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Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors

Author

Listed:
  • Wenhan Chen

    (The University of Queensland
    Garvan Institute of Medical Research)

  • Yang Wu

    (The University of Queensland)

  • Zhili Zheng

    (The University of Queensland)

  • Ting Qi

    (The University of Queensland
    Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine)

  • Peter M. Visscher

    (The University of Queensland)

  • Zhihong Zhu

    (The University of Queensland)

  • Jian Yang

    (The University of Queensland
    Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine)

Abstract

Summary statistics from genome-wide association studies (GWAS) have facilitated the development of various summary data-based methods, which typically require a reference sample for linkage disequilibrium (LD) estimation. Analyses using these methods may be biased by errors in GWAS summary data or LD reference or heterogeneity between GWAS and LD reference. Here we propose a quality control method, DENTIST, that leverages LD among genetic variants to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two. Through simulations, we demonstrate that DENTIST substantially reduces false-positive rate in detecting secondary signals in the summary-data-based conditional and joint association analysis, especially for imputed rare variants (false-positive rate reduced from >28% to

Suggested Citation

  • Wenhan Chen & Yang Wu & Zhili Zheng & Ting Qi & Peter M. Visscher & Zhihong Zhu & Jian Yang, 2021. "Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27438-7
    DOI: 10.1038/s41467-021-27438-7
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