A flexible method for estimating the fraction of fitness influencing mutations from large sequencing data sets

Moon, Seok-Jae; Akey, Joshua M.

doi:10.1101/gr.203059.115

Cited by 10 publications

(13 citation statements)

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“…2 ). As expected from previous findings 18 , a ratio of the fractions of sites under selection pressure (= f ) 19 between loss-of-function variants and synonymous SNV was high in the Finnish population ( = 11.3), while the Japanese population indicated moderate values ( = 3.5–4.0; Fig. 1b ).…”

Section: Resultssupporting

confidence: 89%

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Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese

et al. 2018

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Understanding natural selection is crucial to unveiling evolution of modern humans. Here, we report natural selection signatures in the Japanese population using 2234 high-depth whole-genome sequence (WGS) data (25.9×). Using rare singletons, we identify signals of very recent selection for the past 2000–3000 years in multiple loci (ADH cluster, MHC region, BRAP-ALDH2, SERHL2). In large-scale genome-wide association study (GWAS) dataset (n = 171,176), variants with selection signatures show enrichment in heterogeneity of derived allele frequency spectra among the geographic regions of Japan, highlighted by two major regional clusters (Hondo and Ryukyu). While the selection signatures do not show enrichment in archaic hominin-derived genome sequences, they overlap with the SNPs associated with the modern human traits. The strongest overlaps are observed for the alcohol or nutrition metabolism-related traits. Our study illustrates the value of high-depth WGS to understand evolution and their relationship with disease risk.

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Section: Resultssupporting

confidence: 89%

“…Annotation of ancestral and derived alleles was conducted according to the 1000 Genomes Project Phase3 v5 data 33 and the dbSNP database version 150. SFS and the fraction of sites under selection pressure was calculated using original scripts of Moon et al (see URLs) 19 . Intron and intergenic sites were used as a reference.…”

Section: Methodsmentioning

confidence: 99%

Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese

et al. 2018

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“…2007 ; Akey 2009 ; Grossman et al. 2013 ; Moon and Akey 2016 ). Together, evolutionary information from both short- and long-term time scales is harnessed in our approach.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Landscape of Protein Variation in Human Exomes

Patel

Scheinfeldt

Sanderford

et al. 2018

Molecular Biology and Evolution

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The human genome contains hundreds of thousands of missense mutations. However, only a handful of these variants are known to be adaptive, which implies that adaptation through protein sequence change is an extremely rare phenomenon in human evolution. Alternatively, existing methods may lack the power to pinpoint adaptive variation. We have developed and applied an Evolutionary Probability Approach (EPA) to discover candidate adaptive polymorphisms (CAPs) through the discordance between allelic evolutionary probabilities and their observed frequencies in human populations. EPA reveals thousands of missense CAPs, which suggest that a large number of previously optimal alleles experienced a reversal of fortune in the human lineage. We explored nonadaptive mechanisms to explain CAPs, including the effects of demography, mutation rate variability, and negative and positive selective pressures in modern humans. Many nonadaptive hypotheses were tested, but failed to explain the data, which suggests that a large proportion of CAP alleles have increased in frequency due to beneficial selection. This suggestion is supported by the fact that a vast majority of adaptive missense variants discovered previously in humans are CAPs, and hundreds of CAP alleles are protective in genotype–phenotype association data. Our integrated phylogenomic and population genetic EPA approach predicts the existence of thousands of nonneutral candidate variants in the human proteome. We expect this collection to be enriched in beneficial variation. The EPA approach can be applied to discover candidate adaptive variation in any protein, population, or species for which allele frequency data and reliable multispecies alignments are available.

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“…The DFEs estimated for humans by Eyre-Walker et al (2006) and Boyko et al (2008) have been widely used for human population genetics. For example, the DFEs from these studies have been used to estimate differences in the genetic load across human populations (Henn et al 2016), to model the ancient introgression of Neanderthal alleles into humans (Harris and Nielsen 2016), as a model for the frequency spectrum of deleterious polymorphisms in simulating data for disease studies (Uricchio et al 2016), to evaluate the contribution of background selection to diversity on the Y chromosome in humans (Sayres et al 2014), and to estimate the strength of selection acting on disease genes (Moon and Akey 2016). While these previous studies of the DFE have had considerable impact in the field, it is important to appreciate that the estimates were made using small samples, either in terms of total individuals sequenced or in terms of the proportion of the total genome sequenced.…”

Section: Introductionmentioning

confidence: 99%

Inference of the distribution of selection coefficients for new nonsynonymous mutations using large samples

Kim

Lohmueller

2016

Preprint

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The distribution of fitness effects (DFE) has considerable importance in population genetics. To date, estimates of the DFE come from studies using a small number of individuals. Thus, estimates of the proportion of moderately to strongly deleterious new mutations may be unreliable because such variants are unlikely to be segregating in the data. Additionally, the true functional form of the DFE is unknown, and estimates of the DFE differ significantly between studies. Here we present a flexible and computationally tractable method, called Fit∂a∂i, to estimate the DFE using the site frequency spectrum from a large number of individuals. We apply our approach to the frequency spectrum of 1300 Europeans from the Exome Sequencing Project ESP6400 dataset, 1298 Danes from the LuCamp dataset, and 432Europeans from the 1000 Genomes Project to estimate the DFE of deleterious nonsynonymous mutations.We infer significantly fewer (0.38-0.84x) strongly deleterious mutations with selection coefficient |s| > 0.01 and more (1.24-1.43x) weakly deleterious mutations with selection coefficient |s| < 0.001 compared to previous estimates. Furthermore, a DFE that is a mixture distribution of a point mass at neutrality plus a gamma distribution fits best to two of the three datasets. Our results suggest that nearly neutral forces play a larger role in human evolution than previously thought.

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A flexible method for estimating the fraction of fitness influencing mutations from large sequencing data sets

Cited by 10 publications

References 50 publications

Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese

Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese

Adaptive Landscape of Protein Variation in Human Exomes

Inference of the distribution of selection coefficients for new nonsynonymous mutations using large samples

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