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QTL-based association analyses reveal novel genes influencing pleiotropy of metabolic syndrome (MetS).

ABSTRACT: Metabolic Syndrome (MetS) is a phenotype cluster predisposing to type 2 diabetes and cardiovascular disease. We conducted a study to elucidate the genetic basis underlying linkage signals for multiple representative traits of MetS that we had previously identified at two significant QTLs on chromosomes 3q27 and 17p12.We performed QTL-specific genomic and transcriptomic analyses 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 MetS 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, plasma lipid levels, and lipoprotein densities.Using an approach combining genomic, transcriptomic, and bioinformatic data we identified novel candidate genes for MetS.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC3769476 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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QTL-based association analyses reveal novel genes influencing pleiotropy of metabolic syndrome (MetS).

Zhang Y Y Kent J W JW Olivier M M Ali O O Broeckel U U Abdou R M RM Dyer T D TD Comuzzie A A Curran J E JE Carless M A MA Rainwater D L DL Göring H H H HH Blangero J J Kissebah A H AH

Obesity (Silver Spring, Md.) 20130529 10

<h4>Objective</h4>Metabolic Syndrome (MetS) is a phenotype cluster predisposing to type 2 diabetes and cardiovascular disease. We conducted a study to elucidate the genetic basis underlying linkage signals for multiple representative traits of MetS that we had previously identified at two significant QTLs on chromosomes 3q27 and 17p12.<h4>Design and methods</h4>We performed QTL-specific genomic and transcriptomic analyses in 1,137 individuals from 85 extended families that contributed to the ori ...[more]

PMID: 23418049

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Project description:Polychlorinated biphenyls (PCBs) and dioxin are persistent endocrine disrupting chemicals (EDCs) and have been associated with an increased risk of metabolic syndrome (MetS). The aim of this systematic review and meta-analysis was to assess the associations of PCBs and dioxin with MetS and its risk factors, including obesity, hypertriglyceridaemia (HTG), hypertension (HTN) and diabetes mellitus (DM). We searched three electronic databases for epidemiological studies concerning PCBs and dioxin with MetS published up to the end of 2023. Meta-analysis was performed for MetS itself and each of the MetS risks based on a random-effects meta-analysis model, and odds ratios (ORs) with 95% confidence intervals (CIs) were obtained. Publication bias was assessed based on Egger's test. Eleven studies were included from three databases up to 2023. There were 40,528 participants aged 18-89, where 18-100% of them were males, included in our meta-analysis. The meta-analysis results showed a strong association between PCB exposure and DM (OR = 3.593, 95% CI 2.566, 5.031), while most of the risk factors for MetS, including obesity (OR = 1.875, 95% CI 0.883, 3.979), HTN (OR = 1.335, 95% CI 0.902, 1.976) and HTG (OR = 1.611, 95% CI 0.981, 2.643), were weakly associated with PCB. Furthermore, both PCBs (OR = 1.162, 95% CI 0.994, 1.357) and dioxin (OR = 2.742, 95% CI 1.936, 3.883) were found to be weakly and strongly associated with MetS, respectively. Meta-regression analysis showed that DM in the Asian population is associated with PCB exposure, while HTG in the Northern American population is associated with PCB exposure. Our meta-analysis has demonstrated a strong relationship between DM and PCBs, while the relationship between PCBs with MetS and other risk factors is less pronounced. Additionally, MetS is weakly associated with dioxin exposure. To improve primary care outcomes, healthcare providers should consider incorporating the assessment of patients' risk of exposure to PCBs and dioxins into their evaluation procedures for more targeted medical interventions.

Dataset Information

QTL-based association analyses reveal novel genes influencing pleiotropy of metabolic syndrome (MetS).

Publications

QTL-based association analyses reveal novel genes influencing pleiotropy of metabolic syndrome (MetS).

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