Human Genetics

, Volume 134, Issue 1, pp 77–87 | Cite as

Refinement of schizophrenia GWAS loci using methylome-wide association data

  • Gaurav Kumar
  • Shaunna L. Clark
  • Joseph L. McClay
  • Andrey A. Shabalin
  • Daniel E. Adkins
  • Linying Xie
  • Robin Chan
  • Srilaxmi Nerella
  • Yunjung Kim
  • Patrick F. Sullivan
  • Christina M. Hultman
  • Patrik K. E. Magnusson
  • Karolina A. Aberg
  • Edwin J. C. G. van den Oord
Original Investigation

Abstract

Recent genome-wide association studies (GWAS) have made substantial progress in identifying disease loci. The next logical step is to design functional experiments to identify disease mechanisms. This step, however, is often hampered by the large size of loci identified in GWAS that is caused by linkage disequilibrium between SNPs. In this study, we demonstrate how integrating methylome-wide association study (MWAS) results with GWAS findings can narrow down the location for a subset of the putative casual sites. We use the disease schizophrenia as an example. To handle “data analytic” variation, we first combined our MWAS results with two GWAS meta-analyses (N = 32,143 and 21,953), that had largely overlapping samples but different data analysis pipelines, separately. Permutation tests showed significant overlapping association signals between GWAS and MWAS findings. This significant overlap justified prioritizing loci based on the concordance principle. To further ensure that the methylation signal was not driven by chance, we successfully replicated the top three methylation findings near genes SDCCAG8, CREB1 and ATXN7 in an independent sample using targeted pyrosequencing. In contrast to the SNPs in the selected region, the methylation sites were largely uncorrelated explaining why the methylation signals implicated much smaller regions (median size 78 bp). The refined loci showed considerable enrichment of genomic elements of possible functional importance and suggested specific hypotheses about schizophrenia etiology. Several hypotheses involved possible variation in transcription factor-binding efficiencies.

Keywords

GWAS Study Methylation Signal Methylation Site Autism Spectrum Condition Major Histocompatibility Complex Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This study was supported by the National Institute of Mental Health (Grant 1R01MH097283). The present study is part of a larger project entitled ‘A Large-Scale Schizophrenia Association Study in Sweden’ that is supported by Grants from NIMH (MH077139) and the Stanley Medical Research Institute. Institutions involved in this Project are: Karolinska Institute, Icahn School of Medicine at Mount Sinai, University of North Carolina at Chapel Hill, Virginia Commonwealth University, Broad Institute, and the US National Institute of Mental Health. Library construction and next-generation sequencing was performed by EdgeBio Gaithersburg, MD.

Conflict of interest

The authors declare no competing financial interests.

Supplementary material

439_2014_1494_MOESM1_ESM.docx (42 kb)
Supplementary material 1 (DOCX 41 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Gaurav Kumar
    • 1
  • Shaunna L. Clark
    • 1
  • Joseph L. McClay
    • 1
  • Andrey A. Shabalin
    • 1
  • Daniel E. Adkins
    • 1
  • Linying Xie
    • 1
  • Robin Chan
    • 1
  • Srilaxmi Nerella
    • 1
  • Yunjung Kim
    • 3
  • Patrick F. Sullivan
    • 2
    • 3
  • Christina M. Hultman
    • 2
  • Patrik K. E. Magnusson
    • 2
  • Karolina A. Aberg
    • 1
  • Edwin J. C. G. van den Oord
    • 1
  1. 1.Center for Biomarker Research and Personalized Medicine, School of PharmacyVirginia Commonwealth UniversityRichmondUSA
  2. 2.Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
  3. 3.Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillUSA

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