Cloud gazing: demonstrating paths for unlocking the value of cloud genomics through cross-cohort analysis

Abstract

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.

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

“…The code for all analyses can be found in https://github.com/all-of-us/ ukb-cross-analysis-demo-project 30 Competing interests P.N. reports investigator-initiated grants from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis, personal fees from Apple, Astra-Zeneca, Blackstone Life Sciences, Foresite Labs, Novartis, Roche/ Genentech, is a co-founder of TenSixteen Bio, is a shareholder of gen-eXwell and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work.…”

A One-Shot Lossless Algorithm for Cross-Cohort Learning in Mixed-Outcomes Analysis

“…Extending methods to identify MPA using multiple EHRs is also challenging. Thus far, meta-analysis has been the primary approach to integrate GWAS or PheWAS results from multiple EHR to improve the detection of MPA 13,[26][27][28][29] . However, meta-analyzed PheWAS has been suggested to be less powerful than the pooled mega analysis when detecting MPA across multiple EHRs 14,29 .…”