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Diabetes. Nov 2008; 57(11): 2889–2898.
PMCID: PMC2570381
Learning From Molecular Genetics
Novel Insights Arising From the Definition of Genes for Monogenic and Type 2 Diabetes
Mark I. McCarthy1,2 and Andrew T. Hattersley3
1Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K
2Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
3Diabetes Genetics, Peninsula Medical School, Exeter, U.K
Corresponding authors: Andrew T. Hattersley, andrew.hattersley/at/pms.ac.uk, and Mark I. McCarthy, mark.mccarthy/at/drl.ox.ac.uk
Received March 10, 2008; Accepted July 25, 2008.
Genetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified. The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications. This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes.
Defining monogenic diabetes genes by candidate gene and positional cloning approaches.
There has been rapid progress in defining the etiological genes for monogenic diabetes reflecting the relative simplicity of gene discovery in single gene disorders. The candidate gene approach has been remarkably successful in defining monogenic genes; this reflects that key rate-limiting steps in insulin secretion and action are known, and severe mutations affecting these proteins will result in β-cell dysfunction or insulin resistance. Examples of this approach include the genes encoding insulin (1), glucokinase (2,3), the two ATP-sensitive K+ channel (KATP channel) subunits Kir6.2 (4) and SUR1 (5,6), peroxisome proliferator–activated receptor (PPAR)γ (7), and the insulin receptor (8). Finding human subjects with mutations in these candidate genes has allowed confirmation of a critical role in humans of the encoded protein, helped define structure and function of the protein, and allowed confirmation of the associated pathophysiology (e.g., abnormal glucose sensing in glucokinase mutations) (9), but it has not led to the identification of novel pathways in glucose homeostasis.
Completely unexpected critical pathways for insulin secretion and action have resulted from the positional cloning of novel monogenic diabetes genes. The most striking example was the identification of HNF1A, encoding the transcription factor hepatic nuclear factor (HNF)-1α, as the maturity-onset diabetes of the young (MODY) gene linked to 12q (10). Before this finding it was not known that HNF1A was expressed in the β-cell, and diabetes had not been noticed in the hnf1a-knockout mouse (11), although it was noticed subsequently (12). This result rapidly led to mutations in other hepatic transcription factor genes, HNF4A (13) and HNF1B (14), shown to cause MODY. These findings have led to a whole new area of β-cell biology seeking to explain why haploinsufficency of these genes resulted in progressive β-cell dysfunction (15,16). Mutations in CEL, which encodes the lipolytic enzyme carboxyl-ester lipase, responsible for the hydrolysis of cholesterol esters, was also an unexpected MODY gene identified through positional cloning (17). CEL is only expressed in the pancreatic acinar cell, so it was unexpected that there was β-cell dysfunction. Further studies of the mechanism will lead to new understanding of the close relationship between the exocrine and endocrine pancreas. Familial partial lipodystrophy was shown, following linkage to 1q21, to arise from mutations in LMNA, encoding Lamin A/C (18). Mutations in LMNA can also result in myopathy, dilated cardiomyopathy, or atypical progeria (19), and the biology of how these mutations alter fat distribution is still incompletely understood. Therefore, positional cloning has led to exciting novel pathways of glucose homeostasis.
Most monogenic diabetes genes are β-cell genes.
A key result has been that the vast majority of genes where mutations cause early-onset diabetes have reduced β-cell function rather than increased insulin resistance. Heterozygous haploinsufficency results in dominant early-onset diabetes for many β-cell genes, including GCK, HNF1A, HNF4A, and HNF1B, but this is not seen in insulin resistance genes. This shows that even when faced with severe insulin resistance, a healthy β-cell is usually able to compensate, but there is no compensation possible when faced with marked insulin deficiency. There are many mechanisms of β-cell dysfunction seen in monogenic diabetes, including reduced β-cell development, failure of glucose sensing, and increased destruction of the β-cell (Table 1).
TABLE 1
TABLE 1
Examples of some mechanisms of β-cell dysfunction seen in monogenic diabetes
Gene discovery can also lead to recognition of novel phenotypes.
For many genetic syndromes, such as Wolcott Rallison, and IPEX (immune dysregulation, polyendocrinopathy, enteropathy, X-linked) syndromes, a discrete cluster of clinical features including diabetes was initially recognized as a clinical syndrome, and subsequently the gene responsible was identified (20,21). In these cases, the gene discovery gave new biological insights but only limited insights into the phenotype.
However, in other clinically defined categories, the identification of the etiological genes helped the recognition of novel clinical subgroups. MODY was clinically defined as autosomal dominantly inherited, non–insulin-dependent, early-onset diabetes, but now there are at least eight genetic subgroups of MODY, most of which have a discrete phenotype (Fig. 1) (22,23). Similarly, diabetes diagnosed in the first few months of life was defined clinically as permanent neonatal diabetes mellitus (PNDM) or transient neonatal diabetes mellitus (TNDM), depending on whether the diabetes resolved (22,23). Molecular genetic advances have identified three genetic subgroups of TNDM, four genetic subgroups of PNDM, and five genetic syndromes that include neonatal diabetes (Fig. 2); many of these genetically defined subgroups can, at least in part, be separated by their clinical features (22,23).
FIG. 1.
FIG. 1.
The genetic heterogeneity of clinically defined MODY. The percentages given are based on over 1,000 referrals to the Exeter lab for genetic testing in MODY. (S. Ellard and A.T.H., unpublished data). MODY X represents patients with a clear clinical diagnosis (more ...)
FIG. 2.
FIG. 2.
The genetic heterogeneity of the clinically defined subdivisions of neonatal diabetes. The percentages given are based on over 400 referrals to the Exeter and Salisbury labs for genetic testing for neonatal diabetes (S. Ellard, A.T.H., D. Mackay, and (more ...)
Many entirely novel multisystem clinical syndromes were only recognized clinically after the molecular genetics were defined. The most common is maternally inherited diabetes and deafness, resulting from a heteroplasmic mitochondrial gene mutation at position 3243, which was recognized when the gene was identified in a single large Dutch family (24). Patients with this mitochondrial mutation not only have diabetes typically diagnosed in early adult life and sensorineural deafness, but also other features resulting from the associated mitochondrial dysfunction including myopathy, pigmented retinopathy, cardiomyopathy, and focal glomerulosclerosis (25,26). Prevalence studies have suggested that 3243 accounts for ~1–2% of diabetes in Japanese and 0.2–0.5% in European series (26). Mutations in the transcription factor HNF1B mutations were first described as a subgroup of MODY (14), but it was quickly realized that the most consistent feature was renal cysts (2729); therefore, the novel syndrome renal cysts and diabetes was proposed (30). Further studies have shown that heterozygous mutations or whole gene deletions (31) of this ubiquitous transcription factor result in a wide range of developmental kidney disease and other features including uterine and genital abnormalities, gout, hyperuricemia, exocrine pancreatic dysfunction, abnormal liver function tests, and insulin resistance (32).
Clinical features reflect the function of the encoded protein outside glucose regulation. One striking example of how extra pancreatic features give new insights into the role of the encoded proteins is the way in which birth weight is affected in the different genetic subtypes of MODY (Fig. 3). In utero fetal insulin is a major growth factor, and therefore altered fetal insulin secretion will alter birth weight. Increased birth weight resulting from increased fetal insulin secretion in response to maternal hyperglycemia may be seen in all types of diabetes including monogenic diabetes. A key additional feature of monogenic diabetes is that if the genes alter fetal insulin secretion or fetal insulin action in utero, birth weight is also altered (33).
FIG. 3.
FIG. 3.
The impact on birth weight of a fetus inheriting the four most common MODY gene mutations. Birth weight is presented as centile birth weight with the fetus inheriting the mutation in blue and in comparison a fetus without the mutation in green. Data are (more ...)
Because patients with glucokinase mutations have moderate stable hyperglycemia in the neonatal period (34), it is expected that the glucose sensing abnormality was also present in utero, resulting in reduced fetal insulin secretion and reduced birth weight by 550 g (35). Conversely, in HNF1A mutations, where glucose tolerance is normal at birth and diabetes rarely develops before adolescence, β-cell function was likely to have been normal in utero, hence fetal HNF1A mutations would have no impact on birth weight (38). The marked reduction in birth weight (900 g) with fetal HNF1B mutations (36) suggests that fetal insulin secretion is disrupted even though diabetes typically presents in early adulthood. This is in keeping with the role of the fetal β-cell. HNF1-β is a critical transcription factor in the maturation of the pancreatic stem cell before differentiation into the exocrine and endocrine cells (37). A marked increase in birth weight (790 g) seen with a fetal HNF4A mutation, even when inherited from the father, is an unexpected finding (38). Macrosomia reflects increased insulin secretion (38), and some neonates with HNF4A mutations have transient (38) or prolonged hypoglycemia (39). This means that increased insulin secretion in the fetus progresses to reduced insulin secretion in adolescence or early adulthood. This pattern is also seen in some dominant SUR1 mutations (40).
Defining genetic etiology can alter treatment.
Geneticists have long argued that defining the etiology of subgroups of diabetes should help the development of appropriate treatment, and in monogenic diabetes there are now clear examples of this (41).
The best example of pharmacogenetics has been in the treatment of patients with PNDM resulting from mutations in the Kir6.2 and SUR1 subunits of the KATP channel. These patients frequently present with ketoacidosis and no detectable endogenous insulin secretion, and therefore insulin injections are the only treatment option. Insulin treatment is difficult in a young child, and outstanding glycemic control is rarely achieved. Finding that one-third of the patients with PNDM had mutations in the Kir6.2 channel that reduced channel closure in response to ATP led to the possibility of treating these patients with sulfonylureas that close the channel by an ATP-independent route (4,42). It was then possible to replace insulin injections with high-dose oral sulfonylureas in 90% of patients and also to achieve improved glycemic control without an increase in hypoglycemia (43,44). Insulin secretion is regulated despite the β-cell having a limited response to ATP; this is predominantly mediated through nonclassical pathways for insulin secretion, particularly GLP1 (43). Excellent glycemic control is also seen in the majority of patients with SUR1 mutations treated with sulfonylureas (45). Therefore, ~50% of patients diagnosed before 6 months with permanent diabetes can benefit greatly from a molecular diagnosis. To date, patients with KATP channel mutations have maintained near normoglycemia for over 4 years (A.T.H., unpublished data). Doses tend to reduce over time, suggesting that the effectiveness of this treatment will be long lasting.
There is also clear evidence of pharmacogenetics within the genetic subcategories of MODY. The fall in fasting glucose with sulfonylurea therapy is fourfold greater in HNF1A MODY patients compared with glycemia- and BMI-matched type 2 diabetic subjects (46). This sensitivity to sulfonylureas frequently means that patients who have been misdiagnosed as type 1 diabetic and treated with insulin from diagnosis can successfully transfer to sulfonylureas without deterioration in glycemic control (47). This improved hypoglycemic response to sulfonylureas compared with that in type 2 diabetes represents, in part, greater insulin secretion (46). Animal and cellular models of HNF-1α deficiency have suggested that the major defect in the β-cell is in glucose metabolism, which may explain why these subjects are very responsive to a drug that binds to the KATP channel, which is downstream of glucose metabolism. Pharmacogenetics may result from a specific genetic subgroup having reduced instead of increased response to a drug. In HNF1B MODY there is reduced pancreatic size (48), reflecting reduced development of the endocrine and exocrine pancreas (37). In these patients, treatment with sulfonylureas is relatively unsuccessful and insulin treatment is rapidly required (49). In GCK MODY patients, glucose is regulated to remain at a higher fasting level, hence oral hypoglycemic agents or low/moderate-dose insulin does not alter glycemia. In 24 GCK MODY patients on insulin or oral hypoglycemic agents, A1C was 6.3% on treatment and 6.3% after 3 months off treatment (O. Gill-Carey and A.T.H., unpublished data).
Introduction of monogenic diabetes testing into clinical practice.
Guiding treatment decisions is not the only clinical role for genetic information in monogenic diabetes; it can also be used to make a definitive diagnosis, explain the cluster of clinical features, and predict the clinical course (26). The clear clinical utility of this has led to the very rapid adoption of diagnostic genetic testing in clinical practice with noncommercial and commercial diagnostic services for monogenic diabetes (50). Guidelines for laboratories offering this testing have now been produced (51). As this is a new area of diabetes practice, guidelines are also needed for both patients and physicians (22), and websites such as www.diabetesgenes.org offer both educational material and guidelines about who should be tested. As the costs of genetic testing fall and more advantages are established, genetic testing to detect monogenic diabetes will increase in clinical practice.
Monogenic and syndromic forms account for only a small, though highly informative, proportion of cases of nonautoimmune diabetes. The challenge for medical science lies in bringing equivalent mechanistic insights and translational benefits to the hundreds of millions of people already affected by, or at risk of, more common, typical forms of diabetes. For type 2 diabetes, there is abundant evidence that individual susceptibility is influenced by both the combination of genetic variation at multiple sites and a series of environmental exposures encountered during life (52). Tracking down the specific genetic variants involved has been tougher than for monogenic forms of disease, since the correlations between genotype and phenotype are far weaker (53,54). However, recent efforts have now identified at least 17 confirmed type 2 diabetes–susceptibility variants (Table 2) (5567), a count certain to increase further in the months ahead. Though effective type 2 diabetes gene discovery remains very much in its infancy, several important lessons are emerging.
TABLE 2
TABLE 2
Summary details of the first 17 loci with a proven role in type 2 diabetes susceptibility
Inherited susceptibility to common forms of type 2 diabetes derives from multiple genes of modest effect.
The linkage approaches used in monogenic diabetes are successful precisely because linkage analysis is intrinsically adept at finding highly penetrant variants, irrespective of allele frequency. Efforts to use similar linkage approaches to identify type 2 diabetes–susceptibility genes have met with only limited success, yielding few, if any, consistently replicating signals (68). The lesson is clear: common variants of large effect (what might once have been called “major” genes) do not make an important contribution to type 2 diabetes susceptibility.
Association-based approaches are far better suited to identification of signals of modest effect (69), and development and exploitation of this methodology has had the greatest impact on susceptibility gene discovery. Even so, many of these discoveries have been hard-won. One reason for this is that the “candidate” gene–based approach has proved, with notable exceptions (55,56), to be an inefficient route to susceptibility gene discovery; it is only with the advent of functionally agnostic genome-wide approaches that the floodgates have opened (70). Another reason is that detection of the variants of modest effect that appear to be responsible for much of type 2 diabetes susceptibility (per-allele odds ratios [ORs] 1.10–1.40, for risk-allele frequencies 10–90%) has required association studies conducted in extremely large sample sizes (thousands of individuals) (54). Variants within TCF7L2 have the largest effects seen so far, with a per-allele OR of 1.4 (57): the 15% of Europeans carrying two copies of the risk allele are at approximately twice the lifetime risk of type 2 diabetes as the 40% who have none.
It is important to remember that for many of the newly discovered susceptibility loci (5767), all we have at present is an initial association signal derived from an incomplete survey of genome-wide common variation. Deeper inspection of these association signals, using resequencing to derive more complete inventories of local genomic variation, and fine mapping to explore the relationships between these variants and disease susceptibility may reveal that the variants of current interest are merely weak surrogates for other stronger effects nearby. It is also possible that future discovery efforts—targeting a wider range of types of genome sequence variation than the subset of common single nucleotide polymorphisms captured by current genotyping platforms—will reveal additional type 2 diabetes–susceptibility variants with more impressive effect sizes (see below).
Nevertheless, it seems likely that many of the undiscovered type 2 diabetes–susceptibility variants will have effects similar to, or smaller than, those found thus far; there may well be scores (even hundreds) of these (71). Very large sample sets (requiring collaboration between multiple groups) will be required to detect them, to confirm that they are truly associated, and to identify the causal variants. Researchers planning to examine the consequences of these variants on whole-body physiology, or on molecular events in vitro, can expect that such low-penetrance variants will often have equally subtle effects on intermediary metabolism and cellular function.
From a translational point of view, low-penetrance variants may have limited value for individual prognostication (72) (see below). Nevertheless, they are already providing valuable insights into the biology of type 2 diabetes (Table 2 and Fig. 4). Demonstrating that a particular variant has a genuine effect on type 2 diabetes susceptibility generates the most direct evidence available about pathways critical to the maintenance of normal glucose homeostasis in humans. These pathways might, with luck, be amenable to therapeutic or preventative manipulation. From this perspective, the effect size of the associated variant is irrelevant: more is likely to be learned from discovering a weak (but genuine) genetic effect in an entirely novel pathway than from a much larger effect that highlights once again a process with an established role in pathogenesis.
FIG. 4.
FIG. 4.
Simplified schematic of the processes involved in genetic predisposition to type 2 diabetes. Assignments of loci to particular processes are based on current knowledge of the presumed function of the best candidates within each signal and human physiological (more ...)
Most type 2 diabetes–susceptibility variants impact on β-cell function and/or mass.
Individuals with type 2 diabetes typically display concomitant defects in both insulin secretion and action. While it is axiomatic that hyperglycemia implies some degree of relative or absolute failure of β-cell function, there has been a long-standing debate about the relative importance (even “primacy”) of the two processes in the pathogenesis of type 2 diabetes (73). Notwithstanding the efforts of epidemiologists and physiologists, this may be one debate where genetics (precisely because of its focus on inherited rather than acquired phenomena) may provide the answers.
The relative prevalence of mutations causal for monogenic forms of diabetes suggests that mutations in β-cell–related processes are a more frequent cause of severe early-onset diabetes than those influencing insulin action (see above). Studies of the relative heritabilities of indexes of β-cell function and insulin action in the general population also hint at a preponderance of β-cell effects (52).
Recent gene discovery efforts have provided further evidence to support such assertions. Though, at this point, the identity of some of the genes mechanistically responsible for the association signals uncovered remains uncertain, it remains possible to determine, through studies of healthy populations, whether the type 2 diabetes–susceptibility variants themselves are mediating their effects through disruption of β-cell function or insulin action. With the exception of FTO (known to influence type 2 diabetes risk through a primary effect on adiposity) and PPARG (long implicated in insulin action), all confirmed susceptibility alleles appear to exert their predominant effect on diabetes pathogenesis through abrogation of β-cell function (or mass) (62,7477). It would be wrong to extrapolate too far: the known variants account for only a small proportion of overall genetic risk, and the focus on lean type 2 diabetes cases, which has characterized several of the genome-wide association (GWA) studies (58,59), may have generated a bias toward detection of variants detrimental to β-cell performance. Nonetheless, the picture that emerges is one where alterations of β-cell function seem to be playing the predominant role with respect to the inherited component of disease predisposition.
When it comes to further insights—to the identification of specific pathways responsible, for example—caution is warranted. Colocalization of an association signal to the same interval as a particular gene does not prove a causal connection. In some instances, causal variants may be influencing type 2 diabetes pathogenesis through remote regulatory effects on genes whose coding sequences lie some distance away. Indeed, several of the recently identified type 2 diabetes signals map to “gene deserts,” and others (such as the association within the HHEX/KIF11/IDE region on chromosome 10) map to regions containing several good candidates (5861). Until the causal variants have been defined, and unequivocal connections made to the genes whose function and/or expression is altered, any presumptions about the mechanisms involved must remain provisional. Having said that, certain themes are emerging (Table 2 and Fig. 4). Perhaps the most exciting involve the roles played by variants within the islet Zn transporter (58) and cell-cycle regulators (5962,67) in type 2 diabetes predisposition. The latter discovery, in particular, may help to resolve the controversy over the part played by reduced β-cell mass in the development of diabetes (78).
Unexpected connections between diabetes and other diseases.
One of the most unexpected outcomes of large-scale association approaches has been the identification of variants, and loci, that influence predisposition to multiple diseases. These “pleiotropic” effects often cross conventional nosological boundaries. In the case of type 2 diabetes, the most compelling is the overlap with prostate cancer. It has emerged that several of the genes implicated in type 2 diabetes susceptibility (particularly TCF2/HNF1B and JAZF1) are also involved in predisposition to prostate cancer, a finding that hints at hitherto unsuspected common pathways influencing these two disease processes (64,65,67). Since the predisposition effects seem to lie in opposing directions, this finding also highlights the risk that efforts at pharmaceutical modulation of this pathway designed to benefit diabetes may have adverse consequences for cancer risk.
A further example concerns the region of type 2 diabetes susceptibility mapped to chromosome 9, in the vicinity of the CDKN2A/2B genes (5961). Not only does this region harbor at least two distinct type 2 diabetes–susceptibility signals, it also contains a third, statistically independent, signal with a profound effect on coronary artery disease risk (7981) and aneurysm formation (82). Surprisingly, the diabetes and vascular association signals involve quite distinct sets of variants, though these may act through modulation of similar molecular pathways. In the case of type 2 diabetes, this most plausibly involves an effect on β-cell regeneration mediated by CDKN2A overexpression (83). Since loss of CDKN2A expression is a common feature of many cancers, this may represent another instance of molecular events with reciprocal effects on cancer and diabetes risk.
The common variants so far uncovered have limited capacity to provide individual prediction.
In analysis of large subject groups, it can be shown that the known type 2 diabetes–susceptibility variants influence clinically relevant phenotypes such as disease progression (84), risk of complications, and therapeutic response (85). However, it does not follow that those differences will be sufficient to provide clinically relevant information where individual patients are concerned. Indeed, the modest effect sizes of the variants identified to date mean that their individual impact is likely to be limited.
This is best illustrated by considering variants in TCF7L2 (57). GWA studies have demonstrated that variants in this gene have the strongest effect on diabetes risk currently known, and a genetic test is commercially available. Assuming an average lifetime risk of type 2 diabetes of ~10%, someone with no copies of the risk allele would (all else being equal) find that figure falling to ~7.5%, whereas the lifetime risk for an individual with two copies increases to 14.5%. It is not yet clear that personal information of this kind (particularly when other pertinent factors such as an individual's age, ethnicity, family history, and BMI are not explicitly taken into account) will lead individuals toward beneficial changes in health-related behaviors (86) or alterations in their clinical management. Indeed, if such information were to be poorly presented, there is a danger that overestimation of the deterministic qualities of genetic information could motivate individuals toward counterproductive changes to their lifestyle (through unwarranted fatalism or feelings of personal immunity).
Of course, individual small effects can amount to more when considered collectively, and it is true that genetic testing (for the 17 known genes, for example) can identify subsets of individuals who have inherited particularly high or low numbers of risk alleles and therefore have marked differences in individual risk (87). However, the numbers of individuals in these “extreme” high- and low-risk groups are comparatively small, and for many, their risk will already be obvious through conventional factors (family history, BMI, and previous gestational diabetes, for example). When the information from the known type 2 diabetes–susceptibility variants is examined using approaches such as receiver-operating curve analysis, which are better suited for evaluating the performance of diagnostic tests at the population level, the results look far less spectacular (72,87).
Progress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments. The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known. The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA–based biomarkers, as these emerge) to provide a more accurate assessment of individual risk. It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information. The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions.
Getting from the extremes to a comprehensive view of diabetes genetics.
As described above, success in the identification of genes impacting on individual risk of diabetes has come from two distinct approaches to gene discovery. The first, linkage mapping within monogenic and syndromic families, has delivered causal variants that are rare but highly penetrant. The second, large-scale association mapping, is now yielding growing numbers of common variants: these have, at best, modest effect sizes and low penetrance. Several genes are featured in the lists generated by both approaches. For example, mutations in KCNJ11, PPARG, WFS1, and TCF2 (HNF1B) are causal for syndromic and/or monogenic forms of diabetes, while common variants in these same genes influence predisposition to typical type 2 diabetes (55,56,6466). While common variants in GCK (another gene causal for MODY) do not influence type 2 diabetes risk per se, they have a clear impact on fasting glucose levels within the population (88).
Of course, none of this should come as a surprise. Once a gene has been shown to harbor one variant associated with, or causal for, a diabetes-like phenotype, it becomes far more likely that other nearby variants (provided they exert some effect on the expression and/or function of the gene) will also have a detectable phenotypic effect. By the same token, the genotype-phenotype relationships revealed by these gene discovery efforts highlight the pathways involved as prime candidates for beneficial therapeutic or preventative manipulation, a view reinforced by the fact that at least two of the genes involved in both monogenic and multifactorial forms of diabetes (PPARG, KCNJ11) encode the targets of proven diabetes drugs.
However, it should be obvious that these two “flavors” of polymorphism—rare and highly penetrant on the one hand and low penetrant on the other—are not the only options when it comes to the variants that might influence disease susceptibility. It seems probable, even likely, that between these extremes lies a class of medium frequency, medium penetrance variants that have until now escaped the gaze of the gene mappers.
Such variants would have penetrances too low to generate Mendelian patterns of segregation and frequencies too low to be covered by current GWA approaches. Despite this, such variants have particularly attractive translational properties. For example, a variant where the risk allele has a frequency of 1% and produces in a per-allele OR of ~3 would provide greater predictive power than the known variants in TCF7L2. Variants with such characteristics are increasingly being reported in other disease states (breast cancer and hyperlipidemia) (89,90) and have even been reported in type 2 diabetes (91). In principle, just 30 such variants across the genome could explain the observed familial aggregation of type 2 diabetes in a way that the current set of common, low-penetrance variants cannot. Such a pool of variants would also provide an excellent tool for individual diabetes-risk prediction, generating a discriminative accuracy on receiver-operating characteristic analysis close to 80%. The advent of new high-throughput sequencing technologies, allied to large-scale association analysis, brings variants in this class within the range of genetic discovery and should allow researchers to evaluate the contribution to disease susceptibility attributable to variants that lie between the extremes where previous attention has been focused.
There are many other challenges to be faced and opportunities to be realized in the years ahead. The first of these lies in extending the range of variants that are accessible to scrutiny, beyond the low-frequency variants referred to in the previous paragraph, to a systematic evaluation of structural polymorphisms (insertions, deletions, and duplications) and variants that influence methylation status (92). Another lies in characterizing the association signals that have been found: large-scale resequencing and fine-mapping strategies will be required to recover the full allelic spectrum of causal variants and thereby obtain the most precise quantification of the genetic effects attributable to each locus. The part played by nonadditive interactions between different genetic loci and between susceptibility variants and environmental exposures needs to be charted, and discovery and replication studies need to be extended beyond the European populations that have been the focus of much of the current research.
Moving beyond genetics, there is work to be done to understand the novel (molecular, cellular, and physiological) biology revealed by these discoveries. If, as seems probable, many of the causal variants lie in noncoding regions, often some distance from the nearest coding sequence, they will often have subtle, spatially and/or temporally restricted effects. In such circumstances, gathering experimental evidence of their functional impact will be seriously difficult.
The final challenge lies in placing gene discovery into translational context. The clinical utility and validity of genetic diagnostics are already established in monogenic diabetes, where such testing can influence clinical practice and treatment. However, diagnostic genetic testing, still underutilized by most diabetologists, and further research, development, and education are required. It is a major challenge to establish how to use knowledge from the identification of predisposing polymorphisms in type 2 diabetes to improve the care of the diabetic patient. Definition of the underlying polymorphisms and genes is but a first step on this road.
Acknowledgments
Research from the authors included in this review has been supported by the Wellcome Trust (067463, 076113, and 083948), the Medical Research Council (G0601261), the European Commission (MolPAGE: LSHG-CT-2004-512066 and EURODIA: LSHG-CT-2004-518153), and Diabetes UK.
We acknowledge stimulating discussions from our colleagues in Oxford, Exeter, and beyond.
This article is dedicated to the memory of Robert Turner.
Notes
See accompanying commentary, p. 2918.
1. Stoy J, Edghill EL, Flanagan SE, Ye H, Paz VP, Pluzhnikov A, Below JE, Hayes MG, Cox NJ, Lipkind GM, Lipton RB, Greeley SA, Patch AM, Ellard S, Steiner DF, Hattersley AT, Philipson LH, Bell GI: Insulin gene mutations as a cause of permanent neonatal diabetes. Proc Natl Acad Sci U S A 104: 15040–15044, 2007. [PubMed]
2. Hattersley AT, Turner RC, Permutt MA, Patel P, Tanizawa Y, Chiu KC, O'Rahilly S, Watkins PJ, Wainscoat JS: Linkage of type 2 diabetes to the glucokinase gene. Lancet 339: 1307–1310, 1992. [PubMed]
3. Froguel P, Vaxillaire M, Sun F, Velho G, Zouali H, Butel MO, Lesage S, Vionnet N, Clement K, Fougerousse F, Tanizawa Y, Weissenbach J, Beckmann JS, Lathrop GM, Passa P, Permutt MA, Cohen D: Close linkage of glucokinase locus on chromosome 7p to early-onset non-insulin-dependent diabetes mellitus. Nature 356: 162–164, 1992. [PubMed]
4. Gloyn AL, Pearson ER, Antcliff JF, Proks P, Bruining GJ, Slingerland AS, Howard N, Srinivasan S, Silva JM, Molnes J, Edghill EL, Frayling TM, Temple IK, Mackay D, Shield JP, Sumnik Z, van Rhijn A, Wales JK, Clark P, Gorman S, Aisenberg J, Ellard S, Njolstad PR, Ashcroft FM, Hattersley AT: Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit Kir6.2 and permanent neonatal diabetes. N Engl J Med 350: 1838–1849, 2004. [PubMed]
5. Proks P, Arnold AL, Bruining J, Girard C, Flanagan SE, Larkin B, Colclough K, Hattersley AT, Ashcroft FM, Ellard S: A heterozygous activating mutation in the sulphonylurea receptor SUR1 (ABCC8) causes neonatal diabetes. Hum Mol Genet 15: 1793–1800, 2006. [PubMed]
6. Babenko AP, Polak M, Cave H, Busiah K, Czernichow P, Scharfmann R, Bryan J, Aguilar-Bryan L, Vaxillaire M, Froguel P: Activating mutations in the ABCC8 gene in neonatal diabetes mellitus. N Engl J Med 355: 456–466, 2006. [PubMed]
7. Barroso I, Gurnell M, Crowley VE, Agostini M, Schwabe JW, Soos MA, Maslen GL, Williams TD, Lewis H, Schafer AJ, Chatterjee VK, O'Rahilly S: Dominant negative mutations in human PPARgamma associated with severe insulin resistance, diabetes mellitus and hypertension. Nature 402: 880–883, 1999. [PubMed]
8. Kadowaki T, Bevins CL, Cama A, Ojamaa K, Marcus-Samuels B, Kadowaki H, Beitz L, McKeon C, Taylor SI: Two mutant alleles of the insulin receptor gene in a patient with extreme insulin resistance. Science 240: 787–790, 1988. [PubMed]
9. Byrne MM, Sturis J, Clement K, Vionnet N, Pueyo ME, Stoffel M, Takeda J, Passa P, Cohen D, Bell GI, et al: Insulin secretory abnormalities in subjects with hyperglycemia due to glucokinase mutations. J Clin Invest 93: 1120–1130, 1994. [PMC free article] [PubMed]
10. Yamagata K, Oda N, Kaisaki PJ, Menzel S, Furuta H, Vaxillaire M, Southam L, Cox RD, Lathrop GM, Boriraj VV, Chen X, Cox NJ, Oda Y, Yano H, Le Beau MM, Yamada S, Nishigori H, Takeda J, Fajans SS, Hattersley AT, Iwasaki N, Pedersen O, Polonsky KS, Turner RC, Velho G, Chevre J-C, Froguel P, Bell GI: Mutations in the hepatic nuclear factor 1 alpha gene in maturity-onset diabetes of the young (MODY3). Nature 384: 455–458, 1996. [PubMed]
11. Pontoglio M, Barra J, Hadchouel M, Doyen A, Kress C, Poggi Bach J, Babinet C, Yaniv M: Hepatocyte nuclear factor 1 inactivation results in hepatic dysfunction, phenylketonuria, and renal fanconi syndrome. Cell 84: 575–585, 1996. [PubMed]
12. Dukes ID, Sreenan S, Roe MW, Levisetti M, Zhou YP, Ostrega D, Bell GI, Pontoglio M, Yaniv M, Philipson L, Polonsky KS: Defective pancreatic beta-cell glycolytic signaling in hepatocyte nuclear factor-1alpha-deficient mice. J Biol Chem 273: 24457–24464, 1998. [PubMed]
13. Yamagata K, Furuta H, Oda N, Kaisaki PJ, Menzel S, Cox NJ, Fajans SS, Signorini S, Stoffel M, Bell GI: Mutations in the hepatocyte nuclear factor 4 alpha gene in maturity-onset diabetes of the young (MODY1). Nature 384: 458–460, 1996. [PubMed]
14. Horikawa Y, Iwasaki N, Hara M, Furuta H, Hinokio Y, Cockburn B, Lindner T, Yamagata K, Ogata M, Tomonaga O, Kuroki H, Kasahar T, Iwamoto Y, Bell GI: Mutation in hepatocyte nuclear factor-1b gene (TCF2) associated with MODY. Nat Genet 17: 384–385, 1997. [PubMed]
15. Servitja JM, Ferrer J: Transcriptional networks controlling pancreatic development and beta cell function. Diabetologia 47: 597–613, 2004. [PubMed]
16. Maestro MA, Cardalda C, Boj SF, Luco RF, Servitja JM, Ferrer J: Distinct roles of HNF1beta, HNF1alpha, and HNF4alpha in regulating pancreas development, beta-cell function and growth. Endocr Dev 12: 33–45, 2007. [PubMed]
17. Raeder H, Johansson S, Holm PI, Haldorsen IS, Mas E, Sbarra V, Nermoen I, Eide SA, Grevle L, Bjorkhaug L, Sagen JV, Aksnes L, Sovik O, Lombardo D, Molven A, Njolstad PR: Mutations in the CEL VNTR cause a syndrome of diabetes and pancreatic exocrine dysfunction. Nat Genet 38: 54–62, 2006. [PubMed]
18. Hegele RA, Anderson CM, Wang J, Jones DC, Cao H: Association between nuclear lamin A/C R482Q mutation and partial lipodystrophy with hyperinsulinemia, dyslipidemia, hypertension, and diabetes. Genome Res 10: 652–658, 2000. [PubMed]
19. Capell BC, Collins FS: Human laminopathies: nuclei gone genetically awry. Nat Rev Genet 7: 940–952, 2006. [PubMed]
20. Delepine M, Nicolino M, Barrett T, Golamaully M, Lathrop GM, Julier C: EIF2AK3, encoding translation initiation factor 2-alpha kinase 3, is mutated in patients with Wolcott-Rallison syndrome. Nat Genet 25: 406–409, 2000. [PubMed]
21. Wildin RS, Ramsdell F, Peake J, Faravelli F, Casanova JL, Buist N, Levy-Lahad E, Mazzella M, Goulet O, Perroni L, Bricarelli FD, Byrne G, McEuen M, Proll S, Appleby M, Brunkow ME: X-linked neonatal diabetes mellitus, enteropathy and endocrinopathy syndrome is the human equivalent of mouse scurfy. Nat Genet 27: 18–20, 2001. [PubMed]
22. Hattersley A, Bruining J, Shield J, Njolstad P, Donaghue K: ISPAD clinical practice consensus guidelines 2006–2007: the diagnosis and management of monogenic diabetes in children. Pediatr Diabetes 7: 352–360, 2006. [PubMed]
23. Murphy R, Ellard S, Hattersley AT: Clinical implications of a molecular genetic classification of monogenic beta-cell diabetes. Nat Clin Pract Endocrinol Metab 4: 200–213, 2008. [PubMed]
24. van den Ouweland JM, Lemkes HH, Ruitenbeek W, Sandkuijl LA, de Vijlder MF, Struyvenberg PA, van de Kamp JJ, Maassen JA: Mutation in mitochondrial tRNA(Leu)(UUR) gene in a large pedigree with maternally transmitted type II diabetes mellitus and deafness. Nat Genet 1: 368–371, 1992. [PubMed]
25. Maassen JA, Kadowaki T: Maternally inherited diabetes and deafness: a new diabetes subtype. Diabetologia 39: 375–382, 1996. [PubMed]
26. Murphy R, Turnbull DM, Walker M, Hattersley AT: Clinical features, diagnosis and management of maternally inherited diabetes and deafness (MIDD) associated with the 3243A>G mitochondrial point mutation. Diabet Med 25: 383–399, 2008. [PubMed]
27. Nishigori H, Yamada S, Kohama T, Tomura H, Sho K, Horikawa Y, Bell GI, Takeuchi T, Takeda J: Frameshift mutation, A263fsinsGG, in the hepatocyte nuclear factor-1β gene associated with diabetes and renal dysfunction. Diabetes 47: 1354–1355, 1998. [PubMed]
28. Lindner TH, Njolstad PR, Horikawa Y, Bostad L, Bell GI, Sovik O: A novel syndrome of diabetes mellitus, renal dysfunction and genital malformation associated with a partial deletion of the pseudo-POU domain of hepatocyte nuclear factor-1beta. Human Molecular Genetics 8: 2001–2008, 1999. [PubMed]
29. Bingham C, Ellard S, Allen L, Bulman M, Shepherd M, Frayling T, Berry PJ, Clark PM, Lindner T, Bell GI, Ryffel GU, Nicholls AJ, Hattersley AT: Abnormal nephron development associated with a frameshift mutation in the transcription factor hepatocyte nuclear factor-1 beta. Kidney Int 57: 898–907, 2000. [PubMed]
30. Bingham C, Bulman M, Ellard S, Allen L, Lipkin G, van’t Hoff W, Woolf A, Rizzoni G, Novelli G, Nicholls A, Hattersley A: Mutations in the HNF1-beta gene are associated with familial hypoplastic glomerulocystic kidney disease. Am J Hum Genet 68: 219–224, 2001. [PubMed]
31. Bellanne-Chantelot C, Clauin S, Chauveau D, Collin P, Daumont M, Douillard C, Dubois-Laforgue D, Dusselier L, Gautier JF, Jadoul M, Laloi-Michelin M, Jacquesson L, Larger E, Louis J, Nicolino M, Subra JF, Wilhem JM, Young J, Velho G, Timsit J: Large genomic rearrangements in the hepatocyte nuclear factor-1beta (TCF2) gene are the most frequent cause of maturity-onset diabetes of the young type 5. Diabetes 54: 3126–3132, 2005. [PubMed]
32. Bingham C, Hattersley AT: Renal cysts and diabetes syndrome resulting from mutations in hepatocyte nuclear factor-1{beta}. Nephrol Dial Transplant 19: 2703–2708, 2004. [PubMed]
33. Hattersley AT, Tooke JE: The fetal insulin hypothesis: an alternative explanation of the association of low birth weight with diabetes and vascular disease. Lancet 353: 1789–1792, 1999. [PubMed]
34. Prisco F, Iafusco D, Franzese A, Sulli N, Barbetti F: MODY 2 presenting as neonatal hyperglycaemia: a need to reshape the definition of “neonatal diabetes”? Diabetologia 43: 1331–1332, 2000. [PubMed]
35. Hattersley AT, Beards F, Ballantyne E, Appleton M, Harvey R, Ellard S: Mutations in the glucokinase gene of the fetus in reduced birthweight. Nat Genet 19: 268–270, 1998. [PubMed]
36. Edghill EL, Bingham C, Slingerland AS, Minton JA, Noordam C, Ellard S, Hattersley AT: Hepatocyte nuclear factor-1 beta mutations cause neonatal diabetes and intrauterine growth retardation: support for a critical role of HNF-1beta in human pancreatic development. Diabet Med 23: 1301–1306, 2006. [PubMed]
37. Maestro MA, Boj SF, Luco RF, Pierreux CE, Cabedo J, Servitja JM, German MS, Rousseau GG, Lemaigre FP, Ferrer J: Hnf6 and Tcf2 (MODY5) are linked in a gene network operating in a precursor cell domain of the embryonic pancreas. Hum Mol Genet 12: 3307–3314, 2003. [PubMed]
38. Pearson ER, Boj SF, Steele AM, Barrett T, Stals K, Shield JP, Ellard S, Ferrer J, Hattersley AT: Macrosomia and hyperinsulinaemic hypoglycaemia in patients with heterozygous mutations in the HNF4A gene. PLoS Med 4: e118, 2007. [PMC free article] [PubMed]
39. Kapoor RR, Locke J, Colclough K, Wales J, Conn JJ, Hattersley AT, Ellard S, Hussain K: Persistent hyperinsulinemic hypoglycemia and maturity-onset diabetes of the young due to heterozygous HNF4A mutations. Diabetes 57: 1659–1663, 2008. [PubMed]
40. Huopio H, Otonkoski T, Vauhkonen I, Reimann F, Ashcroft F, Laakso M: A new subtype of autosomal dominant diabetes attributable to a mutation in the gene for the sulphonlyurea receptor 1. Lancet 361: 301–307, 2003. [PubMed]
41. Hattersley AT, Pearson ER: Minireview: pharmacogenetics and beyond: the interaction of therapeutic response, beta-cell physiology, and genetics in diabetes. Endocrinology 147: 2657–2663, 2006. [PubMed]
42. Sagen JV, Raeder H, Hathout E, Shehadeh N, Gudmundsson K, Baevre H, Abuelo D, Phornphutkul C, Molnes J, Bell G, Gloyn AL, Hattersley AT, Molven A, Sovik O, Njolstad PR: Permanent neonatal diabetes due to mutations in KCNJ11 encoding Kir6.2: Patient characteristics and initial response to sulfonylurea therapy. Diabetes 53: 2713–2718, 2004. [PubMed]
43. Pearson ER, Flechtner I, Njolstad PR, Malecki MT, Flanagan SE, Larkin B, Ashcroft FM, Klimes I, Codner E, Iotova V, Slingerland AS, Shield J, Robert JJ, Holst JJ, Clark PM, Ellard S, Sovik O, Polak M, Hattersley AT: Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations. N Engl J Med 355: 467–477, 2006. [PubMed]
44. Tonini G, Bizzarri C, Bonfanti R, Vanelli M, Cerutti F, Faleschini E, Meschi F, Prisco F, Ciacco E, Cappa M, Torelli C, Cauvin V, Tumini S, Iafusco D, Barbetti F: Sulfonylurea treatment outweighs insulin therapy in short-term metabolic control of patients with permanent neonatal diabetes mellitus due to activating mutations of the KCNJ11 (KIR6.2) gene. Diabetologia 49: 2210–2213, 2006. [PubMed]
45. Rafiq M, Flanagan SE, Patch AM, Shields BM, Ellard S, Hattersley AT: Effective treatment with oral sulfonylureas in patients with diabetes due to sulfonylurea receptor 1 (SUR1) mutations. Diabetes Care 31: 204–209, 2008. [PubMed]
46. Pearson ER, Starkey BJ, Powell RJ, Gribble FM, Clark PM, Hattersley AT: Genetic aetiology of hyperglycaemia determines response to treatment in diabetes. Lancet 362: 1275–1281, 2003. [PubMed]
47. Shepherd M, Pearson ER, Houghton J, Salt G, Ellard S, Hattersley AT: No deterioration in glycemic control in HNF-1α maturity-onset diabetes of the young following transfer from long-term insulin to sulphonylureas. Diabetes Care 26: 3191–3192, 2003. [PubMed]
48. Bellanne-Chantelot C, Chauveau D, Gautier JF, Dubois-Laforgue D, Clauin S, Beaufils S, Wilhelm JM, Boitard C, Noel LH, Velho G, Timsit J: Clinical spectrum associated with hepatocyte nuclear factor-1beta mutations. Ann Intern Med 140: 510–517, 2004. [PubMed]
49. Pearson ER, Badman MK, Lockwood CR, Clark PM, Ellard S, Bingham C, Hattersley AT: Contrasting diabetes phenotypes associated with hepatocyte nuclear factor-1α and -1β mutations. Diabetes Care 27: 1102–1107, 2004. [PubMed]
50. Hattersley AT: Molecular genetics goes to the diabetes clinic. Clin Med 5: 476–481, 2005. [PubMed]
51. Ellard S, Bellanne-Chantelot C, Hattersley AT: Best practice guidelines for the molecular genetic diagnosis of maturity-onset diabetes of the young. Diabetologia 51: 546–553, 2008. [PMC free article] [PubMed]
52. Stumvoll M, Goldstein BJ, van Haeften TW: Type 2 diabetes: principles of pathogenesis and therapy. Lancet 365: 1333–1346, 2005. [PubMed]
53. McCarthy MI: Progress in defining the molecular basis of type 2 diabetes mellitus through susceptibility-gene identification. Hum Mol Genet 13: R33–R41, 2004. [PubMed]
54. Hattersley AT, McCarthy MI: A question of standards: what makes a good genetic association study? Lancet 366: 1315–1323, 2005. [PubMed]
55. Altshuler D, Hirschhorn JN, Klannemark M, Lindgren CM, Vohl MC, Nemesh J, Lane CR, Schaffner SF, Bolk S, Brewer C, Tuomi T, Gaudet D, Hudson TJ, Daly M, Groop L, Lander ES: The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet 26: 76–80, 2000. [PubMed]
56. Gloyn AL, Weedon MN, Owen KR, Turner MJ, Knight BA, Hitman G, Walker M, Levy JC, Sampson M, Halford S, McCarthy MI, Hattersley AT, Frayling TM: Large-scale association studies of variants in genes encoding the pancreatic β-cell KATP channel subnunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52: 568–572, 2003. [PubMed]
57. Grant SFA, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, Helgason A, Stefansson H, Emilsson V, Helgadottir A, Styrkars dottir U, Magnusson KP, Walters GB, Palsdottir E, Jonsdottir T, Gudmundsdottir T, Gylfason A, Saemundsdottir J, Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson G, Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K: Variant of transcription factor 7-like 2 (TFC7L2) gene confers risk of type 2 diabetes. Nat Genet 38: 320–323, 2006. [PubMed]
58. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D, Polychronakos C, Froguel P: A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445: 881–885, 2007. [PubMed]
59. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PIW, Chen H, Roix JR, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D: Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316: 1331–1335, 2007. [PubMed]
60. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JRB, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney ASF, Wellcome Trust Case Control Consortium (WTCCC), McCarthy MI, Hattersley AT: Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316: 1336–1341, 2007. [PMC free article] [PubMed]
61. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines CS, Jackson AU, Prokunknina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M: A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316: 1341–1345, 2007. [PMC free article] [PubMed]
62. Steinsthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, Styrkarsdottir U, Gretarsdottir S, Emilsson V, Ghosh S, Baker A, Snorradottir S, Bjarnason H, Ng MCY, Hansen T, Bagger Y, Wilensky RL, Reilly MP, Adeyemo A, Chen Y, Zhou J, Gudnason V, Chen G, Huang H, Lashley K, Doumatey A, So W-Y, Ma RCY, Andersen G, Borch-Johnsen K, Jorgensen T, van Vliet-Ostaptchouk JV, Hofker MH, Wijmenga C, Christiansen C, Rader DK, Rotimi C, Gurney M, Chan JCN, Pedersen O, Sigurdsson G, Gulcher JR, Thorsteinsdottir U, Kong A, Stefansson K: A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 39: 770–775, 2007. [PubMed]
63. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JRB, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch A-M, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin M-R, Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJF, Barroso I, Wareham NJ, Karpe F, Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CNA, Doney ASF, Morris AD, Davey-Smith G, The Wellcome Trust Case Control Consortium, Hattersley AT, McCarthy MI: A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316: 889–894, 2007. [PMC free article] [PubMed]
64. Winckler W, Weedon MN, Graham RR, McCarroll SA, Purcell S, Almgren P, Tuomi T, Gaudet D, Bengtsson Boström K, Walker M, Hitman G, Hattersley AT, McCarthy MI, Ardlie KG, Hirschhorn JN, Daly MJ, Frayling TM, Groop L, Altshuler D: Evaluation of common variants in the six known MODY genes for association with type 2 diabetes. Diabetes 56: 685–693, 2007. [PubMed]
65. Gudmundsson J, Sulem P, Steinthorsdottir V, Bergthorsson JT, Thorleifsson G, Manolescu A, Rafnar T, Gudbjartsoon D, Agnarsson BA, Baker A, Sigurdsson A, Benediktsdottir KR, Jakobsdottir M, Blondal T, Stacey SN, Helgason A, Gunnarsdottir S, Olafsdottir A, Kristinsson KT, Birgisdottir B, Ghosh S, Thorlacius S, Magnusdottir D, Stefansdottir G, Kristjansson K, Bagger Y, Wilensky RL, Reilly MP, Morris AD, Kimber CH, Adeyemo A, Chen Y, Zhou J, So WY, Tong PCY, Ng MCY, Hansen T, Andersen G, Borch-Johnsen K, Jorgensen T, Tres A, Fuertes F, Ruiz-Echarri M, Asin L, Saez B, van Boven E, Klaver S, Swinkels DW, Aben KK, Graif T, Cashy J, Suarez BK, van Vierssen Trip O, Frigge ML, Ober C, Hofker MH, Wijmenga C, Christiansen C, Rader DJ, Palmer CNA, Rotimi C, Chan JCN, Pedersen O, Sigurdsson G, Benediktsson R, Jonsson E, Einarsson GV, Mayordomo JI, Catalona WJ, Kiemeney LA, Barkardottir RB, Gulcher JR, Thorsteinsdottir U, Kong A, Stefansson K: Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet 39: 977–983, 2007. [PubMed]
66. Sandhu MS, Weedon MN, Fawcett KA, Wasson J, Debenham SL, Daly A, Lango H, Frayling TM, Neumann R, Pharoah PD, McCarthy MI, Walker M, Hitman G, Glaser B, Permutt MA, Hattersley AT, Wareham NJ, Barroso I: WFS1 is a type 2 diabetes susceptibility gene. Nat Genet 39: 951–953, 2007. [PMC free article] [PubMed]
67. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, De Bakker PW, Abecasis GR, Almgren P, Andersen G, Ardlie K, Bostrom KB, Bergman RN, Bonnycastle LL, Borch-Johnsen K, Burtt NP, Chen H, Chines PS, Daly MJ, Deodhar P, Ding C, Doney ASF, Duren WL, Elliott KS, Erdos MR, Frayling TM, Freathy RM, Gianniny L, Grallert H, Grarup, Groves CJ, Giuducci C, Hansen T, Herder C, Hitman GA, Hughes TE, Isomaa B, Jackson AU, Jorgensen T, Kong A, Kubalanza K, Kuruvilla FG, Kuusisto J, Langenberg C, Lango H, Lauritzen T, Li Y, Lindgren CM, Lyssenko V, Marvelle AF, Meisinger C, Midthjell K, Mohlke KL, Morken MA, Morris AD, Narisu N, Nilsson P, Owen KR, Palmer CNA, Payne F, Perry JRB, Pettersen E, Platou C, Prokopenko I, Qi L, Qin L, Rayner NW, Rees M, Roix JJ, Sandbaek A, Shields B, Sjogren M, Steinthorsdottir V, Stringham HM, Swift AJ, Thorleifsson G, Thorsteinsdottir U, Timpson NJ, Tuomi T, Tuomilehto J, Walker M, Watanabe RM, Weedon MN, Willer CJ, Wellcome Trust Case Control Consortium, Illig T, Hveem K, Hu FN, Laakson M, Stefansson K, Pedersen O, Wareham NJ, Barroso I, Hattersley AT, Collins FS, Groop L, McCarthy MI, Boehnke M, Altshuler D: Meta-analysis of genome-wide association data and large-scale replication identifies several additional susceptibility loci for type 2 diabetes Nat Genet 40: 638–645, 2008. [PMC free article] [PubMed]
68. Risch N, Merikangas K: The future of genetic studies of complex human diseases. Science 273: 1516–1517, 1996. [PubMed]
69. McCarthy MI: Growing evidence for diabetes susceptibility genes from genome scan data. Curr Diab Rep 3: 159–167, 2003. [PubMed]
70. Frayling TM: Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nat Rev Genet 8: 657–662, 2007. [PubMed]
71. Altshuler D, Daly M: Guilt beyond a reasonable doubt. Nat Genet 39: 813–815, 2007. [PubMed]
72. Janssens ACJW, Pardo MC, Steyerberg EW, van Duijn CM: Revisiting the clinical validity of multiplex genetic testing in complex diseases. Am J Hum Genet 74: 585–588, 2004. [PubMed]
73. Kahn SE: The relative contributions of insulin resistance and beta-cell dysfunction to the pathophysiology of type 2 diabetes. Diabetologia 46: 3–19, 2003. [PubMed]
74. Pascoe L, Tura A, Patel SK, Ibrahim IM, Ferraninni E, The RISC Consortium, The UK Type 2 Diabetes Genetics Consortium, Zeggini E, Weedon MN, Mari A, Hattersley AT, McCarthy MI, Frayling TM, Walker M: Common variants of the novel type 2 diabetes genes, CDKAL1 and HHEX/IDE, are associated with decreased pancreatic beta-cell function Diabetes 56: 3101–3104, 2007. [PubMed]
75. Grarup N, Rose CS, Andersson EA, Andersen G, Nielsen AL, Albrechtsen A, Clausen JO, Rasmussen SS, Jorgensen T, Sandbaek A, Lauritzen T, Schmitz O, Hansen T, Pedersen O: Studies of association of variants near the HHEX, CDKN2A/B, and IGF2BP2 genes with type 2 diabetes and impaired insulin release in 10,705 Danish subjects. Diabetes 56: 3105–3111, 2007. [PubMed]
76. Staiger H, Machicao F: Stefan N, Tschritter O, Thamer C, Kantartzis K, Schafer SA, Kirchhoff K, Fritsche A, Haring HU: Polymorphisms within novel risk loci for type 2 diabetes determine β-cell function. PLoS One 9: e832, 2007. [PMC free article] [PubMed]
77. Staiger H, Stancakova A, Zilinskaite J, Vanttinen M, Hansen T, Marini MA, Hammarstedt A, Jansson P-A, Sesti G, Smith U, Pedersen O, Laakso M, Stefan M, Fritsche A, Haring H-U: A candidate type 2 diabetes polymorphism near the HHEX locus affects acute glucose-stimulated insulin release in European populations. Diabetes 57: 514–517, 2008. [PubMed]
78. Butler PC, Meier JJ, Butler AE, Bhushan A: The replication of beta cells in normal physiology, in disease and for therapy. Nat Clin Pract Endocrinol Metab 3: 758–768, 2006. [PubMed]
79. Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, Jonasdottir A, Sigurdsson A, Baker A, Palsson A, Masson G, Gudbjartsson D, Magnusson KP, Andersen K, Levey AI, Backman VM, Matthiasdottir S, Jonsdottir T, PAlsson S, EInarsdottir H, Gunnarsdottir S, Gylfason A, Vaccarino V, Hooper WC, Reilly MP, Granger CB, Austin H, Rader DJ, Shah SH, Quyyumi AA, Gulcher JR, Thorgeirsson G, Thorsteinsdottir U, Kong A, Stefansson K: A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 316: 1491–1493, 2007. [PubMed]
80. McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, Hinds DA, Pennacchio LA, Tybjaerg-Hansen A, Folsom AR, Boerwinkle E, Hobbs HH, Cohen JC: A common allele on chromosome 9 associated with coronary heart disease. Science 316: 1488–1491, 2007. [PMC free article] [PubMed]
81. Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, Dixon RJ, Meitinger T, Braund P, Wichmann HE, Barrett JH, König IR, Stevens SE, Szymczak S, Tregouet DA, Iles MM, Pahlke F, Pollard H, Lieb W, Cambien F, Fischer M, Ouwehand W, Blankenberg S, Balmforth AJ, Baessler A, Ball SG, Strom TM, Braenne I, Gieger C, Deloukas P, Tobin MD, Ziegler A, Thompson JR, Schunkert H, WTCCC and the Cardiogenics Consortium: Genomewide association analysis of coronary artery disease. N Engl J Med 357: 443–453, 2007. [PMC free article] [PubMed]
82. Helgadottir A, Thorleifsson G, Magnusson KP, Gretarsdottir S, Steinthorsdottir V, Manolescu A, Jones GT, Rinkel GJE, Blankensteijn JD, Ronkainen A, Jaaskelainen JE, Kyo Y, Lenk GM, Sakalihasan N, Kostulas K, Gottsater A, Flex A, Stefansson H, Hansen T, Andersen G, Weinsheimer S, Borch-Johnsen K, Jorgensen T, Shah SH, Quyyumi AA, Granger CB, Reilly MP, Austin H, Levey AI, Vaccarino V, Palsdottir E, Walters GB, Jonsdottir T, Snorradottir S, Magnusdottir D, Gudmundsson G, Ferrell RE, Sveinbjornsdottir S, Hernesniemi J, Niemela M, Limet R, Andersen K, Sigurdsson G, Benediktsson R, Verhoeven ELG, Teijink JAW, Grobbee DE, Rader DJ, Collier DA, Pedersen O, Pola R, Hillert J, Lindblad B, Valdimarsson EM, Magnadottir HB, Wijmenga C, Tromp G, Baas AF, Ruigrok YM, van Rij AM, Kuivaniemi H, Powell JT, Matthiasson SE, Gulcher JR, Thorgeirsson G, Kong A, Thorsteinsdottir U, Stefansson K: The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet 40: 217–224, 2008. [PubMed]
83. Krishnamurthy J, Ramsey MR, Ligon KL, Torrice C, Koh A, Bonner-Weir S, Sharpless NE: p16INK4a induces an age-dependent decline in islet regenerative potential. Nature 443: 453–457, 2006. [PubMed]
84. Lyssenko V, Almgren P, Anevski D, Orho-Melander M, Sjogren M, Saloranta C, Tuomi T, Groop L, Botnia Study Group: Genetic Prediction of future type 2 diabetes. PLoS Med 2: 1299–1307, 2005. [PMC free article] [PubMed]
85. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney ASF, McCarthy MI, Hattersley AT, Morris AD, Palmer CN: Variation in TCF7L2 influences therapeutic response to sulfonylureas: a GoDARTs study. Diabetes 56: 2178–2182, 2007. [PubMed]
86. Janssens ACJW, Gwinn M, Subramonia-Iyer S, Khoury MJ: Does genetic testing really improve the prediction of future type 2 diabetes? PLOS Med 3: e114, 2006. [PMC free article] [PubMed]
87. Weedon MN, McCarthy MI, Hitman G, Walker M, Groves CJ, Zeggini E, Rayner NW, Shields B, Owen KR, Hattersley AT, Frayling TM: Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. PLOS Med 3: e374, 2006. [PMC free article] [PubMed]
88. Weedon MN, Clark VJ, Qian Y, Ben-Shlomo Y, Timpson N, Ebrahim S, Lawlor DA, Pembrey ME, Ring S, Wilkin TJ, Voss LD, Jeffery AN, Metcalf B, Ferrucci L, Corsi AM, Murray A, Melzer D, Knight B, Shields B, Smith GD, Hattersley AT, Di Rienzo A, Frayling TM: 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 79: 991–1001, 2006. [PubMed]
89. Stratton MR, Rahman N: The emerging landscape of breast cancer susceptibility. Nat Genet 40: 17–22, 2008. [PubMed]
90. Romeo S, Pennacchio LA, Fu Y, Boerwinkle E, Tybjaerg-Hansen A, Hobbs HH, Cohen JC: Population-based resequencing of ANGPTL4 uncovers variations that reduce triglycerides and increase HDL. Nat Genet 39: 513–516, 2007. [PMC free article] [PubMed]
91. Hegele RA, Cao H, Harris SB, Hanley AJG, Zinman B: The hepatic nuclear factor-1α G319S variant is associated with early-onset type 2 diabetes in Canadian Oji-Cree. J Clin Endocrinol Metab 84: 1077–1082, 1999. [PubMed]
92. McCarroll SA, Altshuler DM: Copy-number variation and association studies of human disease. Nat Genet 39: S37–S42, 2007. [PubMed]
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