Letter

Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues

Received:
Accepted:
Published online:

Abstract

Genetic association mapping produces statistical links between phenotypes and genomic regions, but identifying causal variants remains difficult. Whole-genome sequencing (WGS) can help by providing complete knowledge of all genetic variants, but it is financially prohibitive for well-powered GWAS studies. We performed mapping of expression quantitative trait loci (eQTLs) with WGS and RNA-seq, and found that lead eQTL variants called with WGS were more likely to be causal. Through simulations, we derived properties of causal variants and used them to develop a method for identifying likely causal SNPs. We estimated that 25–70% of causal variants were located in open-chromatin regions, depending on the tissue and experiment. Finally, we identified a set of high-confidence causal variants and showed that these were more enriched in GWAS associations than other eQTLs. Of those, we found 65 associations with GWAS traits and provide examples in which genes implicated by expression are functionally validated as being relevant for complex traits.

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Acknowledgements

We thank N. Lykoskoufis for assistance with the enrichment analysis. T.S. is supported as an NIHR Senior Research Fellow. This project was supported by a Helse Sør-Øst grant (2011060) to A.B. and an MRC Project Grant (L01999X/1) to K.S., and by grants from the NIH-NIMH (NIH-R01MH101814-GTEx), an IMI-Joint Undertaking of the European Commission (UE7-DIRECT-115317-1), the European Commission (UE7-EUROBATS-259749), the European Research Council (UE7-POPRNASEQ-260927), the Louis Jeantet Foundation, the Swiss National Science Foundation (31003A-149984 and 31003A-170096), and SystemsX (2012/201-SysGenetix) to E.T.D. The TwinsUK study was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007-2013) and the Medical Research Council. The study also received support from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre, based at Guy's and St Thomas' NHS Foundation Trust, in partnership with King's College London. SNP genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via NIH-CIDR. This study used data generated by the UK10K Consortium. Funding for UK10K was provided by the Wellcome Trust under award WT091310. A full list of the investigators who contributed to the generation of the UK10K data is available at http://www.UK10K.org/. This research was supported by grants from the European Research Council. Computation was performed at the Vital-IT Center (http://www.vital-it.ch/) 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.

    • Andrew Anand Brown
    • , Ana Viñuela
    • , Olivier Delaneau
    •  & Emmanouil T Dermitzakis
  2. Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland.

    • Andrew Anand Brown
    • , Ana Viñuela
    • , Olivier Delaneau
    •  & Emmanouil T Dermitzakis
  3. Swiss Institute of Bioinformatics, Geneva, Switzerland.

    • Andrew Anand Brown
    • , Ana Viñuela
    • , Olivier Delaneau
    •  & Emmanouil T Dermitzakis
  4. NORMENT, KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo, Norway.

    • Andrew Anand Brown
  5. Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.

    • Tim D Spector
    •  & Kerrin S Small

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Contributions

A.A.B. and E.T.D. designed the study. A.A.B. ran the analyses. A.A.B., A.V., and E.T.D. interpreted the results. A.A.B., A.V., and E.T.D. wrote the manuscript. O.D. provided methodological suggestions. K.S.S. and T.D.S. contributed data.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Andrew Anand Brown or Emmanouil T Dermitzakis.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–6, Supplementary Tables 1 and 2 and Supplementary Note

  2. 2.

    Life Sciences Reporting Summary

Text files

  1. 1.

    Supplementary Data Set 1

    A full list of all eQTLs discovered in the five experiments, together with P value for association and causal probability score

  2. 2.

    Supplementary Data Set 2

    A list of high confidence causal variants which are also significantly associated with a GWAS trait, together with an estimate produced by coloc of the probability of a shared genetic signal