Integrating Multi-Omics Data to Identify Novel Disease Genes and Single-Neucleotide Polymorphisms

Abstract

Stroke ranks the second leading cause of death among people over the age of 60 in the world. Stroke is widely regarded as a complex disease that is affected by genetic and environmental factors. Evidence from twin and family studies suggests that genetic factors may play an important role in its pathogenesis. Therefore, research on the genetic association of susceptibility genes can help understand the mechanism of stroke. Genome-wide association study (GWAS) has found a large number of stroke-related loci, but their mechanism is unknown. In order to explore the function of single-nucleotide polymorphisms (SNPs) at the molecular level, in this paper, we integrated 8 GWAS datasets with brain expression quantitative trait loci (eQTL) dataset to identify SNPs and genes which are related to four types of stroke (ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke). Thirty-eight SNPs which can affect 14 genes expression are found to be associated with stroke. Among these 14 genes, 10 genes expression are associated with ischemic stroke, one gene for large artery stroke, six genes for cardioembolic stroke and eight genes for small vessel stroke. To explore the effects of environmental factors on stroke, we identified methylation susceptibility loci associated with stroke using methylation quantitative trait loci (MQTL). Thirty-one of these 38 SNPs are at greater risk of methylation and can significantly change gene expression level. Overall, the genetic pathogenesis of stroke is explored from locus to gene, gene to gene expression and gene expression to phenotype.

A Workflow for Selection of Single Nucleotide Polymorphic Markers for Studying of Genetics of Ischemic Stroke Outcomes

“…Like other studies pointed to establish genomic landscape of complex traits, our approach is also based on exploration of data of different types (mRNA transcription, population genetic variations, eQTLs) [33,34]. However, it does not rely on GWAS data which are known to be not good in identifying real causative variants and genes as well [35] and thus it is initially more confident.…”

Identification of susceptibility loci for cardiovascular disease in adults with hypertension, diabetes, and dyslipidemia

“…Locus 3p22.1 [ULK4] of DL-IS risk seems likely to be associated with ULK4; recently, ULK4 was identified by GWAS and brain eQTL to be a susceptibility gene for IS and small vessel stroke in trans-ethnic datasets [45]. However, CTNN1B of the WNT gene family is another possibility.…”

Identification of Susceptibility Loci for Cardiovascular Disease in Adults With Hypertension, Diabetes and Dyslipidemia 

“…Locus 3p22.1 [ULK4] of DL-IS risk seems likely to be associated with ULK4; recently, ULK4 was identi ed by GWAS and brain eQTL to be a susceptibility gene for IS and small vessel stroke in trans-ethnic datasets. [44] However, CTNN1B of the WNT gene family is another possibility. The Wnt signaling pathway contributes to the development of CVDs; both canonical and noncanonical Wnt signaling cascades moderate cell phenotypic modulation of vascular smooth muscles in CVDs.…”