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The Journal of Clinical Endocrinology and Metabolism logoLink to The Journal of Clinical Endocrinology and Metabolism
. 2008 Sep 9;93(12):4633–4642. doi: 10.1210/jc.2008-1345

The Genetics of Type 2 Diabetes: A Realistic Appraisal in 2008

Jose C Florez 1
PMCID: PMC2626447  PMID: 18782870

Abstract

Context: Over the last few months, genome-wide association studies have contributed significantly to our understanding of the genetic architecture of type 2 diabetes. If and how this information will impact clinical practice is not yet clear.

Evidence Acquisition: Primary papers reporting genome-wide association studies in type 2 diabetes or establishing a reproducible association for specific candidate genes were compiled. Further information was obtained from background articles, authoritative reviews, and relevant meeting conferences and abstracts.

Evidence Synthesis: As many as 17 genetic loci have been convincingly associated with type 2 diabetes; 14 of these were not previously known, and most of them were unsuspected. The associated polymorphisms are common in populations of European descent but have modest effects on risk. These loci highlight new areas for biological exploration and allow the initiation of experiments designed to develop prediction models and test possible pharmacogenetic and other applications.

Conclusions: Although substantial progress in our knowledge of the genetic basis of type 2 diabetes is taking place, these new discoveries represent but a small proportion of the genetic variation underlying the susceptibility to this disorder. Major work is still required to identify the causal variants, test their role in disease prediction and ascertain their therapeutic implications.

New genetic discoveries in type 2 diabetes are described; the review places these discoveries in context, answers some common misconceptions, and outlines avenues for future research.

The field of type 2 diabetes genetics is undergoing a genuine revolution. Not too long ago a review of this type would start by introducing the epidemiological underpinnings of diabetes heritability, follow with a discussion of methodological issues, and spend most of its remaining space focusing on rare monogenic syndromes of diabetes, only to end by touching on the handful of genetic variants convincingly associated with common type 2 diabetes as a kind of spirited attempt to highlight a future promise. Not surprisingly, such reviews had significant difficulty in holding the interest of busy practitioners and became the province of specialty journals. Today, the explosion of well-powered and expertly analyzed genome-wide association scans (GWAS) across all aspects of human physiology is yielding a growing number of genetic loci implicated in multiple complex phenotypes. More importantly, there is no longer any doubt about the statistical robustness of these associations, and interested participants have a phenomenally hard time keeping up with new developments while trying to understand and investigate the biology that lurks behind each new observation. There is great hope that these findings will illuminate and alter the way we practice medicine; in several ways, type 2 diabetes has pioneered many of these advances. This review will highlight the state of type 2 diabetes genetics (as of mid-year 2008) and provide a realistic perspective on what clinicians can expect from this unfolding body of knowledge.

Candidate Gene Association Studies

At the end of the last century, linkage analysis and positional cloning allowed geneticists to query the entire genome in their search for genetic causes of disease. Although these approaches had proven extremely successful in identifying rare genetic variants of strong effects for single-gene disorders such as maturity-onset diabetes of the young (MODY) (1,2), they were much less effective in detecting the common alleles that underlie polygenic diseases. It turned out that the dramatic linkage signal initially observed for the HLA region in type 1 diabetes (3,4,5) was the exception rather than the rule, and linkage scans for type 2 diabetes designed with such effect sizes in mind were thereby underpowered (6). Once it became clear that association methods conducted in much larger samples were preferable to linkage analysis (7), investigators generally turned their attention to biological candidate genes that might harbor variants that were overrepresented in disease vs. health. Such an emphasis on single genes was predicated by the inability of association methods to interrogate the entire human genome in a single experiment at that point in time.

Candidate variants were discovered through focused sequencing efforts, which were greatly aided by the parallel progress of the Human Genome Project (8) and the depositing of single-nucleotide polymorphisms (SNPs) in public databases (9). Although conflicting reports of association for many variants were often published, in part due to the adoption of low statistical thresholds before declaring an association, some findings stood the test of time. One of these was the proline→alanine change at position 12 (P12A) in the peroxisome proliferator-activated receptor γ2, encoded by PPARG (10); individuals homozygous for the more common proline allele were more insulin resistant (11) and about 20% more likely to develop type 2 diabetes than alanine carriers, as demonstrated definitively in a landmark metaanalysis (12). Among other things, this study illustrated the need for large sample sizes to detect genetic associations of modest effects (these sample sizes, typically achieved only through metaanalyses of different cohorts, must take into account the possibility of publication bias when based on published studies only; such bias is less of a concern if the metaanalysis is carried out among newly genotyped samples).

PPARG was an attractive candidate gene because it encodes the molecular target for thiazolidinedione medications, and P12A was a plausible causal SNP because it signified a coding change (a missense SNP results in a different residue that alters protein sequence and thus presumably function, in contrast to synonymous coding SNPs, which preserve the same amino acid through the degeneracy of the genetic code, or to noncoding SNPs that lie in intergenic or untranslated regions). Another known drug target in type 2 diabetes was the sulfonylurea receptor; its gene ABCC8 lies a few kilobases upstream of KCNJ11, which encodes its functional partner, the islet ATP-sensitive potassium channel Kir6.2. A missense polymorphism in KCNJ11 (in which glutamate is exchanged by lysine at codon 23, E23K) was associated with type 2 diabetes in an initial metaanalysis (13) and confirmed in a subsequent large-scale association study (14), which produced a summary odds ratio (OR) of about 1.20 and a convincing overall P value (P = 10−5). Furthermore, the risk allele was also associated with impaired insulin secretion (15,16). Notwithstanding the excitement generated by these true positive results, 3 yr (and many hours of painstaking and costly research) had transpired between these two discoveries.

Many other candidate genes continued to be examined during this period. Foremost among these were genes already known to be involved in monogenic forms of diabetes. For example, several studies comprehensively evaluated common variants in the six MODY genes (17,18,19,20,21,22). These efforts produced a conclusive association of an intronic SNP (rs757210) in HNF1B (encoding the hepatocyte nuclear factor 1β) with type 2 diabetes; a combined analysis of more than 15,000 samples yielded an overall OR of 1.12 and a convincing P value of <10−6 (21), with consistent results obtained in another large-scale study (20) and independent confirmation in a recent GWAS (23). This last GWAS, designed to identify variants associated with prostate cancer, showed that an allele in a SNP that is highly correlated with rs757210 simultaneously protects against prostate cancer and increases risk of type 2 diabetes, an intriguing link that requires further investigation (24).

Of the other MODY genes, suggestive evidence not quite reaching genome-wide statistical significance (empirically established at ∼5 × 10−8) (25) has been gathered for two promoter variants in HNF4A (17,26,27,28,29), an appealing candidate gene in that it causes MODY 1 and it lies under a widely replicated linkage peak (see below). A more recent comprehensive metaanalysis of over 18,000 subjects (presented only in abstract form) has reported a very modest association for the original SNPs (combined OR ∼1.07; 95% CI 1.03–1.13; P = 0.003), although affected by substantial heterogeneity (30). The missense variants A98V and I27L in the MODY 3 gene HNF1A (encoding the hepatocyte nuclear factor 1α) have also received substantial attention (18,19); a current metaanalysis for the A98V variant shows an OR of 1.17 (95% CI 1.03–1.33; P = 0.02) (31), whereas the leucine allele at I27L appears to raise the risk of type 2 diabetes in overweight and/or elderly persons (20,32,33,34), is associated with decreased insulin secretion (32,34), and seems to predict type 2 diabetes prospectively (22).

Another monogenic form of diabetes is Wolfram syndrome, caused by mutations in the gene WFS1 (35). A recent evaluation of common variants in 84 candidate genes yielded two SNPs in WFS1 that were robustly (P = ∼10−7) but modestly (OR ∼1.11) associated with type 2 diabetes in a case-control study comprising about 24,000 samples (36). This association has reached genome-wide significance through replication in independent cohorts (37), and the risk variants appear to affect β-cell function (38,39).

Finally, another candidate gene that has been studied extensively is ENPP1, encoding the ectonucleotide pyrophosphatase phosphodiesterase 1. The Q allele in the missense variant K121Q was nominally associated with type 2 diabetes and insulin resistance in a small cohort (40), and although subsequent larger studies reported a positive association with type 2 diabetes (41,42), other well-powered studies failed to replicate the association (43,44). In a recent metaanalysis of about 42,000 samples, we have documented a nominal association of this variant with type 2 diabetes in European populations under a recessive model (OR 1.38; 95% CI 1.10–1.74; P = 0.005) (45); in support of this finding, we have also reported a consistent association with several quantitative glycemic traits in the Framingham Heart Study (46). Although the level of statistical evidence in the current sample size does not quite reach genome-wide significance, this limitation could be due to its recessive model of risk transmission and/or the apparent dependence on a concomitant obesogenic background for the risk-conferring variant to exert its effect (41,42,45,46,47). Nevertheless, the persistent positive reports of association regarding this variant, the multiple lines of molecular and physiological evidence implicating it in insulin resistance, and a plausible explanation for some of the negative studies, place this gene as a highly suggestive biological and genetic candidate.

Exploration of Linkage Peaks

The relative success of association approaches in the earlier part of this decade did not preclude investigators from pushing linkage analyses to the limit. The first report of a positive result to emerge from this line of investigation concerned CAPN10, the gene encoding calpain 10 (48). Although the haplotype described originally does not seem to increase risk of type 2 diabetes across all populations, selected SNPs in the promoter region do display evidence of association on metaanalysis, albeit with modest P values (49,50,51). While the reason for this heterogeneity remains unexplained, functional evidence for a possible role of this protein in glycemic physiology is accumulating (52).

A modest linkage peak in chromosome 20q has been replicated in multiple populations (53,54,55,56,57,58,59). As discussed above, the most attractive candidate gene in the region (HNF4A) has been comprehensively genotyped and analyzed in well-powered association studies. Similarly, linkage with type 2 diabetes or related traits in chromosome 1q has been observed in Pima Indians, the Framingham Heart Study, other U.S., French, and United Kingdom Whites, and Chinese (60,61,62,63,64,65). A detailed fine-mapping and association analysis of this region has been carried out by investigators of the International Chromosome 1q Consortium, although no unifying signal has yet emerged as a convincing locus predisposing to type 2 diabetes.

Finally, a thorough exploration of multiple (and modest) linkage signals by deCODE investigators resulted in the identification of the gene encoding the transcription factor 7-like 2 (TCF7L2) as the locus that confers the strongest effect on type 2 diabetes risk yet found (Fig. 1) (66). This robust association has been replicated in almost every population examined, with an OR of about 1.4 per risk allele and an incontrovertible P value of less than 10−80 (67,68). Detailed resequencing and fine mapping points to an intronic SNP (rs7903146) as the main source of the association signal in European populations (66,69,70). The risk allele leads to impaired insulin secretion (70,71,72), perhaps through a diminished incretin effect (72,73) and/or defects in insulin processing (74,75). Encouragingly, lifestyle modification can significantly attenuate the genetic risk (71,76). An initial retrospective pharmacogenetic study indicates that risk allele carriers are more susceptible to sulfonylurea failure (77). It should be noted that although TCF7L2 happens to lie under the chromosome 10 linkage peak, this association does not explain the linkage signal in the original populations; thus, although GWAS would have undoubtedly reached the same conclusive result on short order (see below), the modest linkage signal at this locus has not yet been ascribed to any specific gene.

Figure 1.

Figure 1

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Chronological accounting of the discovery of type 2 diabetes-associated genes, plotted by year of definitive publication and approximate effect size. Within each group, darker colors indicate biological candidate genes (PPARG, KCNJ11, HNF1B, WFS1, HNF4A, HNF1A, and ENPP1), and lighter colors denote loci identified via agnostic genome-wide approaches. In gray are genes implicated in type 2 diabetes by considerable functional and genetic evidence but for which genome-wide statistical evidence of association has not been reached. Allelic effect sizes are derived from Ref. 109, except for PPARG (110), KCNJ11 (16), TCF7L2 (67), the six genes identified in 2008 (103), HNF4A (30), HNF1A (31), CAPN10 (51), and ENPP1 (45).

GWAS

Several key advances catalyzed the extension of candidate gene association analyses to the unbiased interrogation of the entire genome that has occurred during the second half of this decade. First, building on the completion of the human genome sequence, several million SNPs (including the majority of all common SNPs) were discovered and deposited in public databases; such common variants explain the vast majority of human heterozygosity and can be tested for their role in disease (25). Second, genotyping of 3.8 million SNPs in 270 DNA samples and identification of haplotype-tagged SNPs by the International HapMap Project (HapMap) further increased efficiency by guiding selection of so-called tag SNPs that can serve as proxies for the vast majority of the remaining common variation (25,78). Third, the development of affordable, high-throughput genotyping technologies enabled execution of GWAS at significantly reduced time and expense. Fourth, multiple analytical tools were made available for the cleaning, mining, and interpretation of such large datasets (79,80,81,82,83,84,85,86,87,88). And fifth, several large multicenter collections with well-characterized phenotypes were assembled and shared through international collaborations.

The first GWAS for type 2 diabetes (and all others that followed) was validated by the clear replication of the TCF7L2 association (89). It also discovered a missense SNP in SLC30A8 (OR 1.26; P < 10−6) and common variants in HHEX (OR 1.21; P < 10−5) as novel type 2 diabetes associations. The realization that SLC30A8 encodes a zinc transporter expressed in insulin-containing granules in β cells (90) furnished tantalizing evidence of the relevance of this approach. In addition, HHEX encodes a transcription factor involved in early pancreatic development (91). Shortly thereafter, three other high-density GWAS, which shared results ahead of publication and were published jointly (92,93,94), confirmed the known TCF7L2, KCNJ11, and PPARG loci as well as the HHEX and SCL30A8 findings; they also identified CDKAL1 (OR 1.12; P < 10−10), IGF2BP2 (OR 1.14; P < 10−15), and CDKN2A/B (OR 1.20; P < 10−14) as new type 2 diabetes loci. The putative functional mechanisms by which they may affect type 2 diabetes risk is listed in Table 1. On the same day, a report by deCODE investigators and their collaborators corroborated the HHEX and SCL30A8 associations and independently detected the CDKAL1 signal (95). In parallel, variants in the FTO gene were associated with metabolically deleterious obesity and thereby also contribute to type 2 diabetes (96,97). Other GWAS were simultaneously conducted at lower density and/or in smaller samples (98,99,100,101,102); suggestive results from these studies await replication in additional cohorts before they reach comparable levels of statistical evidence.

Table 1.

Genetic variants associated with type 2 diabetes at or near genome-wide levels of statistical significance, ordered by chromosome (Chr)

Marker Chr Description Gene region Function Risk allele OR P value Ref.
rs10923931 1 Intronic NOTCH2 Transmembrane receptor implicated in pancreatic organogenesis T 1.13 4.1 × 10–8 (103)
rs7578597 2 Missense: T1187A THADA Thyroid adenoma; associates with PPARγ T 1.15 1.1 × 10–9 (103)
rs4607103 3 38 kb upstream ADAMTS9 Secreted metalloprotease expressed in musle and pancreas C 1.09 1.2 × 10–8 (103)
rs4402960 3 Intronic IGF2BP2 Growth factor binding protein; pancreatic development T 1.14 8.9 × 10−16 (109)
rs1801282 3 Missense: P12A PPARG Transcription factor involved in adipocyte development C 1.19 1.5 × 10−7 (110)
rs10010131 4 Intron-exon junction WFS1 Endoplasmic reticulum transmembrane protein G 1.15 4.5 × 10−5 (109)
rs7754840 6 Intronic CDKAL1 Homologous to CDK5RAP1, CDK5 inhibitor; islet glucotoxicity sensor C 1.12 4.1 × 10−11 (109)
rs864745 7 Intronic JAZF1 Transcriptional repressor; associated with prostate cancer T 1.10 5.0 × 10–14 (103)
rs13266634 8 Missense: R325W SLC30A8 &β-cell zinc transporter ZnT8; insulin storage and secretion C 1.12 5.3 × 10−8 (109)
rs10811661 9 125 kb upstream CDKN2A/B Cyclin-dependent kinase inhibitor and p15 tumor suppressor; islet development T 1.20 7.8 × 10−15 (109)
rs12779790 10 Intergenic region CDC123-CAMK1D Cell cycle/protein kinase G 1.11 1.2 × 10–10 (103)
rs7903146 10 Intronic TCF7L2 Transcription factor; transactivates proglucagon and insulin genes T 1.37 1.0 × 10−48 (67)
rs1111875 10 7.7 kb downstream HHEX Transcription factor involved in pancreatic development C 1.13 5.7 × 10−10 (109)
rs5219 11 Missense: E23K KCNJ11 Kir6.2 potassium channel; risk allele impairs insulin secretion T 1.14 6.7 × 10−11 (16)
rs7961581 12 Intronic TSPAN8-LGR5 Cell surface glycoprotein implicated in GI cancers C 1.09 1.1 × 10–9 (103)
rs8050136 16 Intronic FTO Alters BMI in general population A 1.17 1 × 10−12 (109)
rs757210 17 Intronic HNF1B Transcription factor involved in pancreatic development A 1.12 5 × 10−6 (109)
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In a tangible display of fruitful collaboration, the various type 2 diabetes GWAS conducted by the FUSION group (94), the Wellcome Trust Case Control Consortium (93), and the Diabetes Genetics Initiative (92) were combined in a full metaanalysis (103), followed by replication in about 50,000 independent samples including those genotyped in the deCODE GWAS (95); this effort (termed DIAGRAM for Diabetes Genetics Replication And Metaanalysis; http://www.well.ox.ac.uk/DIAGRAM/) yielded six new loci (JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2-ADAM30) associated with type 2 diabetes at genome-wide statistical significance (Table 1 and Fig. 1).

Interestingly, many of the newly discovered variants appear to influence insulin secretion rather than insulin resistance; the potential reasons for this phenomenon are multiple and discussed in detail elsewhere (31). All in all, a little over a year after the publication of the first GWAS for type 2 diabetes, we have been presented with over a dozen new type 2 diabetes-associated loci whose biology and impact on human metabolism and pathophysiology must be fully characterized.

A Realistic Appraisal

Although the revelation of this new body of knowledge may seem exhilarating, it can elicit a variety of different reactions. In an attempt to place these discoveries in perspective, I will now address some of the common attitudes or questions one may encounter.

  1. The impatient may declare that “most of the genetic basis of type 2 diabetes has been explained,” and promote a shift in focus and resources to other modalities of investigation. This opinion usually arises from a misunderstanding of the concept of population-attributable risk (PAR), a mathematical assessment of the contribution of a particular genetic variant to population risk that takes into account both its effect size and its risk allele frequency. The PAR construct expresses the fraction of disease that would be eliminated from the population if the risk variant did not exist. It should be noted that although genetic effects may be additive in an individual, PARs conferred by different loci are not necessarily additive at the population level; for instance, if three genetic variants have PARs of 25, 15, and 10%, respectively, this does not translate into an overall PAR of 50%, and even less so do they support the claim that 50% of the genetic contribution to type 2 diabetes has been explained. Indeed, the current compilation of bona fide type 2 diabetes risk variants may explain as little as 5–10% of the genetic basis of type 2 diabetes. In addition, the SNPs identified thus far merely signal genomic regions–at times hundreds of kilobases away from known genes–where an association has been found, but do not necessarily represent the causal variants: further fine-mapping and functional studies must be carried out before an accurate assessment of the true contribution of these loci to type 2 diabetes can be estimated. Moreover, the genotyping platforms used to date have precluded the comprehensive evaluation of structural variants (e.g. copy number polymorphisms), have not captured rare variants, and have left as much as 20% of common SNPs in the genome suboptimally covered (with a higher percentage of uncovered regions in the more diverse African population) (104). Thus, the genetic architecture of type 2 diabetes remains largely undescribed, and the full universe of human genetic variation contains significant gaps that are at present unexplored.

  2. The cynic may state that “larger and larger sample sizes are just going to uncover smaller and smaller effects.” Although it is true that higher numbers do increase statistical power, it should also be noted that the initial studies had relatively poor power to detect the genetic associations that have been discovered so far. That is to say, a study with only 10% power to detect an OR of 1.2 for a given allele frequency basically ensures that a substantial number of genetic variants of the same effect size remain to be discovered. A power calculation of the statistical power obtained by the DIAGRAM Consortium (the largest discovery panel in type 2 diabetes published to date, comprising approximately 10,000 samples) is illustrated in Fig. 2; although the power of DIAGRAM to identify polymorphisms that increase type 2 diabetes risk by 40% nears 100%, power is more limited for effect sizes of the 1.1–1.2 range that seem to be the norm for type 2 diabetes, with a large majority of variants of OR of about 1.1 yet undiscovered across a broad range of allele frequencies. These observations indicate that 1) although it is unlikely for common SNPs of effects larger than TCF7L2 rs7903146 to have gone undetected in populations of European ancestry, 2) we should expect many SNPs of similar effects to be reported even as sample sizes continue to increase.

  3. The pessimist may announce that “genetic effects are so small that they cannot possibly be clinically relevant.” Here, it must be kept in mind that effect sizes produced by allele frequency differences between cases and controls make no statement about biological or clinical relevance. One would expect that the evolutionary constraints of natural selection would not allow genetic variants of strong deleterious effects to rise to high frequencies in the population, but genetic variants of modest effects may indeed point to specific molecules or pathways that could be targeted for therapeutic intervention. The reader does not have to go very far to find empirical support for this assertion; within type 2 diabetes there exist two genetic variants of very modest effects (PPARG P12A, OR ∼1.2, and KCNJ11 E23K, OR ∼1.15) that happen to lie in genes that encode targets for well established antidiabetic medications, thiazolidinediones and sulfonylureas, respectively. That is to say, if nothing had been known about these targets before the initiation of genetic studies, GWAS would have effectively marked them as possible avenues for intervention. In another relevant example, a recent GWAS has identified a variant in the gene that encodes 3-hydroxy-3-methyl-glutaryl coenzyme A (HMG-CoA) reductase as explaining a small proportion of the variance in low-density lipoprotein-cholesterol (105), but the small size of this effect should not lead anyone to conclude that HMG-CoA reductase is not an adequate target for low-density lipoprotein-cholesterol lowering.

  4. The optimist, in turn, may naively proclaim that “the variants identified will be useful in individual clinical prediction,” heralding a quick and successful implementation of personalized medicine. Although these discoveries may indeed illuminate biology and highlight opportunities for therapeutic intervention, their use as risk factors in disease prediction is much less clear. Current simple clinical tools developed to predict risk of type 2 diabetes perform quite well, with an area under the receiver-operator characteristics curve as high as 85–90% (106). Whether a full set of type 2 diabetes-associated genetic variants will provide enough independent information to improve this figure significantly or allow for reclassification of individuals into alternate risk categories remains to be seen, but it does seem unlikely given the marginal results obtained in a recent cross-sectional study (107). In any case, the widespread clinical use of genetic testing for complex diseases must be preceded by a rigorous scientific assessment of the impact of such testing on patient outcomes, as well as guarantees of individual protection, cost-effectiveness analyses, and education of both health practitioners and the public.

  5. The absolutist may conclude that “all of the genetic contribution to type 2 diabetes acts through β-cell dysfunction.” That person should keep in mind that although the heritability of insulin resistance measures is generally lower than that of insulin secretion measures, a substantial heritable component of insulin resistance does exist (108). Some of the recent GWAS for type 2 diabetes (89,92) focused on enrolling leaner cases, thus deliberately biasing the scan toward genes that increase type 2 diabetes risk without the mediation of obesity. Thus, GWAS that intend to discover insulin resistance genes must be designed with that goal in mind, either accounting for the effect of obesity or searching for insulin resistance as a quantitative trait in population cohorts that display enough variance in this phenotype (31).

Figure 2.

Figure 2

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Power estimates of the DIAGRAM metaanalysis (effective n = 9562) to detect genetic associations of different OR (range 1.1–1.4) across various risk allele frequencies. Although power is excellent for effect sizes of about 1.4 (indicating it is unlikely that SNPs of such effects remain undiscovered, at least in populations of European ancestry), power is much more limited for lower effect sizes (suggesting that only a small fraction of comparable associations has been identified).

In sum, a realistic appraisal of the current status of type 2 diabetes genetics acknowledges the tremendous progress that has ensued as a result of previous efforts to find type 2 diabetes genes, brought about by technological advances and an improved understanding of association testing; recognizes the vast areas that remain unprobed; and assumes moderate enthusiasm regarding the repercussions that these and future genetic discoveries will have on human health, which can be garnered only once the necessary groundwork of biological experimentation and clinical trials has been laid.

Note Added in Proof

Two recent GWAS conducted in Japanese samples have identified common SNPs in the KCNQ1 gene that are robustly associated with type 2 diabetes, with OR in the 1.2–1.4 range, and P values that achieve genome-wide significance (Yasuda et al. 2008 Nat Genet 40:1092–1097 and Unoki et al. 2008 Nat Genet 30:1098–1102). Although the associations can be replicated in non-Asian cohorts, they were not detected in the original GWAS because of the lower allele frequency of the risk variants in populations of European descent.

Footnotes

Supported by National Institutes of Health Research Career Development Award 1 K23 DK65978-05.

Disclosure Statement: J.C.F. has received consulting honoraria from Merck and from Publicis Healthcare Communications Group, a global advertising agency engaged by Amylin Pharmaceuticals.

First Published Online September 9, 2008

Abbreviations: GWAS, Genome-wide association scans; MODY, maturity-onset diabetes of the young; OR, odds ratio; PAR, population-attributable risk; SNP, single-nucleotide polymorphism.

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