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Interpreting polygenic score effects in sibling analysis

Author

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  • Jason Fletcher
  • Yuchang Wu
  • Tianchang Li
  • Qiongshi Lu

Abstract

Researchers often claim that sibling analysis can be used to separate causal genetic effects from the assortment of biases that contaminate most downstream genetic studies (e.g. polygenic score predictors). Indeed, typical results from sibling analysis show large (>50%) attenuations in the associations between polygenic scores and phenotypes compared to non-sibling analysis, consistent with researchers’ expectations about bias reduction. This paper explores these expectations by using family (quad) data and simulations that include indirect genetic effect processes and evaluates the ability of sibling analysis to uncover direct genetic effects of polygenic scores. We find that sibling analysis, in general, fail to uncover direct genetic effects; indeed, these models have both upward and downward biases that are difficult to sign in typical data. When genetic nurture effects exist, sibling analysis creates “measurement error” that attenuates associations between polygenic scores and phenotypes. As the correlation between direct and indirect effect changes, this bias can increase or decrease. Our findings suggest that interpreting results from sibling analysis aimed at uncovering direct genetic effects should be treated with caution.

Suggested Citation

  • Jason Fletcher & Yuchang Wu & Tianchang Li & Qiongshi Lu, 2024. "Interpreting polygenic score effects in sibling analysis," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-12, February.
    • Handle: RePEc:plo:pone00:0282212
    DOI: 10.1371/journal.pone.0282212
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    References listed on IDEAS

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    1. K. Paige Harden & Philipp D. Koellinger, 2020. "Using genetics for social science," Nature Human Behaviour, Nature, vol. 4(6), pages 567-576, June.
    2. Kunling Huang & Yuchang Wu & Junha Shin & Ye Zheng & Alireza Fotuhi Siahpirani & Yupei Lin & Zheng Ni & Jiawen Chen & Jing You & Sunduz Keles & Daifeng Wang & Sushmita Roy & Qiongshi Lu, 2021. "Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder," PLOS Genetics, Public Library of Science, vol. 17(2), pages 1-25, February.
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