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

Abstract

Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winner's curse. In simulations with in-sample LD, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In simulations with mismatched reference LD, PolyFun + SuSiE remained well-calibrated when reducing the maximum number of assumed causal SNPs per locus, which reduces absolute power but still produces large relative improvements. In analyses of 49 UK Biobank traits (average N=318K) with insample LD, PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were fine-mapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures..

Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease

“…Second, it is of interest to assess the informativeness for the common disease of Mendelian disease pathogenicity scores that may be developed in the future, particularly after imputing and denoising these scores using AnnotBoost; this would further elucidate the shared properties between Mendelian disease variants and common disease variants. Third, annotations derived from published and boosted Mendelian pathogenicity scores can be used to improve functionally informed fine-mapping 61,[64][65][66][67] , motivating their inclusion in future large-scale fine-mapping studies. (On the other hand, we anticipate that our new annotations will be less useful for improving functionally informed polygenic risk prediction 68,69 and association mapping 70 , because there is pervasive LD between SNPs in an annotation and SNPs outside of an annotation, such that these annotations do not distinguish which LD blocks contain causal signal.)…”

“…Third, we repeated the classification of fine-mapped SNPs using a single LD-, MAF-, and genomic-element-matched control variant (instead of 10 control variants) for each fine-mapped SNP, and obtained similar results ( Supplementary Data 21). Fourth, we repeated the classification of fine-mapped disease SNPs analysis of Weissbrod et al fine-mapped SNPs using 1379 SNPs that were fine-mapped without using functional information 61 (to ensure that results were not circular), and obtained similar results ( Supplementary Fig. 6, Supplementary Data 21).…”

“…Here, we consider only low-frequency and common SNPs (MAF ≥ 0.5%) and report the total number of unique SNPs (regardless MAF). we assessed the accuracy of classifying five different SNP sets (summarized in Supplementary Data 22): (1) 7333 fine-mapped for 21 autoimmune diseases from Farh et al 59 (of 7747 total SNPs; 95% credible sets), (2) 3768 fine-mapped SNPs for inflammatory bowel disease from Huang et al 60 (of 4311 total SNPs; 95% credible sets), (3) 1851 SNPs (of 2225 SNPs, spanning 3025 SNPtrait pairs; stringently defined by causal posterior probability ≥ 0.95) functionally informed fine-mapped for 49 UK Biobank traits from Weissbrod et al 61 , (4) 1379 (of 1853 total SNPs with causal posterior probability ≥ 0.95) non-functionally informed fine-mapped SNPs for 49 UK Biobank traits from Weissbrod et al 61 , and (5) 14,807 SNPs from the NHGRI GWAS catalog 62,63 (2019-07-12 version; p-value < 5e−8; we note only about 5% of GWAS SNPs are expected to be causal 59 ). For each of these five SNP sets, we matched 10 control SNPs for each positive fine-mapped SNP by matching LD, MAF, and genomic element, as in the previous studies 9,10,47 ; we note that these studies emphasized the need for matching the relative genomic region distribution in performance evaluation.…”

“…Second, we assessed each model's accuracy of classifying three different sets of fine-mapped SNPs (from 10 LD-, MAF-, and genomic-element-matched control SNPs in the reference panel 28 ): 7,333 fine-mapped for 21 autoimmune diseases from Farh et al 59 , 3768 fine-mapped SNPs for inflammatory bowel disease from Huang et al 60 , and 1851 fine-mapped SNPs for 49 UK Biobank traits from Weissbrod et al 61 . We note that with the exception of Weissbrod et al fine-mapped SNPs (stringently defined by causal posterior probability ≥ 0.95; FDR < 0.05), 95% credible fine-mapped SNPs likely include a large fraction of noncausal variants.…”

Leveraging supervised learning for functionally-informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs

“…Specifically, we directly train a predictor of whether a variant modifies expression using 14,807 putative expression-modifying variant-gene pairs in humans as training data and utilizing 6,121 features; we call the resulting prediction the expression modifier score (EMS). Toward the second goal, we use EMS as a prior for statistical fine-mapping of eQTLs (analogous to recently-performed functionally-informed fine-mapping of complex traits [32][33][34] ), increasing fine-mapping resolution and identifying an additional 20,913 variants across 49 tissues. Finally, using UK Biobank (UKBB) 35 phenotypes as an example, we show that EMS can be incorporated into co-localization analysis at scale, and we identify 310 additional candidate genes for UK Biobank phenotypes.…”