QTL-based association analyses reveal novel genes influencing pleiotropy of Metabolic Syndrome (MetS)
| Autor: | Zhang, Y., Kent, J. W., Olivier, M., Ali, O., Broeckel, U., Abdou, R. M., Dyer, T. D., Comuzzie, A., Curran, J. E., Carless, M. A., Rainwater, D. L., Göring, H. H. H., Blangero, J., Kissebah, A. H. |
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| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Adult
Male Genetic Linkage Transmembrane Activator and CAML Interactor Protein Quantitative Trait Loci Polymorphism Single Nucleotide Article Body Mass Index Cohort Studies Young Adult Adipocytes Cell Adhesion Chromosomes Human Humans Genetic Predisposition to Disease RNA Messenger Genetic Association Studies Metabolic Syndrome Gene Expression Profiling Computational Biology RNA-Binding Proteins Cell Differentiation Genetic Pleiotropy Middle Aged Pedigree Phenotype Haplotypes Body Composition Leukocytes Mononuclear Female Transcriptome |
| Zdroj: | Obesity (Silver Spring, Md.) |
| ISSN: | 1930-739X 1930-7381 |
| Popis: | Metabolic Syndrome (MetS) is a phenotype cluster predisposing to type 2 diabetes and cardiovascular diseases. In extended families of Northern European ancestry, we previously identified two significant QTLs 3q27 and 17p12 that were linked with multiple representative traits of MetS. To determine the genetic basis of these linkage signals, QTL-specific genomic and transcriptomic analyses were performed in 1,137 individuals from 85 extended families that contributed to the original linkage. We tested in SOLAR association of MetS phenotypes with QTL-specific haplotype-tagging SNPs as well as transcriptional profiles of peripheral blood mononuclear cells (PBMCs). SNPs significantly associated with phenotypes under the prior hypothesis of linkage mapped to seven genes at 3q27 and seven at 17p12. Prioritization based on biologic relevance, SNP association, and expression analyses identified two genes: insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) at 3q27 and tumor necrosis factor receptor 13B (TNFRSF13B) at 17p12. Prioritized genes could influence cell-cell adhesion and adipocyte differentiation, insulin/glucose responsiveness, cytokine effectiveness and plasma lipids and lipoprotein densities. In summary, our results combine genomic, transcriptomic, and bioinformatic data to identify novel candidate loci for MetS. |
| Databáze: | OpenAIRE |
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