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Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies

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  • Yuchang Wu

    (Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53706; Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706)

  • Xiaoyuan Zhong

    (Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53706)

  • Yunong Lin

    (Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53706; Department of Statistics, University of Wisconsin–Madison, Madison, WI 53706)

  • Zijie Zhao

    (Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53706)

  • Jiawen Chen

    (Department of Statistics, University of Wisconsin–Madison, Madison, WI 53706; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514)

  • Boyan Zheng

    (Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706; Department of Sociology, University of Wisconsin–Madison, Madison, WI 53706)

  • James J. Li

    (Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706; Department of Psychology, University of Wisconsin–Madison, Madison, WI 53706; Waisman Center, University of Wisconsin–Madison, Madison, WI 53706)

  • Jason M. Fletcher

    (Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706; Department of Sociology, University of Wisconsin–Madison, Madison, WI 53706; La Follette School of Public Affairs, University of Wisconsin–Madison, Madison, WI 53706)

  • Qiongshi Lu

    (Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53706; Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706; Department of Statistics, University of Wisconsin–Madison, Madison, WI 53706)

Abstract

Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic, and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower body mass index, less-active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. A polygenic transmission disequilibrium test showed a significant overtransmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

Suggested Citation

  • Yuchang Wu & Xiaoyuan Zhong & Yunong Lin & Zijie Zhao & Jiawen Chen & Boyan Zheng & James J. Li & Jason M. Fletcher & Qiongshi Lu, 2021. "Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(25), pages 2023184118-, June.
  • Handle: RePEc:nas:journl:v:118:y:2021:p:e2023184118
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    Cited by:

    1. Rita Dias Pereira & Pietro Biroli & Titus Galama & Stephanie von Hinke & Hans van Kippersluis & Cornelius A. Rietveld & Kevin Thom, 2022. "Gene-Environment Interplay in the Social Sciences," Papers 2203.02198, arXiv.org, revised Aug 2022.

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