The rapid growth of genomic data has led to a new research paradigm where data are stored centrally in Trusted Research Environments (TREs) such as the All of Us Researcher Workbench (RW) and the UK Biobank Research Analysis Platform (RAP). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conducted a Genome-Wide Association Study (GWAS) of standard lipid measures on the UKB RAP and AoU RW using two approaches: meta-analysis and pooled analysis. We curated lipid measurements for 37,754 All of Us participants with whole genome sequence (WGS) data and 190,982 UK Biobank participants with whole exome sequence (WES) data. For the meta-analysis, we performed a GWAS of each cohort in their respective platform and meta-analyzed the results. We separately performed a pooled GWAS on both datasets combined. We identified 454 and 445 significant variants in meta-analysis and pooled analysis, respectively. Comparison of full summary data from both meta-analysis and pooled analysis with an external study showed strong correlation of known loci with lipid levels (R2~91-98%). Importantly, 84 variants met the significance threshold only in the meta-analysis and 75 variants were significant only in pooled analysis. These method-specific differences may be explained by differences in cohort size, ancestry, and phenotype distributions in All of Us and UK Biobank. Importantly, we noted a significant increase in the proportion of significant variants predominantly from non-European ancestry individuals in the pooled analysis compared to meta-analysis (p=0.01). Pooled analysis required about half as many computational steps as meta-analysis. These findings have important implications for both platform implementations and researchers undertaking large-scale cross-cohort analyses, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations.

All of Us is a biorepository aiming to advance biomedical research by providing various types of data in diverse human populations.Here we present a demonstration project validating the program's genomic data in 98,622 participants. We sought to replicate known genetic associations for three diseases (atrial fibrillation [AF], coronary artery disease, type 2 diabetes [T2D]) and two quantitative traits (height and low-density lipoprotein [LDL]) by conducting common and rare variant analyses. We identified one known risk locus for AF, five loci for T2D, 143 loci for height, and nine loci for LDL. In gene-based burden tests for rare loss-offunction variants, we replicated associations between TTN and AF, GIGYF1 and T2D, ADAMTS17, ACAN, NPR2 and height, APOB, LDLR, PCSK9 and LDL. Our results are consistent with previous literature, indicating that the All of Us program is a reliable resource for advancing the understanding of complex diseases in diverse human populations.

All of Usis a biorepository aiming to advance biomedical research by providing various types of data in diverse human populations. Here we present a demonstration project validating the program’s genomic data in 98,622 participants. We sought to replicate known genetic associations for three diseases (atrial fibrillation [AF], coronary artery disease, type 2 diabetes [T2D]) and two quantitative traits (height and low-density lipoprotein [LDL]) by conducting common and rare variant analyses. We identified one known risk locus for AF, five loci for T2D, 143 loci for height, and nine loci for LDL. In gene-based burden tests for rare loss-of-function variants, we replicated associations betweenTTNand AF,GIGYF1and T2D,ADAMTS17, ACAN, NPR2and height,APOB, LDLR, PCSK9and LDL. Our results are consistent with previous literature, indicating that theAll of Usprogram is a reliable resource for advancing the understanding of complex diseases in diverse human populations.