Elevated factor VII (FVII) level is a risk factor for coronary artery disease (CAD). We investigated the role of R353Q polymorphism in the F7 gene in 139 Indian families with CAD, comprising of 222 affected subjects, 105 unaffected subjects and 126 affected sibling pairs. Plasma per cent FVIIc activity (FVII.c activity) differed significantly across R353Q genotype ($P \lt 0.0001$). Frequency of subjects with RR and QQ genotypes were higher in 4th quartile and 1st quartile of FVII.c activity, respectively ($P \lt 0.0001$). F7 R353Q SNP was able to explain up to 7% of variation in FVII.c activity by regression analysis and an additive genetic component of variance of 28.04% by heritability analysis. Quantitative trait loci analysis showed suggestive linkage evidence of F7 SNP with per cent FVII.c activity (LOD score $-1.82$; $P = 0.002$). Individuals with RR and RQ genotypes carried an OR of 2.071 (95% c.i. = 1.506–2.850) and 2.472 (95% c.i. = 1.679–3.641), respectively, towards CAD risk. There was significant correlation of FVII.c activity with lipid markers, particularly among those with RR and RQ genotype after covariate adjustment. In conclusion, the F7 R353Q SNP appears to moderately influence plasma FVII.c activity and risk of CAD in Indians.

We investigated the promoter polymorphisms of the pituitary growth hormone gene (GH1) and exon 3 deletion polymorphism (GHRd3) in its receptor gene (GHR) in 299 angiographically proven patients with coronary artery disease (CAD) and 231 asymptomatic controls enrolled in the ongoing Indian Atherosclerosis Research Study. Real time PCR based analysis of the GHR variant showed significant association of the GHRd3 deletion allele with CAD (OR 0.48, 95% CI: 0.30–0.76, $P = 0.0014$) and a dominant model of inheritance (Akaike information criterion = 482). The deletion allele showed significant association with high plasma HDL-c levels ($P = 0.001$). Sequencing of the proximal promoter region of GH1 revealed 12 novel polymorphisms and a TAGA haplotype constituted by the functional SNPs rs2005171, rs11568828, rs2005172 and rs6171, that showed significant association with CAD alone (adjusted OR of 3.31 (95% CI = 1.33–8.29, $P = 0.011$) and in CAD patients with diabetes ($P = 0.019$). Mean standardized height was associated with three of the four haplotype-tagging SNPs in the cohort ($P \leq 0.03$). Eleven of the 12 polymorphic promoter SNPs contributed to 14.7% of variation in height in females in the whole dataset ($P = 0.029$). CAD patients with history of stroke exhibited marginally significantly lower mean height as compared to rest of the cohort ($P \lt 0.006$). In conclusion, genetic polymorphisms in the GHR gene and its ligand, GH1, may modulate the risk of CAD in the Asian Indian population.

Molecular mechanism underlying the patho-physiology of coronary artery disease (CAD) is complex. We used global expression profiling combined with analysis of biological network to dissect out potential genes and pathways associated with CAD in a representative case–control Asian Indian cohort. We initially performed blood transcriptomics profiling in 20 subjects, including 10 CAD patients and 10 healthy controls on the Agilent microarray platform. Data was analysed with Gene Spring Gx12.5, followed by network analysis using David v 6.7 and Reactome databases. The most significant differentially expressed genes from microarray were independently validated by real time PCR in 97 cases and 97 controls. A total of 190 gene transcripts showed significant differential expression (fold change > 2, P < 0.05) between the cases and the controls of which 142 genes were upregulated and 48 genes were downregulated. Genes associated with inflammation, immune response, cell regula- tion, proliferation and apoptotic pathways were enriched, while inflammatory and immune response genes were displayed as hubs in the network, having greater number of interactions with the neighbouring genes. Expression of 𝐸𝐺𝑅1/2/3, 𝐼𝐿8, 𝐶𝑋𝐶𝐿1, 𝑃𝑇𝐺𝑆2, 𝐶𝐷69, 𝐼𝐹𝑁𝐺, 𝐹𝐴𝑆𝐿𝐺, 𝐶𝐶𝐿4, 𝐶𝐷𝐶42, 𝐷𝐷𝑋58, 𝑁𝐹𝐾𝐵𝐼𝐷 and 𝑁𝑅4𝐴2 genes were independently validated; 𝐸𝐺𝑅1/2/3 and 𝐼𝐿8 showed >8-fold higher expression in cases relative to the controls implying their important role in CAD. In conclusion, global gene expression profiling combined with network analysis can help in identifying key genes and pathways for CAD.