Open AccessPolygenic risk scores: effect estimation and model optimization
Author(s) -
Zhao Zijie,
Song Jie,
Wang Tuo,
Lu Qiongshi
Publication year - 2021
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.15302/j-qb-021-0238
Subject(s) - linkage disequilibrium , genome wide association study , polygenic risk score , pleiotropy , computer science , genetic association , summary statistics , linkage (software) , machine learning , data science , statistics , biology , genetics , mathematics , single nucleotide polymorphism , gene , genotype , phenotype
Background Polygenic risk score (PRS) derived from summary statistics of genome‐wide association studies (GWAS) is a useful tool to infer an individual’s genetic risk for health outcomes and has gained increasing popularity in human genetics research. PRS in its simplest form enjoys both computational efficiency and easy accessibility, yet the predictive performance of PRS remains moderate for diseases and traits. Results We provide an overview of recent advances in statistical methods to improve PRS’s performance by incorporating information from linkage disequilibrium, functional annotation, and pleiotropy. We also introduce model validation methods that fine‐tune PRS using GWAS summary statistics. Conclusion In this review, we showcase methodological advances and current limitations of PRS, and discuss several emerging issues in risk prediction research.