Integrated analysis of genome-wide genetic and epigenetic association data for identification of disease mechanisms

Abstract

Many human diseases are multifactorial, involving multiple genetic and environmental factors impacting on one or more biological pathways. Much of the environmental effect is believed to be mediated through epigenetic changes. Although many genome-wide genetic and epigenetic association studies have been conducted for different diseases and traits, it is still far from clear to what extent the genomic loci and biological pathways identified in the genetic and epigenetic studies are shared. There is also a lack of statistical tools to assess these important aspects of disease mechanisms. In the present study, we describe a protocol for the integrated analysis of genome-wide genetic and epigenetic data based on permutation of a sum statistic for the combined effects in a locus or pathway. The method was then applied to published type 1 diabetes (T1D) genome-wide- and epigenome-wide-association studies data to identify genomic loci and biological pathways that are associated with T1D genetically and epigenetically. Through combined analysis, novel loci and pathways were also identified, which could add to our understanding of disease mechanisms of T1D as well as complex diseases in general.

Keywords: :

• EWAS
• GWAS
• integrated analysis
• combined effect
• biological pathways
• type 1 diabetes

10.4161/epi.26407

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

The University College London (UCL) Institute of Child Health receives a proportion of funding from the Department of Health's National Institute for Health Research Biomedical Research Centres funding scheme. Part of this work was undertaken at the Centre for Paediatric Epidemiology and Biostatistics, (UCL, London) which benefits from funding support from the MRC in its capacity as the MRC Centre of Epidemiology for Child Health. The Medical Research Council Centre of Epidemiology for Child Health is supported by funds from the UK Medical Research Council (grant G0400546). The authors also wish to thank the Wellcome Trust Case Control Consortium (WTCCC) and the Database of Genotypes and Phenotypes (dbGaP) for providing relevant genotyping data and summary GWAS analysis results in the study. The authors acknowledge the use of the UCL Legion High Performance Computer Facility (Legion@UCL), and associated support service, in the completion of this work.