ACE2 Gene Polymorphism And Coronary Heart Disease / Myocardial Infarction Association Studies And Genome-wide Association Study Pathway Analysis Methods

Association study of ACE2 gene polymorphisms with coronary heart disease and myocardial infarction in Chinese Han populationResults are accumulating that ACE2 (angiotensin I-converting enzyme 2) might act as a protective protein for cardiovascular diseases; however, only a few studies in human populations have been carried out. This prompted us to perform a case-control study to investigate the relationship of ACE2 polymorphisms with CHD (coronary heart disease) and Ml (myocardial infarction). Three single nucleotide polymorphisms in the ACE2 gene (1075A/G, 8790A/G and 16854G/C) were genotyped by PCR-RFLP (restriction-fragment-length polymorphism) in 811 patients with CHD (of which 508 were patients with MI) and 905 normal controls in a Chinese population. The polymorphisms were in linkage disequilibrium (r~2 =0.854-0.973). Analyses were conducted by gender, because the ACE2 gene is on the X chromosome. In females, an association was detected with MI for 1075A/G (P=0.026; odds ratio=1.98) and 16854G/C (P=0.028; odds ratio=1.97) in recessive models after adjusting for covariates. In male subjects, two haplotypes (AAG and GGC) were common in frequency. In male subjects not consuming alcohol, the haplotype GGC was associated with a 1.76-fold risk of CHD [95% CI (confidence interval), 1.15-2.69; P=0.007] and a 1.77-fold risk of MI (95% CI, 1.12-2.81; P=0.015) with environmental factors adjusted, when compared with the most common haplotype AAG. In conclusion, the results of the present study indicate that common genetic variants in the ACE2 gene might impact on Ml in females, and may possibly interact with alcohol consumption to affect the risk of CHD and MI in Chinese males. Variable Set Enrichment Analaysis for Testing Pathway Significance in Genome-wide Association StudiesThe genome-wide association (GWA) approach is revolutionary in the genetic investigation of complex diseases. Currently, single locus analysis is generally assumed in GWAS and reports single-nucleotide polymorphisms (SNPs) and their neighboring genes with the strongest evidence of association. This strategy is limited since there are too much variables in the data, and the background noise is high. As a complementary approach, gene set enrichment analysis (GSEA), which evaluates the significance of biologically plausible pathways, was borrowed by Wang et. al. from gene expression studies. They used the maximum SNP association statistics from a gene as the score to measure association between the gene and the disease of interest. Even thought reasonable, such a choice is affected by the gene sizes and local linkage equilibrium within genes. Calculated on the basis of such gene scores, the summary statistics for gene sets tends to be biased. We propose a variable set enrichment analysis (VSEA) method to utilize multiple seemingly more reasonable algorithesms for gene score calculation, endeavoring to make GSEA method more suited in the GWA setting. The VSEA approach is tested by simulation studies and applied to a small scale GWA data set (LVH pilot study). Results show that, with the new adjusted gene scores, VSEA based on CHI2 and related algorithms have substantial improvement in terms of power compared to Wang's GSEA; Some other algorithms (e.g., principal component analysis based methods) are also potentially more effective. VSEA might complement the traditional methods for GWA analysis and provide additional insights into the interpretation of GWA data on complex diseases.