LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks

Jeffery A. Goldstein, Joshua S. Weinstock, Lisa A. Bastarache, Daniel B. Larach, Lars G. Fritsche, Ellen M. Schmidt, Chad M. Brummett, Sachin Kheterpal, Goncalo R. Abecasis, Joshua C. Denny, Matthew Zawistowski*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.

Original languageEnglish (US)
Article numbere1009077
JournalPLoS genetics
Volume16
Issue number11
DOIs
StatePublished - Nov 11 2020

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics
  • Genetics(clinical)
  • Cancer Research

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