Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets.

ABSTRACT: Polygenic risk prediction is a widely investigated topic because of its promising clinical applications. Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, including coding, conserved, regulatory, and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank (avg N = 373 K as training data). LDpred-funct attained a +4

SUBMITTER: Marquez-Luna C 

PROVIDER: S-EPMC8523709 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature