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Cited by 119Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockForensic and Genetic Research

Population-specific causal disease effect sizes in functionally important regions impacted by selection

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Shi, Huwenbo, Gazal, Steven, Kanai, Masahiro, Koch, Evan M., Schoech, Armin P., Siewert, Katherine M., Kim, Samuel S., Luo, Yang +6 more·2021·Nature Communications
DOI 10.1038/s41467-021-21286-1PMC PMC7889654

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Abstract

Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

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  • The cross-ancestral genetic correlation between EAS and EUR populations for EduYears was 0.873 (s.e. 0.042) using S-LDXR.

    “The genetic correlation across the EAS and EUR populations was 0.873 (s.e. 0.042).”

    From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [31]

    The Results section, “Analysis of baseline-LD-X model annotations across 31 diseases and complex traits” (page unavailable), says the authors used S-LDXR to estimate East Asian–European genetic correlations for 31 traits. Our copy gives the average correlation and some trait-specific estimates, but it does not give 0.873 (s.e. 0.042) for EduYears; searches for EduYears, educational attainment, and the stated numbers found no matching passage. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • The cross-population rg for EduYears closely aligns with the median of EAS-EUR cross-population rg across 31 other traits (median rg = 0.88; range 0.342-1.05).

    “Remarkably, the cross-population rg for EduYears closely aligns with the median of EAS–EUR cross-population rg across the 31 traits (median rg = 0.88; range 0.342–1.05).”

    From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [31]

    The Results section (‘Analysis of baseline-LD-X model annotations across 31 diseases and complex traits’; page unavailable) reports EAS–EUR genetic correlations for 31 traits, with an average of 0.85 and a lowest estimate of 0.34. Our copy does not report a median of 0.88, an upper endpoint of 1.05, or an EduYears correlation; the Methods trait list does not include EduYears. It therefore does not establish the claimed close alignment. (LLM, conf=0.85)

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References 112 · Cited by 119

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Authors
Shi, H., Gazal, S., Kanai, M., Koch, E. M., Schoech, A. P., Siewert, K. M., Kim, S. S., Luo, Y., Amariuta, T., Huang, H., Okada, Y., Raychaudhuri, S., Sunyaev, S. R., & Price, A. L.
Affiliations
  • Broad Institute (US)
  • Brigham and Women's Hospital (US)
  • Immunology Frontier Research Center, The University of Osaka (JP)
Corresponding author
  • Shi, Huwenbo
  • Price, Alkes L.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-02-17
Volume
12
Issue
1
Pages
1098-1098
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-021-21286-1
PMC
PMC7889654
OpenAlex
W3129840172
Cited by
119
References
112
Citations by year
2026: 18 · 2025: 13 · 2024: 24 · 2023: 24 · 2022: 27 · 2021: 12 · 2020: 1
FWCI
12.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R01 HG006399)
  • U.S. Department of Health & Human Services | NIH | Center for Information Technology (U01 HG009379, R01 MH107649, R01 MH101244)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
Keywords
background selection, polygenic risk prediction, East Asians, Europeans, regulatory elements, conserved domains, gene-environment interaction, positive selection, complex traits
MeSH terms
Algorithms, Genetics, Population, Haplotypes, Humans, Models, Genetic, Selection, Genetic, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Genomics, White People, Asian People, Genome-Wide Association Study

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