| Title: | Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets |
| Journal: | Nature Communications |
| Published: | 18 Oct 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34663819/ |
| DOI: | https://doi.org/10.1038/s41467-021-25171-9 |
| URL: | https://www.nature.com/articles/s41467-021-25171-9.pdf |
Publication 5224
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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.7 Authors
- Carla Márquez-Luna
- Steven Gazal
- Po-Ru Loh
- Samuel S. Kim
- Nicholas Furlotte
- Adam Auton
- Alkes L. Price
Enabling scientific discoveries that improve human health