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Quantifying genetic effects on disease mediated by assayed gene expression levels

Abstract

Disease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs), but it remains unclear whether this overlap is driven by gene expression levels ‘mediating’ genetic effects on disease. Here, we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis genetic component of gene expression levels. We applied MESC to GWAS summary statistics for 42 traits (average N = 323,000) and cis-eQTL summary statistics for 48 tissues from the Genotype-Tissue Expression (GTEx) consortium. Averaging across traits, only 11 ± 2% of heritability was mediated by assayed gene expression levels. Expression-mediated heritability was enriched in genes with evidence of selective constraint and genes with disease-appropriate annotations. Our results demonstrate that assayed bulk tissue eQTLs, although disease relevant, cannot explain the majority of disease heritability.

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Fig. 1: Schematic of MESC.
Fig. 2: Simulation results.
Fig. 3: Estimates of proportion of heritability mediated by expression from GTEx.
Fig. 4: Low heritability genes explain more expression-mediated disease heritability.
Fig. 5: Expression-mediated heritability enrichment estimates for functional gene sets.

Data availability

GWAS summary statistics for 42 diseases and complex traits can be found at https://data.broadinstitute.org/alkesgroup/sumstats_formatted/. Genotypes for 1000 Genomes Phase 3 data can be found at ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502. GTEx v.7 data can be found at https://www.gtexportal.org/home/datasets, although to access genotypes one is required to have an approved application. eQTLGen data can be found at https://www.eqtlgen.org/cis-eqtls.html. BaselineLD v.2.0 annotations can be found at https://data.broadinstitute.org/alkesgroup/LDSCORE/. Gene sets can be found from the Macarthur laboratory, https://github.com/macarthur-lab/gene_lists, and Molecular Signatures Database, http://software.broadinstitute.org/gsea/msigdb/collections.jsp. S-LDSC software can be found at https://github.com/bulik/ldsc. BOLT-LMM software can be found at https://data.broadinstitute.org/alkesgroup/BOLT-LMM/downloads/.

Code availability

Software implementing MESC can be found at https://github.com/douglasyao/mesc.

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Acknowledgements

We thank B. Pasaniuc, R. Ophoff, H. Shi, S. Groha, K. Siewert, S. Gazal and A. Liu for helpful discussions. This research was funded by NIH grant nos. T32 HG002295 (D.W.Y.), R01 MH115676 (A.L.P. and A.G.), R01 CA227237 (A.G.), R01 MH107649 (A.L.P.), R01 MH101244 (A.L.P.), R01 HG006399 (A.L.P.) and U01 HG009379 (A.L.P.). This research was conducted using the UK Biobank Resource under Application 16549.

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D.W.Y., L.J.O., A.L.P. and A.G. conceived the project. D.W.Y. and A.G. designed experiments. D.W.Y. performed the experiments and analyzed the data. D.W.Y. and A.G. wrote the manuscript with input from L.J.O. and A.L.P.

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Correspondence to Douglas W. Yao or Alexander Gusev.

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Extended data

Extended Data Fig. 1 Relationship between \(h_{med}^2/h_g^2\) and \(h_g^2\).

\(h_{med}^2/h_g^2\) estimates were obtained using all-tissue meta-analyzed expression scores. \(h_g^2\) estimates were obtained using stratified LD-score regression. Error bars represent jackknife standard errors.

Extended Data Fig. 2 \(h_{med}^2/h_g^2\) estimates for all diseases and expression scores.

Same as Fig. 3a, but containing \(h_{med}^2/h_g^2\) estimates for all 42 traits from all three types of expression scores: ‘All tissues’ (expression scores meta-analyzed across all 48 GTEx tissues), ‘Best tissue group’ (expression scores meta-analyzed within 7 tissue groups), and ‘Best tissue’ (expression scores computed within individual tissues). Here, ‘best’ refers to the tissue/tissue group resulting in the highest estimates of \(h_{med}^2/h_g^2\) compared to all other tissues/tissue groups. Error bars represent jackknife standard errors.

Extended Data Fig. 3 Relationship between individual tissue sample size and magnitude of \(h_{med}^2/h_g^2\).

\(h_{med}^2/h_g^2\) estimates from expression scores estimated in each of 48 individual GTEx tissues were meta-analyzed across 42 complex traits, then plotted against the number of samples in each tissue. We use the following abbreviations: adipose visceral, adipose visceral omentum; brain ACC, brain anterior cingulate cortex BA24; brain CBG, brain caudate basal ganglia; brain CH, brain cerebellar hemisphere; brain FC, brain frontal cortex BA9; brain NABG, brain nucleus accumbens basal ganglia; brain PBG brain putamen basal ganglia; cells CETL, cells EBV-transformed lymphocytes; cells TF, cells transformed fibroblasts; esophagus GJ, esophagus gastroesophageal junction; heart AA, heart atrial appendage; heart LV, heart left ventricle; skin NSES, skin not sun exposed suprapubic; skin SELL, skin sun exposed lower leg; small intestine, small intestine terminal ileum.

Extended Data Fig. 4 \(h_{med}^{2}/h_{g}^{2}\) estimates for 42 diseases and complex traits using data from eQTLGen.

We estimated expression scores for all SNPs using cis-eQTL summary statistics from eQTLGen (N = 31,684 blood samples), then estimated \(h_{med}^2/h_g^2\) using GWAS summary statistics for the same 42 traits analyzed in the main text. Expression cis-heritability estimates for eQTLGen data were obtained using LD-score regression. For sake of comparison, we also display \(h_{med}^2/h_g^2\) estimates obtained from expression scores from GTEx all-tissue meta-analysis and GTEx whole blood only. (a) \(h_{med}^2/h_g^2\) estimates for 42 individual traits, organized into blood/immune and non-blood/immune traits. Error bars represent jackknife standard errors. (b) Results from a meta-analyzed across traits. Error bars represent standard errors from random-effects meta-analysis. Note that low estimates of \(h_{med}^2/h_g^2\) for GTEx whole blood expression scores are caused by the small sample size of the GTEx whole blood data set (N = 369).

Extended Data Fig. 5 Relationship between expression cis-heritability and metrics of gene essentiality.

For each gene, pLI (probability of loss-of-function intolerance) was obtained from Lek et al. 2016 Nature and shet (selection against protein-truncating variants) was obtained from Cassa et al. 2017 Nature Genetics.

Extended Data Fig. 6 \(h_{med}^{2}\) enrichment estimates for all 10 broadly essential gene sets across all 26 complex traits.

Same as Fig. 5a, but showing \(h_{med}^2\) enrichment estimates for individual traits rather than meta-analyzed estimates.

Extended Data Fig. 7 \(h_{med}^{2}\) enrichment estimates for 97 pathway-specific gene sets across all 26 complex traits.

Same as Fig. 5b, but plotting all pathway-specific gene sets (out of 780 total) with FDR-significant \(h_{med}^2\) enrichment in at least one of the 26 complex traits. For ease of display, we grouped together related traits and gene sets.

Extended Data Fig. 8 Comparison between gene set enrichment estimates from MESC, MAGMA, and DEPICT.

See Supplementary Note for details on these analyses. (a) Venn diagram showing the overlap between significantly enriched trait-gene set pairs (FDR < 0.05) identified by the three methods. (b) Scatterplots of -log10 enrichment p-values from MESC vs. MAGMA (left), MESC vs. DEPICT (middle), and MAGMA vs. DEPICT (right). Each point represents a trait-gene set pair. (c) List of all 32 gene sets-complex traits pairs detected as significant by MESC (FDR q-value < 0.05) that are not detected as significant by MAGMA or DEPICT. See Supplementary Table 9 for enrichment estimates for all gene set-complex traits pairs.

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Yao, D.W., O’Connor, L.J., Price, A.L. et al. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat Genet 52, 626–633 (2020). https://doi.org/10.1038/s41588-020-0625-2

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