Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and
diseases by aggregating information across multiple genetic variants identified from genome …

Accurate genetic prediction of complex traits can facilitate disease screening, improve early
intervention, and aid in the development of personalized medicine. Genetic prediction of …

Compared with linear mixed model-based genome-wide association (GWA) methods,
generalized linear mixed model (GLMM)-based methods have better statistical properties …

The predictive utility of polygenic scores is increasing, and many polygenic scoring methods
are available, but it is unclear which method performs best. This study evaluates the …

Polygenic risk scores (PRSs) have been widely explored in precision medicine. However,
few studies have thoroughly investigated their best practices in global populations across …

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS)
summary statistics with functional genomic annotations to improve polygenic prediction of …

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009,
polygenic risk scores (PRSs) have been described for a large number of common complex …

The increasing availability of genotype data in longitudinal population‐and family‐based
samples provides opportunities for using polygenic scores (PGS) to study developmental …

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed
significantly. The increasing number of SNPs that have been identified by large scale GWAS …

Abstract Machine learning (ML) holds promise for precision psychiatry, but its predictive
performance is unclear. We assessed whether ML provided added value over logistic …