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Abstract Type 2 diabetes represents an increasing health burden. Its prevalence is rising among younger age groups and differs among racial/ethnic groups. Little is known about its genetic basis, including whether there is a genetic basis for racial/ethnic disparities. We examined a multi-ethnic sample of 253 healthy children to evaluate associations between insulin-related phenotypes and 142 ancestry-informative markers (AIMs), while adjusting for sex, age, Tanner stage, genetic admixture, total body fat, height and socio-economic status. We also evaluated the effect of measurement errors in the estimation of the individual ancestry proportions on the regression results. We found that European genetic admixture is positively associated with insulin sensitivity (SI), and negatively associated with the acute insulin response to glucose, fasting insulin levels and the homeostasis model assessment of insulin resistance. Our analysis revealed associations between individual AIMs on chromosomes 2, 8 and 15 and these phenotypes. Most notably, marker rs3287 at chromosome 2p21 was found to be associated with SI (p = 5.8 × 10-5). This marker may be in admixture linkage disequilibrium with nearby loci (THADA and BCL11A) that previously have been reported to be associated with diabetes and diabetes-related phenotypes in several genome-wide association and linkage studies. Our results provide further evidence that variation in the 2p21 region containing THADA and BCL11A is associated with type 2 diabetes. Importantly, we have implicated this region in the early development of diabetes-related phenotypes, and in the genetic aetiology of population differences in these phenotypes.

Although hyperlipidemia is traditionally considered a risk factor for type 2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL cholesterol (LDL-C) increases T2D risk. We thus sought to more comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D. Using data from the UK Biobank, we found that levels of circulating LDL-C were negatively associated with T2D prevalence (odds ratio 0.41 [95% CI 0.39, 0.43] per mmol/L unit of LDL-C), despite positive associations of circulating LDL-C with HbA1c and BMI. We then performed the first genome-wide exploration of variants simultaneously associated with lower circulating LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n = 431,167) and the Global Lipids Genetics Consortium (n = 188,577), and data on T2D from the Diabetes Genetics Replication and Meta-Analysis consortium (n = 898,130). We identified 31 loci associated with lower circulating LDL-C and increased T2D, capturing several potential mechanisms. Seven of these loci have previously been identified for this dual phenotype, and nine have previously been implicated in nonalcoholic fatty liver disease. These findings extend our current understanding of the higher T2D risk among individuals with low circulating LDL-C and of the underlying mechanisms, including those responsible for the diabetogenic effect of LDL-C-lowering medications. [ABSTRACT FROM AUTHOR]

Type 2 diabetes is typified by insulin-resistance in adipose tissue, skeletal muscle, and liver, leading to chronic hyperglycemia. Additionally, obesity and type 2 diabetes are characterized by chronic low-grade inflammation. Membrane-associated RING-CH-1 (MARCH1) is an E3 ubiquitin ligase best known for suppression of antigen presentation by dendritic and B cells. MARCH1 was recently found to negatively regulate the cell surface levels of the insulin receptor via ubiquitination. This, in turn, impaired insulin sensitivity in mouse models. Here, we report that MARCH1-deficient (knockout; KO) female mice exhibit excessive weight gain and excessive visceral adiposity when reared on standard chow diet, without increased inflammatory cell infiltration of adipose tissue. By contrast, male MARCH1 KO mice had similar weight gain and visceral adiposity to wildtype (WT) male mice. MARCH1 KO mice of both sexes were more glucose tolerant than WT mice. The levels of insulin receptor were generally higher in insulin-responsive tissues (especially the liver) from female MARCH1 KO mice compared to males, with the potential to account in part for the differences between male and female MARCH1 KO mice. We also explored a potential role for MARCH1 in human type 2 diabetes risk through genetic association testing in publicly-available datasets, and found evidence suggestive of association. Collectively, our data indicate an additional link between immune function and diabetes, specifically implicating MARCH1 as a regulator of lipid metabolism and glucose tolerance, whose function is modified by sex-specific factors. [ABSTRACT FROM AUTHOR]

Lifestyle and genetic factors play a large role in the development of Type 2 Diabetes (T2D). Despite the important role of genetic factors, genetic information is not incorporated into the clinical assessment of T2D risk. We assessed and compared Whole Genome Regression methods to predict the T2D status of 5,245 subjects from the Framingham Heart Study. For evaluating each method we constructed the following set of regression models: A clinical baseline model (CBM) which included non-genetic covariates only. CBM was extended by adding the first two marker-derived principal components and 65 SNPs identified by a recent GWAS consortium for T2D (M-65SNPs). Subsequently, it was further extended by adding 249,798 genome-wide SNPs from a high-density array. The Bayesian models used to incorporate genome-wide marker information as predictors were: Bayes A, Bayes Cπ, Bayesian LASSO (BL), and the Genomic Best Linear Unbiased Prediction (G-BLUP). Results included estimates of the genetic variance and heritability, genetic scores for T2D, and predictive ability evaluated in a 10-fold cross-validation. The predictive AUC estimates for CBM and M-65SNPs were: 0.668 and 0.684, respectively. We found evidence of contribution of genetic effects in T2D, as reflected in the genomic heritability estimates (0.492±0.066). The highest predictive AUC among the genome-wide marker Bayesian models was 0.681 for the Bayesian LASSO. Overall, the improvement in predictive ability was moderate and did not differ greatly among models that included genetic information. Approximately 58% of the total number of genetic variants was found to contribute to the overall genetic variation, indicating a complex genetic architecture for T2D. Our results suggest that the Bayes Cπ and the G-BLUP models with a large set of genome-wide markers could be used for predicting risk to T2D, as an alternative to using high-density arrays when selected markers from large consortiums for a given complex trait or disease are unavailable. [ABSTRACT FROM AUTHOR]

Type 2 Diabetes (T2D) is a chronic disease arising from the development of insulin absence or resistance within the body, and a complex interplay of environmental and genetic factors. The incidence of T2D has increased throughout the last few decades, together with the occurrence of the obesity epidemic. The consideration of variants identified by GenomeWide Association Studies (GWAS) into risk assessment models for T2D could aid in the identification of at-risk patients who could benefit from preventive medicine. In this study, we build several risk assessment models, evaluated with two different classification approaches (Logistic Regression and Neural Networks), to measure the effect of including genetic information in the prediction of T2D. We used data from to the Original and the Offspring cohorts of the Framingham Heart Study, which provides phenotypic and genetic information for 5245 subjects (4306 controls and 939 cases). Models were built by using several covariates: gender, exposure time, cohort, body mass index (BMI), and 65 SNPs associated to T2D. We fitted Logistic Regressions and Bayesian Regularized Neural Networks and then assessed their predictive ability by using a ten-fold cross validation. We found that the inclusion of genetic information into the risk assessment models increased the predictive ability by 2%, when compared to the baseline model. Furthermore, the models that included BMI at the onset of diabetes as a possible effector, gave an improvement of 6% in the area under the curve derived from the ROC analysis. The highest AUC achieved (0.75) belonged to the model that included BMI, and a genetic score based on the 65 established T2D-associated SNPs. Finally, the inclusion of SNPs and BMI raised predictive ability in all models as expected; however, results from the AUC in Neural Networks and Logistic Regression did not differ significantly in their prediction accuracy. [ABSTRACT FROM AUTHOR]

Although over 60 single nucleotide polymorphisms (SNPs) have been identified by meta-analysis of genome-wide association studies for type-2 diabetes (T2D) among individuals of European descent, much of the genetic variation remains unexplained. There are likely many more SNPs that contribute to variation in T2D risk, some of which may lie in the regions surrounding established SNPs - a phenomenon often referred to as allelic heterogeneity. Here, we use the summary statistics from the DIAGRAM consortium meta-analysis of T2D genome-wide association studies along with linkage disequilibrium patterns inferred from a large reference sample to identify novel SNPs associated with T2D surrounding each of the previously established risk loci. We then examine the extent to which the use of these additional SNPs improves prediction of T2D risk in an independent validation dataset. Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p<5×10−8). Using a less stringent threshold (p<5×10−4), we identify 34 additional loci with multiple associated SNPs. The addition of these SNPs slightly improves T2D prediction compared to the use of only the respective lead SNPs, when assessed using an independent validation cohort. Our findings suggest that some currently established T2D risk loci likely harbor multiple polymorphisms which contribute independently and collectively to T2D risk. This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D. [ABSTRACT FROM AUTHOR]

Background Genome-wide association studies ( GWAS) have identified single nucleotide polymorphisms ( SNPs) associated with type 2 diabetes ( T2D), mainly among individuals of European ancestry. In the present study, we examined the frequency of these SNPs and their association with T2D-related traits in an Alaska Native study population with a historically low prevalence of T2D. We also investigated whether dietary characteristics that may protect against T2D, such as n-3 polyunsaturated fatty acid ( PUFA) intake, modify these associations. Methods In 1144 Yup'ik people, we examined 17 SNPs repeatedly identified in GWAS for individual and cumulative associations with T2D-related traits. Cumulative associations were evaluated using a genetic risk score ( GRS) calculated by summing risk alleles. Associations were tested for interactions with sex, body mass index ( BMI), and n-3 PUFA intake. Results The rs7754840 SNP in CDKAL1 is significantly associated with HbA1c ( P = 0.00091). The rs5015480 SNP near HHEX is significantly associated (in opposite direction to that in Europeans) with a combined fasting glucose ( FG) and HbA1c measure ( P = 0.00046) and with homeostatic model assessment of β-cell function ( HOMA- B; P = 0.0014). The GRS is significantly associated with FG and combined FG and HbA1c only when the HHEX SNP is dropped from the GRS. Associations are not modified by BMI or n-3 PUFA intake. Conclusion Our results highlight the potential importance of CDKAL1 and HHEX in glucose homeostasis in this Alaska Native population with a low prevalence of T2D, and suggest that these loci should be examined in greater detail in this population. [ABSTRACT FROM AUTHOR]

Different populations suffer from different rates of obesity and type-2 diabetes (T2D). Little is known about the genetic or adaptive component, if any, that underlies these differences. Given the cultural, geographic, and dietary variation that accumulated among humans over the last 60,000 years, we examined whether loci identified by genome-wide association studies for these traits have been subject to recent selection pressures. Using genome-wide SNP data on 938 individuals in 53 populations from the Human Genome Diversity Panel, we compare population differentiation and haplotype patterns at these loci to the rest of the genome. Using an 'expanding window' approach (100-1,600 kb) for the individual loci as well as the loci as ensembles, we find a high degree of differentiation for the ensemble of T2D loci. This differentiation is most pronounced for East Asians and sub-Saharan Africans, suggesting that these groups experienced natural selection at loci associated with T2D. Haplotype analysis suggests an excess of obesity loci with evidence of recent positive selection among South Asians and Europeans, compared to sub-Saharan Africans and Native Americans. We also identify individual loci that may have been subjected to natural selection, such as the T2D locus, HHEX, which displays both elevated differentiation and extended haplotype homozygosity in comparisons of East Asians with other groups. Our findings suggest that there is an evolutionary genetic basis for population differences in these traits, and we have identified potential group-specific genetic risk factors. [ABSTRACT FROM AUTHOR]