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

Author

Listed:
  • Huwenbo Shi

    (Harvard T.H. Chan School of Public Health
    Program in Medical and Population Genetics, Broad Institute of MIT and Harvard)

  • Steven Gazal

    (Harvard T.H. Chan School of Public Health
    Program in Medical and Population Genetics, Broad Institute of MIT and Harvard)

  • Masahiro Kanai

    (Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
    Analytic and Translational Genetics Unit, Massachusetts General Hospital
    Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT
    Harvard Medical School)

  • Evan M. Koch

    (Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School
    Harvard Medical School)

  • Armin P. Schoech

    (Harvard T.H. Chan School of Public Health
    Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
    Harvard T.H. Chan School of Public Health)

  • Katherine M. Siewert

    (Harvard T.H. Chan School of Public Health
    Program in Medical and Population Genetics, Broad Institute of MIT and Harvard)

  • Samuel S. Kim

    (Harvard T.H. Chan School of Public Health
    Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology)

  • Yang Luo

    (Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
    Harvard Medical School
    Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School
    Harvard Medical School)

  • Tiffany Amariuta

    (Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
    Harvard Medical School
    Harvard Medical School
    Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School)

  • Hailiang Huang

    (Analytic and Translational Genetics Unit, Massachusetts General Hospital
    Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT
    Harvard Medical School)

  • Yukinori Okada

    (Osaka University Graduate School of Medicine
    Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University)

  • Soumya Raychaudhuri

    (Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
    Harvard Medical School
    Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School
    Harvard Medical School)

  • Shamil R. Sunyaev

    (Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School
    Harvard Medical School)

  • Alkes L. Price

    (Harvard T.H. Chan School of Public Health
    Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
    Harvard T.H. Chan School of Public Health)

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.

Suggested Citation

  • Huwenbo Shi & Steven Gazal & Masahiro Kanai & Evan M. Koch & Armin P. Schoech & Katherine M. Siewert & Samuel S. Kim & Yang Luo & Tiffany Amariuta & Hailiang Huang & Yukinori Okada & Soumya Raychaudhu, 2021. "Population-specific causal disease effect sizes in functionally important regions impacted by selection," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21286-1
    DOI: 10.1038/s41467-021-21286-1
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    Cited by:

    1. Tzu-Ting Chen & Jaeyoung Kim & Max Lam & Yi-Fang Chuang & Yen-Ling Chiu & Shu-Chin Lin & Sang-Hyuk Jung & Beomsu Kim & Soyeon Kim & Chamlee Cho & Injeong Shim & Sanghyeon Park & Yeeun Ahn & Aysu Okbay, 2024. "Shared genetic architectures of educational attainment in East Asian and European populations," Nature Human Behaviour, Nature, vol. 8(3), pages 562-575, March.
    2. Mingxuan Cai & Zhiwei Wang & Jiashun Xiao & Xianghong Hu & Gang Chen & Can Yang, 2023. "XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. Alesha A. Hatton & Fei-Fei Cheng & Tian Lin & Ren-Juan Shen & Jie Chen & Zhili Zheng & Jia Qu & Fan Lyu & Sarah E. Harris & Simon R. Cox & Zi-Bing Jin & Nicholas G. Martin & Dongsheng Fan & Grant W. M, 2024. "Genetic control of DNA methylation is largely shared across European and East Asian populations," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Md. Moksedul Momin & Jisu Shin & Soohyun Lee & Buu Truong & Beben Benyamin & S. Hong Lee, 2023. "A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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