Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits

Ap, Schoech; Jordan, Daniel M.; P, Loh; Gazal, Steven; O’Connor, Luke; Balick, Daniel J.; Pf, Palamara; Hk, Finucane; Sunyaev,; Al, Price

doi:10.1101/188086

Cited by 29 publications

(37 citation statements)

References 43 publications

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“…Despite low per-SNP heritability, the per-SNP effect sizes have been shown to be on average higher for SNPs with low MAF, in accordance with a model of negative selection (the relationship between captured heritability h 2 and allele effect b for a given SNP is , with f being the allele frequency. · 1 ) h 2 = 2 b 2 · f · ( − f See Schoech et al 2017;and Zeng et al 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Polygenic architecture of human neuroanatomical diversity

Biton

Traut

Poline

et al. 2019

Preprint

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We analysed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and SNP data from > 20,000 individuals. Our results replicate previous findings of a strong polygenic architecture of neuroanatomical diversity. SNPs captured from 40% to 54% of the variance in the volume of different brain regions. We observed a large correlation between chromosome length and the amount of phenotypic variance captured, r~0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a more local scale, SNPs within genes (~51%) captured~1.5-times more genetic variance than the rest; and SNPs with low minor allele frequency (MAF) captured significantly less variance than those with higher MAF: the 40% of SNPs with MAF<5% captured less than one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG~0.45. Across regions, genetic correlations were in general similar to phenotypic correlations. By contrast, genetic correlations were larger than phenotypic correlations for the left/right volumes of the same region, and indistinguishable from 1. Additionally, the differences in left/right volumes were not heritable, underlining the role of environmental causes in the variability of brain asymmetry. Our analysis code is available at https://github.com/neuroanatomy/genomic-architecture.

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Section: Discussionmentioning

confidence: 99%

Polygenic architecture of human neuroanatomical diversity

Biton

Traut

Poline

et al. 2019

Preprint

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“…Negative selection has been investigated by comparing empirical data to null models of the relationship between frequency and squared effect sizes (Schoech et al. ) or linkage disequilibrium and per‐single nucleotide polymorphism heritability (Gazal et al. ).…”

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confidence: 99%

“…; Schoech et al. ) and positive (Berg and Coop ; Berg et al. ) selection act on complex traits such as height and body mass index (BMI).…”

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confidence: 99%

“…To detect polygenic adaptation, studies have relied on genotype data from multiple populations to probe the frequency and linkage properties of trait-associated alleles as compared to a null based on genome-wide single nucleotide polymorphisms (Turchin et al 2012;Berg and Coop 2014;Racimo et al 2018), or have used haplotype-based statistics and dense sequence data from a single population (Field et al 2016). Negative selection has been investigated by comparing empirical data to null models of the relationship between frequency and squared effect sizes (Schoech et al 2017) or linkage disequilibrium and per-single nucleotide polymorphism heritability ). These studies have argued that selection acts on many traits, and that both negative Schoech et al 2017) and positive (Berg and Coop 2014;Berg et al 2017) selection act on complex traits such as height and body mass index (BMI).…”

mentioning

confidence: 99%

“…Negative selection has been investigated by comparing empirical data to null models of the relationship between frequency and squared effect sizes (Schoech et al 2017) or linkage disequilibrium and per-single nucleotide polymorphism heritability ). These studies have argued that selection acts on many traits, and that both negative Schoech et al 2017) and positive (Berg and Coop 2014;Berg et al 2017) selection act on complex traits such as height and body mass index (BMI).…”

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confidence: 99%

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An evolutionary compass for detecting signals of polygenic selection and mutational bias

et al. 2019

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Selection and mutation shape the genetic variation underlying human traits, but the specific evolutionary mechanisms driving complex trait variation are largely unknown. We developed a statistical method that uses polarized genome‐wide association study (GWAS) summary statistics from a single population to detect signals of mutational bias and selection. We found evidence for nonneutral signals on variation underlying several traits (body mass index [BMI], schizophrenia, Crohn's disease, educational attainment, and height). We then used simulations that incorporate simultaneous negative and positive selection to show that these signals are consistent with mutational bias and shifts in the fitness‐phenotype relationship, but not stabilizing selection or mutational bias alone. We additionally replicate two of our top three signals (BMI and educational attainment) in an external cohort, and show that population stratification may have confounded GWAS summary statistics for height in the GIANT cohort. Our results provide a flexible and powerful framework for evolutionary analysis of complex phenotypes in humans and other species, and offer insights into the evolutionary mechanisms driving variation in human polygenic traits.

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Estimating cross‐population genetic correlations of causal effect sizes

Galinsky

Reshef

Finucane

et al. 2018

Genetic Epidemiology

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Recent studies have examined the genetic correlations of single‐nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated ρ g, the cross‐population correlation of joint‐fit effect sizes at genotyped SNPs. However, the value of ρ g depends both on the cross‐population correlation of true causal effect sizes ( ρ b) and on the similarity in linkage disequilibrium (LD) patterns in the two populations, which drive tagging effects. Here, we derive the value of the ratio ρ g / ρ b as a function of LD in each population. By applying existing methods to obtain estimates of ρ g, we can use this ratio to estimate ρ b. Our estimates of ρ b were equal to 0.55 ( SE = 0.14) between Europeans and East Asians averaged across nine traits in the Genetic Epidemiology Research on Adult Health and Aging data set, 0.54 ( SE = 0.18) between Europeans and South Asians averaged across 13 traits in the UK Biobank data set, and 0.48 ( SE = 0.06) and 0.65 ( SE = 0.09) between Europeans and East Asians in summary statistic data sets for type 2 diabetes and rheumatoid arthritis, respectively. These results implicate substantially different causal genetic architectures across continental populations.

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Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits

Cited by 29 publications

References 43 publications

Polygenic architecture of human neuroanatomical diversity

Polygenic architecture of human neuroanatomical diversity

An evolutionary compass for detecting signals of polygenic selection and mutational bias

Estimating cross‐population genetic correlations of causal effect sizes

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