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

Abstract

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 7858 DNAm sites and 2733 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.

Some statistical consideration in transcriptome‐wide association studies

“…We emphasize that our main conclusion (that the standard TWAS performs well) holds only under the conditions with the large sample size and small effect sizes of genetic variants on complex traits and common diseases. Otherwise, for example, in extensions of TWAS to molecular traits or other endophenotypes (Wu et al, ; Xu, Wu, Pan, & Alzheimer's Disease Neuroimaging Initiative, ), on which genetic variants (or other IVs) may have much larger effect sizes, cautions should be taken: 2SPS as adopted in the standard TWAS may not be even consistent for a nonlinear model in Stage 2.…”

OSCA: a tool for omic-data-based complex trait analysis

“…In practice, the probes are split into two groups by association p-values from a linear regression model (i.e., = J J + + ) at a methylome-wide significant threshold (all the methylome-wide significant probes in the first group and the other probes in the second group). The GLS method described in model [3] can be used to estimate J and its SE for hypothesis testing. Like the exact MOA method, MOMENT is also computationally intensive when applied in a methylome-wide analysis.…”

“…The rapid proliferation of genetic and omic data in large cohort-based samples in the past decade have greatly advanced our understanding of the genetic architecture of omic profiles and the molecular mechanisms underpinning the genetic variation of human complex traits [1][2][3]. These advances include the identification of a large number of genetic variants associated with gene expression [4,5], DNA methylation [6,7], histone modification [8,9], and protein abundance [10,11]; the discovery of omic measures associated with complex traits [12,13]; the improved accuracy in predicting a trait using omic data [14,15]; and the prioritization of gene targets for complex traits by integrating genetic and omic data in large samples [3,13,[16][17][18]. These advances have also led to the development of software tools, focusing on a range of different aspects of omic data analysis.…”

Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation

“…In human data, co-occurence of QTL across various multi-omic data has been used to assess potentially related and connected biological processes; examples include gene expression with chromatin accessibility [7] or regulatory elements [8], and ribosome occupancy with protein abundances [9]. More formal integration through statistical mediation analyses has also been used to investigate relationships between levels of human biological data, such as distal genetic regulation through local gene expression [10,11], and eQTL with regulatory elements [12][13][14] and physiological phenotypes, such as cardiometabolic traits [15].…”