Ranking candidate genes in rat models of type 2 diabetes

Effects of smoking and obesity on the association between CDH13 (rs3865188) and adiponectin among Korean men: the KARE study

Adiponectin is secreted exclusively by adipocytes. Adiponectin is regulated by obesity, smoking, and genetic factors, including CDH13, which may contribute to development of diseases such as cardiovascular disease. Therefore, we aim to explore the joint effect of smoking and obesity on the association between CDH13 (rs3865188) and adiponectin among Korean men. This study included 1,570 Korean men aged 40-69 years who participated in the KARE cohort study (community-based cohorts, South Korea) from 2001. Hypoadiponectinemia was defined as the lowest quartile of adiponectin. In this study, individuals with at least one at-risk allele, the T allele, had an increased risk for hypoadiponectinemia, particularly current smokers with at least one T allele together with obesity when compared to those without the T allele. In addition, individuals with the TT genotype of CDH13 rs3865188, as well as obesity, were significantly associated with a 1.8-fold (odds ratio (OR) = 9.4, 95% confidence interval (CI) = 4.4-19.6, P < 0.001) increased risk for hypoadiponectinemia compared to individuals with the TT genotype of CDH 13 rs3865188 and normal waist circumference (WC) (OR = 5.1, 95% CI = 3.5-7.6, P < 0.001). However, in the joint effect of smoking and obesity, current smokers with the TT genotype of CDH13 rs3865188, as well as obesity, were significantly associated with a 6.2-fold (OR = 24.2, 95% CI = 3.0-196.6, P < 0.001) increased risk for hypoadiponectinemia compared to nonsmokers with the TT genotype of CDH 13 rs3865188 with normal WC (OR = 3.9, 95% CI = 1.7-9.3, P < 0.001). This study suggested that the association between CDH13 and adiponectin can be modified by lifestyle factors, such as smoking and obesity, among Korean men.

Co-clustering phenome-genome for phenotype classification and disease gene discovery

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.