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LabWAS: novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks
- Source :
- PLoS Genetics, Vol 16, Iss 11, p e1009077 (2020), PLoS Genetics
- Publication Year :
- 2020
- Publisher :
- Cold Spring Harbor Laboratory, 2020.
-
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.<br />Author summary Electronic Health Records (EHRs) have emerged as an abundant data source for deriving phenotypes used in genetic association studies. EHRs provide a broad range of clinical data in large health system cohorts and are readily incorporated into large-scale meta-analyses. The abundance of available data in EHRs introduces unique technical challenges, particularly longitudinal clinical lab measurements which lack the structure of more commonly used disease diagnosis codes. Conflicting strategies exist in the literature and it is not clear how portable these strategies are across health systems. In this study we performed a proof-of-principle meta-analysis of 70 clinical lab traits in two large-scale health systems: BioVU from Vanderbilt University and the Michigan Genomics Initiative from Michigan Medicine. Despite the challenges of matching labs across the two health systems, we observed a high replication rate for known genetic variants. Further, we identified 31 novel associations, 22 of which replicated in an independent BioVU cohort, indicating the potential for future meta-analyses. Finally, we explored the impact of various analytic strategies, looking for consistent effects between our two cohorts, to determine optimal strategies for future genetic analysis of EHR-derived lab traits.
- Subjects :
- Michigan
Cancer Research
Future studies
Computer science
Physiology
Electronic Medical Records
Genome-wide association study
Health records
QH426-470
Health data
Cohort Studies
White Blood Cells
Mathematical and Statistical Techniques
0302 clinical medicine
Animal Cells
Red Blood Cells
Medicine and Health Sciences
Electronic Health Records
Medical diagnosis
Genetics (clinical)
Biological Specimen Banks
0303 health sciences
Library Science
Statistics
Genomics
Metaanalysis
Biobank
Body Fluids
Blood
Phenotype
Meta-analysis
Physical Sciences
Cohort
Anatomy
Cellular Types
Information Technology
Research Article
Computer and Information Sciences
Immune Cells
Immunology
MEDLINE
Biology
Research and Analysis Methods
Polymorphism, Single Nucleotide
03 medical and health sciences
Quantitative Trait, Heritable
Genome-Wide Association Studies
Genetics
Humans
Statistical Methods
Molecular Biology
Genetic Association Studies
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Blood Cells
Biology and Life Sciences
Computational Biology
Human Genetics
Health Information Technology
Cell Biology
Genome Analysis
Data science
Health Care
Blood Counts
Catalogs
Mathematics
030217 neurology & neurosurgery
Genome-Wide Association Study
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- PLoS Genetics, Vol 16, Iss 11, p e1009077 (2020), PLoS Genetics
- Accession number :
- edsair.doi.dedup.....976aa621a36709ac2d619fc2442f84bb
- Full Text :
- https://doi.org/10.1101/2020.04.08.19011478