4.8 Article

The impact of structural variation on human gene expression

Journal

NATURE GENETICS
Volume 49, Issue 5, Pages 692-+

Publisher

NATURE PORTFOLIO
DOI: 10.1038/ng.3834

Keywords

-

Funding

  1. NIH [MH101810]
  2. NIH/NHGRI [1UM1HG008853]
  3. Burroughs Wellcome Fund Career Award
  4. Mr. and Mrs. Spencer T. Olin Fellowship for Women in Graduate Study
  5. Lucille P. Markey Biomedical Research Stanford Graduate Fellowship
  6. Stanford Genome Training Program (SGTP) [NIH/NHGRI T32HG000044]
  7. Hewlett-Packard Stanford Graduate Fellowship
  8. Natural Science and Engineering Council of Canada
  9. Office of the Director of the National Institutes of Health
  10. NCI
  11. NHGRI
  12. NHLBI
  13. NIDA
  14. NIMH
  15. NINDS
  16. NCI/SAIC-Frederick, Inc. (SAIC-F) [10XS170, 10XS171, X10S172]
  17. SAIC-F [10ST1035]
  18. [HHSN268201000029C]
  19. [DA006227]
  20. [DA033684]
  21. [N01MH000028]
  22. [MH090941]
  23. [MH101814]
  24. [MH090951]
  25. [MH090937]
  26. [MH101820]
  27. [MH101825]
  28. [MH090936]
  29. [MH101819]
  30. [MH090948]
  31. [MH101782]
  32. [MH101822]

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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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