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Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis

Author

Listed:
  • Nicole Deflaux

    (Verily Life Sciences)

  • Margaret Sunitha Selvaraj

    (Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT
    Harvard Medical School
    Massachusetts General Hospital
    Massachusetts General Hospital)

  • Henry Robert Condon

    (Vanderbilt University Medical Center)

  • Kelsey Mayo

    (Vanderbilt University Medical Center)

  • Sara Haidermota

    (Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT
    Massachusetts General Hospital)

  • Melissa A. Basford

    (Vanderbilt University Medical Center)

  • Chris Lunt

    (All of Us Research Program, National Institutes of Health)

  • Anthony A. Philippakis

    (Broad Institute of Harvard and MIT)

  • Dan M. Roden

    (Vanderbilt University Medical Center
    Vanderbilt University Medical Center
    Vanderbilt University Medical Center)

  • Joshua C. Denny

    (All of Us Research Program, National Institutes of Health)

  • Anjene Musick

    (All of Us Research Program, National Institutes of Health)

  • Rory Collins

    (University of Oxford
    UK Biobank)

  • Naomi Allen

    (University of Oxford
    UK Biobank)

  • Mark Effingham

    (UK Biobank)

  • David Glazer

    (Verily Life Sciences)

  • Pradeep Natarajan

    (Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT
    Harvard Medical School
    Massachusetts General Hospital
    Massachusetts General Hospital)

  • Alexander G. Bick

    (Vanderbilt University Medical Center)

Abstract

Recently, large scale genomic projects such as All of Us and the UK Biobank have introduced a new research paradigm where data are stored centrally in cloud-based Trusted Research Environments (TREs). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conduct a Genome-Wide Association Study of standard lipid measures using two approaches: meta-analysis and pooled analysis. Comparison of full summary data from both approaches with an external study shows strong correlation of known loci with lipid levels (R2 ~ 83–97%). Importantly, 90 variants meet the significance threshold only in the meta-analysis and 64 variants are significant only in pooled analysis, with approximately 20% of variants in each of those groups being most prevalent in non-European, non-Asian ancestry individuals. These findings have important implications, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations.

Suggested Citation

  • Nicole Deflaux & Margaret Sunitha Selvaraj & Henry Robert Condon & Kelsey Mayo & Sara Haidermota & Melissa A. Basford & Chris Lunt & Anthony A. Philippakis & Dan M. Roden & Joshua C. Denny & Anjene Mu, 2023. "Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41185-x
    DOI: 10.1038/s41467-023-41185-x
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    References listed on IDEAS

    as
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