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.
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This work was supported by National Institutes of Health grants LM010098 and AI116794.
The authors declare no competing interests.
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Michigan Genomics Initiative: https://precisionhealth.umich.edu/michigangenomics/
Office of the National Coordinator for Health Information Technology: https://dashboard.healthit.gov/quickstats/pages/physician-ehr-adoption-trends.php
Phenotype KnowledgeBase (PheKB): https://www.phekb.org/
Precision Medicine Initiative: https://ghr.nlm.nih.gov/primer/precisionmedicine/initiative
- Genome-wide association studies
(GWAS). Studies in which associations between genetic variation and a phenotype or trait of interest are identified by genotyping cases (for example, diseased individuals) and controls (for example, healthy individuals) for a set of genetic variants that capture variation across the entire genome.
- Polygenic risk score
(PRS). A weighted score calculated from numerous genetic variants for predicting disease risk. A PRS is calculated as the sum of risk alleles multiplied by their association coefficients.
- Electronic health records
(EHRs). Also known as electronic medical records. Digitally stored patients’ medical history.
Repositories that store biological samples, including blood or tissue samples, for research use. Increasingly, the term biobank is used to denote a population cohort study with stored biological samples.
The physiological traits that are related to a disease trait; for example, for hypertension this could include blood pressure, angiotensin levels or salt sensitivity.
Pertaining to a gene that affects multiple phenotypes or traits.
- Positive predictive value
The proportion of true positives among positive results.
- k-Nearest neighbour
A machine learning method that is based on similarities between samples.
- Decision tree
A machine learning method that learns decision rules from the data and represents them in a tree-like structure. The tree is used to perform classification or regression.
- Random forest
An ensemble approach that learns from multiple decision trees.
- Support vector machine
A supervised machine learning method that uses a hyperplane to perform classification.
- Naive Bayes
A simple classification method that is based on the Bayes’ theorem.
- Lasso logistic regression
A penalized version of regular logistic regression. The additional penalty term forces some features to have zero coefficients.
- Population stratification
The presence of allele frequency differences between subpopulations within a larger population.
- Relative risk
The ratio of the probability of an event (such as disease) occurring in an at-risk group to the probability of it occurring in a population that is not considered at risk.
- Absolute risk
The actual probability of disease occurrence.
- Omnigenic model
A model that proposes that the genetic architecture of a trait or disease is affected by many genes.
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Li, R., Chen, Y., Ritchie, M.D. et al. Electronic health records and polygenic risk scores for predicting disease risk. Nat Rev Genet 21, 493–502 (2020). https://doi.org/10.1038/s41576-020-0224-1