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Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity

Abstract

Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP–gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency.

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Fig. 1: Overview of the S2G framework.
Fig. 2: Accuracy of individual S2G strategies and the cS2G strategy.
Fig. 3: SNPgenedisease triplets identified by cS2G and other S2G strategies.
Fig. 4: Examples of high-confidence SNP–gene–disease triplets identified by cS2G.
Fig. 5: Empirical assessment of disease omnigenicity using cS2G.

Data availability

The list of 19,995 genes, summary statistics of the 63 independent traits, training and validation critical gene sets, S2G and cS2G strategies, SNP annotations, predicted causal SNP–disease pairs from UK Biobank fine-mapping analyses and from the NHGRI-EBI GWAS Catalog and SNP heritability causally explained by SNPs linked to each gene have been made publicly available at https://alkesgroup.broadinstitute.org/cS2G and https://doi.org/10.5281/zenodo.6354007. Links for all data sets used to create S2G strategies are provided in Supplementary Table 26.

Access to the UK Biobank resource is available via application at http://www.ukbiobank.ac.uk/.

The GWAS Catalog is available at https://www.ebi.ac.uk/gwas/api/search/downloads/full.

Open Targets SNP–gene pairs are available at https://raw.githubusercontent.com/opentargets/genetics-gold-standards/master/gold_standards/processed/gwas_gold_standards.191108.tsv.

SNP–gene pairs from ref. 48 are available at https://www.dropbox.com/s/kz2c49rpm2yanf5/all_byCS_rev1.txt?dl=0.

Code availability

The code to estimate precision and recall of S2G strategies and the code to create combined S2G strategies have been made publicly available at https://alkesgroup.broadinstitute.org/cS2G/code and https://doi.org/10.5281/zenodo.6415925.

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Acknowledgements

We thank X. Jiang, C. Boix and M. Kellis for helpful discussion. S.G. is funded by National Institutes of Health grant R00 HG010160. A.L.P. is funded by National Institutes of Health grants U01 HG009379, R01 MH101244, R37 MH107649, R01 MH115676, R01 MH109978, U01 HG012009 and R01 HG006399. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This research was conducted using the UK Biobank Resource under application 16549.

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S.G. and A.L.P. designed experiments. S.G. performed experiments. S.G., O.W., F.H., K.D., J.N., and K.J. analyzed data. D.W., H.S., C.P.F., L.OC., B.P. and J.M.E. provided suggestions on the analyses. S.G. and A.L.P., with assistance from all authors, wrote the manuscript.

Corresponding authors

Correspondence to Steven Gazal or Alkes L. Price.

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C.P.F. is now an employee of Bristol Myers Squibb. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 S2G strategy linking each SNP to best gene leads to higher precision than linking SNPs to multiple target genes.

We report the precision of S2G strategies linking SNPs to target genes using three difference approaches for converting raw linking values into linking scores: by assigning to each gene with non-zero raw linking value the same linking score (unweighted), by assigning to each gene a linking score proportional to its raw linking value (weighted), and by retaining only the gene(s) with the highest linking score (best gene). Values were estimated using non-trait-specific training critical gene set and meta-analyzed across 63 independent traits. Error bars represent 95% confidence intervals around meta-analyzed values. For most of the S2G strategies the precision was very similar (except for EpiMap, ABC and Open Targets), but the precision was generally highest for the ‘best gene’ strategy. However, we note that this choice does not reflect biological reality, in which a regulatory element may target more than one gene, and that refinements to this choice are a direction for future research.

Extended Data Fig. 2 Precision of 27 S2G strategies based on physical distance to TSS.

We report precision of the closest TSS strategy as a function of the distance between a SNP and its closest TSS (a) (numbers between parentheses represent the fraction of common SNPs linked by the strategy), and the precision of the ith closest TSS (each strategy links 100% of the SNPs) (b). Values were estimated using trait-specific validation critical gene sets and meta-analyzed across 63 independent traits. Error bars represent 95% confidence intervals around meta-analyzed values. The mean value of 0.043 for 6th-20th closest TSS suggests that genes located relatively close to causal disease genes have a slightly elevated probability of being causal. Numerical results including values of recall and corresponding standard errors are reported in Supplementary Table 5.

Extended Data Fig. 3 Precision of functional S2G strategies using all available cell types and tissues or restricted to blood and immune cell types and tissues.

We report the precisions of functional S2G strategies built using either all available cell types and tissues (All CT; in light color) and/or blood and immune cell types and tissues (Blood CT; in dark color) meta-analyzed across 63 independent traits (All traits; in blue) and 11 blood cell traits and autoimmune diseases (Blood traits; in red) (UK Biobank all autoimmune diseases, Crohn’s Disease, Rheumatoid Arthritis, Ulcerative Colitis, Lupus, Celiac, Platelet Count, Red Blood Cell Count, Red Blood Cell Distribution Width, Eosinophil Count, White Blood Cell Count; see Supplementary Table 3). Error bars represent 95% confidence intervals around meta-analyzed values. We considered 5 S2G strategies with data available for cell types and tissues: GTEx cis-eQTLs (GTEx), GTEx fine-mapped cis-eQTL (GTEx fine-mapped), Roadmap enhancer-gene linking (Roadmap), EpiMap enhancer-gene linking (EpiMap), and Activity-By-Contact (ABC). We considered 3 S2G strategies with data available only for blood and immune cell types and tissues: eQTLGen fine-mapped blood cis-eQTL (eQTLGen fine-mapped), PCHi-C (blood), and Cicero blood/basal (Cicero). We observed 1) that S2G strategies using data from all cell types and tissues were more precise than S2G strategies restricted to blood and immune cell types and tissues in both analyses of all traits (light blue vs. dark blue) and blood cell traits and autoimmune diseases (light red vs. dark red), and 2) that S2G strategies using data from blood and immune cell types and tissues are more precise in all traits than in blood cell traits and autoimmune diseases (dark blue vs. dark red).

Extended Data Fig. 4 Proportion of common and low-frequency variant heritability linked to genes explained by each individual gene.

We report the proportion of common and low-frequency variant heritability linked to genes (\(h_{gene,common}^2\) and \(h_{gene,low - freq}^2\), respectively) explained by each individual gene in 16 independent UK Biobank traits. Genes in the top 200 genes (top 1% of all genes) contributing to both \(h_{gene,common}^2\) and \(h_{gene,low - freq}^2\) are denoted in red (median of 26 genes across the 16 traits), genes in the top 200 genes contributing to only \(h_{gene,common}^2\) (resp. \(h_{gene,low - freq}^2\)) are colored in black (resp. blue) (median of 174 genes each), and remaining genes are colored in gray (median of 19,621 genes, with values close to 0 on both axes). We observe low concordance between per-gene contributions to gene architectures for common vs. low-frequency SNPs.

Extended Data Fig. 5 Excess overlap between top genes contributing to common and low-frequency variant heritability linked to genes and disease-specific Mendelian disorder genes.

We report the excess overlap between phenotype-specific Mendelian disorder genes72 and the top 200 genes contributing to common and low-frequency variant heritability linked to genes (left), and the gene enrichment of disease-specific Mendelian disorder genes (that is [SNP heritability linked to Mendelian disorder genes / SNP heritability linked to all genes] / [number of Mendelian disorder genes / total number of genes]) across common and low-frequency variants (right). Each dot represents a disease/trait - Mendelian disorder gene set pair, and is colored by the Mendelian disorder gene set. These two results suggest that both the set of top 200 genes and the per-gene heritability estimates are unlikely to be driven by noisy estimates arising from finite sample size. We restricted analyses to 21 traits analyzed in ref. 72.

Extended Data Fig. 6 Excess overlap between top genes contributing to common and low-frequency variant heritability linked to genes and differentially expressed gene sets.

We report the excess overlap between 205 differentially expressed gene sets41 and the top 200 genes contributing to common and low-frequency variants heritability linked to genes across 16 independent UK Biobank traits. Each dot represents a differentially expressed gene set, and is colored by the tissue category. We generally observed excess overlap for disease-critical tissues/cell types. We observed high correlations between excess overlaps for common vs. low-frequency variant architectures, suggesting that common and low-frequency variants architectures are driven by different genes pertaining to similar biological processes.

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Gazal, S., Weissbrod, O., Hormozdiari, F. et al. Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity. Nat Genet 54, 827–836 (2022). https://doi.org/10.1038/s41588-022-01087-y

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