Prostate cancer risk prediction using a polygenic risk score

PRSice: Polygenic Risk Score software

SUMMARY A polygenic risk score (PRS) is a sum of trait-associated alleles across many genetic loci, typically weighted by effect sizes estimated from a genome-wide association study. The application of PRS has grown in recent years as their utility for detecting shared genetic aetiology among traits has become appreciated; PRS can also be used to establish the presence of a genetic signal in un...

Performance of an Adipokine Pathway-Based Multilocus Genetic Risk Score for Prostate Cancer Risk Prediction

Few biomarkers are available to predict prostate cancer risk. Single nucleotide polymorphisms (SNPs) tend to have weak individual effects but, in combination, they have stronger predictive value. Adipokine pathways have been implicated in the pathogenesis. We used a candidate pathway approach to investigate 29 functional SNPs in key genes from relevant adipokine pathways in a sample of 1006 men...

Schizophrenia polygenic risk score and psychotic risk detection.

Evaluation of three polygenic risk score models for the prediction of breast cancer risk in Singapore Chinese

Genome-wide association studies (GWAS) have proven highly successful in identifying single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. The majority of these studies are on European populations, with limited SNP association data in other populations. We genotyped 51 GWAS-identified SNPs in two independent cohorts of Singaporean Chinese. Cohort 1 comprised 1294 BC cas...

Multiethnic polygenic risk scores improve risk prediction in diverse populations.

Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but...