Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data.

Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene-gene interactions involved in these susceptible pathways with their protein-protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.

Association between apolipoprotein B EcoRI polymorphisms and coronary heart disease : A meta-analysis.

OBJECTIVE: The study was carried out to examine the association between apolipoprotein B (ApoB) EcoRI polymorphism (E- vs. E+) (rs1042031) and coronary heart disease (CHD) risk by systematically analyzing multiple independent studies. METHODS: The Hardy-Weinberg equilibrium (HWE) test was applied to assess genotype frequency distribution in healthy controls. The quality of the studies was assessed using the Newcastle-Ottawa scale (NOS). Power analysis was performed with Power and Precision V4 software. A fixed effect model was used because no deviation from homogeneity was found. Publication bias was quantified and examined with Begg's funnel plot test and Egger's linear regression method. The meta-analysis was performed by Stata 12.0 software. RESULTS: A total of 21 eligible association studies were merged in this meta-analysis and the pooled sample consisted of 2994 CHD patients and 3258 healthy controls. No significant publication bias and heterogeneity were observed in these studies. The pooled odds ratio (OR) and 95% confidence interval (CI) of E- vs. E+ were 1.18 (1.06-1.32). The pooled OR (95% CI) of E+ E- + E- E- vs. E+ E+ was 1.18 (1.04-1.34). CONCLUSIONS: This meta-analysis indicated that ApoB EcoRI confers a moderate risk for CHD and the E- allele at this locus might be a susceptibility allele for the development of CHD.

Association Between Apolipoprotein B XbaI Polymorphism and Coronary Heart Disease in Han Chinese Population: A Meta-Analysis.

OBJECTIVE: To examine the association between apolipoprotein B (ApoB) XbaI polymorphisms (rs693) and coronary heart disease (CHD) risk among the Han Chinese population by systematically analyzing multiple independent studies. METHODS: The Hardy-Weinberg equilibrium test was applied to check genetic equilibrium among genotypes for the selected literatures. The quality of the studies was assessed by using the NewcastleOttawa Scale. Power analysis was performed with Power and Precision V4 software. A fixed or random effect model was used on the basis of heterogeneity. Publication bias was quantified and examined with Begg's funnel plot test and Egger's linear regression test. The meta-analysis was performed by Stata 12.0 software. RESULTS: A total of 10 eligible association studies were included in this meta-analysis, and the pooled sample consisted of 1195 CHD patients and 1178 health controls. No consistent inference regarding publication bias for the included studies was obtained by using the two above-mentioned methods. The pooled odds ratios (95% confidence intervals [CIs]) for X(-) versus X(+) allele and X(+)X(+) + X(+)X(-) versus X(-)X(-) genotype were 2.25 (1.40-3.62) and 2.21 (1.39-3.50), respectively. CONCLUSIONS: This meta-analysis indicated that ApoB XbaI allele confers a significant risk towards the development of CHD among the Han Chinese population.

[A novel biological pathway expansion method based on the knowledge of protein-protein interactions].

Biological pathways have been widely used in gene function studies; however, the current knowledge for biological pathways is per se incomplete and has to be further expanded. Bioinformatics prediction provides us a cheap but effective way for pathway expansion. Here, we proposed a novel method for biological pathway prediction, by intergrating prior knowledge of protein?protein interactions and Gene Ontology (GO) database. First, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to which the interacting neighbors of a targe gene (at the level of protein?protein interaction) belong were chosen as the candidate pathways. Then, the pathways to which the target gene belong were determined by testing whether the genes in the candidate pathways were enriched in the GO terms to which the target gene were annotated. The protein?protein interaction data obtained from the Human Protein Reference Database (HPRD) and Biological General Repository for Interaction Datasets (BioGRID) were respectively used to predict the pathway attribution(s) of the target gene. The results demanstrated that both the average accuracy (the ratio of the correctly predicted pathways to the totally pathways to which all the target genes were annotated) and the relative accuracy (of the genes with at least one annotated pathway being successful predicted, the percentage of the genes with all the annotated pathways being correctly predicted) for pathway predictions were increased with the number of the interacting neighbours. When the number of interacting neighbours reached 22, the average accuracy was 96.2% (HPRD) and 96.3% (BioGRID), respectively, and the relative accuracy was 93.3% (HPRD) and 84.1% (BioGRID), respectively. Further validation analysis of 89 genes whose pathway knowledge was updated in a new database release indicated that 50 genes were correctly predicted for at least one updated pathway, and 43 genes were accurately predicted for all the updated pathways, giving an estimate of the relative accuracy of 86.0%. These results demonstrated that the proposed approach was a reliable and effective method for pathway expansion.

Pathway-based analysis of the hidden genetic heterogeneities in cancers.

Many cancers apparently showing similar phenotypes are actually distinct at the molecular level, leading to very different responses to the same treatment. It has been recently demonstrated that pathway-based approaches are robust and reliable for genetic analysis of cancers. Nevertheless, it remains unclear whether such function-based approaches are useful in deciphering molecular heterogeneities in cancers. Therefore, we aimed to test this possibility in the present study. First, we used a NCI60 dataset to validate the ability of pathways to correctly partition samples. Next, we applied the proposed method to identify the hidden subtypes in diffuse large B-cell lymphoma (DLBCL). Finally, the clinical significance of the identified subtypes was verified using survival analysis. For the NCI60 dataset, we achieved highly accurate partitions that best fit the clinical cancer phenotypes. Subsequently, for a DLBCL dataset, we identified three hidden subtypes that showed very different 10-year overall survival rates (90%, 46% and 20%) and were highly significantly (P=0.008) correlated with the clinical survival rate. This study demonstrated that the pathway-based approach is promising for unveiling genetic heterogeneities in complex human diseases.