Human Genetics

, Volume 134, Issue 1, pp 77–87

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.

Supplementary material

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

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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