Estimating the causal tissues for complex traits and diseases

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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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.

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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.

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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.

Corresponding authors

Correspondence to Halit Ongen or Emmanouil T Dermitzakis.

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The authors declare no competing financial interests.

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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)

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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

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