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Abundant associations with gene expression complicate GWAS follow-up

Nature Genetics volume 51, pages 768–769 (2019)Cite this article

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To the Editor — Genome-wide association studies (GWAS) are rapidly expanding the catalog of trait- and disease-associated variants. With increasing cohort size and phenotyping, GWAS have identified more than 70,000 associated variants1. Because as many as 90% of GWAS variants fall within non-coding regions, most of them have unknown functional importance2. To aid in interpreting these variants, expression quantitative trait locus (eQTL) studies provide data on whether a variant of interest is also associated with gene expression levels.

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Fig. 1: Distinguishing candidates from false-positive genes by using LocusCompare.

Data availability

A bash script to download all GWAS datasets is available at https://github.com/mikegloudemans/gwas-download. GTEx eQTL data can be accessed via https://gtexportal.org/home/datasets. Data for coronary artery smooth muscle cells are available at https://stanford.app.box.com/s/e6e8hyft5u7wix1nzg5mjfqa084c4tin. Data for retinal pigment epithelium cells are available at https://stanford.box.com/s/asrxy0o66xxe1j7mfj56p3z3d405gijj. Methylation QTL data for brain and blood are available at https://cnsgenomics.com/software/smr/#DataResource.

Code availability

LocusCompare is hosted at http://locuscompare.com and is also available as open source software at https://github.com/boxiangliu/locuscompare. LocusCompareR is open source and is available at https://github.com/boxiangliu/locuscomparer. Our pipeline for running colocalization tests is available at https://bitbucket.org/mgloud/production_coloc_pipeline/. The LocusCompareR package was built with R version 3.2.3.

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Acknowledgements

B.L. is supported by the Stanford Center for Evolution and Human Genomics fellowship and National Key R&D Program of China, 2016YFD0400800 and Baidu Research. M.J.G. is funded by NLM training grant T15 LM 007033 and a Stanford Graduate Fellowship. E.I. is supported by R01DK106236. S.B.M. is supported by R33HL120757 (NHLBI), U01HG009431 (NHGRI; ENCODE4), R01MH101814 (NIH Common Fund; GTEx Program), R01HG008150 (NHGRI; Non-Coding Variants Program), R01HL142015 (NHLBI; TOPMED), U01HG009080 (NHGRI; GSPAC) and the Edward Mallinckrodt Jr. Foundation. We acknowledge A. Shcherbina for support in this project and N. Cyr for support with graphical illustration.

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Author notes

  1. These authors contributed equally: Boxiang Liu, Michael J. Gloudemans.

Authors and Affiliations

  1. Department of Biology, Stanford University, Stanford, CA, USA

    Boxiang Liu

  2. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Boxiang Liu, Michael J. Gloudemans, Abhiram S. Rao & Stephen B. Montgomery

  3. Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA

    Michael J. Gloudemans

  4. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Abhiram S. Rao

  5. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Erik Ingelsson

  6. Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA

    Erik Ingelsson

  7. Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA

    Erik Ingelsson

  8. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Stephen B. Montgomery

Authors
  1. Boxiang Liu
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  2. Michael J. Gloudemans
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  3. Abhiram S. Rao
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  4. Erik Ingelsson
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  5. Stephen B. Montgomery
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Contributions

B.L. and S.B.M. designed the study. B.L., M.J.G. and A.S.R. performed analyses. E.I. provided study support and feedback. B.L., M.J.G. and S.B.M. wrote the paper. B.L. and M.J.G. contributed equally to this work.

Corresponding authors

Correspondence to Boxiang Liu or Stephen B. Montgomery.

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Competing interests

S.B.M. is on the SAB of Prime Genomics.

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Figs. 1–6

Reporting Summary

Supplementary Tables 1 and 2

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Liu, B., Gloudemans, M.J., Rao, A.S. et al. Abundant associations with gene expression complicate GWAS follow-up. Nat Genet 51, 768–769 (2019). https://doi.org/10.1038/s41588-019-0404-0

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