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Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants

Author

Listed:
  • Guanghao Qi

    (University of Washington)

  • Surya B. Chhetri

    (Johns Hopkins University
    Johns Hopkins University)

  • Debashree Ray

    (Johns Hopkins University
    Johns Hopkins University)

  • Diptavo Dutta

    (National Cancer Institute)

  • Alexis Battle

    (Johns Hopkins University
    Johns Hopkins University
    Johns Hopkins University)

  • Samsiddhi Bhattacharjee

    (Biotechnology Research and Innovation Council-National Institute of Biomedical Genomics (BRIC-NIBMG))

  • Nilanjan Chatterjee

    (Johns Hopkins University
    Johns Hopkins University)

Abstract

Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with $$\ge 2$$ ≥ 2 traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value $$

Suggested Citation

  • Guanghao Qi & Surya B. Chhetri & Debashree Ray & Diptavo Dutta & Alexis Battle & Samsiddhi Bhattacharjee & Nilanjan Chatterjee, 2024. "Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51075-5
    DOI: 10.1038/s41467-024-51075-5
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