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A strategy for analyzing gene-nutrient interactions in type 2 diabetes.

ABSTRACT: Type 2 diabetes mellitus (T2DM), like all chronic diseases, results from interactions between multiple genes and multiple environmental factors. Nevertheless, many research studies focus on either nutrition or genetic factors independently of each other. The challenges of analyzing gene-nutrient interactions in T2DM are the (i) genetic heterogeneity in humans, (ii) complexity of environmental factors, particularly dietary chemicals, and (iii) diverse physiologies that produce the same apparent disease. Many of these variables are not accounted for in the design or study of T2DM or, indeed, most chronic diseases, although exceptions are noteworthy. Establishing experimental paradigms to analyze the complexity of these interactions and physiologies is challenging, but possible. This article provides a strategy to extend nutrigenomic experimental strategies to include early environmental influences that may promote adult-onset disease.

SUBMITTER: Wise C

PROVIDER: S-EPMC2769961 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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A strategy for analyzing gene-nutrient interactions in type 2 diabetes.

Wise Carolyn C Kaput Jim J

Journal of diabetes science and technology 20090701 4

Type 2 diabetes mellitus (T2DM), like all chronic diseases, results from interactions between multiple genes and multiple environmental factors. Nevertheless, many research studies focus on either nutrition or genetic factors independently of each other. The challenges of analyzing gene-nutrient interactions in T2DM are the (i) genetic heterogeneity in humans, (ii) complexity of environmental factors, particularly dietary chemicals, and (iii) diverse physiologies that produce the same apparent d ...[more]

PMID: 20144318

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Project description:BackgroundIdentification of disease genes for Type 2 Diabetes (T2D) by traditional methods has yielded limited success. Based on our previous observation that T2D may result from disturbed protein-protein interactions affected through disrupting modular domain interactions, here we have designed an approach to rank the candidates in the T2D linked genomic regions as plausible disease genes.ResultsOur approach integrates Weight value (Wv) method followed by prioritization using clustering coefficients derived from domain interaction network. Wv for each candidate is calculated based on the assumption that disease genes might be functionally related, mainly facilitated by interactions among domains of the interacting proteins. The benchmarking using a test dataset comprising of both known T2D genes and non-T2D genes revealed that Wv method had a sensitivity and specificity of 0.74 and 0.96 respectively with 9 fold enrichment. The candidate genes having a Wv > 0.5 were called High Weight Elements (HWEs). Further, we ranked HWEs by using the network property-the clustering coefficient (Ci). Each HWE with a Ci < 0.015 was prioritized as plausible disease candidates (HWEc) as previous studies indicate that disease genes tend to avoid dense clustering (with an average Ci of 0.015). This method further prioritized the identified disease genes with a sensitivity of 0.32 and a specificity of 0.98 and enriched the candidate list by 6.8 fold. Thus, from the dataset of 4052 positional candidates the method ranked 435 to be most likely disease candidates. The gene ontology sharing for the candidates showed higher representation of metabolic and signaling processes. The approach also captured genes with unknown functions which were characterized by network motif analysis.ConclusionsPrioritization of positional candidates is essential for cost-effective and an expedited discovery of disease genes. Here, we demonstrate a novel approach for disease candidate prioritization from numerous loci linked to T2D.

Project description:BACKGROUND We investigated the association between 8 single-nucleotide polymorphisms (SNPs) at 3 genetic loci (CDKAL1, CDKN2A/2B and FTO) with type 2 diabetes (T2D) in a Uyghur population. MATERIAL AND METHODS A case-control study of 879 Uyghur patients with T2D and 895 non-diabetic Uyghur controls was conducted at the Hospital of Xinjiang Medical University between 2010 and 2013. Eight SNPs in CDKAL1, CDKN2A/2B and FTO were analyzed using Sequenom MassARRAY®SNP genotyping. Factors associated with T2D were assessed by logistic regression analyses. Gene-gene and gene-environment interactions were analyzed by generalized multifactor dimensionality reduction. RESULTS Genotype distributions of rs10811661 (CDKN2A/2B), rs7195539, rs8050136, and rs9939609 (FTO) and allele frequencies of rs8050136 and rs9939609 differed significantly between diabetes and control groups (all P<0.05). While rs10811661, rs8050136, and rs9939609 were eliminated after adjusting for covariates (P>0.05), rs7195539 distribution differed significantly in co-dominant and dominant models (P<0.05). In gene-gene interaction analysis, after adjusting for covariates the two-locus rs10811661-rs7195539 interaction model had a cross-validation consistency of 10/10 and the highest balanced accuracy of 0.5483 (P=0.014). In gene-environment interaction analysis, the 3-locus interaction model TG-HDL-family history of diabetes had a cross-validation consistency of 10/10 and the highest balanced accuracy of 0.7072 (P<0.001). The 4-locus interaction model, rs7195539-TG-HDL-family history of diabetes had a cross-validation consistency of 8/10 (P<0.001). CONCLUSIONS Polymorphisms in CDKN2A/2B and FTO, but not CDKAL1, may be associated with T2D, and alleles rs8050136 and rs9939609 are likely risk alleles for T2D in this population. There were potential interactions among CDKN2A/2B (rs10811661) - FTO (rs7195539) or FTO (rs7195539)-TG-HDL-family history of diabetes in the pathogenesis of T2D in a Uyghur population.

Project description:BackgroundEvidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.Methods/principal findingsLogistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7-5.3]; P<or=0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7-3.4; P<or=0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4-2.8; P<or=0.0001] and 2.3 [95% CI = 1.2-4.4; P<or=0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.ConclusionsThese results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P<or=0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk.

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A strategy for analyzing gene-nutrient interactions in type 2 diabetes.

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A strategy for analyzing gene-nutrient interactions in type 2 diabetes.

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