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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 +4.6% relative improvement in average prediction accuracy (avg prediction R2 = 0.144; highest R2 = 0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (N = 1107 K) increased prediction R2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits.

SUBMITTER: Marquez-Luna C 

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

REPOSITORIES: biostudies-literature

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

Márquez-Luna Carla C   Gazal Steven S   Loh Po-Ru PR   Kim Samuel S SS   Furlotte Nicholas N   Auton Adam A   Price Alkes L AL  

Nature communications 20211018 1


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 e  ...[more]

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