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Modeling tissue co-regulation estimates tissue-specific contributions to disease

Nature Genetics volume 55, pages 1503–1511 (2023)Cite this article

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Abstract

Integrative analyses of genome-wide association studies and gene expression data have implicated many disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues impedes distinguishing biologically causal tissues from tagging tissues. In the present study, we introduce tissue co-regulation score regression (TCSC), which disentangles causal tissues from tagging tissues by regressing gene–disease association statistics (from transcriptome-wide association studies) on tissue co-regulation scores, reflecting correlations of predicted gene expression across genes and tissues. We applied TCSC to 78 diseases/traits (average n = 302,000) and gene expression prediction models for 48 GTEx tissues. TCSC identified 21 causal tissue–trait pairs at a 5% false discovery rate (FDR), including well-established findings, biologically plausible new findings (for example, aorta artery and glaucoma) and increased specificity of known tissue–trait associations (for example, subcutaneous adipose, but not visceral adipose, and high-density lipoprotein). TCSC also identified 17 causal tissue–trait covariance pairs at 5% FDR. In conclusion, TCSC is a precise method for distinguishing causal tissues from tagging tissues.

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Fig. 1: Overview of TCSC regression.
Fig. 2: Robustness and power of TCSC regression in simulations.
Fig. 3: TCSC estimates tissue-specific contributions to disease and complex trait heritability.
Fig. 4: Comparison of disease-critical tissues identified by RTC Coloc, LDSC-SEG and TCSC.
Fig. 5: Cross-trait TCSC estimates tissue-specific contributions to the genetic covariance of two diseases/traits.

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Data availability

We have made 78 GWAS summary statistics and 41 brain-specific summary statistics publicly available at https://github.com/TiffanyAmariuta/TCSC/tree/main/sumstats (https://doi.org/10.5281/zenodo.8030594)73, gene expression prediction models publicly available at https://alkesgroup.broadinstitute.org/TCSC/GeneExpressionModels, TWAS association statistics publicly available at https://alkesgroup.broadinstitute.org/TCSC/TWAS_sumstats, tissue co-regulation scores publicly available at https://github.com/TiffanyAmariuta/TCSC/tree/main/coregulation_scores and TCSC output publicly available at https://github.com/TiffanyAmariuta/TCSC/tree/main/results. Gene expression and genotype data were acquired from the GTEx v.8 eQTL dataset (dbGaP, accession no. phs000424.v8.p2) and 1000 Genomes phase 3 data were downloaded from https://data.broadinstitute.org/alkesgroup/FUSION/LDREF.tar.bz2. Source data are provided with this paper.

Code availability

TCSC software including a quick start tutorial is available at https://github.com/TiffanyAmariuta/TCSC/ (https://doi.org/10.5281/zenodo.8030594)73. The Mancuso Lab TWAS Simulator is available at https://github.com/mancusolab/twas_sim. The FUSION software is available at http://gusevlab.org/projects/fusion. The simulation code for RTC Coloc is available at https://github.com/TiffanyAmariuta/TCSC/tree/main/simulation_analysis.

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Acknowledgements

We thank H. Shi, M. Zhang and B. Strober for helpful discussions. This work was funded by US National Institutes of Health grants (nos. U01 HG009379, R01 MH101244, R37 MH107649, R01 HG006399, R01 MH115676 and U01 HG012009 awarded to A.L.P.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA

    Tiffany Amariuta

  2. Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Tiffany Amariuta

  3. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Tiffany Amariuta, Katherine Siewert-Rocks & Alkes L. Price

  4. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Tiffany Amariuta, Katherine Siewert-Rocks & Alkes L. Price

  5. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Alkes L. Price

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  1. Tiffany Amariuta
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Contributions

T.A. and A.L.P. conceived and designed the study. T.A. conducted simulation and real data analysis. K.S.-W. helped design the simulation framework. T.A. and A.L.P. wrote the initial draft of the manuscript. All authors contributed to the final manuscript.

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Correspondence to Tiffany Amariuta or Alkes L. Price.

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Extended data

Extended Data Fig. 1 Comparison of tissue-trait association methods with TCSC in simulations.

(a) Percentage of estimates of \({h}_{ge(t^{\prime} )}^{2}\) for noncausal tissues that were significantly positive at P < 0.05. \({h}_{ge(t\,causal)}^{2}\) is set to 10% and GWAS sample size is set to 10,000. (b) Percentage of estimates of \({h}_{ge(t{\prime} )}^{2}\) for causal tissues that were significantly positive at p < 0.05. In panels A and B, we performed n = 1,000 independent simulated genetic architectures for TCSC, LDSC-SEG, CoCoNet, and RolyPoly and n = 100 independent simulated genetic architectures for RTC Coloc due to the computationally intensive nature of the method across each eQTL sample size (n = 100, 200, 300, 500, 1000, 1500); we used a one-sided z-test and the genomic block jackknife standard error to obtain p-values and data are presented as mean values ± 1.96 × s.e.m. (c) Receiver operating characteristic (ROC) curves for each method, including cross-trait TCSC, across 1,000 uniformly spaced p-values between 0 and 1 used as the threshold to identify a causal tissue at a simulation eQTL sample size of 300, most closely matching real data analysis. We note that CoCoNet cannot be compared here because the method identifies the single most likely causal tissue using maximum likelihood estimation rather than via p-value. Numerical results are provided in Supplementary Tables 1, 2.

Source data

Extended Data Fig. 2 Robustness and power of cross-trait TCSC in simulations.

(a) Bias in estimates of genetic covariance explained by the cis-genetic component of gene expression in tissue t′ (\({\omega }_{ge(t{\prime} )}\)) for causal (purples) and noncausal (grays) tissues, across n = 1,000 independent simulations per eQTL sample size (n = 100, 200, 300, 500, 1000, 1500). Light purple (resp. gray) indicates that \({G}_{t{\prime} }\) was set to the total number of true cis-heritable genes across tissues, dark purple (resp. gray) indicates that \({G}_{t{\prime} }\) was set to the number of significantly cis-heritable genes detected in each tissue. The dashed line indicates the true value of \({\omega }_{ge(t{\prime} )}\) for causal tissues. (b) Percentage of estimates of \({\omega }_{ge(t{\prime} )}\) for noncausal tissues that were significantly positive at p < 0.05. (c) Percentage of estimates of \({\omega }_{ge(t{\prime} )}\) for causal tissues that were significantly positive at p < 0.05. For panels B and C, we performed n = 1,000 independent simulations per eQTL sample size (n = 100, 200, 300, 500, 1000, 1500) and used a one-sided z-test to obtain p-values. In all panels, data are presented as mean values ± 1.96 × s.e.m. Numerical results are reported in Supplementary Table 3.

Source data

Extended Data Fig. 3 Robustness and power of TCSC regression in simulations with different values of \({{\boldsymbol{h}}}_{{\boldsymbol{ge}}({\boldsymbol{t}}{\prime} )}^{2}\).

(a) Type I error per true value of \({h}_{ge(t{\prime} )}^{2}\) in the causal tissue. False positive event is defined as \({h}_{ge(t{\prime} )}^{2}\)> 0 for noncausal tissues at p < 0.05. (b) Power to detect the causal tissue per true value of \({h}_{ge(t{\prime} )}^{2}\) in the causal tissue. A true positive event is defined as \({h}_{ge(t{\prime} )}^{2}\) > 0 for causal tissues at p < 0.05. (c) Bias on causal estimates of \({h}_{ge(t{\prime} )}^{2}\) for different true values of the causal tissue \({h}_{ge(t{\prime} )}^{2}\). Dashed lines indicate true values of \({h}_{ge(t{\prime} )}^{2}\). (d) Bias on noncausal estimates of \({h}_{ge(t{\prime} )}^{2}\) for different true values of the causal tissue \({h}_{ge(t{\prime} )}^{2}\). In all panels, we performed n = 1,000 independent simulated genetic architectures across different eQTL sample sizes (n = 100, 200, 300, 500, 1000, 1500); we used a one-sided z-test and the genomic block jackknife standard error to obtain p-values and data are presented as mean values ± 1.96 × s.e.m. The value of \({G}_{t{\prime} }\) is set to the total number of unique cis-heritable genes across all tissues.

Source data

Extended Data Fig. 4 Robustness and power of cross-trait TCSC regression in simulations with different values of \({{\boldsymbol{\omega }}}_{{\boldsymbol{ge}}({\boldsymbol{t}}{\prime} )}\).

(a) Type I error per true value of \({\omega }_{ge(t{\prime} )}\) in the causal tissue. False positive event is defined as \({\omega }_{ge(t{\prime} )}\)> 0 for noncausal tissues at p < 0.05. (b) Power to detect the causal tissue per true value of \({\omega }_{ge(t{\prime} )}\) in the causal tissue. A true positive event is defined as \({\omega }_{ge(t{\prime} )}\) > 0 for causal tissues at p < 0.05. (c) Bias on causal estimates of \({\omega }_{ge(t{\prime} )}\) for different true values of the causal tissue \({\omega }_{ge(t{\prime} )}\). Dashed lines indicate true values of \({\omega }_{ge(t{\prime} )}\). (d) Bias on noncausal estimates of \({\omega }_{ge(t{\prime} )}\) for different true values of the causal tissue \({\omega }_{ge(t{\prime} )}\). In all panels, we performed n = 1,000 independent simulated genetic architectures across different eQTL sample sizes (n = 100, 200, 300, 500, 1000, 1500); we used a one-sided z-test and the genomic block jackknife standard error to obtain p-values and data are presented as mean values ± 1.96 × s.e.m. The value of \({G}_{t{\prime} }\) is set to the total number of unique cis-heritable genes across all tissues.

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Extended Data Fig. 5 Robustness and power of TCSC regression in simulations with different numbers of noncausal tissues.

(a) Type I error involving a variable number of noncausal tissues in the presence of a single causal tissue. False positive event defined as \({h}_{ge(t{\prime} )}^{2}\) > 0 for noncausal tissues at p < 0.05. Note, when there are 0 tagging tissues, there is no measurement of type I error. (b) Power to detect the causal tissue in which \({h}_{ge(t{\prime} )}^{2} > 0\) for causal tissues at p < 0.05. (c) Bias on estimates of \({h}_{ge(t{\prime} )}^{2}\) for the causal tissue, while changing the number of noncausal tissues in the model. The dashed line indicates that the true value of \({h}_{ge(t{\prime} )}^{2}\). (d) Bias on estimates of \({h}_{ge(t{\prime} )}^{2}\) for noncausal tissues, while changing the number of noncausal tissues in the model. In all panels, we performed n = 1,000 independent simulated genetic architectures across different eQTL sample sizes (n = 100, 200, 300, 500, 1000, 1500); we used a one-sided z-test and the genomic block jackknife standard error to obtain p-values and data are presented as mean values ± 1.96 × s.e.m. The value of \({G}_{t{\prime} }\) is set to the total number of unique cis-heritable genes across all tissues.

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Extended Data Fig. 6 Robustness and power of cross-trait TCSC regression in simulations with different numbers of noncausal tissues.

(a) Type I error involving a variable number of noncausal tissues in the presence of a single causal tissue. False positive event defined as \({\omega }_{ge(t{\prime} )}\) > 0 for noncausal tissues at p < 0.05. Note, when there are 0 tagging tissues, there is no measurement of type I error. (b) Power to detect the causal tissue in which \({\omega }_{ge(t{\prime} )} > 0\) for causal tissues at p < 0.05. (c) Bias on estimates of \({\omega }_{ge(t{\prime} )}\) for the causal tissue, while changing the number of noncausal tissues in the model. The dashed line indicates that the true value of \({\omega }_{ge(t{\prime} )}\). (d) Bias on estimates of \({\omega }_{ge(t{\prime} )}\) for noncausal tissues, while changing the number of noncausal tissues in the model. In all panels, we performed n = 1,000 independent simulated genetic architectures across different eQTL sample sizes (n = 100, 200, 300, 500, 1000, 1500); we used a one-sided z-test and the genomic block jackknife standard error to obtain p-values and data are presented as mean values ± 1.96 × s.e.m. The value of \({G}_{t{\prime} }\) is set to the total number of unique cis-heritable genes across all tissues.

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Extended Data Fig. 7 Tissue-specific contributions to disease and complex trait heritability in secondary analysis of 23 tissues, removing tissues with small eQTL sample size.

We report estimates of the proportion of disease heritability explained by the cis-genetic component of gene expression in tissue \(t{\prime}\) (\({\pi }_{t{\prime} }\)). Results are shown for tissue-trait pairs with FDR <= 10%; full boxes denote FDR of 5% or lower and partial boxes denote FDR between 5% and 10%. Tissues are ordered alphabetically. Color corresponds to \({\pi }_{t{\prime} }\), the proportion of common variant heritability causally explained by predicted gene expression in tissue \(t{\prime}\). These results are largely consistent with the analysis of 39 GTEx tissues (Fig. 4). WHRadjBMI: waist-hip-ratio adjusted for body mass index. HDL: high-density lipoprotein. DBP: diastolic blood pressure. BMI: body mass index. FEV1/FVC: forced expiratory volume in one second divided by forced vital capacity. Cereb. Cortex Ar.: cerebral cortex surface area. WBC Count: white blood cell count. RBC Count: red blood cell count. MDD: major depressive disorder. Daggers next to a tissue indicate a meta-tissue.

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Extended Data Fig. 8 Tissue-specific contributions to disease and complex trait heritability in brain-specific analysis.

We separately analyzed results for 41 brain-related diseases and complex traits and 13 brain tissues. Results are shown for tissue-trait pairs with FDR <= 10%; full boxes denote FDR of 5% or lower and partial boxes denote FDR between 5% and 10%. Tissues are ordered alphabetically. Each tissue has an eQTL sample size ranging from 101 to 189 individuals. Color corresponds to \({\pi }_{t{\prime} }\), the proportion of common variant heritability causally explained by predicted gene expression in tissue t'. Caudate Vol: caudate volume. BMI: body mass index. Scz: schizophrenia. Bipolar: bipolar disorder. Brainstem Vol: brainstem volume. Cereb. Cortex Width: cerebral cortex width. ADHD: attention-deficit/hyperactivity disorder. Brainstem Vol: brainstem volume .

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Extended Data Fig. 9 Comparison of disease-critical tissues identified by RTC Coloc, LDSC-SEG and TCSC for all 17 disease/traits with causal tissue-trait associations identified by TCSC.

FDR significance of trait-tissue associations across three different methods for each of 17 traits with a significant tissue found by TCSC from Fig. 3 and 7 causal tissues, plus each tissue’s most highly genetically correlated GTEx tissue. Full boxes denote FDR of 5% or lower and partial boxes denote FDR between 5% and 10%. Thicker black lines separate the causal tissue found in the primary TCSC analysis (left) from its most highly genetically correlated GTEx tissue (right), with two exceptions. First, breast tissue was the most highly genetically correlated tissue for two causal tissues, adipose subcutaneous and thyroid; therefore, these three tissues appear as a trio. Second, the aorta artery and tibial artery are each other’s most highly genetically correlated tissue and both are a causal tissue different traits by TCSC. (a) RTC Coloc (Ongen 2017 Nat Genet), (b) LDSC-SEG (Finucane 2018 Nat Genet), (c) TCSC. Per-trait FDR in panels A and C, FDR across traits and tissues in panel B. WHRadjBMI: waist-hip-ratio conditional on body mass index. HDL: high-density lipoprotein. DBP: diastolic blood pressure. BMI: body mass index. FEV1/FVC: forced expiratory volume in one second divided by forced vital capacity. Cereb. Cortex Ar.: cerebral cortex surface area. AST: aspartate aminotransferase. LDL: low-density lipoprotein. WBC: white blood cell count. MDD: major depressive disorder. Daggers next to a tissue indicate a meta-tissue. For BMI, fecundity, and cereb. cortex ar., LDSC-SEG brain-specific analysis results are used.

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Extended Data Fig. 10 Comparison of disease-critical tissues identified by RTC Coloc, LDSC-SEG and TCSC for brain-specific analysis.

a–c, Brain-specific tissue-trait pairs identified by three methods: RTC Coloc (a), LDSC-SEG (b) and TCSC (c). Full boxes denote FDR of 5% or lower and partial boxes denote FDR between 5% and 10%. Each tissue has an eQTL sample size ranging from 100 to 189 individuals. Additionally, we include each tissue’s most highly genetically correlated GTEx tissue. Thicker black lines separate the causal tissue found in the primary TCSC analysis (left) from its most highly genetically correlated GTEx tissue (right), with one exception: brain accumbens, caudate, putamen, hippocampus, and amygdala are all highly genetically correlated, with no pairs of exclusively high genetic correlation. Caudate Vol: caudate volume. BMI: body mass index. Scz: schizophrenia. Bipolar: bipolar disorder. Brainstem Vol: brainstem volume. Cereb. Cortex Width: cerebral cortex width. ADHD: attention-deficit/hyperactivity disorder. Brainstem Vol: brainstem volume.

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Supplementary information

Supplementary Information

Supplementary Note, Figs. 1–22 and table legends.

Reporting Summary

Supplementary Tables 1–21

Numerical results.

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Source Data

Source data for Figs. 2–5 and Extended Data Figs. 1–10.

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Amariuta, T., Siewert-Rocks, K. & Price, A.L. Modeling tissue co-regulation estimates tissue-specific contributions to disease. Nat Genet 55, 1503–1511 (2023). https://doi.org/10.1038/s41588-023-01474-z

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