Richard M. Watanabe1
(1)
Department of Preventive Medicine, Keck School of Medicine University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, CA 90089-9011, USA
Richard M. Watanabe
Email: rwatanab@usc.edu
Abstract
Identifying genes underlying complex diseases hold the promise of new drug targets, improved interventions, and the advent of so-called “personalized medicine.” For almost 2 decades, investigators have attempted to identify genes underlying gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM), but until recently were mostly unsuccessful. Improvements in genetic information and technology changed the landscape of complex disease genetics. Prior to 2006, only 3 genes were accepted as bonafide T2DM susceptibility genes and there were none for GDM. Currently, approximately 21 genes underlying susceptibility to T2DM and related traits have been identified. However, our knowledge of the genetics underlying GDM has lagged. This chapter reviews the current state of knowledge as to the genetics of both GDM and T2DM, and related traits. The potential relationship between genes underlying these 2 forms of diabetes is discussed and some cautionary notes regarding interpretation of this wealth of genetic knowledge are offered.
13.1 Introduction
Positional cloning is the process of identifying disease susceptibility genes through various gene mapping techniques when little is known about the genes underlying the disease. The success of positional cloning in single-gene diseases led to the possibility that the same process could be used to identify genes underlying complex diseases like diabetes.1,2
However, years before any of the major efforts to positionally clone diabetes genes began, a book entitled “The Genetics of Diabetes Mellitus” appeared with a lead chapter prophetically entitled “Diabetes Mellitus – A Geneticist’s Nightmare”.3 This chapter, written by James V. Neel, considered the father of human genetics, warned of the complexity surrounding diabetes, small gene effects, confounding due to environmental factors, and interactions among genes and environment, and the challenge that would be faced in uncovering the genetic architecture of diabetes.
Scientific and technological advances led to an explosion of discoveries regarding genes underlying type 2 diabetes mellitus (T2DM) and related traits. Unfortunately, knowledge regarding the genetic basis for gestational diabetes mellitus (GDM) lags behind that of T2DM. In this chapter, we will summarize the general state of knowledge regarding the genetic basis for T2DM and whether there is a unique genetic basis underlying GDM.
13.2 Genetics of T2DM
13.2.1 State of Knowledge Prior to 2007
Over 25 genome-wide linkage scans for T2DM4 and countless candidate gene studies had been performed between 1976 and 2006. There were numerous successes related to autosomal dominant forms of diabetes,5 altered forms of insulin,6–9 and rare forms of diabetes like maternally inherited diabetes and deafness.10 However, by 2006 peroxisome proliferator-activated receptor γ (PPARG11,12), potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11 13), and transcription factor 7-like 2 (TCF7L214) were the only “accepted” T2DM susceptibility genes. Other genes, such as calpain 10 (CAPN10 15), protein tyrosine phosphatase 1B (PTPN116), and hepatocyte nuclear factor-4α (HNF4A17–19) showed evidence of association with T2DM, but most of these were not consistently replicated across study populations, raising some question as to their validity as T2DM susceptibility genes. Unfortunately, the inability to identify diabetes genes simply reflected the nightmare originally described by Dr. Neel and the reality that the technology and methods of the time would not allow for identification of small gene effects.
In 1996, Risch and Merikangas suggested that complex disease susceptibility genes could be identified by genotyping a number of single nucleotide polymorphisms (SNP) in each gene across the human genome20 and performing association analysis. However, the ability to genotype the requisite number of SNPs in a sufficiently large sample was not feasible at that time. Also, many recognized that the framework proposed by Risch and Merikangas was gene-centric and did not take into account the possibility of regulatory elements outside the coding region of genes or intronic SNPs that might result in alternative splice forms of the gene.
However, three milestones altered the genomics landscape. The Human Genome Project (http://genome.ucsc.edu), an effort to decode our DNA, released the first draft of the human genome in 2000.21 The HapMap Project (http://www.hapmap.org), an effort to identify genetic differences across human populations, has to-date identified over six million common SNPs across the human genome.22,23 Finally, improved technology now allows for genotyping of hundreds of thousands of SNPs in thousands of individuals in a span of weeks.
13.2.2 State of Knowledge After 2006: Genome-Wide Association
Results from the first genome-wide association (GWA) study for T2DM appeared in February 200724 and was based on ∼400,000 SNPs genotyped in approximately 2,800 T2DM cases and controls with promising associations in this first stage followed-up in a larger sample of ∼5,500 cases and controls. This 2-stage approach was employed to reduce study cost, while maintaining statistical power close to what would have been achieved if all subjects had been genotyped for all SNPs.25 The analysis identified five regions of the genome showing evidence of association with T2DM that survived stringent statistical criteria that accounted for the large number of tests performed (Table 13.1). Among the loci identified was TCF7L2, which acted as a positive control in this study and provided added confidence these loci were indeed T2DM susceptibility genes. Another was a nonsynonymous variant in the solute carrier family 30 (zinc transporter), member 8 (SLC30A8) encoding the zinc transporter regulating zinc concentration in insulin secretory vesicles of the β-cell. There were two regions of extended linkage disequilibrium harboring multiple genes. One region included hematopoietically expressed homeobox (HHEX), insulin-degrading enzyme (IDE), and kinesin family member 11 (KIF11), all of which could be logically tied to the biology underlying T2DM. The second region included exostoses (multiple) 2 (EXT2) and aristaless-like homeobox 4 (ALX4), which although less obvious candidates, nonetheless have plausible biologic ties to T2DM. Interestingly, the final SNP was in a region on chromosome 11 that did not harbor any known human genes; a so-called “gene desert,” which raised interesting questions regarding human genome architecture. This study demonstrated that large-scale anonymous interrogation of the human genome by association could indeed identify susceptibility genes for a complex disease.
Table 13.1
Current type 2 diabetes susceptibility genes and their reported effect sizes
|
Chromosome |
SNP |
Gene region |
Description |
Reported odds ratio |
References |
|
1 |
rs10923931 |
NOTCH1 |
Intronic |
1.13 |
97 |
|
2 |
rs7578597 |
THADA |
Nonsynonymous |
1.15 |
97 |
|
3 |
rs4607103 |
ADAMTS9 |
Upstream from the gene |
1.09 |
97 |
|
3 |
rs4402960 |
IGF2BP2 |
Intronic |
1.14 |
26–28 |
|
3 |
rs1801282 |
PPARG |
Nonsynonymous |
0.79 |
12 |
|
4 |
rs10010131 |
WFS1 |
Intronic |
0.90 |
98 |
|
6 |
rs7754840 |
CDKAL1 |
Intronic |
1.12 |
26–28 |
|
7 |
rs864745 |
JAZF1 |
Intronic |
1.10 |
97 |
|
8 |
rs13266634 |
SLC30A8 |
Nonsynonymous |
1.18 |
24 |
|
9 |
rs10811661 |
CDKN2A/2B |
Upstream from the genes |
1.20 |
26–28 |
|
10 |
rs12779790 |
CDC123-CAMK1D |
Intergenic |
1.11 |
97 |
|
10 |
rs7903146 |
TCF7L2 |
Intronic |
1.37 |
14 |
|
10 |
rs1111875 |
HHEX-IDE-KIF11 |
Downstream from the genes |
1.19 |
24 |
|
11 |
rs7480010 |
- - - |
Intergenic |
1.14 |
24 |
|
11 |
rs5219 |
KCNJ11 |
Nonsynonymous |
1.23 |
13 |
|
11 |
rs3740878 |
EXT2 |
Intronic |
1.26 |
24 |
|
11 |
rs2237892 |
KCNQ1 |
Intronic |
1.49 |
99,100 |
|
11 |
rs10830963 |
MTNR1B |
Intronic |
1.09 |
41 |
|
12 |
rs7961581 |
TSPAN8-LGR5 |
Intergenic |
1.09 |
97 |
|
16 |
rs8050136 |
FTO |
Intronic |
1.17 |
26–28 |
|
17 |
rs757210 |
HNF1B |
Intronic |
1.13 |
101 |
This initial success was immediately followed in April 2007 by a series of three GWA studies that collaborated to improve the power of their individual studies by performing a meta-analysis.26–28 These groups replicated some of the findings by the original GWA study, such as TCF7L2, SLC30A8, the HHEX region, and even the intragenic region on chromosome 11, but also identified additional new loci (Table 13.1). This meta-analysis approach has become the current standard for GWA studies and as of this writing, 21 T2DM susceptibility genes have been identified (Table 13.1).
13.2.3 T2DM-Related Quantitative Traits
A general rule of thumb in statistics is that analysis of a continuous variable will be statistically more powerful than an analysis that categorizes the continuous variable. For a variety of reasons, this maxim may not fully extend to the analysis of disease-related quantitative traits. For example, as one progresses toward disease both the disease process and disease treatment could significantly alter one tail of the trait distribution. However, in principle the analysis of such traits could provide additional insights into the genetic architecture underlying a complex disease. Many groups have begun to apply GWA to search for genes underlying variation in disease-related quantitative traits, hypothesizing that a gene regulating variability in a trait may also be a disease susceptibility variant. To date, genes underlying height,29, 30lipids,31,32 anthropometrics,33–37 and fasting glucose38–41 have been identified.
One obvious trait to examine with respect to diabetes is fasting glucose. Among the first loci to be identified as regulating fasting glucose concentrations was a promoter variant in glucokinase (GCK) first reported in the mid to late 1990s42,43 and subsequently confirmed in a large meta-analysis in 2005 by Weedon et al.44,45 This promoter variant appears to contribute to mild hyperglycemia and does not contribute to risk for T2DM, consistent with observations regarding coding variants in GCK and MODY2.46 47 Interestingly, this variant also appears to be associated with birth weight,44,45 suggesting implications for fetal development. Recent results from the Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) have shown that variation in GCK is associated with both fasting glucose levels and risk for type 2 diabetes (Dupuis et al., Nat Genet. 2010;42:105-116 (PMID 20081858).
A recent GWA meta-analysis of fasting glucose reported association with glucose-6-phosphatase, catalytic unit 2 (G6PC238, 48), which was replicated in a separate study.39 This association was interesting in that G6PC2appeared to only be associated with fasting glucose and showed no evidence of association with T2DM.38,39 Most recently, a large GWA-based meta-analysis by the Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) reported that melatonin receptor-1B (MTNR1B) was associated with fasting glucose,41 but in contrast to G6PC2, also showed evidence for association with T2DM.41 These observations were independently reported by Bouatia-Naji et al.40 Overall, these results suggest that there may be genes that only contribute to the day-to-day regulation of fasting glucose levels, while others may also contribute to susceptibility to T2DM.
MAGIC also demonstrated that MTNR1B was associated with insulin secretion and β-cell function.49 These results demonstrate that while there may be an initial association between a genetic variant and specific quantitative trait, the primary effect of the gene may be elsewhere in the biologic pathway. Therefore, it is important to examine the association with other traits to best characterize the specific biology underlying the gene. A second example is the fat and obesity associated (FTO) gene, which was initially identified as being associated with T2DM,26–28 but has subsequently been shown to be primarily associated with body mass index and body fat.33,50
It is also noteworthy that like T2DM susceptibility genes, genetic variants underlying quantitative traits typically have very small effect sizes. For example, G6PC2 and MTNR1B each independently have a per allele effect of ∼0.07 mM, accounting for a very small proportion of the variation in fasting glucose.38–41 The fact that only a small proportion of the variation in these traits is accounted for by genes suggests additional loci for this and other traits.
13.3 Genetics of GDM
13.3.1 Heritability of GDM
Despite successful identification of genes associated with T2DM and T2DM-related traits, surprisingly there has been relatively little research in the area of GDM genetics. This may partly reflect the fact that the study of GDM genetics is fraught with difficulties. An essential first step in genetics research has been the determination of evidence for a genetic basis for the disease, typically achieved through assessment of heritability or familial clustering. However, performing such studies in a prospective fashion is hindered by, among other things, identification of multiple GDM cases in a single family. Similarly, retrospective studies are hampered by the evolution of the clinical definition of GDM and the fact that definitions differ (Chap. 1.2).51–55 Furthermore, screening for GDM in the United States has not been routine until the 1990s, leading to possible ascertainment bias. There are also difficulties in ascertaining sufficient numbers of GDM cases, given its relatively low prevalence. Finally, many existing cohort studies typically lack DNA samples and resampling for DNA is either not possible or a Herculean task.
No published studies have estimated familiality with GDM. There has been only one unpublished attempt to estimate familiality of GDM. Williams and colleagues used the state-wide medical record system in the state of Washington to link sisters diagnosed with GDM and estimated the sibling GDM risk ratio to be 1.75 (Michelle Williams, personal communication), suggesting some evidence for a genetic basis for GDM. Although the risk ratio of 1.75 is likely an underestimate of the true sibling risk ratio, it does provide some evidence for familiality of GDM and suggests the genetic basis for GDM may fall within the range of T2DM.56
There have been studies examining whether GDM clusters with type 1 diabetes mellitus or T2DM. Dorner et al showed that offspring with type 1 diabetes whose mothers had GDM showed increased familial aggregation of diabetes on the maternal side compared with the paternal side.57 There is also evidence for clustering of impaired glucose tolerance and T2DM in parents of a woman with GDM58 and evidence for higher prevalence of T2DM specifically in mothers of women with GDM.59 Kim and colleagues examined the association between family history of diabetes and history of GDM in the National Health and Nutrition Examination Survey and also found that sibling history of diabetes increased the odds of GDM (Catherine Kim, personal communication). Thus, there exists evidence of a link between both autoimmune and nonautoimmune forms of diabetes and GDM. The fact that GDM clusters with both autoimmune and nonautoimmune forms of diabetes suggests a potential overlap in genetic susceptibility among these various forms of the disease.
13.3.2 Candidate Gene Studies
Candidate genes related to both autoimmune and nonautoimmune forms of GDM have been assessed in a variety of cohorts60–69 and candidate genes in GDM are also reviewed by Robitaille and Grant.70 It appears that many of the candidate gene associations reported with modest evidence of association are likely to represent under-powered studies. However, the reader is cautioned to carefully evaluate results for genetic studies based on the sample collected and the analyses performed. The fact that a study does not achieve a certain, somewhat arbitrary, level of statistical significance, or is not of a sample size approaching that of a GWA should not be grounds for outright dismissal. For example, gene association studies are essentially signal-to-noise ratio problems. While one solution to this problem is to increase sample size to rise above the “noise,” another approach is selective sample collection to reduce the “noise.” Both can yield equally valid results.
One candidate gene of particular interest for GDM is GCK.62, 71–74 Although the frequency of GCK variants among GDM patients is low,71 the important contribution of GCK variation to risk for GDM can be observed in MODY2 families, where a large proportion of female members present with GDM.72 Saker et al speculated that because GCK variants typically result in subclinical hyperglycemia,47,75 the frequency of GCK variants may be higher and only detectable upon pregnancy.72 Their speculation was supported by Ellard et al who estimated that the prevalence of GCK variants may be as high as 80% in a small subset of women selected using stringent criteria.74
The BetaGene study is unique among studies examining candidate genes for GDM in that it is studying Mexican American families of probands with or without a diagnosis of GDM and has focused on T2DM-related quantitative traits, rather than GDM per se.76–78 Another unique aspect of BetaGene is the examination of whether the association between a gene and trait is modified by adiposity. For example, the study found that TCF7L2 was associated with both GDM and with insulin secretion and β-cell function.76 However, the association between TCF7L2 and insulin secretion and β-cell function differed by the subject’s level of body fat. Similar observations were made for the association between insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) and insulin sensitivity.78 These are among the first observations to suggest that the effect of genes may be modified by other factors, like adiposity. The BetaGene investigators have also examined gene–gene interactions,77 another critical component of the genetic architecture of complex diseases.
13.3.3 Genes for GDM?
The question of GDM genes is closely tied to the debate surrounding whether GDM represents a unique disease state, or whether pregnancy results in metabolic derangements revealing individuals already on a trajectory towards T2DM. If T2DM susceptibility variants are also associated with GDM, from a purely genetic perspective it would be difficult to argue a unique genetic predisposition for GDM. This does not take into account the possibility of unique environmental exposures related to pregnancy that may interact with genes to alter disease risk or may affect pregnancy outcomes.
One group has recently assessed a subset of T2DM susceptibility loci for association with GDM.79 They selected a case–control set of 238 women with a history of GDM and 2,446 normal glucose tolerant women from the population-based Inter99 cohort80 and eleven T2DM susceptibility loci were tested for association with GDM. Among the 11 T2DM genes tested, all but 1 showed estimated odds ratios >1.0 and among these loci 4 (TCF7L2, CDKAL1, TCF2, and FTO) showed nominal evidence for association with GDM. While not all 11 loci were associated with T2DM, a highly significant additive effect of all 11 loci on risk for GDM (OR = 1.18; p = 3.2 × 10−6) was found, suggesting that the genetic basis for GDM and T2DM may, in fact, be the same.
13.4 What Does the Future Hold?
The new GWA era means it is now likely possible to identify large GDM case–control samples and perform GWA studies to identify susceptibility genes for GDM. The unanswered question of whether GDM per se has a genetic basis may also be directly addressed using the GWA framework, by incorporating carefully selected samples of T2DM cases as a secondary contrast group into the study design. Also, it should be possible to begin to understand how gene variants in the mother and fetus interact in terms of pregnancy outcomes, an area that has received very little attention.81, 82
Should GDM have unique genetic susceptibility loci, this information could be critical in developing new interventional strategies to not just treat GDM, but also prevent progression to T2DM, which appears to occur in a large proportion of GDM cases.83,84
Three important cautionary notes arise. First, one should not directly correlate the relatively small odds ratios associated with these gene variants in terms of risk for T2DM or GDM and their relative importance in terms of disease biology. A common misconception is that with such small odds ratios, these gene variants are not likely to be playing a large role in disease pathogenesis and are therefore clinically irrelevant. However, one cannot deny the important role PPARG plays in terms of T2DM treatment85–87 and possibly prevention88–90 via thiazolidinedione therapy. Similarly, KCNJ11 is another important therapeutic target of the sulfonylurea class of medications.87,91–93 Yet, the odds ratio for T2DM risk associated with PPARG is ∼1.2 and for KCNJ11 is ∼1.1.
Second, it is insufficient to simply know that a genetic variant is associated with a disease or disease-related trait. The relative role played by any gene in terms of disease pathogenesis requires additional molecular and physiologic study. This is exemplified by the role of PPARG and KCNJ11 in terms of diabetes therapy and the interactions with adiposity observed in the BetaGene study.
Finally, knowledge of the genes underlying disease will not be useful in predicting future disease until the biologic effect of genes is well understood. Several studies have already attempted to determine if gene variants can be used to “predict” T2DM94–96 or GDM.79 Each of these analyses shows that gene variants alone do not predict T2DM nor do they significantly improve predictability over traditional clinical predictors. Rather, genetic information should be leveraged to improve clinical care through development of new pharmacologic agents or interventional strategies.
Our knowledge of the genetics underlying both T2DM and GDM will continue to improve over the next several years. At the time of this writing, I am aware of at least an additional eight genes related to T2DM or T2DM-related traits that will soon be appearing in the literature. “Next generation” sequencing methods allow for sequencing of the genome of a single individual in a relatively short time and at relatively low cost and will likely introduce yet another wave of genetic discoveries. Already this technology is being applied in the 1,000 Genomes Project (http://www.1000genomes.org), which has set a goal to completely sequence the genomes of 2,000 individuals selected from around the world.
Pharmacogenetics is likely to be an area that will significantly impact clinical care as we learn more about the relationship between genetic variation and drug responses. Also, genetic findings are raising interest in specific areas of diabetes research. For example, the MTNR1B findings have increased interest in studies of circadian rhythms and sleep disorders and their relation to diabetes and obesity; an area many clinicians have not considered a part of their interventional arsenal. Our own BetaGene study is now looking at the possible effect of genetic variation on longitudinal change in T2DM-related traits, based on our finding of interactions with adiposity.76,78 In the end, as we learn more about the intricacies of the genetic architecture underlying GDM and T2DM, it is easy to envision a time, in the not too distant future, when genetic information will be used for so-called “personalized medicine.”
Acknowledgments
I would like to thank my colleagues on the FUSION and BetaGene studies and in MAGIC, who have made many of the contributions discussed in this chapter. RMW was or is supported as principle investigator or coinvestigator on grants from the American Diabetes Association (05-RA-140), National Institutes of Health (DK69922, DK62370, and DK61628), and Merck & Co.
References
1.
Morton NE, Collins A. Toward positional cloning with SNPs. Curr Opin Mol Ther. 2002;4:259-264.
2.
Collins FS. Identifying human disease genes by positional cloning. Harvey Lect. 1991;86:149-164.
3.
Neel JV. Diabetes mellitus – a geneticist’s nightmare. In: Creutzfeldt W, Kobberling J, Neel JV, eds. The Genetics of Diabetes. New York: Springer; 1976:1-11.CrossRef
4.
McCarthy MI. Growing evidence for diabetes susceptibility genes from genome scan data. Curr Diab Rep. 2003;3:159-167.CrossRef
5.
Fajans SS, Bell GI, Polonsky KS. Molecular mechanisms and clinical pathophysiology of maturity onset diabetes of the young. N Engl J Med. 2001;345:971-980.CrossRef
6.
Keefer LM, Piron MA, De MP, et al. Impaired negative cooperativity of the semisynthetic analogues human [LeuB24]- and [LeuB25]-insulins. Biochem Biophys Res Commun. 1981;100:1229-1236.CrossRef
7.
Kwok SC, Steiner DF, Rubenstein AH, et al. Identification of a point mutation in the human insulin gene giving rise to a structurally abnormal insulin (insulin Chicago). Diabetes. 1983;32:872-875.CrossRef
8.
Nanjo K, Sanke T, Kondo M, et al. Mutant insulin syndrome: identification of two families with [LeuA3]insulin and determination of its biological activity. Trans Assoc Am Physicians. 1986;99:132-142.
9.
Nanjo K, Miyano M, Kondo M, et al. Insulin Wakayama: familial mutant insulin syndrome in Japan. Diabetologia. 1987;30:87-92.
10.
Kadowaki T, Kadowaki H, Mori Y. A subtype of diabetes mellitus associated with a mutation of mitochondrial DNA. N Engl J Med. 1994;330:962-966.CrossRef
11.
Deeb SS, Fajas L, Nemoto M, et al. A Pro12Ala substitution in PPARγ2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet. 1998;20:284-287.CrossRef
12.
Altshuler D, Hirschhorn JN, Klannemark M, et al. The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet. 2000;26:76-80.CrossRef
13.
Gloyn AL, Weedon MN, Owen KR, et al. Large-scale association studies of variants in genes encoding the pancreatic β-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes. 2003;52:568-572.CrossRef
14.
Grant SF, Thorleifsson G, Reynisdottir I, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006;38:320-323.CrossRef
15.
Horikawa Y, Oda N, Cox NJ, et al. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet. 2000;26:163-175.CrossRef
16.
Bento JL, Palmer ND, Mychaleckyj JC, et al. Association of protein tyrosine phosphatase 1B gene polymorphisms with type 2 diabetes. Diabetes. 2004;53:3007-3012.CrossRef
17.
Silander K, Mohlke KL, Scott LJ, et al. Genetic variation near the hepatocyte nuclear factor-4α gene predicts susceptibility to type 2 diabetes. Diabetes. 2004;53:1141-1149.CrossRef
18.
Love-Gregory LD, Wasson J, Ma J, et al. A common polymorphism in the upstream promoter region of the hepatocyte nuclear factor-4alpha gene on chromosome 20q is associated with type 2 diabetes and appears to contribute to the evidence for linkage in an Ashkenazi Jewish population. Diabetes. 2004;53:1134-1140.CrossRef
19.
Johansson S, Raeder H, Eide SA, et al. Studies in 3, 523 Norwegians and meta-analysis in 11, 571 subjects indicate that variants in the hepatocyte nuclear factor 4 alpha (HNF4A) P2 region are associated with type 2 diabetes in Scandanavians. Diabetes. 2007;56:3112-3117.CrossRef
20.
Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273:1516-1517.CrossRef
21.
International Human Genome Mapping Consortium. A physical map of the human genome. Nature. 2001;409:934-941.CrossRef
22.
The International HapMap Consortium. The international HapMap project. Nature. 2003;426:789-796.CrossRef
23.
The International HapMap Consortium. A haplotype map of the human genome. Nature. 2005;437:1299-1320.PubMedCentralCrossRef
24.
Sladek R, Rocheleau G, Rung J, et al. A genome-wide association study identified novel risk loci for type 2 diabetes. Nature. 2007;445:881-885.CrossRef
25.
Skol AD, Scott LJ, Abecasis GR, et al. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006;38:209-213.CrossRef
26.
Diabetes Genetics Initiative of Broad Institute of Harvard and MIT LUaNIfBR. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316:1331-1336.CrossRef
27.
Scott LJ, Mohlke KL, Bonnycastle LL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316:1341-1345.PubMedCentralCrossRef
28.
Zeggini E, Weedon MN, Lindgren CM, et al. Replication of genome-wide association signals in U.K. samples reveals risk loci for type 2 diabetes. Science. 2007;316:1336-1341.PubMedCentralCrossRef
29.
Sanna S, Jackson AU, Nagaraja R, et al. Common variants in the GDF5-UQCC region are associated with variation in human height. Nat Genet. 2008;40:198-203.PubMedCentralCrossRef
30.
Weedon MN, Lango H, Lindgren CM, et al. Genome-wide association analysis identifies 20 loci that influence adult height. Nat Genet. 2008;40:575-583.PubMedCentralCrossRef
31.
Willer CJ, Sanna S, Jackson AU, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40:161-169.CrossRef
32.
Kathiresan S, Willer CJ, Peloso GM, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet. 2009;41:56-65.PubMedCentralCrossRef
33.
Frayling TM, Timpson NJ, Weedon MN, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889-894.PubMedCentralCrossRef
34.
Scuteri A, Sanna S, Chen W-M, et al. Genome-wide asociation scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007;3:1200-1210.CrossRef
35.
Loos RJ, Lindgren CM, Li S, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet. 2008;40:768-775.PubMedCentralCrossRef
36.
Chambers JC, Elliott P, Zabaneh D, et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet. 2008;40:716-718.CrossRef
37.
Thorleifsson G, Walters GB, Gudbjartsson DF, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009;41:18-24.CrossRef
38.
Chen W-M, Erdos MR, Jackson AU, et al. Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels. J Clin Invest. 2008;118:2609-2628.
39.
Bouatia-Naji N, Rocheleau G, Van Lommel L, et al. A polymorphism within the G6PC2 gene is associated with fasting plasma glucose levels. Science. 2008;320:1085-1088.CrossRef
40.
Bouatia-Naji N, Bonnefond A, Cavalcanti-Proenca C, 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.
41.
Prokopenko I, Langenberg C, Florez JC, et al. Variants in MTNR1B influence fasting glucose levels. Nat Genet. 2009;41:77-81.PubMedCentralCrossRef
42.
Stone LM, Kahn SE, Deeb SS, et al. Glucokinase gene variations in Japanese-Americans with a family history of NIDDM. Diabetes Care. 1994;17:1480-1483.CrossRef
43.
Stone LM, Kahn SE, Fujimoto WY, et al. A variation at position -30 of the β-cell glucokinase gene promoter is associated with reduced β-cell function in middle-aged Japanese-American men. Diabetes. 1996;45:428.CrossRef
44.
Weedon MN, Frayling TM, Shields B, et al. Genetic regulation of birth weight and fasting glucose by a common polymorphism in the islet promoter of the glucokinase gene. Diabetes. 2005;54:576-581.CrossRef
45.
Weedon MN, Clark VJ, Qian Y, et al. A common haplotype of the glucokinase gene alters fasting glucose and birth weight: Association in six studies and population-genetics analyses. Am J Hum Genet. 2006;79:991-1001.PubMedCentralCrossRef
46.
Froguel Ph, Zouali H, Vionnet N, et al. Familial hyperglycemia due to mutations in glucokinase. N Engl J Med. 1993;328:697-702.CrossRef
47.
Hattersley AT. Maturity-onset diabetes of the young: clinical heterogeneity explained by genetic heterogeneity. Diabet Med. 1998;15:15-24.CrossRef
48.
Chen WM, Jackson AU, Scuteri A, et al. Genome-wide association scans in cohorts from Sardinia and Finland identify a locus for fasting glucose levels. [Abstract Program Number 259]. Presented at the annual meeting of the American Society of Human Genetics, October 2007, San Diego, CA. http://www.ashg.org/cgi-bin/ashg07s/ashg07?author=watanabe&sort=ptimes&sbutton=Detail&absno=11220&sid=462012
49.
Lyssenko V, Nagorny CL, Erdos MR, et al. Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet. 2009;41:82-88.PubMedCentralCrossRef
50.
Dina C, Meyre D, Gallina S, et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet. 2007;39:724-726.CrossRef
51.
National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes. 1979;28:1039-1057.CrossRef
52.
World Health Organization: Diabetes Mellitus: Report of a WHO Study Group, 1985.
53.
The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care. 1997;20:1183-1197.
54.
World Health Organization: Definition, diagnosis and classification of diabetes mellitus and its complications. Report of a WHO Consultation, 1999.
55.
Metzger BE. Summary and recommendation of the Third International Workshop-Conference on Gestational Diabetes Mellitus. Diabetes. 1991;40:197-201.CrossRef
56.
Rich SS. Mapping genes in diabetes. Diabetes. 1990;39:1315-1319.CrossRef
57.
Dorner G, Plagemann A, Reinagel H. Familial diabetes aggregation in type I diabetics: Gestational diabetes an apparent risk factor for increased diabetes susceptibility in the offspring. Exp Clin Endocrinol. 1987;89:84-90.CrossRef
58.
McLellan JA, Barrow BA, Levy JC, et al. Prevalence of diabetes mellitus and impaired glucose tolerance in parents of women with gestational diabetes. Diabetologia. 1995;38:693-698.CrossRef
59.
Martin AO, Simpson JL, Ober C, et al. Frequency of diabetes mellitus in mothers of probands with gestational diabetes: Possible maternal influence on the predisposition to gestational diabetes. Am J Obstet Gynecol. 1985;151:471-475.CrossRef
60.
Freinkel N, Metzger BE, Phelps RL, et al. Gestational diabetes mellitus: a syndrome with phenotypic and genotypic heterogeneity. Horm Metab Res. 1986;18:427-439.CrossRef
61.
Ober C, Xiang KS, Thisted RA, et al. Increased risk for gestational diabetes mellitus associated with insulin receptor and insulin-like growth factor II restriction fragment length polymorphisms. Genet Epidemiol. 1989;6:559-569.CrossRef
62.
Chiu KC, Go RC, Aoki M, et al. Glucokinase gene in gestational diabetes mellitus: population association study and molecular scanning. Diabetologia. 1994;37:104-110.CrossRef
63.
Festa A, Krugluger W, Shnawa N, et al. Trp64Arg polymorphismof the β3-adrenergic receptor gene in pregnancy: Association with mild gestational diabetes mellitus. J Clin Endocrinol Metab. 1999;84:1695-1699.CrossRef
64.
Rissanen J, Mykkänen L, Markkanen A, et al. Sulfonylurea receptor 1 gene variants are associated with gestational diabetes and type 2 diabetes but not with altered secretion of insulin. Diabetes Care. 2000;23:70-73.CrossRef
65.
Chen Y, Liao WX, Roy AC, et al. Mitochondrial gene mutations in gestational diabetes mellitus. Diabetes Res Clin Pract. 2000;48:29-35.CrossRef
66.
Aggarwal P, Gill-Randall R, Wheatley T, et al. Identification of mtDNA mutation in a pedigree with gestational diabetes, deafness, Wolff-Parkinson-White syndrome and placenta accreta. Hum Hered. 2001;51:114-116.CrossRef
67.
Megia A, Gallart L, Fernandez-Real JM, et al. Mannose-binding lectin gene polymorphisms are associated with gestational diabetes mellitus. J Clin Endocrinol Metab. 2004;89:5081-5087.CrossRef
68.
Lauenborg J, Damm P, Ek J, et al. Studies of the ADA/VAL98 polymorphism of the hepatocyte nuclear factor-1α gene and the relationship to β-cell function during an OGTT in glucose-tolerant women with and without previous gestational diabetes mellitus. Diabet Med. 2004;21:1310-1315.CrossRef
69.
Weng J, Ekelund M, Lehto M, et al. Screening for MODY mutations, GAD antibodies, and type 1 diabetes-associated HLA genotypes in women with gestational diabetes mellitus. Diabetes Care. 2002;25:68-71.CrossRef
70.
Robitaille J, Grant AM. The genetics of gestational diabetes mellitus: evidence for relationship with type 2 diabetes mellitus. Genet Med. 2008;10:240-250.CrossRef
71.
Stoffel M, Bell KL, Blackburn CL, et al. Identification of glucokinase mutations in subjects with gestational diabetes mellitus. Diabetes. 1993;42:937-940.CrossRef
72.
Saker PJ, Hattersley AT, Barrow B, et al. High prevalence of a missense mutation of the glucokinase gene in gestational diabetic patients due to a founder-effect in a local population. Diabetologia. 1996;39:1325-1328.CrossRef
73.
Allan CJ, Argyropoulos G, Bowker M, et al. Gestational diabetes mellitus and gene mutations which affect insulin secretion. Diabetes Res Clin Pract. 1997;36:135-141.CrossRef
74.
Ellard S, Beards F, Allen LI, et al. A high prevalence of glucokinase mutations in gestational diabetic subjects selected by clinical criteria. Diabetologia. 2000;43:250-253.CrossRef
75.
Page RC, Hattersley AT, Levy JC, et al. Clinical characteristics of subjects with a missense mutation in glucokinase. Diabet Med. 1995;12:209-217.CrossRef
76.
Watanabe RM, Allayee H, Xiang AH, et al. Transcription factor 7-like 2 (TCF7L2) is associated with gestational diabetes mellitus and interacts with adiposity to alter insulin secretion in Mexican Americans. Diabetes. 2007;56:1481-1485.PubMedCentralCrossRef
77.
Black MH, Fingerlin TE, Allayee H, et al. Evidence of interaction between peroxisome proliferator-activated receptor-γ2 and hepatocyte nuclear factor-4α contributing to variation in insulin sensitivity in Mexican Americans. Diabetes. 2008;57:1048-1056.CrossRef
78.
Li X, Allayee H, Xiang AH, et al. Variation in IGF2BP2 interacts with adiposity to alter insulin sensitivity in Mexican Americans. Obesity. 2009;17:729-736.
79.
Lauenborg J, Grarup N, Damm P, et al. Common type 2 diabetes risk gene variants asociated with gestational diabetes. J Clin Endocrinol Metab. 2009;94:145-150.
80.
Glümer C, Jørgensen T, Borch-Johnsen K, et al. Prevalences of diabetes and impaired glucose regulation in a Danish population: the Inter99 study. Diabetes Care. 2003;26:2335-2340.CrossRef
81.
Singh R, Pearson ER, Clark PM, et al. The long-term impact on offspring of exposure to hyperglycaemia in utero due to maternal glucokinase gene mutations. Diabetologia. 2007;50:620-624.CrossRef
82.
Pearson ER, Boj SF, Steele AM, et al. Macrosomia and hyperinsulinaemic hypoglycaemia in patients with heterozygous mutations in the HNF4A gene. PLoS Med. 2007;4:e118.PubMedCentralCrossRef
83.
Kjos SL, Peters RK, Xiang A, et al. Predicting future diabetes in Latino women with gestational diabetes. Diabetes. 1995;44:586-591.CrossRef
84.
Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes. Diabetes Care. 2002;25:1862-1868.CrossRef
85.
Schwartz S, Raskin P, Fonseca V, et al. Effect of troglitazone in insulin-treated patients with type II diabetes mellitus. N Engl J Med. 1998;338:861-866.CrossRef
86.
Baba S. Pioglitazone: a review of Japanese clinical studies. Curr Med Res Opion. 2001;17:166-189.
87.
Kahn SE, Haffner SM, Heise MA, et al. Glycemic durability of rosiglitazone, metformin, or glyburide monotherapy. N Engl J Med. 2006;355:2427-2443.CrossRef
88.
Buchanan TA, Xiang AH, Peters RK, et al. Preservation of pancreatic β-cell function and prevention of type 2 diabetes by pharmacological treatment of insulin resistance in high-risk hispanic women. Diabetes. 2002;51:2796-2803.CrossRef
89.
Xiang AH, Peters RK, Kjos SL, et al. Effect of pioglitazone on pancreatic β-cell function and diabetes risk in Hispanic women with prior gestational diabetes. Diabetes. 2006;55:517-522.PubMedCentralCrossRef
90.
The DREAM Trial Investigators. Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial. Lancet. 2006;368:1096-1105.CrossRef
91.
Minuk HL, Vranic M, Marliss EB, et al. Glucoregulatory and metabolic response to exercise in obese noninsulin-dependent diabetes. Am J Physiol. 1981;240:E458-E464.
92.
Prigeon RL, Jacobson RK, Porte D Jr, et al. Effect of sulfonylurea withdrawal on proinsulin levels, B cell function, and glucose disposal in subjects with noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab. 1996;81:3295-3298.CrossRef
93.
Shapiro ET, Van Cauter E, Tillil H, et al. Glyburide enhances the responsiveness of the β-cell to glucose by does not correct the abnormal patterns of insulin secretion in noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab. 1989;69:571-576.CrossRef
94.
Balkau B, Lange C, Fezeu L, et al. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care. 2008;31:2056-2061.CrossRef
95.
van HM, Dehghan A, Witteman JC, et al. Predicting type 2 diabetes based on polymorphisms from genome-wide association studies: a population-based study. Diabetes. 2008;57:3122-3128.
96.
Lango H, Palmer CN, Morris AD, et al. Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes. 2008;57:3129-3135.CrossRef
97.
Zeggini E, Scott LJ, Saxena R, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40:638-645.PubMedCentralCrossRef
98.
Sandhu MS, Weedon MN, Fawcett KA, et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet. 2007;39:951-953.PubMedCentralCrossRef
99.
Yasuda K, Miyake K, Horikawa Y, et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat Genet. 2008;40:1092-1097.CrossRef
100.
Unoki H, Takahashi A, Kawaguchi T, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet. 2008;40:1098-1102.CrossRef
101.
Winckler W, Weedon MN, Graham RR, et al. Evaluation of common variants in the six known maturity-onset diabetes of the young (MODY) genes for association with type 2 diabetes. Diabetes. 2007;56:685-693.CrossRef