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

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 Musick, Rory Collins, Naomi Allen, Mark Effingham, David Glazer, Pradeep Natarajan and Alexander G. Bick ()
Additional contact information
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
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
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
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
Naomi Allen: University of Oxford
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
Alexander G. Bick: Vanderbilt University Medical Center

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-41185-x

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