Feature Review Genetic prediction of complex traits with polygenic scores: a statistical review

Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a poly genic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.

Automated summary

Accurate genetic prediction is crucial for disease screening and personalized medicine, with the development of polygenic scores (PGS) playing a key role in this process. The review presents 46 methods for PGS construction and connects them through a multiple linear regression framework, providing insights into prediction performance for traits with distinct genetic architectures, as well as discussing challenges and future directions in PGS method development.