Integromic Analysis of Genetic Variation and Gene Expression Identifies Networks for Cardiovascular Disease Phenotypes | Journal Scan
Can biological pathways related to cardiovascular disease (CVD) be identified through integration of genetic, expression, and phenotype data?
A CVD network was constructed using 1,512 single nucleotide polymorphisms (SNPs) associated with 21 CVD traits identified by genome-wide association studies (GWAS). Traits were crosslinked by virtue of their shared SNP associations. Whole blood gene expression in relation to these SNPs in 5,257 participants in the Framingham Heart Study was examined.
A total of 370 cis-eQTLs (SNPs associated with altered expression of nearby genes) and 44 trans-eQTLs (SNPs associated with altered expression of remote genes) were identified. The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, glucose, body mass index) in the same individuals, examples where the expression of eQTL genes were associated with these CVD phenotypes were identified. In addition, a subset of SNPs previously associated with CVD phenotypes in GWAS appeared to exert their function by altering expression of eQTL genes (e.g., LDLR, and PCSK7) that in turn may promote inter-individual variation in phenotypes.
Using a network approach to analyze CVD traits, complex networks of SNP-phenotype and SNP-transcript connections were identified. Integrating the CVD network with phenotypic data, biological pathways were identified that may provide insights into potential drug targets for treatment or prevention of CVD.
The clinical utility of GWAS findings related to CVD remains unclear. More accurate assessment of relevant pathways and risk may require integration of different databases. This study provides several examples of complex interactions between SNPs, transcripts, and phenotypes that are revealed through integration of the various data sets. Biological targets may be more accurately identified through these analyses.
Keywords: Blood Pressure, Blood Pressure Determination, Cardiovascular Diseases, Gene Expression, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Risk Factors, Lipids, Phenotype
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