Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits

Yang Wu, Jian Zeng, Futao Zhang, Zhihong Zhu, Ting Qi, Zhili Zheng, Lloyd-Jones Luke R, Riccardo Marioni, Nicolas G. Martin, Grant W Montgomery, Ian J Deary, Peter Visscher, Allan F. Mcrae, Jian Yang

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The identification of genes and regulatory elements underlying the associations discovered by GWAS is essential to understanding the aetiology of complex traits (including diseases). Here, we demonstrate an analytical paradigm of prioritizing genes and regulatory elements at GWAS loci for follow-up functional studies. We perform an integrative analysis that uses summary-level SNP data from multi-omics studies to detect DNA methylation (DNAm) sites associated with gene expression and phenotype through shared genetic effects (i.e., pleiotropy). We identify pleiotropic associations between 7,858 DNAm sites and 2,733 genes. These DNAm sites are enriched in enhancers and promoters, and >40% of them are mapped to distal genes. Further pleiotropic association analyses, which link both the methylome and transcriptome to 12 complex traits, identify 149 DNAm sites and 66 genes, indicating a plausible mechanism whereby the effect of a genetic variant on phenotype is mediated by genetic regulation of transcription through DNAm.
Original languageEnglish
Article number918
Pages (from-to)1-14
Number of pages14
JournalNature Communications
Early online date2 Mar 2018
Publication statusE-pub ahead of print - 2 Mar 2018

Keywords / Materials (for Non-textual outputs)

  • data integration
  • epigenomics
  • genetics


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