Title: Electronic health records and polygenic risk scores for predicting disease risk.
Authors: Li, Ruowang; Chen, Yong; Ritchie, Marylyn D; Moore, Jason H
Published In Nat Rev Genet, (2020 08)
Abstract: Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.
PubMed ID:
32235907
MeSH Terms: Algorithms; Computational Biology/methods; Electronic Health Records*; Genetic Association Studies*; Genetic Predisposition to Disease*; Genome-Wide Association Study; Genomics/methods; Genotype; Humans; Multifactorial Inheritance*; Phenotype; Reproducibility of Results; Risk Assessment; Risk Factors