Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Analysis
  • Published:

Estimating the causal tissues for complex traits and diseases

Nature Genetics volume 49, pages 1676–1683 (2017)Cite this article

Subjects

Abstract

How to interpret the biological causes underlying the predisposing markers identified through genome-wide association studies (GWAS) remains an open question. One direct and powerful way to assess the genetic causality behind GWAS is through analysis of expression quantitative trait loci (eQTLs). Here we describe a new approach to estimate the tissues behind the genetic causality of a variety of GWAS traits, using the cis-eQTLs in 44 tissues from the Genotype-Tissue Expression (GTEx) Consortium. We have adapted the regulatory trait concordance (RTC) score to measure the probability of eQTLs being active in multiple tissues and to calculate the probability that a GWAS-associated variant and an eQTL tag the same functional effect. By normalizing the GWAS–eQTL probabilities by the tissue-sharing estimates for eQTLs, we generate relative tissue-causality profiles for GWAS traits. Our approach not only implicates the gene likely mediating individual GWAS signals, but also highlights tissues where the genetic causality for an individual trait is likely manifested.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Estimates of tissue sharing for eQTLs among the 44 GTEx tissues.
Figure 2: Finding the most likely set of tissues where an eQTL effect is active.
Figure 3: RTC score compared to other pairwise variant metrics.
Figure 4: Patterns of tissue causality of GWAS traits.
Figure 5: eQTL effects at the coronary artery disease (CAD)- and lipid levels–associated 1p13 locus.
Figure 6: Enrichment over the null of tissues signifying their contribution to the genetic causality of complex diseases.

Similar content being viewed by others

References

  1. Bush, W.S. & Moore, J.H. Chapter 11: Genome-wide association studies. PLoS Comput. Biol. 8, e1002822 (2012).

    Article  CAS  Google Scholar 

  2. McCarthy, M.I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9, 356–369 (2008).

    Article  CAS  Google Scholar 

  3. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP–trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    Article  CAS  Google Scholar 

  4. Dermitzakis, E.T. From gene expression to disease risk. Nat. Genet. 40, 492–493 (2008).

    Article  CAS  Google Scholar 

  5. Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

    Article  CAS  Google Scholar 

  6. Nica, A.C. & Dermitzakis, E.T. Using gene expression to investigate the genetic basis of complex disorders. Hum. Mol. Genet. 17 R2, R129–R134 (2008).

    Article  CAS  Google Scholar 

  7. Montgomery, S.B. & Dermitzakis, E.T. From expression QTLs to personalized transcriptomics. Nat. Rev. Genet. 12, 277–282 (2011).

    Article  CAS  Google Scholar 

  8. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  9. Flutre, T., Wen, X., Pritchard, J. & Stephens, M. A statistical framework for joint eQTL analysis in multiple tissues. PLoS Genet. 9, e1003486 (2013).

    Article  CAS  Google Scholar 

  10. Brown, C.D., Mangravite, L.M. & Engelhardt, B.E. Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs. PLoS Genet. 9, e1003649 (2013).

    Article  CAS  Google Scholar 

  11. Hore, V. et al. Tensor decomposition for multiple-tissue gene expression experiments. Nat. Genet. 48, 1094–1100 (2016).

    Article  CAS  Google Scholar 

  12. Nica, A.C. et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6, e1000895 (2010).

    Article  Google Scholar 

  13. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    Article  CAS  Google Scholar 

  14. Dimas, A.S. et al. Common regulatory variation impacts gene expression in a cell type–dependent manner. Science 325, 1246–1250 (2009).

    Article  CAS  Google Scholar 

  15. Gutierrez-Arcelus, M. et al. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLoS Genet. 11, e1004958 (2015).

    Article  Google Scholar 

  16. Nicolae, D.L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    Article  Google Scholar 

  17. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  Google Scholar 

  18. Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

  19. Nguyen, P. et al. Liver lipid metabolism. J. Anim. Physiol. Anim. Nutr. (Berl.) 92, 272–283 (2008).

    Article  CAS  Google Scholar 

  20. Saltiel, A.R. & Kahn, C.R. Insulin signalling and the regulation of glucose and lipid metabolism. Nature 414, 799–806 (2001).

    Article  CAS  Google Scholar 

  21. Maiuolo, J., Oppedisano, F., Gratteri, S., Muscoli, C. & Mollace, V. Regulation of uric acid metabolism and excretion. Int. J. Cardiol. 213, 8–14 (2016).

    Article  Google Scholar 

  22. Taki, Y. et al. Correlation among body height, intelligence, and brain gray matter volume in healthy children. Neuroimage 59, 1023–1027 (2012).

    Article  Google Scholar 

  23. Buckner, R.L., Andrews-Hanna, J.R. & Schacter, D.L. The brain's default network: anatomy, function, and relevance to disease. Ann. NY Acad. Sci. 1124, 1–38 (2008).

    Article  Google Scholar 

  24. Harrison, P.J. The neuropathology of schizophrenia. A critical review of the data and their interpretation. Brain 122, 593–624 (1999).

    Article  Google Scholar 

  25. Han, S.K. et al. Activation of gonadotropin-releasing hormone neurons by kisspeptin as a neuroendocrine switch for the onset of puberty. J. Neurosci. 25, 11349–11356 (2005).

    Article  CAS  Google Scholar 

  26. Myocardial Infarction Genetics Consortium. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat. Genet. 41, 334–341 (2009).

  27. Samani, N.J. et al. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357, 443–453 (2007).

    Article  CAS  Google Scholar 

  28. Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    Article  CAS  Google Scholar 

  29. Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).

    Article  CAS  Google Scholar 

  30. Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006).

    Article  CAS  Google Scholar 

  31. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature http://dx.doi.org/10.1038/nature24277 (2017).

  32. Ongen, H., Buil, A., Brown, A.A., Dermitzakis, E.T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).

    Article  CAS  Google Scholar 

  33. Delaneau, O. et al. A complete tool set for molecular QTL discovery and analysis. Nat. Commun. 8, 15452 (2017).

    Article  CAS  Google Scholar 

  34. McVean, G.A. et al. The fine-scale structure of recombination rate variation in the human genome. Science 304, 581–584 (2004).

    Article  CAS  Google Scholar 

  35. Kent, W.J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  Google Scholar 

  36. 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  37. Su, Z., Marchini, J. & Donnelly, P. HAPGEN2: simulation of multiple disease SNPs. Bioinformatics 27, 2304–2305 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research was supported by grants from the US National Institutes of Health (NIH-R01MH101814), European Commission Framework Programme 7 (UE7-SYSCOL-258236), the European Research Council (UE7-POPRNASEQ-260927), the Swiss National Science Foundation (31003A-149984 and 31003A-170096), and the Louis Jeantet Foundation. Computations were performed at the Vital-IT Centre for High-Performance Computing of the SIB Swiss Institute of Bioinformatics.

Author information

Authors and Affiliations

  1. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland

    Halit Ongen, Andrew A Brown, Olivier Delaneau, Nikolaos I Panousis, Alexandra C Nica & Emmanouil T Dermitzakis

  2. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland

    Halit Ongen, Andrew A Brown, Olivier Delaneau, Nikolaos I Panousis & Emmanouil T Dermitzakis

  3. Swiss Institute of Bioinformatics, Geneva, Switzerland

    Halit Ongen, Andrew A Brown, Olivier Delaneau, Nikolaos I Panousis & Emmanouil T Dermitzakis

Authors
  1. Halit Ongen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew A Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olivier Delaneau
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nikolaos I Panousis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexandra C Nica
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Emmanouil T Dermitzakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

GTEx Consortium

Contributions

H.O. and E.T.D. designed the study. H.O., A.A.B., and O.D. conducted the analysis and developed software. A.C.N. designed the original RTC method. N.I.P. tested the software. H.O. wrote and E.T.D. edited the manuscript. The GTEx Consortium generated the data.

Corresponding authors

Correspondence to Halit Ongen or Emmanouil T Dermitzakis.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

A list of members and affiliations appears in the Supplementary Note.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–19 and Supplementary Note (PDF 1417 kb)

Life Sciences Reporting Summary (PDF 85 kb)

Supplementary Table 1

Mean eQTL tissue sharing probabilities across 44 tissues. (XLSX 35 kb)

Supplementary Table 2

Proportions of the number of tissues an eQTL (FDR = 5%) is active in, for the 44 GTEx tissues. (XLSX 31 kb)

Supplementary Table 3

Frequency of tissues being included in the most likely set of tissues for all eQTLs discovered. (XLSX 33 kb)

Supplementary Table 4

Various error statistics for r2. (XLSX 17 kb)

Supplementary Table 5

Enrichment over the null for the GWAS traits. (XLSX 315 kb)

Supplementary Table 6

Nominal P values for enrichments over the null for the GWAS traits. (XLSX 261 kb)

Supplementary Table 7

Normalized tissue causality profile for the GWAS traits. (XLSX 333 kb)

Supplementary Table 8

GWAS–eQTL probabilities. (XLSX 1923 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ongen, H., Brown, A., Delaneau, O. et al. Estimating the causal tissues for complex traits and diseases. Nat Genet 49, 1676–1683 (2017). https://doi.org/10.1038/ng.3981

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3981

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing