Differences in mtDNA haplogroup distribution among 3 Jewish populations alter susceptibility to T2DM complications

BACKGROUND: Recent genome-wide association studies searching for candidate susceptibility loci for common complex diseases such as type 2 diabetes mellitus (T2DM) and its common complications have uncovered novel disease-associated genes. Nevertheless these large-scale population screens often overlook the tremendous variation in the mitochondrial genome (mtDNA) and its involvement in complex disorders. RESULTS: We have analyzed the mitochondrial DNA (mtDNA) genetic variability in Ashkenazi (Ash), Sephardic (Seph) and North African (NAF) Jewish populations (total n = 1179). Our analysis showed significant differences (p < 0.001) in the distribution of mtDNA genetic backgrounds (haplogroups) among the studied populations. To test whether these differences alter the pattern of disease susceptibility, we have screened our three Jewish populations for an association of mtDNA genetic haplogroups with T2DM complications. Our results identified population-specific susceptibility factors of which the best example is the Ashkenazi Jewish specific haplogroup N1b1, having an apparent protective effect against T2DM complications in Ash (p = 0.006), being absent in the NAF population and under-represented in the Seph population. We have generated and analyzed whole mtDNA sequences from the disease associated haplogroups revealing mutations in highly conserved positions that are good candidates to explain the phenotypic effect of these genetic backgrounds. CONCLUSION: Our findings support the possibility that recent bottleneck events leading to over-representation of minor mtDNA alleles in specific genetic isolates, could result in population-specific susceptibility loci to complex disorders

Gene-Gene Interactions Lead to Higher Risk for Development of Type 2 Diabetes in an Ashkenazi Jewish Population

Evidence 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.Logistic 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.These 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

Parental diabetes status reveals association of mitochondrial DNA haplogroup J1 with type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>Although mitochondrial dysfunction is consistently manifested in patients with Type 2 Diabetes mellitus (T2DM), the association of mitochondrial DNA (mtDNA) sequence variants with T2DM varies among populations. These differences might stem from differing environmental influences among populations. However, other potentially important considerations emanate from the very nature of mitochondrial genetics, namely the notable high degree of partitioning in the distribution of human mtDNA variants among populations, as well as the interaction of mtDNA and nuclear DNA-encoded factors working in concert to govern mitochondrial function. We hypothesized that association of mtDNA genetic variants with T2DM could be revealed while controlling for the effect of additional inherited factors, reflected in family history information.</p> <p>Methods</p> <p>To test this hypothesis we set out to investigate whether mtDNA genetic variants will be differentially associated with T2DM depending on the diabetes status of the parents. To this end, association of mtDNA genetic backgrounds (haplogroups) with T2DM was assessed in 1055 Jewish patients with and without T2DM parents ('DP' and 'HP', respectively).</p> <p>Results</p> <p>Haplogroup J1 was found to be 2.4 fold under-represented in the 'HP' patients (p = 0.0035). These results are consistent with a previous observation made in Finnish T2DM patients. Moreover, assessing the haplogroup distribution in 'DP' versus 'HP' patients having diabetic siblings revealed that haplogroup J1 was virtually absent in the 'HP' group.</p> <p>Conclusion</p> <p>These results imply the involvement of inherited factors, which modulate the susceptibility of haplogroup J1 to T2DM.</p

Predicting Diabetic Nephropathy Using a Multifactorial Genetic Model

AIMS: The tendency to develop diabetic nephropathy is, in part, genetically determined, however this genetic risk is largely undefined. In this proof-of-concept study, we tested the hypothesis that combined analysis of multiple genetic variants can improve prediction. METHODS: Based on previous reports, we selected 27 SNPs in 15 genes from metabolic pathways involved in the pathogenesis of diabetic nephropathy and genotyped them in 1274 Ashkenazi or Sephardic Jewish patients with Type 1 or Type 2 diabetes of >10 years duration. A logistic regression model was built using a backward selection algorithm and SNPs nominally associated with nephropathy in our population. The model was validated by using random "training" (75%) and "test" (25%) subgroups of the original population and by applying the model to an independent dataset of 848 Ashkenazi patients. RESULTS: The logistic model based on 5 SNPs in 5 genes (HSPG2, NOS3, ADIPOR2, AGER, and CCL5) and 5 conventional variables (age, sex, ethnicity, diabetes type and duration), and allowing for all possible two-way interactions, predicted nephropathy in our initial population (C-statistic = 0.672) better than a model based on conventional variables only (C = 0.569). In the independent replication dataset, although the C-statistic of the genetic model decreased (0.576), it remained highly associated with diabetic nephropathy (χ(2) = 17.79, p<0.0001). In the replication dataset, the model based on conventional variables only was not associated with nephropathy (χ(2) = 3.2673, p = 0.07). CONCLUSION: In this proof-of-concept study, we developed and validated a genetic model in the Ashkenazi/Sephardic population predicting nephropathy more effectively than a similarly constructed non-genetic model. Further testing is required to determine if this modeling approach, using an optimally selected panel of genetic markers, can provide clinically useful prediction and if generic models can be developed for use across multiple ethnic groups or if population-specific models are required

Post Genome-Wide Association Studies of Novel Genes Associated with Type 2 Diabetes Show Gene-Gene Interaction and High Predictive Value

Recently, several Genome Wide Association (GWA) studies in populations of European descent have identified and validated novel single nucleotide polymorphisms (SNPs), highly associated with type 2 diabetes (T2D). Our aims were to validate these markers in other European and non-European populations, then to assess their combined effect in a large French study comparing T2D and normal glucose tolerant (NGT) individuals. rs7903146 SNP, were combined (8.68-fold for the 14% of French individuals carrying 18 to 30 risk alleles with an allelic OR of 1.24). With an area under the ROC curve of 0.86, only 15 novel loci were necessary to discriminate French individuals susceptible to develop T2D. strongly associate with T2D in French individuals, and mostly in populations of Central European descent but not in Moroccan subjects. Genes expressed in the pancreas interact together and their combined effect dramatically increases the risk for T2D, opening avenues for the development of genetic prediction tests

Common variants in WFS1 confer risk of type 2 diabetes

We studied genes involved in pancreatic beta cell function and survival, identifying associations between SNPs in WFS1 and diabetes risk in UK populations that we replicated in an Ashkenazi population and in additional UK studies. In a pooled analysis comprising 9,533 cases and 11,389 controls, SNPs in WFS1 were strongly associated with diabetes risk. Rare mutations in WFS1 cause Wolfram syndrome; using a gene-centric approach, we show that variation in WFS1 also predisposes to common type 2 diabetes

Differences in mtDNA haplogroup distribution among 3 Jewish populations alter susceptibility to T2DM complications

Abstract Background Recent genome-wide association studies searching for candidate susceptibility loci for common complex diseases such as type 2 diabetes mellitus (T2DM) and its common complications have uncovered novel disease-associated genes. Nevertheless these large-scale population screens often overlook the tremendous variation in the mitochondrial genome (mtDNA) and its involvement in complex disorders. Results We have analyzed the mitochondrial DNA (mtDNA) genetic variability in Ashkenazi (Ash), Sephardic (Seph) and North African (NAF) Jewish populations (total n = 1179). Our analysis showed significant differences (p Conclusion Our findings support the possibility that recent bottleneck events leading to over-representation of minor mtDNA alleles in specific genetic isolates, could result in population-specific susceptibility loci to complex disorders.</p

The multifactorial model: ORs and 95% CI for different SNPs and interactions in the model (expressed in logarithmic form).

<p>For the exact values see estimates in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018743#pone-0018743-t003" target="_blank">Table 3</a>. All variables, single or interactions, contribute to the model significantly, but in different ways.</p

ROC Curve and area under the curve (C Statistic) for the “full” model in the replication dataset (dotted line; C = 0.576).

<p>The ROC curve and C statistic for the same model in the original population (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018743#pone-0018743-g001" target="_blank">Figure 1A</a>) is shown for comparison (solid line).</p

Model parameters with and without genetic factors.

<p>1– The intercept and the predictor variables in the model. – see <i>Statistical Analysis and Modeling</i> section for description of how the variables were coded.</p><p>2– Binary logit regression estimates for the parameters in the model. In the logistic regression equation log[p/(1-p)] =  <i>a+βx</i> where p is the probability that nephropathy  =  1, the estimate of each variable contributes to β.</p><p>3– Standard errors of the individual regression coefficients.</p><p>4– Test statistic; the squared ratio of the Estimate to the SE of the respective predictor.</p><p>5- The probability that a particular Chi-Square test statistic (1 df) is as extreme as, or more so, than what has been observed under the null hypothesis; the null hypothesis is that all of the regression coefficients in the model are equal to zero. The numbers in the column are the associated p-values.</p><p>6– The logistic regression estimate when all variables in the model are evaluated at zero. In the above equation intercept contributes to the <i>α</i>-coefficient.</p