Obesity results from the interaction of genetic and environmental factors. To search for sequence variants that affect variation in two common measures of obesity, weight and body mass index (BMI), both of which are highly heritable, we performed a genome-wide association (GWA) study with 305,846 SNPs typed in 25,344 Icelandic, 2,998 Dutch, 1,890 European Americans and 1,160 African American subjects and combined the results with previously published results from the Diabetes Genetics Initiative (DGI) on 3,024 Scandinavians. We selected 43 variants in 19 regions for follow-up in 5,586 Danish individuals and compared the results to a genome-wide study on obesity-related traits from the GIANT consortium. In total, 29 variants, some correlated, in 11 chromosomal regions reached a genome-wide significance threshold of P < 1.6 x 10(-7). This includes previously identified variants close to or in the FTO, MC4R, BDNF and SH2B1 genes, in addition to variants at seven loci not previously connected with obesity.

Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity

It has long been appreciated that there is a genetic component to type 2 diabetes, but progress in gene finding has been slow because the genetic component is complex. Accumulating data on various diabetes-related phenotypes suggest that so-called “type 2 diabetes” is likely a collection of many diseases due to varying but often overlapping underlying mechanisms. As such, the search for the once hoped-for simple genetic basis of type 2 diabetes has proved elusive.

This year marks the application of the next gene-finding tool to the study of type 2 diabetes, the genome-wide association study (GWA). This type of study is the logical extension of the candidate gene association study, an approach in which a few (1–20 or so) single nucleotide polymorphisms (SNPs) within a single gene are tested for association with the phenotype of interest. By that approach, the candidate gene is chosen based on biochemistry or physiology related to that phenotype. The candidate gene approach has identified some genes but has not yielded the definitive picture of the genetic contribution to type 2 diabetes (Table 1; reviewed in [1]).

In contrast, the GWA approach tests every gene by testing the association of SNPs in every known gene (∼100,000 SNPs) or in both known genes and in regions outside of genes throughout the genome (∼300,000 to 1 million). The GWA is therefore not biased by a priori assumptions based on prior observations of the phenotype (e.g., kinetics of insulin signaling, glucose-mediated insulin secretion, etc). Therefore, the strength of the GWA is that it has the potential to identify genes of high genetic effect that were previously unsuspected as candidates. The latter might be because little was known about the genes previously or because investigators simply had not addressed the action of the products of these genes …

https://diabetes.diabetesjournals.org/content/diabetes/56/12/2844.full.pdf

Genome-wide association: which do you want first: the good news, the bad news, or the good news?

Contemporary genetic association studies may test hundreds of thousands of genetic variants for association, often with multiple binary and continuous traits or under more than one model of inheritance. Many of these association tests may be correlated with one another because of linkage disequilibrium between nearby markers and correlation between traits and models. Permutation tests and simulation-based methods are often employed to adjust groups of correlated tests for multiple testing, since conventional methods such as Bonferroni correction are overly conservative when tests are correlated. We present here a method of computing P values adjusted for correlated tests (PACT) that attains the accuracy of permutation or simulation-based tests in much less computation time, and we show that our method applies to many common association tests that are based on multiple traits, markers, and genetic models. Simulation demonstrates that PACT attains the power of permutation testing and provides a valid adjustment for hundreds of correlated association tests. In data analyzed as part of the Finland–United States Investigation of NIDDM Genetics (FUSION) study, we observe a near one-to-one relationship (r2>.999) between PACT and the corresponding permutation-based P values, achieving the same precision as permutation testing but thousands of times faster.

/pdf/so-many-correlated-tests-so-little-time-rapid-adjustment-of-1neaey9ov9.pdf

So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests.

Although type 2 diabetes has been traditionally understood as a metabolic disorder initiated by insulin resistance, it has recently become apparent that an impairment in insulin secretion contributes to its manifestation and may play a prominent role in its early pathophysiology. The genetic dissection of Mendelian and, more recently, polygenic types of diabetes confirms the notion that primary defects in insulin synthesis, processing and/or secretion often give rise to the common form of this disorder. This concept, first advanced with the discovery and physiological characterisation of various genetic subtypes of MODY, has been extended to other forms of monogenic diabetes (e.g. neonatal diabetes). It has also led to the identification of common risk variants via candidate gene approaches (e.g. the E23K polymorphism in KCNJ11 or common variants in the MODY genes), and it has been validated by the description of the robust physiological effects conferred by polymorphisms in the TCF7L2 gene. More recently, the completion and integration of genome-wide association scans for this disease has uncovered a number of heretofore unsuspected variants, several of which also affect insulin secretion. This review provides an up-to-date account of genetic loci that influence risk of common type 2 diabetes via impairment of beta cell function, outlines their presumed mechanisms of action, and places them in the context of gene-gene and/or gene-environment interactions. Finally, a strategy for the analogous discovery of insulin resistance genes is proposed.

/pdf/newly-identified-loci-highlight-beta-cell-dysfunction-as-a-40tlrxqhtt.pdf

Newly identified loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes?

Cloned 20 years ago, GLUT2 is a facilitative glucose transporter in the liver, pancreas, intestine, kidney, and brain. It ensures large bidirectional fluxes of glucose in and out the cell due to it...

GLUT2 mutations, translocation, and receptor function in diet sugar managing.

More than 120 published reports have described associations between single nucleotide polymorphisms (SNPs) and type 2 diabetes. However, multiple studies of the same variant have often been discordant. From a literature search, we identified previously reported type 2 diabetes-associated SNPs. We initially genotyped 134 SNPs on 786 index case subjects from type 2 diabetes families and 617 control subjects with normal glucose tolerance from Finland and excluded from analysis 20 SNPs in strong linkage disequilibrium (r(2) > 0.8) with another typed SNP. Of the 114 SNPs examined, we followed up the 20 most significant SNPs (P < 0.10) on an additional 384 case subjects and 366 control subjects from a population-based study in Finland. In the combined data, we replicated association (P < 0.05) for 12 SNPs: PPARG Pro12Ala and His447, KCNJ11 Glu23Lys and rs5210, TNF -857, SLC2A2 Ile110Thr, HNF1A/TCF1 rs2701175 and GE117881_360, PCK1 -232, NEUROD1 Thr45Ala, IL6 -598, and ENPP1 Lys121Gln. The replication of 12 SNPs of 114 tested was significantly greater than expected by chance under the null hypothesis of no association (P = 0.012). We observed that SNPs from genes that had three or more previous reports of association were significantly more likely to be replicated in our sample (P = 0.03), although we also replicated 4 of 58 SNPs from genes that had only one previous report of association.

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Eboni Smith

Papers

Screening of 134 single nucleotide polymorphisms (SNPs) previously associated with type 2 diabetes replicates association with 12 SNPs in nine genes.