Printer Friendly

Genetics of type 2 diabetes.

Type 2 diabetes (T2D) [2] is a common complex disorder with an increasing prevalence worldwide. In 2010 it was estimated that 6.6% of the world population of individuals 20-79 years old have diabetes, and that T2D constitutes approximately 90% of diabetes cases (1). This number is expected to increase epidemically as a consequence of an aging population and changes in lifestyle.

Prevalence and Heritability of T2D

The risk of developing T2D is determined by both genetic and environmental factors. The genetic component can be analyzed by comparing the risk of developing disease between relatives of patients with T2D and the background population, often referred to as sibling relative risk ([[lambda].sub.S]), which is around 3 in most populations (2).

The lifetime risk of developing T2D is 40% for individuals who have 1 parent with T2D and almost 70% if both parents are affected (3, 4). Interestingly, the risk is higher if the mother, rather than the father, is affected (4). In addition, the concordance rate of T2D in monozygotic twins is about 70% whereas the concordance in dizygotic twins is only 20%-30% (5, 6). Large differences in prevalence between ethnic groups also exist and seem to depend on genetic factors. In Sweden, immigrants from the Middle East have a 2- to 3-fold increased risk of T2D compared to native Swedes. In addition, these immigrants seem to have a slightly different form of diabetes with an earlier onset and lower C-peptide concentrations than Swedish patients. It is also more common for patients from the Middle East to have first-degree relatives with T2D (7).

The Role of the Environment

Although genetic factors are important in T2D, it is obvious that environmental factors play a key role in the development of the disease. The change toward a more affluent Western lifestyle that has taken place during the last 50 years has started a worldwide epidemic increase in the prevalence of T2D and obesity defined by body mass index (BMI in kg/[m.sup.2]). It is estimated that nearly 80% of individuals older than 15 years in the US and more than 60% of the same population in the UK are overweight (BMI >25 kg/[m.sup.2]), and that half of these individuals are obese (BMI >30 kg/[m.sup.2]) (8). This has happened in a relatively short period, most likely due to changes in diet and physical activity, while our genes have not changed. This does not diminish the role of genes in diabetes development, because our response to the changed environment is also genetically determined. Affluence is not a problem per se, the problem is that humans seem to be programmed to over consume. One likely reason is that genetic selection has favored energy-preserving genotypes; for individuals living in an environment with a scarce and unstable food supply, such as hunters and nomads, maximizing energy storage would mean maximizing probability of survival (9). In the modern Western culture individuals with these genotypes are at increased risk of obesity and these genes are therefore also candidates for increasing risk ofT2D.

Linkage Analysis

The goal of genetic mapping is to identify variants in the genome that increase susceptibility to a disease or affect a physiological trait. The methods used to map disease-causing genes have evolved rapidly in the last decades. The traditional method of mapping disease genes is to use the long stretches of linkage disequilibrium (LD) in affected families by performing linkage analysis. By genotyping about 400-500 genetic markers, disease loci can be mapped on a genomewide level. Finding that affected family members share a certain marker that is identical by descent, i.e., identical because it was inherited from the same parent, more often than expected by chance, is evidence that a disease-causing variant is in LD with the genotyped marker. Although this strategy has been very successful for mapping genetic diseases with strong penetrance and a known inheritance mode, it has been less useful for identifying genes that cause complex diseases, such as T2D. Although great efforts have been put into linkage studies ofT2D, only 2 genes have been reported to have been identified by linkage: calpain 10 (CAPN10) [3] and transcription factor 7-like 2 (T-cell specific, HMG-box) (TCF7L2) (Table 1 and Table 1 in the Data Supplement that accompanies the online version of this article at http://www.clinchem.org/content/vol57/issue2).

In the case of TCF7L2, a T2D locus was mapped to chromosome 10q in both an Icelandic and a Mexican-American population (10, 11). This region was later fine-mapped in the Icelandic population by use of 228 microsatellite markers covering a 10.5-Mbp region, pinpointing the locus to intron 3 of the TCF7L2 gene (12). The association between T2D and a number of single-nucleotide polymorphisms (SNPs) in the TCF7L2 gene has since been confirmed in numerous studies in different ethnic groups. The risk allele confers a relative risk of approximately 1.4 compared to homozygous carriers of the nonrisk allele, making this the strongest association with T2D by far (13).

The mechanisms by which TCF7L2 affects diabetes susceptibility are still not completely understood. The transcription factor-7-like 2, encoded by TCF7L2, is part of a bipartite transcription factor, which also included [beta]-catenin, that is a key effector in the Wnt-signaling pathway. In a study of Scandinavian individuals, we showed that the T-allele of SNP rs7903146 is associated with riskofT2D, impaired insulin secretion, incretin effects, and an enhanced rate of hepatic glucose production (14). We also found that homozygous carriers of the risk allele had a 5-fold increase in TCF7L2 mRNA expression in their islets (14). In addition, TCF7L2 has been suggested to regulate proglucagon gene expression, and thus glucagon-like peptide 1 (GLP-1) synthesis, from intestinal endocrine L cells (15). However, studies in humans have shown that risk-allele carriers have concentrations of GLP-1 that are within reference intervals but have impaired insulin secretion in response to GLP-1 infusion, indicating that the effect lies at the level of GLP-1 action on [beta]-cells rather than on GLP-1 secretion (14, 16).

The other gene mapped by linkage analysis is a locus on chromosome 2 that was first mapped in 1996 by Hanis et al. (17). In 2000 the locus was fine mapped and the causative gene shown to be CAPN10, the gene for calpain 10, a cysteine protease with largely unknown functions in glucose metabolism (18). Despite a number of negative replication studies, several metaanalyses have shown consistent association of CAPN10 with T2D (19,20). Nevertheless, none of the large genomewide association studies (GWAS) has identified CAPN10 as being associated with T2D.

Candidate Gene Studies

Identification of disease genes can also be made on the basis of association testing in populations rather than in families. Because of the short LD stretches in unrelated individuals, a very large number of markers are needed to perform mapping on a genomewide level. Until recently, the only feasible strategy was to study candidate genes that had high probability of affecting the studied trait based on the known function of the gene. Although a number of studies have reported association of functional or positional candidates, only 6 genes have been consistently associated with T2D: peroxisome proliferator-activated receptor gamma (PPARG), insulin receptor substrate 1 (IRS1), potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11), Wolfram syndrome 1 (wolframin) (WFS1), HNF1 homeobox A (HNF1A), and HNF1 homeobox B (HNF1B) (Table 1).

The PPARG gene, encoding the nuclear receptor PPAR-[gamma], was the first gene reproducibly associated with T2D (21). The PPAR-[gamma] receptor is a molecular target for thiazolidinedione compounds, a class of insulin-sensitizing drugs used to treat T2D, a characteristic that makes this a very strong candidate gene. The gene expressed in adipose tissue has an extra exon B and a substitution of a proline for alanine at position 12 of this protein, which is seen in about 15% of the European population and has been shown to be associated with increased transcriptional activity, increased insulin sensitivity, and protection against T2D (21). Although the initial report was followed by a number of negative studies, in a family-based study, with the use of a transmission disequilibrium test, we observed excess transmission of the Pro allele to affected offspring (22). It soon became clear that most negative studies had been underpowered, and that combining the data from all published studies in a metaanalysis yielded strong support for association between the Pro12Ala variant and T2D (22-24).

IRS1 encodes a protein that is phosphorylated by insulin receptor tyrosine kinase and is essential to insulin function. The association between a Gly972Arg polymorphism and T2D was reported in 1993 (25) but could not be replicated consistently by subsequent studies. It was, however, found to be strongly associated with T2D in a recent GWAS that also showed an association with reduced basal concentrations of IRS1 protein and decreased insulin induction of IRS1associated phosphatidylinositol-3-OH kinase activity in human skeletal muscle biopsies (26).

Common Variants in Genes Causing Monogenic Disorders Increase Risk of T2D

KCNJ11 encodes a protein, Kir6.2, that together with SUR1 (sulfonylurea receptor 1) forms an ATP-sensitive potassium channel that regulates membrane potential, and thereby glucose-dependent insulin secretion, in pancreatic [beta]-cells. Activating mutations in this gene cause severe neonatal diabetes (27).A Glu23Lys polymorphism has been associated with modest impairment of insulin secretion and T2D (28). Large-scale studies and metaanalyses have confirmed the association and shown that the lysine variant increases activation of the channel by 2-fold, resulting in a 1.15 times higher risk of developing T2D (29-31). The association has also been confirmed in GWAS (32-34).

WFS1 encodes Wolframin, a protein that is defective in individuals suffering from the Wolfram syndrome, characterized by diabetes insipidus, juvenile diabetes, optic atrophy, and deafness. WFS1 was first suggested to be associated with T2D in a small family-based association study (35). The gene was later included as a candidate in a study of 1536 SNPs in 84 candidate genes. Of these 84 genes only WFS1 was associated with T2D and could be replicated in 9533 cases and 11 389 controls (36). The association has also been confirmed in a large metaanalysis comprising 14 000 cases and 16 000 controls (37).

Mutations in the HNF1A gene cause the most common form of monogenic diabetes, maturity onset diabetes of the young 3 (MODY3). It was long debated whether more common variants in the HNF1A gene could contribute to T2D (38, 39), but several pieces of evidence suggest that the variants can increase risk of T2D: common variants result in decreased transcriptional activity (39), they have been shown to predict future risk of T2D in large prospective studies (40), and they have been associated with T2D in a recent large GWAS metaanalysis [the Diabetes Genetics, Replication and Metaanalysis (DIAGRAM)-plus, consisting of the DIAGRAM consortium complemented with additional GWAS cohorts from Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Europe (deCODE) genetics, the Diabetes Gene Discovery Group, the Cooperative Health Research in the Region of Augsburg (KORA) group, the Rotterdam study, and the European Special Population Research Network (EUROSPAN)] (41).

HNF1B, also known as transcription factor 2, is another transcription factor that can heterodimerize with HNF1A. Mutations in the HNF1B gene cause MODY5, which in affected individuals is associated with early-onset diabetes and also urogenital and renal pathology. The HNF1B gene is important for pancreatic islet development and function and was first identified as a T2D gene in a study in which common variants in known MODY genes were tested for association with T2D (38). A second variant of this gene was found to be associated with T2D in a GWAS (42).

Whole-Genome Association Studies

Rapid improvement in high-throughput technology for SNP genotyping, which has allowed simultaneous genotyping of hundreds of thousands of SNPs, has opened new possibilities for association studies. The HapMap project provided another important tool, and Demonstrated that genotyping of approximately 500 000 SNPs is enough to cover about 75% of the common variants (minor allele frequency >5%) in the genome.

An important breakthrough in our understanding of the genetics of T2D occurred in 2007 with the publication of the results of several GWAS in which DNA chips were used with >500 000 SNPs to perform genotyping in large numbers patients with T2D as well as healthy controls (32-34, 43, 44).

The results of the first GWAS on T2D were published in February 2007 by Sladek et al. (43). This GWAS conducted in 661 cases and 614 controls from France and led to the identification of 2 new diabetes loci: hematopoietically expressed homeobox (HHEX) and solute carrier family 30 (zinc transporter), member 8(SLC30A8). A few months later this study was followed by another 4 studies, all performed in European populations with a case-control setup, including the Wellcome Trust Case Control Consortium study in 1924 cases and 2938 controls from the UK (45); the Diabetes Genetics Initiative study in 1464 cases and 1467 controls from Sweden and Finland (32); the FUSION (Finland-United States Investigation of NIDDM [non-insulin-dependent diabetes mellitus]) genetics study in 1161 cases and 1174 controls from Finland (33); and a study by Steinthorsdottir et al. in 1399 cases and 5275 controls from the Icelandic population (44).

The first 3 studies shared results before publication and considered only positive results that were seen and replicated in all 4 studies. Two new loci were identified, cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)/cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4) (CDKN2A/CDKN2B) and insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) and several of the previously known loci were confirmed. CDK5 regulatory subunit associated protein 1-like 1 (CDKAL1) was independently identified as anew T2D locus in all 4 studies.

About the same time, the fat mass and obesity associated (FTO) gene was identified as a major susceptibility locus for obesity, and therefore indirectly also for T2D (46, 47). Because the effect of FTO on diabetes is through obesity, this gene was not detected in the GWAS of T2D that were matched on BMI.

Results of the first GWAS on T2D in non-European populations were published in 2008; both of these studies used multistage approaches (48, 49). Yasuda et al. first genotyped 100 000 SNPs in 187 T2D cases and 2 different control populations, each including 752 individuals, thereby identifying 2800 candidate SNPs for follow-up in replication cohorts. In total 19 930 individuals of Asian and European descent were genotyped for the strongest signals. Unoki et al. genotyped 268 068 SNPs in 194 cases and 1558 controls, and then the most highly associated SNPs in another 4924 individuals with T2D and 2618 controls, all of Japanese origin (48). In both studies the potassium voltage-gated channel, KQT-like subfamily, member 1 (KCNQ1) was identified, a new T2D gene that encodes the pore-forming [alpha] subunit of the [I.sub.Ks][K.sup.+] channel (or voltage-gated potassium channel), and results of both studies showed that KCNQ1 is expressed in the pancreas. The risk allele of the SNP rs2237895 has also been found to be associated with reduced insulin secretion in Swedish and Finnish cross-sectional and prospective cohorts, conferring a T2D risk of 1.23 in Swedes and Finns (50) compared to 1.31 in Asians (48).

Another study in an Asian population was performed by Tsai et al. in 2010 (51). These authors conducted a 2-stage GWAS, including 2798 cases and 2367 controls from a Han Chinese population, and identified 2 novel T2D loci: rs17584499 in, protein tyrosine phosphatase, receptor type, D (PTPRD) and rs391300 in serine racemase (SRR).

In 2009 Kong et al. performed a genomewide association study in the Icelandic population by analyzing parental-origin-specific effects, thereby identifying a new candidate SNP close to YY1 associated protein 1 (YY1AP1) (52). The marker rs2334499 was only weakly associated with T2D in a standard case-control analysis. However, when parental origin was taken into account the paternally inherited allele increased risk of T2D with genomewide significance. Interestingly, the maternally inherited allele showed nominally significant evidence of a protective effect on T2D.

Large-Scale Metaanalysis Studies

The first GWAS metaanalysis ofT2D was performed by the DIAGRAM consortium in 2008 (53). The authors combined analysis from 3 GWAS, the Diabetes Genetics Initiative, FUSION, and Wellcome Trust Case Control Consortium, and extended SNP coverage by imputing untyped SNPs using a 3 stage approach. In the first stage 2 202 892 SNPs were tested for association followed by 69 SNPs tested in replication cohorts in stage 2, and finally 11 SNPs were followed up in the deCODE, KORA, Danish, HUNT (Nord-Trondelag Health Study), NHS (National Health Service), GEM (Genetics of Energy Metabolism) Consortium [CCC (Cambridgeshire Case-Control), EPIC (European Prospective Investigation into Cancer), ADDITION (Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen Detected Diabetes in Primary Care)/Ely, Norfolk)] and METSIM (Metabolic Syndrome in Men) study samples (effective sample size, 53 975) (53). The DIAGRAM consortium could thereby identify 6 novel loci, including JAZF zinc finger 1 (JAZF1), cell division cycle 123 homolog-calcium/calmodulin-dependent protein kinase ID (CDC123-CAMK1D), tetraspanin 8-leucine-rich repeat-containing G protein-coupled receptor 5 (TSPAN8-LGR5), thyroid adenoma associated (THADA), ADAM metallopeptidase with thrombospondin type 1 motif, 9 (ADAMTS9), and notch 2(NOTCH2) (Table 1).

Qi et al. identified a T2D-associated variant in the RNA binding motif, single stranded interacting protein 1(RBMS1) gene on chromosome 2 in a nested case-control study that combined 2 prospective studies in the discovery phase and 11 studies in the validation phase, reaching a total number of 13 462 cases and 76 787 controls (54).

In June 2010 the DIAGRAM consortium was complemented with additional GWAS cohorts from deCODE genetics, the Diabetes Gene Discovery group, the KORA group, the Rotterdam study, and EUROSPAN to form DIAGRAM plus, with a higher effective sample size (n = 22 044) (41).

A total of 2 426 886 SNPs were tested in stage 1, and those showing an association of P < [10.sup.-5] were selected for phase 2, in which in silico data from 3 GWAS (Atherosclerosis Risk in Communities study, Nurses' Health study, and Framingham Heart study) not included in the primary metaanalysis, for a maximum possible stage 2 sample size of 34 412 cases and 59 925 controls, all individuals of European descent. Fourteen loci reached genome wide significance: 2 previously identified genes, [melatonin receptor 1B (MTNR1B) and IRS1] and 12 novel loci [B-cell CLL/lymphoma 11A (zinc finger protein) (BCL11A); zinc finger, BED-type containing 3 (ZBED3); Kruppel-like factor 14 (KLF14); tumor protein p53 inducible nuclear protein 1 (TP53INP1); transducin-like enhancer of split 4 (TLE4); coiled-coil-helix-coiled-coil-helix domain containing 9 (CHCHD9); KCNQ1; ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 1(ARAP1); high mobility group AT-hook 2 (HMGA2); HNF1A; zinc finger, AN1-type domain 6 (ZFAND6); protein regulator of cytokinesis 1 (PRC1); and dual specificity phosphatase 9 (D17SP9)] (Table 1). MTNR1B had previously been identified by the Metaanalysis of Glucose and Insulin-Related Traits (MAGIC) consortium as a fasting-glucose locus but was also found to affect T2D (41).

The most recent MAGIC effort involved metaanalysis of 21 GWAS cohort data for 46 186 nondiabetic individuals of European decent, including fasting-glucose information (55). An initial 25 lead SNPs were followed up in 76 558 additional study participants, and 9 new loci were found to be associated (P < 5 x [10.sup.-8]) with fasting glucose (and/or HOMA-B) [adenylate cyclase 5 (ADCY5); MAP-kinase activating death domain (MADD); cryptochrome 2 (photolyase-like) (CRY2); adrenergic, alpha-2A-, receptor (ADRA2A); fatty acid desaturase 1 (FADS1); prospero homeobox 1 (PROX1); solute carrier family 2 (facilitated glucose transporter), member 2 (SLC2A2); GLIS family zinc finger 3 (GLIS3); and C2 calcium-dependent domain containing 4B (C2CD4B)] and 1 was found to be associated with fasting insulin and HOMA-IR concentrations [insulin-like growth factor 1 (somatomedin C) (IGF1)]. Five of these loci [glucokinase (hexokinase 4) (GCK); diacylglycerol kinase, beta 90kDa-transmembrane protein 195 (DGKB-TMEM195); glucokinase (hexokinase 4) regulator (GCKR); ADCY5; PROX1] were also associated with T2D risk when tested in 40 655 diabetic cases and 87 022 controls (Table 2).

FUNCTIONS OF ASSOCIATED GENES

The loci recently identified by GWAS and metaanalysis can be subgrouped on the basis of their association with phenotypes with a key role in T2D etiology.

INSULIN SECRETION

MTNR1B has been reported to be associated with fasting glucose and T2D risk itself in 3 publications based on GWAS (56-58). Melatonin works as a chronobiotic factor, adjusting the timing of the biological clock (59, 60). Its receptors are present in the pancreas, and melatonin is proposed to contribute to the nocturnal lowering of insulin in humans. Recently Lyssenko et al. have shown that the MTNR1B risk genotype is associated with impaired early insulin release to both oral and intravenous glucose and that insulin secretion deteriorates over time in the risk allele carriers (56).

Mulder et al. proposed a mechanism of altered melatonin signaling in T2D: the risk allele increases the mRNA concentration of MTNR1B in [beta]-cells, thereby decreasing the cAMP concentrations via inhibitory G proteins. In addition, the insulinotropic pressure (e.g., that exerted by GLP-1) may be diminished, resulting in a decrease in insulin secretion, contributing to increased fasting plasma glucose, lower circulating insulin concentrations, and increased risk of T2D (61).

Lyssenko et al. reported that genetic variants in the IGF2BP2, SLC30A8, and CDKN2A/CDKN2B gene/loci resulted in impaired [beta]-cell function over time in a prospective study (62). Grarup et al. reported that variants near the JAZF1, CDC123/CAMK1D, and TSPAN8 loci are associated with impaired glucose-stimulated insulin secretion (63).

The CDKAL1 gene is expressed both in skeletal muscle and pancreatic islets but has a largely unknown function (33,34). However, homozygous risk allele carriers were found to have 20% reduced insulin response, suggesting that the risk to T2D is conferred through low insulin secretion.

The recently identified genes from the MAGIC consortium were replicated in a white cohort (European and US; n = 29 084) by Ingelson et al. (64), who found that fasting-glucose-increasing alleles of the MADD, gastric inhibitory polypeptide receptor (GIPR), GCK, FADS, DGKB, PROX1, TCF7L2, SLC30A8, and C2CD4B were associated with either abnormal insulin processing or secretion, whereas GCKR and IGF1 were associated with oral glucose tolerance test-based disposition indices and [beta]-cell function (64). The DIAGRAM plus consortium observed that the T2D risk alleles at PPARG, FTO, IRS1, and the newly discovered gene KLF14, were associated with higher fasting insulin, with primary effect on insulin action, whereas 3 loci (TCF7L2, ARAP1, and CDKAL1) were found to be associated with reduced fasting insulin suggestive of [beta]-cell dysfunction (41).

The ADRA2A locus is associated with impaired insulin granule docking and reduced [beta]-cell exocytosis in congenic GK (Goto-Kakizaki) rats (65). Rosengren et al. reported that human carriers of the ADRA2A risk variant (rs553668) have reduced fasting insulin and decreased insulin secretion (65) as a consequence of increased expression of the ADRA2 receptor in pancreatic islets. It is well known that epinephrine excess can suppress insulin secretion and cause diabetes. Pretreatment of human islets with the ADRA2-blocking agent yohimbine normalized insulin secretion in risk genotype carriers.

INSULIN RESISTANCE

Insulin resistance is a condition in which peripheral tissues fail to respond adequately to insulin. The healthy [beta]-cell can compensate by producing more insulin; in genetically predisposed individuals [beta]-cell may not be able to do so, and diabetes may develop.

GCKR, first identified in the Diabetes Genetics Initiative study (32) along with IGF1 variants (rs780094 and rs35767), was found in the MAGIC study to be associated with fasting insulin and higher insulin resistance (HOMA-IR) (55). In a metaanalysis study by Orho-Melander et al. (66) with more than 45 000 study participants, the GCKR locus (rs780094) was found to show opposite associations with fasting plasma glucose and triglyceride concentrations along with its association with C-reactive protein concentrations. IGF1 codes for insulin-like growth factor 1, and a null mutation for IGF1 has previously been correlated with improper glucose homeostasis with resistance to insulin and high concentrations of circulating insulin (67). Recently the DIAGRAM-plus consortium found an association of novel T2D loci KLF14 (rs972283 G allele) and insulin resistance (HOMA-IR) in addition to 2 previously known T2D/obesity loci, FTO and PPARG (41). KLF14 (a widely expressed, intronless member of the Kruppel-like family of transcription factors) is maternally expressed, and the variant at this locus appears to have a primary effect on insulin action (68).

OBESITY

Obesity is an important predictor and cause of T2D and cardiovascular disease. Genes increasing susceptibility to obesity are thus important candidates for T2D risk as well. Increased free fatty acid concentrations are often seen in obese individuals, in whom they cause defective glucose metabolism through insulin resistance development (69-71). Free fatty acids also have a direct lipotoxic effect on [beta]-cells by lipid accumulation, leading to reduced insulin secretion and apoptosis (72).

Obesity shows a heritability ranging from 55%-85% for BMI (73). T2D GWAS detected strong associations of common SNPs at the FTO locus and T2D risk (33); the risk of T2D could completely be explained by the effect on BMI (33, 47). Subsequently, new GWAS have identified many novel obesity-associated loci in both children and adults. Six of these loci [FTO; melanocortin 4 receptor (MC4R); transmembrane protein 18 (TMEM18); glucosamine-6-phosphate deaminase 2 (GNPDA2); SH2B adaptor protein 1 (SH2B1); and neuronal growth regulator (NEGR1)] are also associated with T2D risk (Table 3) (47, 74-78).

The FTO gene confers risk for T2D in Europeans, with each A allele increasing BMI by approximately 0.4 kg/[m.sup.2] (47). However, results have been variable for replication in other ethnic populations such as Hispanics (79), Asians, Oceanics (80), and blacks (81). The FTO gene encodes for a protein 2-oxoglutaratedependent nucleic acid demethylase involved in fatty acid metabolism, DNA repair, and posttranslational modifications (82). Studies in mice suggest that Fto might affect neuropeptide Y expression in the hypothalamus, which in turn is known to impact feeding behavior (83). Fto-knock-out mice have a reduced size and body weight with improved insulin sensitivity and increased adrenaline concentrations in blood, suggesting that their energy expenditure occurs in the presence of increased sympathetic activation so they are lean even after hyperphagia (84). Olszewski et al. have reported that high body weight in mice downregulates the Fto gene in hypothalamus, which suggests that the Fto gene affects energy metabolism and expenditure and not feeding (85).

The second most important obesity gene, MC4R, is expressed in the hypothalamus and regulates energy expenditure, insulin sensitivity, and energy intake (86). Leptin, an anorexogenic adipokine, stimulates the production of proopiomelanocortin products, which bind to MC4R, resulting in decreased food intake and increased energy expenditure (86). The MC4R variant also confers increased T2D risk in Europeans (87, 88).

In a recent GWAS, Thorleifsson et al. identified additional loci for obesity (74); this study was followed by a metaanalysis confirming the results in Europeans (89). In this investigation, 2 loci each at chromosome 6p21 and 12q13 were found associated with T2D, rs2844479 [close to allograft inflammatory factor 1 (AIF1) and natural cytotoxicity triggering receptor 3 (NCR3)] and rs7138803 [close to BCDIN3 domain containing (BCDIN3D) and Fas apoptotic inhibitory molecule 2 (FAIM2)] even after adjustment for BMI. Chambers et al. also conducted a GWAS on waist circumference and insulin-resistance traits and found the MC4R locus to be associated with HOMA-IR in Asian Indians and Europeans, whereas association with T2D was observed only in Europeans (88). Risk-allele frequencies of the newly identified obesity susceptibility loci vary between 27% and 91% in European populations, suggesting that although they have small effect sizes a substantial part of the risk for T2D development could be attributable to obesity alleles (76).

The Missing Heritability of T2D

Although a considerable number of variants have been found to affect the risk of T2D, these variants still account for only a small proportion of the total heritability. Most of the identified variants have very modest effect sizes in the range of 1.1 to 1.3. Some of these effect sizes may even be overestimated owing to winner's curse, i.e., serendipitously large effects in the discovery population leading to their discovery; however, others will prove to merely be proxies of variants with larger effects. The DIAGRAM-plus consortium estimated that the presented >30 T2D-associated SNPs could explain only about 10%-15% of the sibling relative risk of about 3 (3, 41).

The key question is thus, what can explain the missing heritability? One possibility is of course that the heritability estimates are wrong. This error would not be surprising for T2D, for which the familial risk of 3 has been derived from family studies, but most GWAS studies have not requested that study participants have another family member with T2D. Other possible explanations include a much larger number of variants of even smaller effects that remain to be identified, low frequency (minor allele frequency 0.5%-5%), and rare (<0.5%) variants with larger effects, as well as structural variants (such as copy-number variants and copy-neutral variants) that are poorly captured by existing arrays. Many of these effects could possibly be identified by the currently used strategies by increasing genotyping coverage and sample sizes even further. However, it is likely that many variants escape detection owing to mechanisms such as parental-origin-specific effects, gene-gene interactions, gene-environment interactions, and epigenetic effects, among others. The hunt for these variants will benefit from the development of new detection strategies and methods. In the years to come, next-generation sequencing in families in which the heritability has been estimated should be able to answer some of these questions.

Rare Variants

So far the GWAS performed have been designed to find relatively common variants, typically focusing on variants with allele frequencies more than 5%, owing to the low statistical power to detect associations with more rare alleles. It is plausible that some of the genetic variance is explained by relatively rare variants, some of which could have larger effect sizes than the common variants identified so far. A substantial part of the missing heritability could be due to variants with large or intermediate effect sizes and relatively low frequencies that are likely to have escaped detection by current methods, having too low penetrance to allow linkage analysis and being too rare to detect in GWAS. The strategies to identify such variants largely depend on the frequency of the variants. Some variants in the range of 1% to 5% should be possible to identify by simply increasing genotype density and cohort sizes. An important tool to achieve this is the 1000 Genomes Project, which is extending the catalogue of known human variants to frequencies close to or below 1%. However, detection of many of these rare or intermediate variants will require next-generation sequencing rather than traditional GWAS or genotyping (90).

Variants that are relatively rare in the European population could also be identified by studying other ethnic groups where the variant is more common. For example, variants in the KCNQ1 gene were first identified in Asians where the minor allele frequency of the key SNPs (rs2237892, rs2237897, rs2074196) was 30%-40%, which is much higher than in Europeans with a frequency < 10% (48, 49).

Gene-Gene and Gene-Environment Interactions

Gene-gene interactions with nonadditive effects are another potential source of missing heritability. How much such epistatic effects contribute to the genetic variation in the predisposition to T2D is unknown, and so far there is little evidence in human studies (91-93) that epistatic changes contribute to disease pathology. In contrast, epistatic effects seem to be very common in animal models, in which they can be identified by using selective breeding strategies. For example, Shao et al. compared 41 quantitative traits (14 of them metabolic) that differed significantly between the parental strains in chromosome substitution strains and found that for 40 of 41 traits the cumulative phenotypic effect of all chromosomes was greater than the parental difference. This finding suggests that nonlinear effects is a rule rather than an exception (if all effects were additive the sum of effects would equal the parental difference) (94). Studies of gene-gene interactions in outbred populations are difficult because the number of tests required usually exceeds our calculation and power capacity (95). Therefore, a common strategy to limit the number of tests is to restrict the analysis to loci with significant main effects (45, 96) However, it is well known from studies in plants and animals that epistatic effects are often detected in the absence of main effects (97, 98). An illustrative study was performed by Carlborg et al., who mapped quantitative trait loci affecting body size in chickens in a cross between 2 lines that had been selectively bred for 40 generations, resulting in a 6-fold difference in body weight. In spite of the large genetic variance, only 1 significant locus, with a minor effect, was identified in standard quantitative-trait loci analysis. However, when epistatic interactions were considered, an additional 5 loci turned out to have significant effects and together the 6 genes formed an interaction network accounting for 45% of the difference between the 2 parental lines (97). Similar networks have been shown to determine obesity in mice (99). There are no reasons to assume that the genetics of human metabolism will be less complex.

Gene-environment interactions are another potential source of missing heritability that could have effects similar to those of gene-gene interactions, but these are also poorly investigated in humans owing to the fact that the potentially relevant environmental factors are huge in number and by their nature difficult to measure in a standardized fashion to allow analysis in adequately large sample sizes.

Epigenetics

Epigenetics has been defined as heritable changes in gene function that occur without a change in the nucleotide sequence. Epigenetic modifications can be passed from one cell generation to the next (mitotic inheritance) or between generations of individuals (meiotic inheritance). Although meiotic inheritance is well established in plants, there is only limited information about the inheritance of epigenetic traits between generations in animals and humans so far (100,101). These types of epigenetic effects could explain some of the missing "genetic" variance component of complex diseases. Epigenetic effects can also occur during life, stochastically or in response to environmental stimuli, thereby influencing the effects of genetic variants and thus acting as a mechanism of gene-environment interaction.

Both DNA methylation and histone modifications can change the response of our genome to the environment during life. DNA methylation often results in decreased expression of a gene, which in its extreme form becomes completely suppressed (imprinted). Posttranslational modifications on the N-terminal histone tails in the chromatin also play an essential role in regulation of gene expression and function. Modification of the chromatin structure can allow access or prevent access of proteins to binding with a transcription factor, which has been shown for TCF7L2 (102).

In the pathogenesis of T2D, special attention has been given to the role of intrauterine DNA methylation and imprinting for the programming of diabetogenic effects later in life (103). An interesting study by Dabelea et al. has shown that intrauterine exposure to diabetes increases the risk of diabetes and obesity in the offspring compared to siblings who were born before their mothers' onset of diabetes (104). However, the exact mechanism for this maternal effect remains to be determined.

Intrauterine growth retardation has also been associated with increased DNA methylation of the Pdx1 promoter in islets from experimental animals (105).A few additional epigenetic studies have been carried out in target tissues from individuals with T2D (106-108). One such study describes hypermethylation of the PPRG1A gene promoter in islets from T2D patients, resulting in decreased gene expression, and glucose-stimulated insulin secretion (106). Methylation of the insulin gene promoter also seems to affect insulin secretion (109). In addition, some studies have suggested a role for epigenetic regulation of genes involved in energy metabolism, appetite control, and [beta]-cell function, i.e., peroxisome proliferator-activated receptor alpha (PPARA) (110), leptin (LEP) (111), and pancreatic and duodenal homeobox 1 (PDX1) (105).

Prediction of Disease

Several studies have investigated the predictive value of genetic markers in comparison with clinical risk factors with similar results (62,112-114). The largest study was performed by our own group in 2 Scandinavian cohorts. Inclusion of genetic information from 16 genotyped risk variants increased the area under the ROC curve from 0.74, for a model including only clinical risk factors, to 0.75. Notably the difference in predictive value increased the earlier the genetic analyses were performed (62). The use of genetic markers in diagnosis of T2D is thus not very useful so far, although identification of new variants with larger effects could change this rapidly. It should also be noted that the effect size of a locus is not at all correlated with the potential for the involved pathway as a target for clinical therapy.

Conclusions

It is evident that existing genetic markers explain only a modest (<15%) part of the heritability of T2D. The question to be answered by future studies is whether the missing heritability will be accounted for by the identification of an increasing number of common variants, as shown for Crohn disease (explaining over 40% of heritability) and genetic variants that determine lipid concentrations (>35%), or whether finding the answer will require the use of next-generation sequencing to identify rare variants with stronger effects.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: No authors declared any potential conflicts of interest.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or preparation or approval of manuscript.

References

(1.) International Federation of Diabetes. IDF diabetes atlas. http://www.diabetesatlas.org/ (Accessed January 2011).

(2.) Lyssenko V, Almgren P, Anevski D, Perfekt R, Lahti K, Nissen M, et al. Predictors of and longitudinal changes in insulin sensitivity and secretion preceding onset of type 2 diabetes. Diabetes 2005;54:166-74.

(3.) Kobberling J, Tillil H. Empirical risk figures for first-degree relatives of non-insulin dependent diabetics. In: Kobberling J, Tattersall R, eds. The genetics of diabetes mellitus. London: Academic Press; 1982:201-9.

(4.) Groop L, Forsblom C, Lehtovirta M, Tuomi T, Karanko S, Nissen M, et al. Metabolic consequences of a family history of NIDDM (the Botnia study): evidence for sex-specific parental effects. Diabetes 1996;45:1585-93.

(5.) Kaprio J, Tuomilehto J, Koskenvuo M, Romanov K, Reunanen A, Eriksson J, et al. Concordance for type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia 1992;35:1060-7.

(6.) Newman B, Selby JV, King MC, Slemenda C, Fabsitz R, Friedman GD. Concordance for type 2 (non-insulin-dependent) diabetes mellitus in male twins. Diabetologia 1987;30:763-8.

(7.) Glans F, Elgzyri T, Shaat N, Lindholm E, Apelqvist J, Groop L. Immigrants from the Middle-East have a different form of Type 2 diabetes compared with Swedish patients. Diabet Med 2008;25:303-7.

(8.) WHO. Obesity. http://www.who.int/topics/obesity/ (Accessed January 2011).

(9.) Neel JV. Diabetes mellitus: a "thrifty" genotype rendered detrimental by "progress"? Am J Hum Genet 1962;14:353-62.

(10.) Duggirala R, Blangero J, Almasy L, Dyer TD, Williams KL, Leach RJ, et al. Linkage of type 2 diabetes mellitus and of age at onset to a genetic location on chromosome 10q in Mexican Americans. Am J Hum Genet 1999;64: 1127-40.

(11.) Reynisdottir I, Thorleifsson G, Benediktsson R, Sigurdsson G, Emilsson V, Einarsdottir AS, et al. Localization of a susceptibility gene for type 2 diabetes to chromosome 5q34-q35.2. Am J Hum Genet 2003;73:323-35.

(12.) Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet 2006;38:320-3.

(13.) Tong Y, Lin Y, Zhang Y, Yang J, Liu H, Zhang B. Association between TCF7L2 gene polymorphisms and susceptibility to type 2 diabetes mellitus: a large Human Genome Epidemiology (HuGE) review and meta-analysis. BMC Med Genet 2009;10:15.

(14.) Lyssenko V, Lupi R, Marchetti P, Del Guerra S, Orho-Melander M, Almgren P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007;117:2155-63.

(15.) Yi F, Brubaker PL, Jin T. TCF-4 mediates cell type-specific regulation of proglucagon gene expression by beta-catenin and glycogen synthase kinase-3beta. J Biol Chem 2005;280: 1457-64.

(16.) Schafer SA, Tschritter O, Machicao F, Thamer C, Stefan N, Gallwitz B, et al. Impaired glucagons-like peptide-1-induced insulin secretion in carriers of transcription factor 7-like 2 (TCF7L2) gene polymorphisms. Diabetologia 2007;50:2443-50.

(17.) Hanis CL, Boerwinkle E, Chakraborty R, Ellsworth DL, Concannon P, Stirling B, et al. A genome-wide search for human non-insulin-dependent (type 2) diabetes genes reveals a major susceptibility locus on chromosome 2. Nat Genet 1996;13:161-6.

(18.) Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M, et al. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet 2000;26: 163-75.

(19.) Song Y, Niu T, Manson JE, Kwiatkowski DJ, Liu S. Are variants in the CAPN10 gene related to risk of type 2 diabetes? A quantitative assessment of population and family-based association studies. Am J Hum Genet 2004;74:208-22.

(20.) Weedon MN, Schwarz PE, Horikawa Y, Iwasaki N, Illig T, Holle R, et al. Meta-analysis and a large association study confirm a role for calpain-10 variation in type 2 diabetes susceptibility. Am J Hum Genet 2003;73:1208-12.

(21.) Deeb SS, Fajas L, Nemoto M, Pihlajamaki J, Mykkanen L, Kuusisto J, et al. A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet 1998;20:284-7.

(22.) Altshuler D, Hirschhorn JN, Klannemark M, Lindgren CM, Vohl MC, Nemesh J, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet 2000;26:76-80.

(23.) Tonjes A, Scholz M, Loeffler M, Stumvoll M. Association of Pro12Ala polymorphism in peroxisome proliferator-activated receptor gamma with pre-diabetic phenotypes: meta-analysis of 57 studies on nondiabetic individuals. Diabetes Care 2006;29:2489-97.

(24.) Gouda HN, Sagoo GS, Harding AH, Yates J, Sandhu MS, Higgins JP. The association between the peroxisome proliferator-activated receptor-gamma2 (PPARG2) Pro12Ala gene variant and type 2 diabetes mellitus: a HuGE review and meta-analysis. Am J Epidemiol 2010; 171:645-55.

(25.) Almind K, Bjorbaek C, Vestergaard H, Hansen T, Echwald S, Pedersen O. Aminoacid polymorphisms of insulin receptor substrate-1 in non-insulin-dependent diabetes mellitus. Lancet 1993;342:828-32.

(26.) Rung J, Cauchi S, Albrechtsen A, Shen L, Rocheleau G, Cavalcanti-Proenca C, et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat Genet 2009;41:1110-5.

(27.) Gloyn AL, Pearson ER, Antcliff JF, Proks P, Bruining GJ, Slingerland AS, et al. Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit Kir6.2 and permanent neonatal diabetes. N Engl J Med 2004;350: 1838-49.

(28.) Hani EH, Boutin P, Durand E, Inoue H, Permutt MA, Velho G, Froguel P. Missense mutations in the pancreatic islet beta cell inwardly rectifying K+ channel gene (KIR6.2/BIR): a meta-analysis suggests a role in the polygenic basis of type II diabetes mellitus in Caucasians. Diabetologia 1998;41:1511-5.

(29.) Gloyn AL, Weedon MN, Owen KR, Turner MJ, Knight BA, Hitman G, et al. Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 2003;52:568-72.

(30.) Florez JC, Burtt N, de Bakker PI, Almgren P, Tuomi T, Holmkvist J, et al. Haplotype structure and genotype-phenotype correlations of the sulfonylurea receptor and the islet ATP-sensitive potassium channel gene region. Diabetes 2004; 53:1360-8.

(31.) van Dam RM, Hoebee B, Seidell JC, Schaap MM, de Bruin TW, Feskens EJ. Common variants in the ATP-sensitive K+ channel genes KCNJ11 (Kir6.2) and ABCC8 (SUR1) in relation to glucose intolerance: population-based studies and metaanalyses. Diabet Med 2005;22:590-8.

(32.) Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science (Wash DC) 2007; 316:1331-6.

(33.) Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science (Wash DC) 2007;316:1341-5.

(34.) Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science (Wash DC) 2007;316:1336-41.

(35.) Minton JA, Hattersley AT, Owen K, McCarthy MI, Walker M, Latif F, et al. Association studies of genetic variation in the WFS1 gene and type 2 diabetes in U.K. populations. Diabetes 2002; 51:1287-90.

(36.) Sandhu MS, Weedon MN, Fawcett KA, Wasson J, Debenham SL, DalyA, et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet 2007;39:951-3.

(37.) Franks PW, Rolandsson O, Debenham SL, Fawcett KA, Payne F, Dina C, et al. Replication of the association between variants in WFS1 and risk of type 2 diabetes in European populations. Diabetologia 2008;51:458-63.

(38.) Winckler W, Weedon MN, Graham RR, McCar roll SA, Purcell S, Almgren P, et al. Evaluation of common variants in the six known maturity-onset diabetes of the young (MODY) genes for association with type2 diabetes. Diabetes2007; 56:685-93.

(39.) Holmkvist J, Cervin C, Lyssenko V, Winckler W, Anevski D, Cilio C, et al. Common variants in HNF-1 alpha and risk of type 2 diabetes. Diabetologia 2006;49:2882-91.

(40.) Holmkvist J, Almgren P, Lyssenko V, Lindgren CM, Eriksson KF, Isomaa B, et al. Common variants in maturity-onset diabetes of the young genes and future risk of type 2 diabetes. Diabetes 2008;57:1738-44.

(41.) Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 2010;42:579-89.

(42.) Gudmundsson J, Sulem P, Steinthorsdottir V, Bergthorsson JT, Thorleifsson G, Manolescu A, et al. Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet 2007; 39:977-83.

(43.) Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445:881-5.

(44.) Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 2007;39: 770-5.

(45.) The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661-78.

(46.) Dina C, Meyre D, Gallina S, Durand E, Korner A, Jacobson P, et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet 2007;39:724-6.

(47.) Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science (Wash DC) 2007;316: 889-94.

(48.) Unoki H, Takahashi A, Kawaguchi T, Hara K, Horikoshi M, Andersen G, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet 2008;40:1098-102.

(49.) Yasuda K, Miyake K, Horikawa Y, Hara K, Osawa H, Furuta H, et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008;40:1092-7.

(50.) Jonsson A, Isomaa B, Tuomi T, Taneera J, Salehi A, Nilsson P, et al. A variant in the KCNQ1 gene predicts future type 2 diabetes and mediates impaired insulin secretion. Diabetes 2009;58:2409-13.

(51.) Tsai FJ, Yang CF, Chen CC, Chuang LM, Lu CH, Chang CT, et al. A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet. 6:e1000847.

(52.) Kong A, Steinthorsdottir V, Masson G, Thorleifsson G, Sulem P, Besenbacher S, et al. Parental origin of sequence variants associated with complex diseases. Nature 2009;462:868-74.

(53.) Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008;40:638-45.

(54.) Qi L, Cornelis MC, Kraft P, Stanya KJ, Linda Kao WH, Pankow JS, et al. Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet. 19:2706-15.

(55.) Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 2010;42:105-16.

(56.) Lyssenko V, Nagorny CL, Erdos MR, Wierup N, Jonsson A, Spegel P, et al. Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet 2009;41:82-8.

(57.) Bouatia-Naji N, Bonnefond A, Cavalcanti-Proenca C, Sparso T, Holmkvist J, Marchand M, et al. A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat Genet 2009;41:89-94.

(58.) Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G, et al. Variants in MTNR1B influence fasting glucose levels. Nat Genet 2009;41:77-81.

(59.) Arendt J, Skene DJ. Melatonin as a chronobiotic. Sleep Med Rev 2005;9:25-39.

(60.) von Gall C, Stehle JH, Weaver DR. Mammalian melatonin receptors: molecular biology and signal transduction. Cell Tissue Res 2002;309:15162.

(61.) Mulder H, Nagorny CL, Lyssenko V, Groop L. Melatonin receptors in pancreatic islets: good morning to a novel type 2 diabetes gene. Diabetologia 2009;52:1240-9.

(62.) Lyssenko V, Jonsson A, Almgren P, Pulizzi N, Isomaa B, Tuomi T, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med 2008;359:2220-32.

(63.) Grarup N, Andersen G, Krarup NT, Albrechtsen A, Schmitz O, Jorgensen T, et al. Association testing of novel type 2 diabetes risk alleles in the JAZF1, CDC123/CAMK1D, TSPAN8, THADA, ADAMTS9, and NOTCH2 loci with insulin release, insulin sensitivity, and obesity in a population-based sample of 4,516 glucose-tolerant middle-aged Danes. Diabetes 2008;57: 2534-40.

(64.) Ingelsson E, Langenberg C, Hivert MF, Prokopenko I, Lyssenko V, Dupuis J, et al. Detailed physiologic characterization reveals diverse mechanisms for novel genetic loci regulating glucose and insulin metabolism in humans. Diabetes 2010;59:1266-75.

(65.) Rosengren AH, Jokubka R, Tojjar D, Granhall C, Hansson O, Li DQ, et al. Overexpression of alpha2A-adrenergic receptors contributes to type 2 diabetes. Science (Wash DC) 2010;327:217-20.

(66.) Orho-Melander M, Melander O, Guiducci C, Perez-Martinez P, Corella D, Roos C, et al. Common missense variant in the glucokinase regulatory protein gene is associated with increased plasma triglyceride and C-reactive protein but lower fasting glucose concentrations. Diabetes 2008;57:3112-21.

(67.) Clemmons DR Role of insulin-like growth factor in maintaining normal glucose homeostasis. Horm Res 2004;62(Suppl 1):77-82.

(68.) Parker-Katiraee L, Carson AR, Yamada T, Arnaud P, Feil R, Abu-Amero SN, et al. Identification of the imprinted KLF14 transcription factor undergoing human-specific accelerated evolution. PLoS Genet 2007;3:e65.

(69.) Randle PJ, Garland PB, Hales CN, Newsholme EA. The glucose fatty-acid cycle. Its role in insulin sensitivity and the metabolic disturbances of diabetes mellitus. Lancet 1963;1:785-9.

(70.) Krssak M, Falk Petersen K, Dresner A, DiPietro L, Vogel SM, Rothman DL, et al. Intramyocellular lipid concentrations are correlated with insulin sensitivity in humans: a 1H NMR spectroscopy study. Diabetologia 1999;42:113-6.

(71.) Griffin ME, Marcucci MJ, Cline GW, Bell K, Barucci N, Lee D, et al. Free fatty acid-induced insulin resistance is associated with activation of protein kinase C theta and alterations in the insulin signaling cascade. Diabetes 1999;48: 1270-4.

(72.) Unger RH, Zhou YT. Lipotoxicity of beta-cells in obesity and in other causes of fatty acid spillover. Diabetes 2001;50 Suppl 1:S118-21.

(73.) Souren NY, Paulussen AD, Loos RJ, Gielen M, Beunen G, Fagard R, et al. Anthropometry, carbohydrate and lipid metabolism in the East Flanders Prospective Twin Survey: heritabilities. Diabetologia 2007;50:2107-16.

(74.) Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet 2009;41:18-24.

(75.) Meyre D, Delplanque J, Chevre JC, Lecoeur C, Lobbens S, Gallina S, et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet 2009;41:157-9.

(76.) Loos RJ. Recent progress in the genetics of common obesity. Br J Clin Pharmacol 2009;68:811-29.

(77.) Scherag A, Dina C, HinneyA, Vatin V, Scherag S, Vogel CI, et al. Two new Loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and german study groups. PLoS Genet 2010;6:e1000916.

(78.) Lindgren CM, Heid IM, Randall JC, Lamina C, Steinthorsdottir V, Qi L, et al. Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution. PLoS Genet 2009;5:e1000508.

(79.) Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J, et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet 2007;3:e115.

(80.) Chang YC, Liu PH, Lee WJ, Chang TJ, Jiang YD, Li HY, et al. Common variation in the fat mass and obesity-associated (FTO) gene confers risk of obesity and modulates BMI in the Chinese population. Diabetes 2008;57:2245-52.

(81.) Bressler J, Kao WH, Pankow JS, Boerwinkle E. Risk of type 2 diabetes and obesity is differentially associated with variation in FTO in whites and African-Americans in the ARIC study. PLoS One 2010;5:e10521.

(82.) Clifton IJ, McDonough MA, Ehrismann D, Kershaw NJ, Granatino N, Schofield CJ. Structural studies on 2-oxoglutarate oxygenases and related double-stranded beta-helix fold proteins. J Inorg Biochem 2006;100:644-69.

(83.) Gerken T, Girard CA, Tung YC, Webby CJ, Saudek V, Hewitson KS, et al. The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science (Wash DC) 2007;318:1469-72.

(84.) Fischer J, Koch L, Emmerling C, Vierkotten J, Peters T, Bruning JC, Ruther U. Inactivation of the Fto gene protects from obesity. Nature 2009;458:894-8.

(85.) Olszewski PK, Fredriksson R, Olszewska AM, Stephansson O, Alsio J, Radomska KJ, et al. Hypothalamic FTO is associated with the regulation of energy intake not feeding reward. BMC Neurosci 2009;10:129.

(86.) Ellacott KL, Cone RD. The role of the central melanocortin system in the regulation of food intake and energy homeostasis: lessons from mouse models. Philos Trans R Soc Lond B Biol Sci 2006;361:1265-74.

(87.) Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 2008;40:768-75.

(88.) Chambers JC, Elliott P, Zabaneh D, Zhang W, Li Y, Froguel P, et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet 2008;40: 716-8.

(89.) Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 2009;41: 25-34.

(90.) Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet 2010;11: 415-25.

(91.) Sparso T, Grarup N, Andreasen C, Albrechtsen A, Holmkvist J, Andersen G, et al. Combined analysis of 19 common validated type 2 diabetes susceptibility gene variants shows moderate discriminative value and no evidence of genegene interaction. Diabetologia 2009;52:1308-14.

(92.) Cauchi S, Meyre D, Durand E, Proenca C, Marre M, Hadjadj S, et al. Post genome-wide association studies of novel genes associated with type 2 diabetes show gene-gene interaction and high predictive value. PLoS One 2008;3:e2031.

(93.) Weedon MN, McCarthy MI, Hitman G, Walker M, Groves CJ, Zeggini E, et al. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. PLoS Med 2006;3:e374.

(94.) Shao H, Burrage LC, Sinasac DS, Hill AE, Ernest SR, O'Brien W, et al. Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc Natl Acad SciUSA2008; 105:19910-4.

(95.) Gauderman WJ. Sample size requirements for association studies of gene-gene interaction. Am J Epidemiol 2002;155:478-84.

(96.) Carlson CS, Eberle MA, Kruglyak L, Nickerson DA. Mapping complex disease loci in whole-genome association studies. Nature 2004;429: 446-52.

(97.) Carlborg O, Jacobsson L, Ahgren P, Siegel P, Andersson L. Epistasis and the release of genetic variation during long-term selection. Nat Genet 2006;38:418-20.

(98.) Johannesson M, Karlsson J, Wernhoff P, Nandakumar KS, Lindqvist AK, Olsson L, et al. Identification of epistasis through a partial advanced intercross reveals three arthritis loci within the Cia5 QTL in mice. Genes Immun 2005;6:175-85.

(99.) Stylianou IM, Korstanje R, Li R, Sheehan S, Paigen B, Churchill GA. Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci. Mamm Genome 2006;17:22-36.

(100.) Ng SF, Lin RC, Laybutt DR, Barres R, Owens JA, Morris MJ. Chronic high-fat diet in fathers programs beta-cell dysfunction in female rat offspring. Nature. 467:963-6.

(101.) Gluckman PD, Hanson MA, Beedle AS. Non genomic transgenerational inheritance of disease risk. Bioessays 2007;29:145-54.

(102.) Gaulton KJ, Nammo T, Pasquali L, Simon JM, Giresi PG, Fogarty MP, et al. A map of open chromatin in human pancreatic islets. Nat Genet 2010;42:255-9.

(103.) Simmons RA. Developmental origins of beta-cell failure in type 2 diabetes: the role of epigenetic mechanisms. Pediatr Res 2007;61:64R-7R.

(104.) Dabelea D, Hanson RL, Lindsay RS, Pettitt DJ, Imperatore G, Gabir MM, et al. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sib ships. Diabetes 2000;49:2208-11.

(105.) Park JH, Stoffers DA, Nicholls RD, Simmons RA. Development of type 2 diabetes following intrauterine growth retardation in rats is associated with progressive epigenetic silencing of Pdx1. J Clin Invest 2008;118:2316-24.

(106.) Ling C, Del Guerra S, Lupi R, Ronn T, Granhall C, Luthman H, et al. Epigenetic regulation of PPARGC1A in human type 2 diabetic islets and effect on insulin secretion. Diabetologia 2008; 51:615-22.

(107.) Ling C, Poulsen P, Simonsson S, Ronn T, Holmkvist J, Almgren P, et al. Genetic and epigenetic factors are associated with expression of respiratory chain component NDUFB6 in human skeletal muscle. J Clin Invest 2007;117: 3427-35.

(108.) Ronn T, Poulsen P, Hansson O, Holmkvist J, Almgren P, Nilsson P, et al. Age influences DNA methylation and gene expression of COX7A1 in human skeletal muscle. Diabetologia 2008;51: 1159-68.

(109.) Kuroda A, Rauch TA, Todorov I, Ku HT, Al Abdullah IH, Kandeel F, et al. Insulin gene expression is regulated by DNA methylation. PLoS One 2009;4:e6953.

(110.) Tateishi K, Okada Y, Kallin EM, Zhang Y. Role of Jhdm2a in regulating metabolic gene expression and obesity resistance. Nature 2009;458:757 61.

(111.) Milagro FI, Campion J, Garcia-Diaz DF, Goyenechea E, Paternain L, Martinez JA. High fat diet-induced obesity modifies the methylation pattern of leptin promoter in rats. J Physiol Biochem 2009;65:1-9.

(112.) Lango H, Palmer CN, Morris AD, Zeggini E, Hattersley AT, McCarthy MI, et al. Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes 2008;57:3129-35.

(113.) Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 2008;359:2208-19.

(114.) van Hoek M, Dehghan A, Witteman JC, van Duijn CM, Uitterlinden AG, Oostra BA, et al. Predicting type 2 diabetes based on polymorphisms from genome-wide association studies: a population-based study. Diabetes 2008;57: 3122-8.

(115.) Winckler W, Burtt NP, Holmkvist J, Cervin C, de Bakker PI, Sun M, et al. Association of common variation in the HNF1alpha gene region with risk of type 2 diabetes. Diabetes 2005;54:2336-42.

(116.) Renstrom F, Payne F, Nordstrom A, Brito EC, Rolandsson O, Hallmans G, et al. Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden. Hum Mol Genet 2009;18: 1489-96.

Emma Ahlqvist, [1] Tarunveer Singh Ahluwalia, [1] and Leif Groop [1] *

[1] Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, Skane University Hospital, Malmo, Sweden.

[2] Nonstandard abbreviations: T2D, type 2 diabetes; BMI, body mass index; LD, linkage disequilibrium; SNP, single-nucleotide polymorphism; GLP-1, glucagon-like peptide 1; GWAS, genomewide association study; MODY, maturity-onset diabetes of the young; DIAGRAM, Diabetes Genetics, Replication and Metaanalysis; deCODE, Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Europe; KORA, Cooperative Health Research in the Region of Augsburg; EUROSPAN, European Special Population Research Network; MAGIC, Metaanalysis of Glucose and Insulin-Related Traits.

[3] Human genes: CAPNW, calpain 10; TCF7L2, transcription factor 7-like 2 (T-cell specific, HMG-box); PPARG, peroxisome proliferator-activated receptor gamma; IRS1, insulin receptor substrate 1; KCNJ11,potassium inwardly-rectifying channel, subfamily J, member 11; WFS1, Wolfram syndrome 1 (wolframin); HNF1A, HNF1 homeobox A; and HNF1B, HNF1 homeobox B; HHEX, hematopoietically expressed homeobox; SLC30A8, solute carrier family 30 (zinc transporter), member 8; CDKN2A/CDKN2B, cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)/cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4); IGF2BP2, insulin-like growth factor 2 mRNA binding protein 2 CDKAL1, CDK5 regulatory subunit associated protein 1-like 1; FTO, fat mass and obesity associated; KCNQ1, potassium voltage-gated channel, KQT-like subfamily, member 1; PTPRD, protein tyrosine phosphatase, receptor type, D; SRR, serine racemase; YY1AP1, YY1 associated protein 1; JAZF1, JAZF zinc finger 1; CDC123, cell division cycle 123 homolog; CAMK1D, calcium/calmodulin dependent protein kinase ID; TSPAAN8-LGR5, tetraspanin 8-leucine-rich repeat-containing G protein-coupled receptor 5; THADA, thyroid adenoma associated; ADAMTS9, ADAM metallopeptidase with thrombospondin type 1 motif, 9; NOTCH2, notch 2; RBMS1, RNA binding motif, single stranded interacting protein 1; MTNR1B, melatonin receptor 1B; BCL11A, B-cell CLL/lymphoma 11A (zinc finger protein); ZBED3, zinc finger, BED-type containing 3; KLF14, Kruppel-like factor 14; TP53INP1, tumor protein p53 inducible nuclear protein 1; TLE4, transducin-like enhancer of split 4; CHCHD9, coiled-coil-helix-coiled-coil-helix domain containing 9; ARAP1, ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 1; HMGA2, high mobility group AT-hook 2; ZFAND6, zinc finger, AN1-type domain 6; PRC1, protein regulator of cytokinesis 1; DUSP9, dual specificity phosphatase 9; ADCY5, adenylate cyclase 5; MADD, MAP-kinase activating death domain; CRY2, cryptochrome 2 (photolyase-like); ADRA2A, adrenergic, alpha-2A-, receptor; FADS1, fatty acid desaturase 1; PROX1, prospero homeobox 1; SLC2A2, solute carrier family 2 (facilitated glucose transporter), member 2; GLIS3, GLIS family zinc finger 3; C2CD4B, C2 calcium-dependent domain containing 4B; IGF1, insulin-like growth factor 1 (somatomedin C); GCK, glucokinase (hexokinase 4); DGKB-TMEM195, diacyl-glycerol kinase, beta 90kDa-transmembrane protein 195; GCKR, glucokinase (hexokinase 4) regulator; GIPR, gastric inhibitory polypeptide receptor; FADS, MC4R, melanocortin 4 receptor; TMEM18, transmembrane protein 18; GNPDA2, glucosamine-6-phosphate deaminase 2; SH2B1, SH2B adaptor protein 1; MTCH2, mitochondrial carrier homolog 2; NEGR1, neuronal growth regulator 1; AIF1, allograft inflammatory factor 1; NCR3, natural cytotoxicity triggering receptor 3; BCDIN3D, BCDIN3 domain containing; FAIM2, Fas apoptotic inhibitory molecule 2; PPARA, peroxisome proliferator-activated receptor alpha; LEP, leptin; PDX1, pancreatic and duodenal homeobox 1; ADAM30, ADAM metal-lopeptidase domain 30; TRA2B, transformer 2 beta homolog; ETV5, ets variant 5; DGKG, diacylglycerol kinase, gamma 90kDa.

* Address correspondence to this author at: Department of Clinical Sciences/Diabetes and Endocrinology, Lund University, CRC, Entrance 72, Building 91, Level 12, Skane University Hospital, SE-205 02, Malmo, Sweden. Fax +46-40391222; e-mail Leif.Groop@med.lu.se.

Received September 27, 2010; accepted November 10, 2010.

Previously published online at DOI: 10.1373/clinchem.2010.157016
Table 1. Genetic variants associated with T2D.

Variants Nearest Full gene name
 gene

rs4607103, ADAMTS9 ADAM metallopeptidase
 rs6795735 with thrombospondin
 type 1 motif, 9

rs1552224 ARAP1 ArfGAP with RhoGAP
 (CENTD2) domain, ankyrin repeat
 and PH domain 1

rs243021 BCL11A B-cell CLL/lymphoma
 11A (zinc finger
 protein)

rs2975760, CAPN10 Calpain 10
 rs3792267

rs12779790 CDC123/CAMK1D Cell division cycle
 123 homolog-calcium/
 calmodulin-dependent
 protein kinase ID

rs7754840, CDKAL1 CDK5 regulatory
 rs10440833 subunit associated
 protein 1-like 1

rs13292136 CHCHD9/TLE4 Coiled-coil-helix
 -coil-coiled-helix
 domain containing
 9/transducin-like
 enhancer of split 4

rs10811661, CDKN2A/2B Cyclin-dependent
 rs10965250 kinase inhibitor 2A
 (melanoma, p16,
 inhibits CDK4)
 --cyclin-dependent
 kinase inhibitor 2B
 (p15, inhibitsCDK4)

rs5945326 DUSP9 Dual specificity
 phosphatase 9

rs1111875, HHEX Hematopoietically
 rs5015480 expressed homeobox

rs1531343 HMGA2 High mobility
 group AT-hook 2

rs7957197 HNF1A HNF1 homeobox A

rs1920792, HNF1B HNF1 homeobox B
 rs7501939,
 rs757210,
 rs4430796

rs2334499 HCCA2 YY1 associated
 protein 1

rs4402960, IGF2BP2 Insulin-like growth
 rs1470579 factor 2 mRNA
 binding protein 2

rs2943641, IRS1 insulin receptor
 rs7578326 substrate 1

rs864745, JAZF1 JAZF zinc finger 1
 rs849134

rs5219 KCNJ11 Potassium inwardly
 -rectifying channel,
 subfamily J,
 member 11

rs2237892, KCNQ1 Potassium
 rs163184, voltage-gated
 rs231362 channel, KQT-like
 subfamily, member 1

rs972283 KLF14 Kruppel-like
 factor 14

rs10923931 NOTCH2/ADAM30 Notch 2-ADAM
 metallopeptidase
 domain 30

rs1801282, PPARG Peroxisome
 rs13081389 proliferator
 -activated
 receptor gamma

rs8042680 PRC1 Protein regulator
 of cytokinesis 1

rs17584499 PTPRD Protein tyrosine
 phosphatase,
 receptor
 type, D

rs7593730 RBMS1 RNA binding motif,
 single stranded
 interacting
 protein 1

rs13266634, SLC30A8 Solute carrier
 rs11558471, family 30 (zinc
 rs3802177 transporter),
 member 8

rs391300 SRR Serine racemase

rs7903146, TCF7L2 Transcription
 rs12255372, factor 7-like 2
 rs4506565, (T-cell specific,
 rs11196218 HMG-box)

rs7578597, THADA Thyroid adenoma
 rs11899863 associated

rs896854 TP53INP1 Tumor protein p53
 inducible nuclear
 protein 1

rs7961581, TSPAN8-LGR5 Tetraspanin
 rs4760790 8-leucine-rich
 repeat-containing
 G protein-coupled
 receptor 5

rs10010131, WFS1 Wolfram syndrome 1
 rs6446482, (wolframin)
 rs1801214

rs4457053 ZBED3 Zinc finger,
 BED-type
 containing 3

rs11634397 ZFAND6 Zinc finger,
 AN1-type domain 6

Variants OR T2D Study
 type (a)

rs4607103, 1.09-1.1 MA
 rs6795735

rs1552224 1.13-1.14 MA

rs243021 1.08-1.09 MA

rs2975760, 1.17 LA
 rs3792267

rs12779790 1.09 MA

rs7754840, 1.12-1.25 GWAS
 rs10440833

rs13292136 1.11-1.20 MA

rs10811661, 1.20 GWAS
 rs10965250

rs5945326 1.27 MA

rs1111875, 1.13-1.18 GWAS
 rs5015480

rs1531343 1.10-1.20 MA

rs7957197 1.07-1.14 MA

rs1920792, 1.1-1.17 CS
 rs7501939,
 rs757210,
 rs4430796

rs2334499 1.35 GWAS

rs4402960, 1.14 GWAS
 rs1470579

rs2943641, 1.09-1.12 CS
 rs7578326

rs864745, 1.12-1.13 MA
 rs849134

rs5219 1.09-1.14 CS

rs2237892, 1.08-1.23 GWAS
 rs163184,
 rs231362

rs972283 1.07-1.1 MA

rs10923931 1.14 MA

rs1801282, 1.14-1.24 CS
 rs13081389

rs8042680 1.07-1.1 MA

rs17584499 1.57 GWAS

rs7593730 1.11 MA

rs13266634, 1.12-1.15 GWAS
 rs11558471,
 rs3802177

rs391300 1.28 GWAS

rs7903146, 1.4 LA
 rs12255372,
 rs4506565,
 rs11196218

rs7578597, 1.15-1.17 MA
 rs11899863

rs896854 1.06-1.1 MA

rs7961581, 1.11 MA
 rs4760790

rs10010131, 1.11-1.13 CS
 rs6446482,
 rs1801214

rs4457053 1.08-1.16 MA

rs11634397 1.06-1.11 MA

Variants References

rs4607103, Voight et al. (41),
 rs6795735 Zeggini et al. (53)

rs1552224 Voight et al. (41)

rs243021 Voight et al. (41)

rs2975760, Weedon et al. (20)
 rs3792267

rs12779790 Voight et al. (41),
 Zeggini et al. (53)

rs7754840, Saxena et al. (32),
 rs10440833 Scott et al. (33),
 Voight et al. (41),
 Steinthorsdottir
 et al. (44)

rs13292136 Voight et al. (41)

rs10811661, Saxena et al. (32),
 rs10965250 Scott et al. (33),
 Voight etal. (41) )

rs5945326 Voight et al. (41)

rs1111875, Saxena et al. (32),
 rs5015480 Voight et al. (41),
 Sladek et al. (43)

rs1531343 Voight et al. (41)

rs7957197 Voight et al. (41)

rs1920792, Winckler et al. (115),
 rs7501939, Voight et al. (41),
 rs757210, Gudmundsson et al.
 rs4430796 (42)

rs2334499 Kong et al. (52)

rs4402960, Saxena et al. (32),
 rs1470579 Scott et al. (33),
 Voight et al. (41)

rs2943641, Almind et al. (25),
 rs7578326 Voight et al. (41)

rs864745, Voight et al. (41),
 rs849134 Zeggini et al. (53)

rs5219 Hani et al. (28),
 Saxena et al. (32),
 Voight et al. (41)

rs2237892, Voight et al. (41),
 rs163184, Unoki et al. (48),
 rs231362 Yasuda et al. (49)

rs972283 Voight et al. (41)

rs10923931 Voight et al. (41),
 Zeggini et al. (53)

rs1801282, Deeb et al. (21),
 rs13081389 Saxena et al. (32),
 Voight et al. (41)

rs8042680 Voight et al. (41)

rs17584499 Tsai et al. (51)

rs7593730 Qi et al. (54)

rs13266634, Saxena et al. (32),
 rs11558471, Voight et al. (41),
 rs3802177 Sladek et al. (43),
 Dupuis et al. (55)

rs391300 Tsai et al. (51)

rs7903146, Grant et al. (12),
 rs12255372, Saxena et al. (32),
 rs4506565, Voight et al. (41)
 rs11196218

rs7578597, Voight et al. (41),
 rs11899863 Zeggini et al. (53)

rs896854 Voight et al. (41)

rs7961581, Voight et al. (41),
 rs4760790 Zeggini et al. (53)

rs10010131, Minton et al. (35),
 rs6446482, Sandhu et al. (36),
 rs1801214 Voight et al. (41)

rs4457053 Voight et al. (41)

rs11634397 Voight et al. (41)

(a) MA, metaanalysis; LA, linkage
analysis; CS, candidate study.

Table 2. Genetic variants associated with
glucose or insulin concentrations.

Variants Nearest gene Full gene name

rs11708067 ADCY5 Adenylate
 cyclase 5

rs2191349 DGKB/TMEM195 Diacylglycerol
 kinase, beta 90kDa
 --transmembrane
 protein 195

rs4607517 GCK Glucokinase
 (hexokinase 4)

rs780094 GCKR Glucokinase
 (hexokinase 4)
 regulator

rs1387153, MTNR1B Melatonin
 rs10830963 receptor 1B

rs340874 PROX1 Prospero
 homeobox 1

Variants OR T2D Study
 type (a)

rs11708067 1.06 MA

rs2191349 1.06 MA

rs4607517 1.07 MA

rs780094 1.06 MA

rs1387153, 1.09-1.13 MA
 rs10830963

rs340874 1.07 MA

Variants References

rs11708067 Dupuis et al. (55)

rs2191349 Dupuis et al. (55)

rs4607517 Dupuis et al. (55)

rs780094 Dupuis et al. (55)

rs1387153, Voight et al. (41),
 rs10830963 Dupuis et al. (55),
 Prokopenko et al. (58)

rs340874 Dupuis et al. (55)

(a) MA, metaanalysis.

Table 3. Genetic variants associated with obesity.

Variants Nearest gene Full gene name

rs7138803 BCDIN3D/FAIM2 BCDIN3 domain
 containing--Fas
 apoptotic
 inhibitory
 molecule 2

rs2844479 AIF1/NCR3 Allograft
 inflammatory
 factor 1/natural
 cytotoxicity
 triggering
 receptor 3

rs8050136, FTO Fat mass
 rs9939609, and obesity
 rs11642841 associated

rs10938397 GNPDA2 Glucosamine-6
 -phosphate
 deaminase 2

rs17782313, MC4R Melanocortin
 rs12970134 4 receptor

rs310336, NEGR1 Neuronal growth
 rs2568958 regulator 1

rs7647305 SFRS10/ETV5/DGKG Transformer 2
 --ets variant
 5--diacylglycerol
 kinase, gamma
 90kDa

rs7498665 SH2B1 SH2B adaptor
 protein 1

rs6548238 TMEM18 Transmembrane
 protein 18

Variants OR T2D Study
 type (a)

rs7138803 1.11 MA

rs2844479 1.05 MA

rs8050136, 1.17 GWAS
 rs9939609,
 rs11642841

rs10938397 1.13-1.17 MA

rs17782313, 1.09-1.11 GWAS
 rs12970134

rs310336, 1.06 MA
 rs2568958

rs7647305 1.08 MA

rs7498665 1.11 MA

rs6548238 1.19-1.21 MA

Variants References

rs7138803 Thorleifsson et al. (74)

rs2844479 Thorleifsson et al. (74)

rs8050136, Voight et al. (41),
 rs9939609, Frayling et al. (47)
 rs11642841

rs10938397 Willer et al. (89),

 Renstrom et al. (116)
rs17782313, Loos et al. (87),
 rs12970134 Chambers et al. (88)

rs310336, Thorleifsson et al. (74)
 rs2568958

rs7647305 Thorleifsson et al. (74)

rs7498665 Willer et al. (89),
 Renstrom et al. (116)

rs6548238 Willer et al. (89),
 Renstrom et al. (116)

(a) MA, metaanalysis.
COPYRIGHT 2011 American Association for Clinical Chemistry, Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2011 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Ahlqvist, Emma; Ahluwalia, Tarunveer Singh; Groop, Leif
Publication:Clinical Chemistry
Date:Feb 1, 2011
Words:11835
Previous Article:Personalized medicine in diabetes.
Next Article:Hemoglobin [A.sub.1c] to diagnose diabetes: why the controversy over adding a new tool?

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters