Using whole genome scores to compare three clinical phenotyping methods in complex diseases View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Wenyu Song, Hailiang Huang, Cheng-Zhong Zhang, David W. Bates, Adam Wright

ABSTRACT

Genome-wide association studies depend on accurate ascertainment of patient phenotype. However, phenotyping is difficult, and it is often treated as an afterthought in these studies because of the expense involved. Electronic health records (EHRs) may provide higher fidelity phenotypes for genomic research than other sources such as administrative data. We used whole genome association models to evaluate different EHR and administrative data-based phenotyping methods in a cohort of 16,858 Caucasian subjects for type 1 diabetes mellitus, type 2 diabetes mellitus, coronary artery disease and breast cancer. For each disease, we trained and evaluated polygenic models using three different phenotype definitions: phenotypes derived from billing data, the clinical problem list, or a curated phenotyping algorithm. We observed that for these diseases, the curated phenotype outperformed the problem list, and the problem list outperformed administrative billing data. This suggests that using advanced EHR-derived phenotypes can further increase the power of genome-wide association studies. More... »

PAGES

11360

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-018-29634-w

    DOI

    http://dx.doi.org/10.1038/s41598-018-29634-w

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1105781395

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30054501


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