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Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits

Nature Genetics volume 50, pages 1041–1047 (2018)Cite this article

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Abstract

There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10−31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10−35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures.

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Fig. 1: S-LDSC and GCTA estimates for Topcis-QTL are upward biased in simulations.
Fig. 2: Fine-mapped eQTLs are strongly enriched for disease and trait heritability.
Fig. 3: Relationship between eQTL sample size and the annotation effect size (τ*).
Fig. 4: Tissue-specific fine-mapped eQTL enrichments for blood- and brain-related traits.
Fig. 5: Heritability enrichment of fine-mapped eQTLs is concentrated in disease-relevant gene sets.
Fig. 6: Fine-mapped eQTL, hQTL, sQTL and meQTL annotations are enriched for disease and trait heritability.

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Acknowledgements

We thank S. Raychaudhuri, N. Zaitlen, B. Pasaniuc, M. Nivard, J.-H. Sul and F. Hormozdiari for helpful discussions. This research was funded by NIH grants U01 HG009379, R01 MH101244, R01 MH109978, T32 DK110919 and R01 MH107649. This research was conducted using the UK Biobank Resource under application 16549.

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Authors and Affiliations

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

    Farhad Hormozdiari, Steven Gazal, Bryce van de Geijn, Hilary K. Finucane, Armin Schoech, Yakir Reshef, Xuanyao Liu, Luke O’Connor & Alkes L. Price

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

    Farhad Hormozdiari, Steven Gazal, Bryce van de Geijn, Hilary K. Finucane, Po-Ru Loh, Armin Schoech, Yakir Reshef, Xuanyao Liu & Alkes L. Price

  3. Department of Computer Science, University of California, Los Angeles, CA, USA

    Chelsea J.-T. Ju & Eleazar Eskin

  4. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Po-Ru Loh & Alexander Gusev

  5. Program in Bioinformatics and Integrative Genomics, Harvard Graduate School of Arts and Sciences, Boston, MA, USA

    Luke O’Connor

  6. Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Alexander Gusev

  7. Department of Human Genetics, University of California, Los Angeles, CA, USA

    Eleazar Eskin

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Contributions

F.H. and A.L.P. designed experiments. F.H. performed experiments. F.H., S.G., B.v.d.G., H.K.F., C.J.-T.J., P.-R.L., A.S., Y.R., X.L., L.O., A.G. and E.E. analyzed data. F.H. and A.L.P. wrote the manuscript with assistance from all authors.

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Correspondence to Farhad Hormozdiari or Alkes L. Price.

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Integrated supplementary information

Supplementary Figure 1 Enrichment and τ* of different QTL annotations for whole blood in the GTEx dataset without conditioning on the baselineLD model.

a, Meta-analysis results of enrichment for whole blood tissue from the GTEx datasets. b, Meta-analysis results of τ* for whole blood. The y axis is the meta-analyzed value, and error bars represent jackknife 95% confidence intervals. These values were computed by meta-analyzing 41 independent traits (n = 41 independent simulations to derive the statistics). Numerical results are reported in Supplementary Table 9.

Supplementary Figure 2 Simulations results at different eQTL sample sizes.

We report annotation effect size (τ*) estimates at different eQTL sample sizes simulated under the alternative simulation framework (Methods). We simulated eQTL studies where we ranged the eQTL sample size as 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1,000. The x axis is the eQTL sample size, and the y axis is the estimated annotation effect size (τ*). We observed a linear relationship between eQTL sample size and τ* (R2 = 0.84, P = 9.39 × 10–5).

Supplementary Figure 3 Histogram of values for MaxCPP annotation in GTEx.

The x axis is the MaxCPP value, and the y axis is the frequency corresponding to each MaxCPP value.

Supplementary Figure 4 Pairwise correlation among all baselineLD model annotations and our six molecular QTL MaxCPP annotations.

We compute the pairwise correlation between annotation values.

Supplementary Figure 5 Pairwise correlation among LD scores of all baselineLD model annotations and six molecular QTL MaxCPP annotations.

We compute the pairwise correlation between the LD score of each annotation.

Supplementary Figure 6 Pairwise correlation among all baselineLD model annotations and GTEx blood and Brain+Nerve MaxCPP annotations.

We compute the pairwise correlation between annotation values.

Supplementary Figure 7 Pairwise correlation among LD scores of all baselineLD model annotations and GTEx blood and Brain+Nerve MaxCPP annotations.

We compute the pairwise correlation between the LD score of each annotation.

Supplementary Figure 8 Histogram of values for MaxCPP annotation for FE-Meta-Tissue for each molecular QTL in BLUEPRINT.

a, eQTL. b, H3K27ac hQTL. c, H3K4me1 hQTL. d, meQTL. e, sQTL. All these annotations were obtained by creating the MaxCPP annotation for each QTL dataset.

Supplementary Figure 9 MaxCPP enrichment estimates are not sensitive to the maximum number of causal variants per locus modeled by CAVIAR.

We report MaxCPP enrichment estimates for each of 44 GTEx tissues with CAVIAR modeling either up to six or up to three causal variants per locus. We determined that results were not statistically different. The y axis is the enrichment meta-analyzed value, and error bars represent 95% confidence intervals. These values were computed by meta-analyzing 41 independent traits (n = 41 independent traits to derive the statistics). Numerical results are reported in Supplementary Table 37.

Supplementary Figure 10 MaxCPP τ* estimates are not sensitive to the maximum number of causal variants per locus modeled by CAVIAR.

We report MaxCPP τ* estimates for each of 44 GTEx tissues with CAVIAR modeling either up to six or up to three causal variants per locus. We determined that results were not statistically different. The y axis is the τ* meta-analyzed value, and error bars represent 95% confidence intervals. These values are computed by meta-analyzing 41 independent traits (n = 41 independent traits to derive the statistics). Numerical results are reported in Supplementary Table 37.

Supplementary Figure 11 Simulations confirm that S-LDSC estimates for τ* are unique to the focal annotation.

We generated simulated data under a model in which only baselineLD and GTEx-FE-Meta-Tissue MaxCPP annotations directly influence trait heritability, using estimated τ* values from meta-analysis of 41 independent traits (estimated using a model that contains only baselineLD and GTEx-FE-Meta-Tissue MaxCPP annotations). We then estimated τ* values using a model that contains baselineLD, GTEx-FE-Meta-Tissue MaxCPP, and GTEx whole-blood maxCPP annotations. Results are averaged across 2,000 simulations. a, We report τ* estimates for each annotation. The y axis is the mean of τ* values, and error bars represent 95% confidence intervals. We computed the mean and confidence intervals using 400 simulations (n = 400 independent simulations to derive the statistics). τ* estimates for GTEx whole-blood MaxCPP were not significantly different from 0. b, We report the false positive rate for different P-value thresholds of τ* estimates for GTEx whole-blood MaxCPP. We observed correct null calibration. We computed the false positive rate using 400 simulations (n = 400 independent simulations to derive the statistics).

Supplementary information

Supplementary Figures, Text and Tables

Supplementary Figures 1–11 and Supplementary Tables 1–5, 7–15, 17–26 and 28–36

Reporting Summary

Supplementary Table 6

List of 47 datasets analyzed in this study

Supplementary Table 16

τ* of MaxCPP annotations for individual tissues and individual traits

Supplementary Table 27

Enrichment and τ* for CD14+ monocytes, CD16+ neutrophils, naive CD4+ T cells and FE-Meta-Tissue in the BLUEPRINT dataset

Supplementary Table 37

MaxCPP enrichment and τ* estimates are not sensitive to the maximum number of causal variants per locus modeled by CAVIAR

Supplementary Table 38

Results using baselineLD model v1.1 and baselineLD model v1.0 are highly concordant

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Hormozdiari, F., Gazal, S., van de Geijn, B. et al. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat Genet 50, 1041–1047 (2018). https://doi.org/10.1038/s41588-018-0148-2

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