Quantifying genetic heterogeneity between continental populations for human height and body mass index

Guo, Jing; Bakshi, Andrew; Wang, Ying; Jiang, Longda; Yengo, Loïc; Goddard, Michael E.; Visscher, Peter M.; Yang, Jian

doi:10.1101/839373

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…On the other hand, several studies have shown that heterogeneity in genetic architectures limits transferability of polygenic risk scores (PRSs) across populations; 5,[41][42][43][44][45][46][47][48] critically, if applied in a clinical setting, existing PRSs may exacerbate health disparities among ethnic groups. 49 The population specificity of existing PRSs as well as estimates of transethnic genetic correlations less than one reported in the literature 30,[50][51][52][53] indicate that (1) LD tagging and allele frequencies of shared causal variants vary across populations, (2) that a sizeable number of causal variants are population specific, and/or (3) that causal effect sizes vary across populations due to, for example, different gene-environment interactions. In a region with population-specific LD, a single genetic variant that is significantly associated with a trait in two populations may actually be tagging distinct population-specific causal variants (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data

Shi

Burch

Johnson

et al. 2020

The American Journal of Human Genetics

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Despite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze nine complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.83 enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWASs due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

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Section: Introductionmentioning

confidence: 99%

Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data

Shi

Burch

Johnson

et al. 2020

The American Journal of Human Genetics

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“…This WEIRD gene sampling bias in GWAS is deeply problematic. Polygenic scores do not translate well across ancestry groups (Bitarello and Mathieson 2020;Guo et al 2019;Curtis 2018;Kim et al 2018;Martin et al 2017). For example, European ancestry-derived polygenic scores have only 42% of the effect size in African ancestry samples (Duncan et al 2019).…”

Section: Weird Gene Problemmentioning

confidence: 99%

Cultural Evolution of Genetic Heritability

Uchiyama

Spicer

Muthukrishna

2020

Preprint

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Behavioral genetics and cultural evolution have both revolutionized our understanding of human behavior, but largely independently of each other. Here we reconcile these two fields using a dual inheritance approach, which offers a more nuanced understanding of the interaction between genes and culture, and a resolution to several long-standing puzzles. For example, by neglecting how human environments are extensively shaped by cultural dynamics, behavioral genetic approaches systematically inflate heritability estimates and thereby overestimate the genetic basis of human behavior. A WEIRD (Western, educated, industrialized, rich, democratic) gene problem obscures this inflation. Considering both genetic and cultural evolutionary forces, heritability scores become less a property of a trait and more a moving target that responds to cultural and social changes. Ignoring cultural evolutionary forces leads to an over-simplified model of gene-to-phenotype causality. When cumulative culture functionally overlaps with genes, genetic effects become masked, or even reversed, and the causal effect of an identified gene is confounded with features of the cultural environment, specific to a particular society at a particular time. This framework helps explain why it is easier to discover genes for deficiencies than genes for abilities. With this framework, we predict the ways in which heritability should differ between societies, between socioeconomic levels within some societies but not others, and over the life course. An integrated cultural evolutionary behavioral genetics cuts through the nature–nurture debate and elucidates controversial topics such as general intelligence.

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“…On the other hand, several studies have shown 31 that heterogeneity in genetic architectures limits transferability of polygenic risk scores (PRS) across 32 populations 5, [41][42][43][44][45][46][47][48] ; critically, if applied in a clinical setting, existing PRS may exacerbate health disparities 33 among ethnic groups 49 . The population-specificity of existing PRS as well as estimates of transethnic 34 genetic correlations less than one reported in the literature 30,[50][51][52][53] indicate that (1) LD tagging and allele 35 frequencies of shared causal variants vary across populations, (2) that a sizeable number of causal variants 36 are population-specific, and/or (3) that causal effect sizes vary across populations due to, for example, 37 different gene-environment interactions. For example, due to population-specific LD, a single genetic 38 variant that is significantly associated with a trait in two populations may actually be tagging distinct 39 population-specific causal variants ( Figure 1).…”

Section: Introduction 18mentioning

confidence: 97%

Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

Shi

Burch²,

Johnson

et al. 2019

Preprint

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1Despite strong transethnic genetic correlations reported in the literature for many complex traits, the non-2 transferability of polygenic risk scores across populations suggests the presence of population-specific 3 components of genetic architecture. We propose an approach that models GWAS summary data for one 4 trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. 5In simulations across various genetic architectures, we show that our approach yields approximately 6 unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze 9 7 complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 8 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide 9 estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-10posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated 11 effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8x 12 enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor 13 shared causal SNPs that are undetected in the other GWAS due to differences in LD, allele frequencies, 14 and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific 15 functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-16 effect common SNPs. 17 respect to the set of variants included in the GWAS and can contain variants with indirect nonzero effects 52 that are statistical rather than biological in nature (this is analogous to the definition of SNP-heritability, 53 which is also a function of a specific set of SNPs 11,54-56 ). Through extensive simulations, we show that our 54 method yields approximately unbiased estimates of the proportions of population-specific/shared causal 55 variants if in-sample LD is used and slightly upward-biased estimates if LD is estimated from an external 56 reference panel. We then show that using these estimates as priors to perform fine-mapping (Methods) 57 produces well-calibrated per-SNP posterior probabilities and enrichment test statistics. We note that the 58 definition of enrichment used here is related to, but conceptually distinct from, definitions of SNP-59 heritability enrichment 13,16 . Under our framework, an enrichment of causal SNPs greater than 1 indicates 60 that, compared to the genome-wide background, there are more causal SNPs in that region than expected 57,58 61 (Methods). In contrast, an enrichment of SNP-heritability greater than 1 indicates that the average per-SNP 62 effect size in the region is larger than the genome-wide average per-SNP effect size. 63We apply our approach to publicly available GWAS summary statistics for 9 complex traits and 64 diseases in individuals of East Asian (EAS) and European (EUR) ancestry (average NEAS = 94,621, N...

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Quantifying genetic heterogeneity between continental populations for human height and body mass index

Cited by 5 publications

References 56 publications

Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data

Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data

Cultural Evolution of Genetic Heritability

Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

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