweep

Akbari, Ali; Vitti, Joseph J.; Iranmehr, Arya; Bakhtiari, Mehrdad; Sabeti, Pardis C.; Mirarab, Siavash; Bafna, Vineet

doi:10.1038/nmeth.4606

Cited by 71 publications

(109 citation statements)

References 42 publications

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“…Individual simulations were converted into two-dimensional matrices, or feature vector images, built from 89 rows corresponding to different summary statistics, and 11 columns corresponding to adjacent sub-windows. The 89 statistics include 17 that are implemented in diploS/HIC along with 72 derivatives of the recently developed SNP-specific SAFE statistic [32]. We defined the four completed hard/soft and partial hard/soft selective sweep states as containing a sweep within the central, focal sub-window.…”

Section: Resultsmentioning

confidence: 99%

Discovery of ongoing selective sweeps withinAnophelesmosquito populations using deep learning

Xue

Schrider²,

Kern

2019

Preprint

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Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce a deep learning approach that uses a convolutional neural network for image processing, which is trained with coalescent simulations incorporating population-specific history, to discover selective sweeps from population genomic data. This approach distinguishes between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate the performance of our deep learning classifier partialS/HIC, which exhibits unprecedented resolution for detecting partial sweeps. We also apply our method to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a catalog of candidate adaptive loci that may aid mosquito control efforts. More broadly, the success of our supervised machine learning approach introduces a powerful method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics. Author Summary Recent successful efforts to reduce malaria transmission are in danger of collapse due to evolving insecticide resistance in the mosquito vector Anopheles gambiae. We aim to understand the genetic basis of current adaptation to vector control efforts by deploying a novel method that can classify multiple categories of selective sweeps from population genomic data. In recent years, there has been great progress made in the identification of completed selective sweeps through the use of supervised machine learning (SML), but SML techniques have rarely been applied to partial or ongoing selective sweeps. Partial sweeps represent an important facet of evolution as they reflect present-day selection and thus may give insight into future dynamics. However, the genomic impact left by partial sweeps is more subtle than that left by completed sweeps, making such signatures more difficult to detect. To this end, we extend a recent SML method to partial sweep inference and apply it to elucidate ongoing selective sweeps from Anopheles population genomic samples.

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Section: Resultsmentioning

confidence: 99%

Discovery of ongoing selective sweeps withinAnophelesmosquito populations using deep learning

Xue

Schrider²,

Kern

2019

Preprint

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“…those with q < 0.01 in at least one population) compared to genes lacking known GWAS associations (Table S5; see Material and Methods). While such patterns may also be influenced by demographic structure, the availability of large numbers of high-coverage ancient human genomes in the next decade will make it possible to improve the resolution of candidate sweeps, potentially to the level of causal mutations (Akbari et al, 2018). In addition, information about the temporal relatedness of past genetic changes could help identify key interacting genes or networks, guiding research efforts investigating target loci.…”

Section: Discussionmentioning

confidence: 99%

Admixture has obscured signals of historical hard sweeps in humans

Souilmi

Tobler

Johar

et al. 2020

Preprint

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The role of selection in shaping genetic diversity in natural populations is an area of intense interest in modern biology, especially the characterization of adaptive loci.Within humans, the rapid increase in genomic information has produced surprisingly few well-defined adaptive loci, promoting the view that recent human adaptation involved numerous loci with small fitness benefits. To examine this we searched for signatures of hard sweeps -the selective fixation of a new or initially rare beneficial variant -in 1,162 ancient western Eurasian genomes and identified 57 sweeps with high confidence. This unexpectedly extensive signal was concentrated on proteins acting at the cell surface, and potential selection pressures include cold adaptation in early Eurasian populations, and oxidative stress from carbohydrate-rich diets in farming populations. Critically, these sweep signals have been obscured in modern European genomes by subsequent population admixture, especially during the Bronze Age (5-3kya) and empires of classical antiquity.

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“…Ultimately, we expect inferences deriving from SS-H12 analysis to assist in formulating and guiding more informed questions about discovered candidates across diverse organisms for which sequence data-phased and unphased-exist. After establishing the timing and softness of a shared sweep, appropriate follow-up analyses can include inferring the age of a sweep [Smith et al, 2018], identifying the favored allele or alleles [Akbari et al, 2018], or identifying other populations connected to the shared sweep. We believe that our approach will serve to enhance investigations into a diverse variety of study systems, and facilitate the emergence of new perspectives and paradigms.…”

Section: Discussionmentioning

confidence: 99%

Identifying and classifying shared selective sweeps from multilocus data

Harris

DeGiorgio

2018

Preprint

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Positive selection causes beneficial alleles to rise to high frequency, resulting in a selective sweep of the diversity surrounding the selected sites. Accordingly, the signature of a selective sweep in an ancestral population may still remain in its descendants. Identifying signatures of selection in the ancestor that are shared among its descendants is important to contextualize the timing of a sweep, but few methods exist for this purpose. We introduce the statistic SS-H12, which can identify genomic regions under shared positive selection across populations and is based on the theory of the expected haplotype homozygosity statistic H12, which detects recent hard and soft sweeps from the presence of high-frequency haplotypes. SS-H12, is distinct from other statistics that detect shared sweeps because it requires a minimum of only two populations, and properly identifies and differentiates between independent convergent sweeps and true ancestral sweeps, with high power and robustness to a variety of demographic models. Furthermore, we can apply SS-H12 in conjunction with the ratio of a different set of expected haplotype homozygosity statistics to further classify identified shared sweeps as hard or soft. Finally, we identified both previously-reported and novel shared sweep candidates from wholegenome sequences of global human populations. Previously-reported candidates include the well-characterized ancestral sweeps at LCT and SLC24A5 in Indo-European populations, as well as GPHN worldwide. Novel candidates include an ancestral sweep at RGS18 in sub-Saharan African populations involved in regulating the platelet response and implicated in sudden cardiac death, and a convergent sweep at C2CD5 between European and East Asian populations that may explain their different insulin responses. inference of ancient human demography from individual genome sequences. Nat. Genet., 43:1031-1034, 2011. J B S Haldane. A mathematical theory of natural and artificial selection. V. selection and mutation. Math.

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Identifying the favored mutation in a positive selective sweep

Cited by 71 publications

References 42 publications

Discovery of ongoing selective sweeps withinAnophelesmosquito populations using deep learning

Discovery of ongoing selective sweeps withinAnophelesmosquito populations using deep learning

Admixture has obscured signals of historical hard sweeps in humans

Identifying and classifying shared selective sweeps from multilocus data

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