Abstract
Complex diseases often have distinct mechanisms spanning multiple tissues. We propose tissue–gene fine-mapping (TGFM), which infers the posterior inclusion probability (PIP) for each gene–tissue pair to mediate a disease locus by analyzing summary statistics and expression quantitative trait loci (eQTL) data; TGFM also assigns PIPs to non-mediated variants. TGFM accounts for co-regulation across genes and tissues and models uncertainty in cis-predicted expression models, enabling correct calibration. We applied TGFM to 45 UK Biobank diseases or traits using eQTL data from 38 Genotype–Tissue Expression (GTEx) tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease or trait, of which 11% were gene–tissue pairs. Causal gene–tissue pairs identified by TGFM reflected both known biology (for example, TPO–thyroid for hypothyroidism) and biologically plausible findings (for example, SLC20A2–artery aorta for diastolic blood pressure). Application of TGFM to single-cell eQTL data from nine cell types in peripheral blood mononuclear cells (PBMCs), analyzed jointly with GTEx tissues, identified 30 additional causal gene–PBMC cell type pairs.
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Data availability
TGFM PIPs for gene–tissue pairs, gene–PBMC cell type pairs, genes and non-mediated variants across 45 diseases or traits (for both analyses of 38 GTEx tissues and the analyses of 38 GTEx tissues and nine PBMC cell types) are publicly available at https://doi.org/10.7910/DVN/S26PFI; GTEx cis-predicted expression models for all gene–tissue pairs are publicly available at https://doi.org/10.7910/DVN/8IPOPK; pseudobulk PBMC cis-predicted expression for all gene–PBMC pairs are publicly available at https://doi.org/10.7910/DVN/8UL8XB; PBMC cis-predicted expression models for all gene–PBMC cell type pairs are publicly available at https://doi.org/10.7910/DVN/A6K9QW; and GWAS summary statistics for all 45 diseases or traits are publicly available at https://doi.org/10.7910/DVN/GTEGPE. To limit the use of computational resources, we refer the reader to UKBB in-sample linkage disequilibrium (337,000 unrelated British ancestry samples) from a previous work36, which is publicly available at https://registry.opendata.aws/ukbb-ld. The UKBB resource is publicly available by application (http://www.ukbiobank.ac.uk). Gene expression and genotype data were acquired from the GTEx v.8 eQTL dataset (dbGaP accession no. phs000424.v8.p2) and a previously published dataset33 (GEO accession number GSE174188 and dbGap accession number phs002812.v1.p1). Source data are provided with this paper.
Code availability
TGFM software (v.1.0) is available at https://github.com/BennyStrobes/TGFM (https://doi.org/10.5281/zenodo.13823621)79.
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Acknowledgements
We are grateful to A. Durvasula and X. Jiang for helpful discussions. This research was conducted using the UKBB resource under application no. 16549 and funded by National Institutes of Health (NIH) grants R01 MH101244 (A.L.P.), R37 MH107649 (A.L.P.), R01 HG006399 (A.L.P.), R01 MH115676 (A.L.P.), U01 HG012009 (A.L.P.), R56 HG013083 (A.L.P.) and F32 HG012889 (B.J.S.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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A.L.P. and B.J.S. proposed the idea for the project. B.J.S. developed the TGFM model and performed the analysis. B.J.S. and A.L.P. wrote the manuscript. A.L.P., M.J.Z., T.A. and J.R. provided suggestions that aided the development of the TGFM model and suggested relevant downstream analyses to perform.
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Extended data
Extended Data Fig. 1 Comparison of tissue-gene fine-mapping power at same level of FDR in simulations.
Average gene-tissue fine-mapping power (x-axis) at a specific level of FDR (y-axis) across 100 simulations for various fine-mapping methods (see legend) at eQTL sample size 100-300 (a), 300 (b), 500 (c), and 1000 (d). We note that all methods other than TGFM (cTWAS-TG, cTWAS, FOCUS-TG, FOCUS, coloc, JLIM, SMR, and SMR + HEIDI) are severely mis-calibrated, with high FDR at even the most stringent p-value or posterior probability thresholds, as evident by no method other than TGFM achieving an FDR < = 0.34 at any threshold. JLIM, SMR, and SMR + HEIDI compute p-values for each gene-tissue pair, whereas TGFM, cTWAS-TG, cTWAS, FOCUS-TG, FOCUS, and coloc calculate posterior probabilities for each gene-tissue pair. SMR corresponds to using the SMR p-value to assess the significance of a gene-tissue pair, whereas SMR + HEIDI corresponds to using the SMR p-value to assess the significance of a gene-tissue pair after filtering to gene-tissue pairs with HEIDI p-value <= 0.05. We do not visualize FDR and power of any p-value or posterior probability threshold containing fewer than 2 gene-tissue pairs in order to remove highly uncertain FDR and power estimates from the visualization.
Extended Data Fig. 2 Calibration and power of tissue-gene fine-mapping for various versions of TGFM in simulations.
(a,b) Average tissue-gene fine-mapping FDR across 100 simulations for various fine-mapping methods (see legend) across eQTL sample sizes (x-axis) at PIP = 0.5 (a) and PIP = 0.9 (b). Single thick, dashed horizontal line denotes 1 – PIP threshold (see main text). The thin dashed horizontal lines specific to each bar denotes (1 – average PIP) (where average is taken across all genetic elements belonging to that bar; see main text). (c,d) Average tissue-gene fine-mapping power across 100 simulations for various fine-mapping methods (see legend) across eQTL sample sizes (x-axis) at PIP = 0.5 (c) and PIP = 0.9 (d). Error bars denote 95% confidence intervals. This supplementary figure is similar to Fig. 1, except it shows calibration and power of additional tissue-gene fine-mapping methods. ‘TGFM (no sampling, uniform prior)’ corresponds to TGFM (Gene-Tissue) with a uniform prior and a single cis-predicted expression model (based on posterior mean causal cis-eQTL effect sizes) instead of averaging results across 100 sampled cis-predicted expression models. ‘TGFM (uniform prior)’ corresponds to TGFM (Gene-Tissue) with a uniform prior. ‘TGFM’ corresponds to the default version of TGFM (Gene-Tissue) (shown in Fig. 1).
Extended Data Fig. 3 Enrichment of fine-mapped TGFM genes within non-disease-specific gene sets.
(a) Enrichment of genes with TGFM (Gene) PIP > 0.5 within non-disease-specific gene sets meta-analyzed over 16 independent traits. Error bars represent 95% confidence intervals. (b) Enrichment of genes with TGFM (Gene) PIP > 0.25, 0.5, and 0.75 (see legend) within non-disease-specific gene sets meta-analyzed over 16 independent traits. Error bars represent 95% confidence intervals. Odds ratios and standard errors on the odds ratio were computed using logistic regression. Numerical results reported in Supplementary Table 14.
Extended Data Fig. 4 Comparison of TGFM (Gene) and cTWAS calibration and power using silver standard gene set of 69 known LDL cholesterol genes.
Empirical FDR (y-axis) using silver-standard gene set of 69 known LDL cholesterol genes at PIP greater than or equal to a range of PIP thresholds (x-axis) for TGFM (Gene) (a) and cTWAS applied to GTEx liver (b). Light shading denotes 95% confidence intervals. Black dashed line denotes (1 – average PIP), a less conservative choice than (1 – PIP threshold). (c) Average gene fine-mapping power (x-axis) at a specific level of FDR (y-axis) based on silver-standard gene set of 69 known LDL cholesterol genes for TGFM (gene) and cTWAS applied to GTEx liver (see legend). PIPs for cTWAS applied to GTEx liver extracted from Supplementary Table 2 of ref. 26.
Extended Data Fig. 5 Properties of disease gene fine-mapping methods.
For each disease gene fine-mapping method, we report whether or not the method jointly models tissues and genes; models non-mediated variants; and models uncertainty in cis-predicted gene expression. *: FOCUS allows for modeling of non-mediated genetic effects via a single genotype intercept term shared across all variants, but this functionality is not enabled in the default version of FOCUS (and does not ameliorate mis-calibration; Supplementary Fig. 22).
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Strober, B.J., Zhang, M.J., Amariuta, T. et al. Fine-mapping causal tissues and genes at disease-associated loci. Nat Genet 57, 42–52 (2025). https://doi.org/10.1038/s41588-024-01994-2
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DOI: https://doi.org/10.1038/s41588-024-01994-2