LDpred-funct: incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets

Abstract

15Genetic variants in functional regions of the genome are enriched for complex trait heritabil-16 ity. Here, we introduce a new method for polygenic prediction, LDpred-funct, that leverages 17 trait-specific functional enrichments to increase prediction accuracy. We fit priors using the 18 recently developed baseline-LD model, which includes coding, conserved, regulatory and LD-19 related annotations. We analytically estimate posterior mean causal e↵ect sizes and then use 20 cross-validation to regularize these estimates, improving prediction accuracy for sparse architec-21 tures. LDpred-funct attained higher prediction accuracy than other polygenic prediction methods 22 in simulations using real genotypes. We applied LDpred-funct to predict 21 highly heritable traits 23 in the UK Biobank. We used association statistics from British-ancestry samples as training data 24 (avg N =365K) and samples of other European ancestries as validation data (avg N =22K), to 25 minimize confounding. LDpred-funct attained a +9% relative improvement in average predic-26 tion accuracy (avg prediction R 2 =0.145; highest R 2 =0.413 for height) compared to LDpred (the 27 best method that does not incorporate functional information), consistent with simulations. For 28 height, meta-analyzing training data from UK Biobank and 23andMe cohorts (total N =1107K; 29 higher heritability in UK Biobank cohort) increased prediction R 2 to 0.429. Our results show 30 that modeling functional enrichment improves polygenic prediction accuracy, consistent with the 31 functional architecture of complex traits. 32 Genetic variants in functional regions of the genome are enriched for complex trait heritability 1-6 . 34 In this study, we aim to leverage functional enrichment to improve polygenic prediction 7, eral studies have shown that incorporating prior distributions on causal e↵ect sizes can improve 36 prediction accuracy 9-12 , compared to standard Best Linear Unbiased Prediction (BLUP) or Prun-37 ing+Thresholding methods [13][14][15] . Recent e↵orts to incorporate functional information have produced 38 promising results 16,17 , but may be limited by dichotomizing between functional and non-functional 39 variants 16 or restricting their analyses to genotyped variants 17 . 40Here, we introduce a new method, LDpred-funct, for leveraging trait-specific functional enrich-41 ments to increase polygenic prediction accuracy. We fit functional priors using our recently devel-42 oped baseline-LD model 18 , which includes coding, conserved, regulatory and LD-related annotations. 43LDpred-funct first analytically estimates posterior mean causal e↵ect sizes, accounting for functional 44 priors and LD between variants. LDpred-funct then uses cross-validation within validation samples 45 to regularize causal e↵ect size estimates in bins of di↵erent magnitude, improving prediction accuracy 46 for sparse architectures. We show that LDpred-funct attains higher polygenic prediction accuracy 47 than other methods in simulations with real genotypes, analys...

Bayesian analysis of GWAS summary data reveals differential signatures of natural selection across human complex traits and functional genomic categories

“…Despite these limitations, our study highlights the impact of negative selection on the genetic architecture across complex traits and in different functional genomic regions. In addition to better understanding of the genetic architecture, our methods can also be applied to genetic mapping and polygenic risk prediction through the use of the joint SNP effect estimates or the characterised underlying distributions of effect sizes as prior knowledge for other methods 45 .…”

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

“…We applied SuSiE and PolyFun + SuSiE to fine-map 47 traits in the UK Biobank, including 31 traits analyzed in refs. 34,35 , 9 blood cell traits analyzed in ref. 12 , and 7 recently released metabolic traits (average N=317K; Supplementary Table 5), using the same data and the same parameter settings described in the Finemapping simulations section.…”

“…12 , and 7 recently released metabolic traits (average N=317K; Supplementary Table 5), using the same data and the same parameter settings described in the Finemapping simulations section. We performed basic QC on each trait as described in our previous publications 34,35 . Specifically, we removed outliers outside the reasonable range for each quantitative trait, and quantile normalizing within sex strata after correcting for covariates for non-binary traits with non-normal distributions.…”

Screening human embryos for polygenic traits has limited utility

“…Additionally, the use of multiple related phenotypes has been demonstrated to enhance the predictive power of PS [47]; for example, the combination of educational attainment and intelligence GWAS may permit a doubling of cognitive ps 2 [48]. Finally, it has recently been suggested that enrichment of certain subcategories of functional variation (e.g., coding, conserved, regulatory, and LD-related genomic annotations) in GWAS results can be leveraged to further enhance prediction accuracy [49,50].…”