Association Study Between Genes On The Lipid Metabolism Pathway And Inflammatory Pathway With Coronary Artery Disease

[Background]Coronary atherosclerotic disease (CAD), as the most common type of heart disease, is very dangerous for human health. As a kind of complex disease, CAD results from the interaction of a number of susceptibility genes and environmental factors. With the discovery of more and more susceptibility genes, there form many kinds of hypothesis about the pathogenesis of CAD. At present, fatty infiltration hypothesis and inflammatory hypothesis are most accepted by researchers.In my research,7 candidate susceptibility genes were selected. LRP5 (low density lipoprotein receptor-related protein 5), LRP6 (low density lipoprotein receptor-related protein 6), APOC1 (Apolipoprotein C-I), ABCG1 (ATP-binding cassette, sub-family G, member 1) and PCSK9 (proprotein convertase subtilisin/kexin type 9), these five genes were related with lipid metabolism. TNFSF4 (Tumor necrosis factor superfamily, number 4) and TNFRSF4 (tumor necrosis factor receptor superfamily, member 4), these two genes were related with inflammation.[Objective]By applying population-based case-control study design, we used many kinds of statistical methods to explore the association between CAD and 5 candidate susceptibility genes (LRP5, LRP6, APOC1, ABCG1, PCSK9) in lipid metabolism pathway and the association between CAD and 2 candidate susceptibility genes (TNFSF4, TNFRSF4) in inflammatory pathway in Chinese Han population.[Methods]1. We collected 498 cases and 509 controls from Qilu Hospital of Shandong University and conducted statistical description of the clinical data of them.2.2 SNPs of LRP5 (rs41494349, rs3736228),9 SNPs of LRP6 (rs2160525, rs2284396, rs4477532, rs2302685, rs7305037, rs12823243, rs1181333, rs11054731, rs17848270),1 SNP of APOC1 (rs4420638),4 SNPs of ABCG1 (rs4148082, rs1893590, rs1378577, rs1044317),3 SNPs of PCSK9 (rs572512, rs2483205, rs2495477),5 SNPs of TNFSF4 (rs1234314, rs45454293, rs3850641, rs1234313, rs3861950) and 1 SNP of TNFRSF4 (rs2298212) were genotyped.3. We used Armitage trend test to detect the association between every single SNP and CAD.4. For every single gene, we put the SNPs selected from the gene and the confounding factors (sex, age, Body Mass Index, Systolic Blood Pressure, Diastolic Blood Pressure, Total Cholesterol, Triglyceride, Glucose) into logistic regression model. After eliminating the impact of confounding factors, we detected the association between every single SNP and CAD.5. For genes with more than 1 selected SNP, we used Genetics module of SAS 9.1.3 software to do haplotype analysis.6. For genes with more than 1 selected SNP, we used principal component-based logistic analysis to detect the association between every gene and CAD.7. We applied partial least squares path model to study the association between these 7 genes and CAD. This was done by using PLS-PM software package of R software.[Results] 1. The results of Armitage trend test suggested that, rs4420638 of APOC1 (P=0.0001), rs572512 of PCSK9 (P=0.0308) and rs3861950 of TNFSF4 (P=0.0324) these 3 SNPs were significantly associated with CAD.2. The results of logistic analysis adjusting confounding factors suggested that, rs41494349 of LRP5 (P=0.0372), rs4477532 of LRP6 (P=0.0130), rsl2823243 of LRP6 (P=0.0117), rs 11054731 of LRP6 (P=0.0024), rs4420638 of APOC1 (P=0.0021 and rs2483205 of PCSK9 (P=0.0402) these 6 SNPs were significantly associated with CAD.3. The results of haplotype analysis suggested that, there existed haplotypes which were significantly different between case and control in LRP6, ABCG1 and TNFSF4.4. The results of principal component-based logistic analysis suggested that, the first principal component of TNFSF4 has statistical significance (P=0.0236).5. The results of applying PLS-PM to detect the interaction between LRP5 and LRP6 suggested that, the path coefficient was significant between case and control (P=0.0099). The results of applying PLS-PM to detect the interaction between TNFSF4 and TNFRSF4 suggested that, the path coefficient was not significant between case and control (P=0.4455). The results of applying PLS-PM to detect the interactions between LRP5, LRP6, APOC1, ABCG1, PCSK9 this 5 genes and blood lipid (BL) suggested that, the path coefficients of LRP6->BL (P=0.0196) and PCSK9->BL (P=0.0392) were significant between case and control.[Conclusions]1. When applying principal component-based logistic analysis, TNFSF4 is associated with CAD, but LRP5, LRP6, ABCG1 and PCSK9 these 4 genes are not associated with CAD.2. The interaction between LRP5 and LRP6 is associated with CAD. The interaction between TNFSF4 and TNFRSF4 is not associated with CAD. In the 5 genes (LRP5, LRP6, APOC1, ABCG1, PCSK9) which are connected with lipid metabolism, LRP6 and PCSK9 are significantly associated with blood lipid between case and control.