Until recently, dissecting the genetics of complex polygenic diseases in which environmental factors interact with genetic variants in the predisposition to the disease has not been a trivial task and success has been limited. In contrast, dissection of monogenic diseases has been a success story and mutations giving rise to six forms of monogenic forms of maturity onset diabetes of the young have been identified [1–3]. At the other end of the spectrum, the polygenic type 2 diabetes is caused by ‘mild’ variations in several genes, which interact with environmental triggers to cause late onset of the disease.
There is ample evidence that type 2 diabetes has a strong genetic component. The concordance of type 2 diabetes in monozygotic twins is approximately 70% compared with 20–30% in dizygotic twins [4,5]. The lifetime risk of developing the disease is about 40% in offspring of one parent with type 2 diabetes – greater if the mother is affected – , the risk approaching 70% if both parents have diabetes. In prospective studies we have demonstrated that first-degree family history is associated with two-fold increased risk of future type 2 diabetes [7,8••]. The challenge has been to find genetic markers which explain the excess risk associated with family history of diabetes. During the last years, the common variant–common disease hypothesis has emerged which assumes that common single-nucleotide polymorphisms (SNPs) (frequency >5%) increase susceptibility to polygenic diseases like type 2 diabetes and obesity. However, there are examples of rare variants influencing metabolic traits in the population [9,10], suggesting that both rare and common variants may contribute to the development of common diseases.
Genome-wide association study approach for mapping genetic variability
A problem in the field of genetics of complex diseases is the difficulty to replicate an initial association. The main reason for this is underpowered studies due to small sample size. The International HapMap Project (http://www.hapmap.org) and a public–private SNP consortium (http://www.ncbi.nlm.nih.gov) have provided a catalogue of 10 million common genetic variants in the human genome. With the improvement of genotyping technology it has become technically possible to genotype a large number of SNPs at affordable costs, which paved the way for the so called genome-wide association studies (GWAS). In recent years GWAS have contributed to discoveries of a number of common genetic variants increasing susceptibility to different diseases including type 2 diabetes [11••,12••,13,14,15••], coronary heart disease [16–19], breast cancer [20–22], Crohn's disease , and so on. In our collaborative study with the Broad Institute and Novartis (Diabetes Genetic Initiative, DGI) we performed a GWAS in 1464 patients with type 2 diabetes and 1467 nondiabetic control individuals from Finland and Sweden [11••]. Prior to publication we shared the results with researchers from the FUSION (Finnish USA Study of NIDDM) and WTCCC (Wellcome Trust Case Control Consortium) groups [12••,15••]. We only considered positive results, which were seen and replicated in all three studies on the basis of DNA from 32 000 individuals. In the past year, a consortium of several GWAS for type 2 diabetes (DIAGRAM) has been established aiming to identify novel loci by increasing the number of people studied. Together, these consortia have identified 17 common variants increasing susceptibility to type 2 diabetes [11••,12••,13,14,15••,24]. Recently, common variants in the KCNQ1 and MTNR1B genes have been added to this list [25••,26••,27]. Interestingly, some of these novel loci are also associated with prostate cancer (TCF2, JAZF1) and Crohn's disease (CDKAL1), but in opposite direction.
Establishing causal relationship between genetic variant and phenotype
Although GWAS have identified a number of novel SNPs increasing disease susceptibility, it will be a challenge to understand the underlying biology.
Transcription factor 7 like 2 (TCF7L2) is the gene in which genetic variants have the largest effects on the risk of type 2 diabetes and which has been most extensively replicated, but we still do not know the mechanisms by which the variants in this gene increase risk of type 2 diabetes. There is clear evidence that risk genotype carriers have impaired insulin secretion [28–30]. In support of this, we have shown a deterioration in beta-cell function in the risk allele carriers over time in individuals who converted to diabetes (Fig. 1a) . There is a binding site for TCF7L2 in the promoter of the preproglucagon suggesting that TCF7L2 might influence the effect of incretins on islets. In our study carriers of the risk T allele of SNP rs7903146 showed a weaker response to oral than to intravenous glucose suggesting a defective enteroinsular axis (Fig. 1b). These findings have subsequently been replicated by showing impaired glucagon-like peptide-1 (GLP-1) stimulated insulin secretion in carriers of the risk genotype . Although the concentrations of glucose-dependent insulinotropic polypeptide (GIP), GLP-1, and glucagon do not seem to differ between carriers of different TCF7L2 genotypes , we observed a strong correlation between GIP and glucagon, particularly in carriers of the risk T allele . Interestingly, GIP levels correlated with insulin levels at 2 h of the OGTT in CC but not in TT genotype carriers. In another study, TCF7L2 risk allele carriers showed reduced GLP-1-stimulated insulin secretion in individuals with normal or impaired glucose tolerance during hyperglycemic clamp . Also expression of TCF7L2 was five-fold higher in islets from patients with type 2 diabetes than in islets from nondiabetic cadaver donors (Fig. 1c) suggesting a link between increased expression of TCF7L2 mRNA and level of glycemia. Taken together, the data suggest that risk variants in TCF7L2 most likely increase risk of diabetes by influencing insulin secretion and incretin action, and altered expression of the gene in islets.
One way to get more insights into pathophysiology of disease is to study genome-wide association between genetic markers and disease-related phenotypes. Recently, GWAS for glucose and insulin traits revealed that variation in the melatonin receptor 1B gene (MTNR1B) was associated with insulin and glucose concentrations [25••,26••]. To this end, we have shown that the risk genotype of this SNP was associated with increased risk of future type 2 diabetes in two large prospective studies with an odds ratio (OR) of 1.12 (P = 0.002). Furthermore, risk carriers of the MTNR1B variant demonstrated impairment of early insulin response to both oral (insulinogenic and disposition index) and intravenous glucose (first-phase insulin response) and showed faster deterioration of insulin secretion over time (Fig. 1d). We have also shown that the MTNR1B mRNA is expressed in human islets, and immunocytochemistry confirms that it is primarily localized in beta-cells in islets. Nondiabetic individuals carrying the risk allele and patients with type 2 diabetes showed increased expression of the receptor in islets (Fig. 1e). Insulin release from clonal beta-cells in response to glucose was inhibited in the presence of melatonin (Fig. 1f). In view of these results, blocking the melatonin ligand-receptor system could be a therapeutic avenue in type 2 diabetes.
Personalized prediction of complex disease?
Identification of genetic variants increasing susceptibility to disease and its combined information ultimately might aid in personalized prediction of disease risk. Recently we have evaluated effect of clinical and genetic factors to predict progression to diabetes in two prospective cohorts [8••]. As these studies included a large number of participants with long follow-up we were in a unique position to address the question whether genetic risk factors added to clinical risk factors could improve prediction of future diabetes. The results demonstrated that adding genetic markers to the clinical risk factors modestly improved the discriminatory power as assessed by the area under the receiver operating characteristic (ROC) curves [from area under curve (AUC) 0.73 to 0.74] [8••]. In keeping with findings from previous case-control studies [33–35], the discriminatory power of genes alone was relatively low (0.62). Individuals with high genetic risk (top 20% of the population with 12 or more number of risk alleles), had a 1.95-fold (1.69–2.25, P = 2.5 × 10−20) increased risk of future diabetes as compared to those with a low genetic risk (bottom 20% of the population carrying eight or less risk alleles). Although this effect appears to be too small to allow individual risk prediction it could be useful in reducing number of persons needed to be included in intervention studies aiming at prevention of type 2 diabetes. An important factor defining the discriminative power of clinical and genetic risk factors is duration of follow-up. We also assessed the area under the ROC curves to determine the discriminative ability of clinical and genetic risk factors in relation to quintiles of time of follow-up. We observed a decrease in AUC for the clinical model (P = 0.01) and an increase in the AUC for the genetic risk score (P = 0.01) with increasing duration of follow-up. These findings suggest that an individual genetic profile could be valuable from birth, long before exposure to most environmental risk factors takes place.
Simulation studies have suggested that using a larger number of common variants can make possible an accurate prediction of genetic risk, which theoretically can attain the same level as that of traditional risk factors as shown for predicting cardiovascular disease [36,37]. However, this figure might look different if we find rare variants with stronger effect size, for example with OR greater than 2–3. In a recent study by Janssens et al., authors have estimated that for an AUC of 0.80 to predict diabetes it will be necessary to genotype 50 risk variants with allele frequencies of 10% and OR of 1.5. Nevertheless, until now, no gene, except TCF7L2, has been shown to have an OR of more than 1.5. This suggests that one will need about 10 rare variants with strong effect size (OR of 2–3) to explain the genetic risk of type 2 diabetes. This figure might change completely if environmental factors could be identified which markedly would increase the risk of a genetic variant. A recent study [38••] from our group showed that a panel of lipid genes predicted future cardiovascular death independently of lipid measurements.
Taken together, genetic tests cannot be offered yet to predict disease. The main reason is the marginally increased risk associated with each risk variant. One should also remember that risk alleles often represent the common alleles in the population possibly reflecting the evolutionary advantage of thrifty genes.
An ultimate goal of gene identification is to improve understanding of the pathophysiology of disease, and to use this information to improve diagnosis, prevention, and treatment. It has been suggested that adverse side effects and therapeutic failure of drugs may both have a strong genetic component . Toward this end, application of GWAS has been recently shown in individuals with statin-induced myopathy [40••]. A SNP in the transporter gene SLCO1B1 was associated with an increased risk of myopathy during statin treatment with an OR of 16.9 for homozygous carriers and of 4.5 for heterozygous carriers. Therefore, one of the promises is that genetic testing will aid in predicting response to treatment and side effects, that is an important step towards personalized medicine.
Dissecting the genetic architecture of a complex disease such as type 2 diabetes is a rather challenging task. Significant advances have been made in the past 2 years, but we are far from the goal. Today we have approximately 20 common variants increasing susceptibility to type 2 diabetes. However, we need to find rare mutations of larger effect in these loci which explain a substantial proportion of disease risk. The role of copy number variations (CNVs) in the pathogenesis of disease has been highlighted in the past years but the tools to detect these CNVs have limited further exploration. This problem may be solved with the introduction of new DNA chips with a much better coverage of CNVs. Having identified novel loci increasing susceptibility to the disease, the fundamental step is to systematically study the molecular mechanisms by which they do so. Furthermore, the real task will be to study the interaction between genes, environment, and treatment and how to bring these results back to patients who have developed type 2 diabetes or are at risk of developing the disease to improve risk prediction and treatment. Finally, rapid progress in genetics of type 2 diabetes has posed promising challenges for potential drug discoveries over the coming years.
References and recommended reading
Papers of particular interest, published within the annual period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Additional references related to this topic can also be found in the Current World Literature section in this issue (p. 135).
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