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- Title
Polygenic risk scores: effect estimation and model optimization.
- Authors
Zijie Zhao; Jie Song; Tuo Wang; Qiongshi Lu
- Abstract
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.
- Subjects
GENOME-wide association studies; GENETIC markers; HEALTH outcome assessment; FOLLOW-up studies (Medicine); MONOGENIC &; polygenic inheritance (Genetics); SINGLE nucleotide polymorphisms
- Publication
Quantitative Biology, 2021, Vol 9, Issue 2, p133
- ISSN
2095-4689
- Publication type
Article
- DOI
10.15302/J-QB-021-0238