Analysis

Estimating the causal tissues for complex traits and diseases

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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.

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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

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

Consortia

  1. GTEx Consortium

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

Authors

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Halit Ongen or Emmanouil T Dermitzakis.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–19 and Supplementary Note

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Mean eQTL tissue sharing probabilities across 44 tissues.

  2. 2.

    Supplementary Table 2

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

  3. 3.

    Supplementary Table 3

    Frequency of tissues being included in the most likely set of tissues for all eQTLs discovered.

  4. 4.

    Supplementary Table 4

    Various error statistics for r2.

  5. 5.

    Supplementary Table 5

    Enrichment over the null for the GWAS traits.

  6. 6.

    Supplementary Table 6

    Nominal P values for enrichments over the null for the GWAS traits.

  7. 7.

    Supplementary Table 7

    Normalized tissue causality profile for the GWAS traits.

  8. 8.

    Supplementary Table 8

    GWAS–eQTL probabilities.