Presenting Author:

YOONJUNG YOONIE JOO, M.S.

Principal Investigator:

M Geoffrey Hayes, Ph.D

Department:

Medicine

Keywords:

Phenome-wide association studies, PheWAS, Genome-wide association studies, GWAS, Polycystic Ovary Syndrome, PCOS, Precis... [Read full text] Phenome-wide association studies, PheWAS, Genome-wide association studies, GWAS, Polycystic Ovary Syndrome, PCOS, Precision Medicine, Translational Research, Women Health, Electronic Medical Records, Electronic Health Records, EMR, EHR, The Electronic Medical Records and Genomics (eMERGE) Network, eMERGE network [Shorten text]

Location:

Ryan Family Atrium, Robert H. Lurie Medical Research Center

C39 - Clinical Women's Health Research

Phenome-wide association studies of Polycystic Ovary Syndrome (PCOS)

Polycystic ovary syndrome (PCOS) is the most common reproductive disorder among women affecting 5-20% of reproductive age women worldwide. Despite its clinical importance, the underlying genetic etiology of PCOS is unresolved. To date, four PCOS GWAS studies have been conducted in European (Hayes et al., Nat Commun 2015; Day et al., Nat Commun 2015) and Han Chinese ancestry cohorts (Shi et al.,Nat Genet 2012; Chen et al., Nat Genet 2011) in which 17 susceptibility loci were found to be significantly associated with PCOS. The recent explosion of electronic health records (EHR) usage has greatly increased the availability of patient phenotypic measures, clinical diagnoses, medication regimens, and demographic information. When linked with patient genetic data, EHR data has become an enormously powerful resource for exploring genotype-phenotype associations. The Electronic Medical Records and Genomics (eMERGE) network is a collaborative network of multiple medical institutions in the US that use large-scale, high-throughput genomic data of patients linked to electronic health records (EHR) for genomic research, which has been successful in identifying novel genotype-phenotype associations using EHR-derived phenotype algorithms for several disorders and quantitative traits. Using electronic health record (EHR) data from all adult eMERGE subjects, we performed a phenome-wide association study (PheWAS) to identify unique phenotypic risk factors with high pleiotropy and/or comorbidity with GWAS-identified PCOS susceptibility loci. We used logistic regression adjusted for principal components of ancestry assuming an additive genetic model, on the eMERGE adult cohort of ~38,000 individuals using the R PheWAS package. We identified several phenotypic risk markers for PCOS that are either symptomatic or potentially cryptogenic, including several skeletal disorders and inflammatory diseases such as congenital spondylolisthesis, osteoarthrosis, and bacteremia. We expect that individual risk susceptible loci have several pleiotropic effects on distinct disease processes and/or pathways, aiming to better characterize the etiology of PCOS.