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SPAGRM: effectively controlling for sample relatedness in large-scale genome-wide association studies of longitudinal traits

Author

Listed:
  • He Xu

    (Peking University)

  • Yuzhuo Ma

    (Peking University)

  • Lin-lin Xu

    (Peking University First Hospital; Peking University Institute of Nephrology)

  • Yin Li

    (Peking University Health Science Center
    Peking University)

  • Yufei Liu

    (Peking University)

  • Ying Li

    (Peking University)

  • Xu-jie Zhou

    (Peking University First Hospital; Peking University Institute of Nephrology)

  • Wei Zhou

    (Massachusetts General Hospital
    The Broad Institute of MIT and Harvard)

  • Seunggeun Lee

    (Seoul National University)

  • Peipei Zhang

    (Peking University Health Science Center
    Peking University)

  • Weihua Yue

    (National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)
    Peking University
    Chinese Institute for Brain Research)

  • Wenjian Bi

    (Peking University
    Peking University
    Peking University
    Peking University)

Abstract

Sample relatedness is a major confounder in genome-wide association studies (GWAS), potentially leading to inflated type I error rates if not appropriately controlled. A common strategy is to incorporate a random effect related to genetic relatedness matrix (GRM) into regression models. However, this approach is challenging for large-scale GWAS of complex traits, such as longitudinal traits. Here we propose a scalable and accurate analysis framework, SPAGRM, which controls for sample relatedness via a precise approximation of the joint distribution of genotypes. SPAGRM can utilize GRM-free models and thus is applicable to various trait types and statistical methods, including linear mixed models and generalized estimation equations for longitudinal traits. A hybrid strategy incorporating saddlepoint approximation greatly increases the accuracy to analyze low-frequency and rare genetic variants, especially in unbalanced phenotypic distributions. We also introduce SPAGRM(CCT) to aggregate the results following different models via Cauchy combination test. Extensive simulations and real data analyses demonstrated that SPAGRM maintains well-controlled type I error rates and SPAGRM(CCT) can serve as a broadly effective method. Applying SPAGRM to 79 longitudinal traits extracted from UK Biobank primary care data, we identified 7,463 genetic loci, making a pioneering attempt to conduct GWAS for these traits as longitudinal traits.

Suggested Citation

  • He Xu & Yuzhuo Ma & Lin-lin Xu & Yin Li & Yufei Liu & Ying Li & Xu-jie Zhou & Wei Zhou & Seunggeun Lee & Peipei Zhang & Weihua Yue & Wenjian Bi, 2025. "SPAGRM: effectively controlling for sample relatedness in large-scale genome-wide association studies of longitudinal traits," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56669-1
    DOI: 10.1038/s41467-025-56669-1
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    References listed on IDEAS

    as
    1. Zachary R. McCaw & Jacqueline M. Lane & Richa Saxena & Susan Redline & Xihong Lin, 2020. "Operating characteristics of the rank‐based inverse normal transformation for quantitative trait analysis in genome‐wide association studies," Biometrics, The International Biometric Society, vol. 76(4), pages 1262-1272, December.
    2. Yaowu Liu & Jun Xie, 2020. "Cauchy Combination Test: A Powerful Test With Analytic p-Value Calculation Under Arbitrary Dependency Structures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 393-402, January.
    3. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
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    5. Kira J. Stanzick & Yong Li & Pascal Schlosser & Mathias Gorski & Matthias Wuttke & Laurent F. Thomas & Humaira Rasheed & Bryce X. Rowan & Sarah E. Graham & Brett R. Vanderweff & Snehal B. Patil & Cass, 2021. "Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
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