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

Carla Márquez-Luna (), Steven Gazal, Po-Ru Loh, Samuel S. Kim, Nicholas Furlotte, Adam Auton and Alkes L. Price ()
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Carla Márquez-Luna: Harvard T.H. Chan School of Public Health
Steven Gazal: Broad Institute of Harvard and MIT
Po-Ru Loh: Broad Institute of Harvard and MIT
Samuel S. Kim: Broad Institute of Harvard and MIT
Nicholas Furlotte: 23andMe Inc.
Adam Auton: 23andMe Inc.
Alkes L. Price: Harvard T.H. Chan School of Public Health

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-25171-9

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