SynthesisPLoS genetics2020
LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks.
Synthesis in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 25 citations in OpenAlex.
- Association Between Hemostatic Profiles and Hypertensive Disorders of Pregnancy: A Multi-Omics Mendelian Randomization Study.International journal of women's health · 2026Article
- Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment.The Journal of clinical investigation · 2025Review
- Exploring beyond diagnoses in electronic health records to improve discovery: a review of the phenome-wide association study.JAMIA open · 2025Review
- Phenome-wide association study in 25,639 pregnant Chinese women reveals loci associated with maternal comorbidities and child health.Cell genomics · 2024Article
- Genetic determinants and phenotypic consequences of blood T-cell proportions in 207,000 diverse individuals.Nature communications · 2024Article
- Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records.Nature communications · 2024Article
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Phenomewide Association Study of Health Outcomes Associated With the Genetic Correlates of 25 Hydroxyvitamin D Concentration and Vitamin D Binding Protein Concentration.Twin research and human genetics : the official journal of the International Society for Twin Studies · 2024Article
- To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks.medRxiv : the preprint server for health sciences · 2024Article
- A longitudinal genome-wide association study of bone mineral density mean and variability in the UK Biobank.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2023Article
- The phenotype-genotype reference map: Improving biobank data science through replication.American journal of human genetics · 2023Article
- The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients.Cell genomics · 2023Article
- Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies.Journal of personalized medicine · 2022Review
- A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation.Nature genetics · 2022Article
- GWAS of longitudinal trajectories at biobank scale.American journal of human genetics · 2022Article
- Developing real-world evidence from real-world data: Transforming raw data into analytical datasets.Learning health systems · 2022Article
- Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS.Annual review of biomedical data science · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.