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

Schoech, Armin; Jordan, Daniel M.; Loh, Po−Ru; Gazal, Steven; O’Connor, Luke J; Balick, Daniel J.; Palamara, Pier Francesco; Finucane, Hilary; Sunyaev, Shamil; Price, Alkes L.

doi:10.1038/s41467-019-08424-6

Cited by 125 publications

(183 citation statements)

References 45 publications

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“…We have recently developed a Bayesian method (BayesS) to estimate the effect size-MAF relationship, which was considered as a free parameter (S) in the model 12 . We detected negative S for a number of complex traits in humans, highlighting an important role of negative selection in shaping the genetic architecture, consistent with the findings from other studies based on genome-wide variance estimation approaches 7,10,13,14 . The BayesS model also allows us to estimate the SNP-based heritability and polygenicity (the proportion of SNPs with nonzero effects) to better describe the genetic architecture for a trait.…”

Section: Introductionsupporting

confidence: 90%

“…A negative relationship between effect size and MAF is a signature of negative (or purifying) selection 2,3 , which prevents mutations with large deleterious effects becoming frequent in the population. Understanding how natural selection has shaped genetic variation helps researchers to improve experimental designs of genetic association studies 4 and the estimation of SNP-based heritability (the proportion of phenotypic variance explained by the SNPs) [5][6][7][8] . Inference on natural selection is also a critical step towards the understanding of the genetic architecture of complex traits.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian analysis of GWAS summary data reveals differential signatures of natural selection across human complex traits and functional genomic categories

Zeng

Xue

Jiang

et al. 2019

Preprint

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Understanding how natural selection has shaped the genetic architecture of complex traits and diseases is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level data to estimate multiple features of genetic architecture, including signatures of natural selection. Here, we present an enhanced method (SBayesS) that only requires GWAS summary statistics and incorporates functional genomic annotations. We analysed GWAS data with large sample sizes for 155 complex traits and detected pervasive signatures of negative selection with diverse estimates of SNP-based heritability and polygenicity. Projecting these estimates onto a map of genetic architecture obtained from evolutionary simulations revealed relatively strong natural selection on genetic variants associated with cardiorespiratory and cognitive traits and relatively small number of mutational targets for diseases. Averaging across traits, the joint distribution of SNP effect size and MAF varied across functional genomic regions (likely to be a consequence of natural selection), with enrichment in both the number of associated variants and the magnitude of effect sizes in regions such as transcriptional start sites, coding regions and 5'-and 3'-UTRs.

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Section: Introductionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Bayesian analysis of GWAS summary data reveals differential signatures of natural selection across human complex traits and functional genomic categories

Zeng

Xue

Jiang

et al. 2019

Preprint

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“…Fifth, we determined that polygenic localization estimates were slightly larger when not stratifying the lowest-heritability bin into 10 MAF bins ( Supplementary Table 28). Sixth, We determined that % estimates using all MAF≥0.001 SNPs were 1.4x larger (vs. 2.8x larger SNP set) compared to analyses of common SNPs, confirming that rare and low-frequency SNPs are depleted for high-heritability SNPs 26,44,48 ( Supplementary Table 28).…”

Section: Functionally Informed Fine-mapping Of 47 Complex Traits In Tmentioning

confidence: 71%

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Weissbrod

Hormozdiari

Benner

et al. 2019

Preprint

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162

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Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (and/or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winner's curse. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In analyses of 47 UK Biobank traits (average N=317K), PolyFun + SuSiE identified 3,025 fine-mapped varianttrait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were finemapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 25 (hair color) to 3,400 (height) to 550,000 (chronotype). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

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“…Second, ps 2 can be enhanced by the addition of increasingly rare variation to the discovery GWAS [30], especially since negative selection results in larger per-allele effect sizes at the lower end of the frequency spectrum [40]. Current imputation panels are limited in their ability to accurately assess variants with frequencies below 1%, but will continuously improve as imputation panels increase in size and representation of varying populations [41,42].…”

Section: Discussionmentioning

confidence: 99%

Screening human embryos for polygenic traits has limited utility

Karavani

Zuk²,

Zeevi³

et al. 2019

Preprint

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Genome-wide association studies have led to the development of polygenic score (PS) predictors that explain increasing proportions of the variance in human complex traits. In parallel, progress in preimplantation genetic testing now allows genome-wide genotyping of embryos generated via in vitro fertilization (IVF). Jointly, these developments suggest the possibility of screening embryos for polygenic traits such as height or cognitive function. There are clear ethical, legal, and societal concerns regarding such a procedure, but these cannot be properly discussed in the absence of data on the expected outcomes of screening. Here, we use theory, simulations, and real data to evaluate the potential gain of PS-based embryo selection, defined as the expected difference in trait value between the top-scoring embryo and an average, unselected embryo. We observe that the gain increases very slowly with the number of embryos, but more rapidly with increased variance explained by the PS. Given currently available polygenic predictors and typical IVF yields, the average gain due to selection would be ≈2.5cm if selecting for height, and ≈2.5 IQ (intelligence quotient) points if selecting for cognitive function. These mean values are accompanied by wide confidence intervals; in real data drawn from nuclear families with up to 20 offspring each, we observe that the offspring with the highest PS for height was the tallest only in 25% of the families. We discuss prospects and limitations of PS-based embryo selection for the foreseeable future.Polygenic scores (PS) are derived from large-scale genome-wide association studies (GWAS) of complex traits, which can be quantitative (such as intelligence or height) or categorical (such as disease status, in which case they are often referred to as 'polygenic risk scores') [8]. A PS is the count of effect alleles in an individual's genome, weighted by each allele's strength of association with the trait of interest in an independent GWAS [9]. The predictive power of a PS is usually represented by ps 2 , or the proportion of variance of the quantitative trait explained by the PS. To date, the largest GWAS of intelligence [10,11] has demonstrated a relatively modest out-of-sample ps 2 (≈5%), despite large sample sizes (n≈300,000 individuals). By contrast, recent large-scale GWASs of height have attained ps 2 of approximately 25%, while demonstrating a highly polygenic genetic architecture similar to intelligence [12]. Consequently, in the present report, we analyze height in addition to cognitive function, which also allows us to exploit several datasets in which height data, but not intelligence data, are available.PSs are typically evaluated on a cohort basis, and are not used to differentiate one individual from another (although a recent report has demonstrated that, for an extraordinarily tall NBA player, the PS for height was >4 standard deviations above the population mean [13]). In order for polygenic embryo selection to hold potential utility (independent of ethical considera...

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

Cited by 125 publications

References 45 publications

Bayesian analysis of GWAS summary data reveals differential signatures of natural selection across human complex traits and functional genomic categories

Bayesian analysis of GWAS summary data reveals differential signatures of natural selection across human complex traits and functional genomic categories

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Screening human embryos for polygenic traits has limited utility

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