Framework for the Integration of Genomics, Epigenomics and Transcriptomics in Complex Diseases

Hum Hered. 2015;79(3-4):124-36. doi: 10.1159/000381184. Epub 2015 Jul 28.

Abstract

Objectives: Different types of '-omics' data are becoming available in the post-genome era; still a single -omics assessment provides limited insights to understand the biological mechanism of complex diseases. Genomics, epigenomics and transcriptomics data provide insight into the molecular dysregulation of neoplastic diseases, among them urothelial bladder cancer (UBC). Here, we propose a detailed analytical framework necessary to achieve an adequate integration of the three sets of -omics data to ultimately identify previously hidden genetic mechanisms in UBC.

Methods: We built a multi-staged framework to study possible pair-wise combinations and integrated the data in three-way relationships. SNP genotypes, CpG methylation levels and gene expression levels were determined for a total of 70 individuals with UBC and with fresh tumour tissue available.

Results: We suggest two main hypothesis-based scenarios for gene regulation based on the -omics integration analysis, where DNA methylation affects gene expression and genetic variants co-regulate gene expression and DNA methylation. We identified several three-way trans-association 'hotspots' that are found at the molecular level and that deserve further studies.

Conclusions: The proposed integrative framework allowed us to identify relationships at the whole-genome level providing some new biological insights and highlighting the importance of integrating -omics data.

Publication types

  • Research Support, N.I.H., Intramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • CpG Islands / genetics
  • DNA Methylation / genetics
  • Disease / genetics*
  • Epigenomics*
  • Female
  • Gene Expression Profiling*
  • Gene Expression Regulation
  • Gene Frequency / genetics
  • Genome-Wide Association Study
  • Genotyping Techniques
  • Humans
  • Male
  • Middle Aged
  • Polymorphism, Single Nucleotide / genetics
  • Quantitative Trait Loci / genetics
  • Statistics as Topic*