Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5–6.8% of eQTLs—a substantially higher fraction than prior estimates—and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance of rare high-impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies.

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The authors thank R.E. Handsaker for advice on Genome STRiP, H.J. Abel for helpful statistical discussions and R.M. Layer for software contributions. This work was supported by the NIH (MH101810) (D.F.C.), the NIH/NHGRI (1UM1HG008853) (I.M.H.), a Burroughs Wellcome Fund Career Award (I.M.H.), a Mr. and Mrs. Spencer T. Olin Fellowship for Women in Graduate Study (A.J.S.), a Lucille P. Markey Biomedical Research Stanford Graduate Fellowship (J.R.D.), the Stanford Genome Training Program (SGTP; NIH/NHGRI T32HG000044) (J.R.D.), a Hewlett-Packard Stanford Graduate Fellowship (E.K.T.), and a doctoral scholarship from the Natural Science and Engineering Council of Canada (E.K.T.). The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health. Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI/SAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171) and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through an SAIC-F subcontract to the Van Andel Institute (10ST1035). Additional data repository and project management were provided by SAIC-F (HHSN261200800001E). The Brain Bank was supported by supplements to University of Miami grants DA006227 & DA033684 and to contract N01MH000028. Statistical Methods development grants were made to the University of Geneva (MH090941 and MH101814), the University of Chicago (MH090951, MH090937, MH101820 and MH101825), the University of North Carolina—Chapel Hill (MH090936 and MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University at St. Louis (MH101810) and the University of Pennsylvania (MH101822).

Author information


  1. McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, USA.

    • Colby Chiang
    • , Alexandra J Scott
    • , Liron Ganel
    •  & Ira M Hall
  2. Department of Pathology, Stanford University School of Medicine, Stanford, California, USA.

    • Joe R Davis
    • , Emily K Tsang
    • , Xin Li
    •  & Stephen B Montgomery
  3. Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.

    • Joe R Davis
    •  & Stephen B Montgomery
  4. Biomedical Informatics Program, Stanford University School of Medicine, Stanford, California, USA.

    • Emily K Tsang
  5. Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.

    • Yungil Kim
    • , Farhan N Damani
    •  & Alexis Battle
  6. Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA.

    • Tarik Hadzic
  7. Department of Computer Science, Stanford University, Stanford, California, USA.

    • Stephen B Montgomery
  8. Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, Missouri, USA.

    • Donald F Conrad
    •  & Ira M Hall
  9. Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA.

    • Donald F Conrad
  10. Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.

    • Ira M Hall


  1. GTEx Consortium

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


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C.C., A.B., S.B.M., D.F.C. and I.M.H. designed the experiments. C.C. and A.J.S. performed SV discovery and genotyping. C.C. performed common eQTL mapping, causality analyses, LD tagging and candidate GWAS analyses. J.R.D., E.K.T., X.L., Y.K. and F.N.D. identified gene expression outliers. C.C. and A.J.S. analyzed rare SVs. L.G. and I.M.H. designed SVScore annotation. D.F.C. and T.H. performed microarray-based CNV detection. C.C., D.F.C. and I.M.H. wrote the manuscript.

Competing interests

D.F.C. is a paid consultant of PierianDx. The authors declare no other competing financial interests.

Corresponding authors

Correspondence to Donald F Conrad or Ira M Hall.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–27, Supplementary Tables 1, 3, 4 and 6–9, and Supplementary Note.

Excel files

  1. 1.

    Supplementary Table 2

    Excel file of all SV-only and joint eQTLs, along with causality scores.

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    Supplementary Table 5

    Excel file of all SV-eQTL GWAS hits.

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