Whole-Exome Sequencing of 2,000 Danish Individuals and the Role of Rare Coding Variants in Type 2 Diabetes

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m² and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk.     

Mouse models of type 1 and type 2 diabetes derived from the same closed colony: genetic susceptibility shared between two types of diabetes

Except for rare subtypes of diabetes, both type 1 and type 2 diabetes are multifactorial diseases in which genetic factors consisting of multiple susceptibility genes and environmental factors contribute to the disease development. Due to complex interaction among multiple susceptibility genes and between genetic and environmental factors, genetic analysis of multifactorial diseases is difficult in humans. Inbred animal models, in which the genetic background is homogeneous and environmental factors can be controlled, are therefore valuable in genetic dissection of multifactorial diseases. We are fortunate to have excellent animal models for both type 1 and type 2 diabetes--the nonobese diabetic (NOD) mouse and the Nagoya-Shibata-Yasuda (NSY) mouse, respectively. Congenic mapping of susceptibility genes for type 1 diabetes in the NOD mouse has revealed that susceptibility initially mapped as a single locus often consists of multiple components on the same chromosome, indicating the importance of congenic mapping in defining genes responsible for polygenic diseases. The NSY mouse is an inbred animal model of type 2 diabetes established from Jcl:ICR, from which the NOD mouse was also derived. We have recently mapped three major loci contributing to type 2 diabetes in the NSY mouse. Interestingly, support intervals where type 2 diabetes susceptibility genes were mapped in the NSY mouse overlapped the regions where type 1 diabetes susceptibility genes have been mapped in the NOD mouse. Although additional evidence is needed, it may be possible that some of the genes predisposing to diabetes are derived from a common ancestor contained in the original closed colony, contributing to type 1 diabetes in the NOD mouse and type 2 diabetes in the NSY mouse. Such genes, if they exist, will provide valuable information on etiological pathways common to both forms of diabetes, for the establishment of effective methods for prediction, prevention, and intervention in both type 1 and type 2 diabetes.     

Genome-wide association studies and type 2 diabetes

In recent years, the search for genetic determinants of type 2 diabetes (T2D) has changed dramatically. Although linkage and small-scale candidate gene studies were highly successful in the identification of genes, which, when mutated, caused monogenic forms of T2D, they were largely unsuccessful when applied to the more common forms of the disease. To date, these approaches have only identified two loci (PPARG, KCNJ11) robustly implicated in T2D susceptibility. The ability to perform large-scale association analysis, including genome-wide association studies (GWAS) in many thousands of samples from different populations, and subsequently, the shift to form large international collaborations to perform meta-analyses across many studies has taken the number of independent loci showing genome-wide significant associations with T2D to 44. This number includes six loci identified initially through the analysis of quantitative glycaemic phenotypes, illustrating the usefulness of this approach both to identify new disease genes and gain insight into the mechanisms leading to disease. Combined, these loci still only account for ～10% of the observed familial clustering in Europeans, leaving much of the variance unexplained. In this review, we will describe what GWAS have taught us about the genetic basis of T2D and discuss possible next steps to uncover the remaining heritability.     

Genetics of type 2 diabetes

Identification and characterization of genetic variants that either cause or predispose to diabetes are a major focus of biomedical research. As of early 2007, the molecular basis of most forms of monogenic diabetes resulting from beta-cell dysfunction is known and, in particular, there has been recent success in delineating the genetic aetiology of neonatal diabetes. Finding genes predisposing to more common, multifactorial forms of type 2 diabetes represents a far greater challenge, and only a handful of robust, well-replicated examples have been established. Nevertheless, 2006 heralded identification of the most important type 2 diabetes susceptibility gene known so far, TCF7L2, and in 2007 large-scale genome-wide association studies are destined to provide novel insights into the genetic architecture and biology of type 2 diabetes.     

Type 2 Diabetes Mellitus: New Genetic Insights will Lead to New Therapeutics

Type 2 diabetes is a disorder of dysregulated glucose homeostasis. Normal glucose homeostasis is a complex process involving several interacting mechanisms, such as insulin secretion, insulin sensitivity, glucose production, and glucose uptake. The dysregulation of one or more of these mechanisms due to environmental and/or genetic factors, can lead to a defective glucose homeostasis. Hyperglycemia is managed by augmenting insulin secretion and/or interaction with hepatic glucose production, as well as by decreasing dietary caloric intake and raising glucose metabolism through exercise. Although these interventions can delay disease progression and correct blood glucose levels, they are not able to cure the disease or stop its progression entirely. Better management of type 2 diabetes is sorely needed. Advances in genotyping techniques and the availability of large patient cohorts have made it possible to identify common genetic variants associated with type 2 diabetes through genome-wide association studies (GWAS). So far, genetic variants on 19 loci have been identified. Most of these loci contain or lie close to genes that were not previously linked to diabetes and they may thus harbor targets for new drugs. It is also hoped that further genetic studies will pave the way for predictive genetic screening. The newly discovered type 2 diabetes genes can be classified based on their presumed molecular function, and we discuss the relation between these gene classes and current treatments. We go on to consider whether the new genes provide opportunities for developing alternative drug therapies.Key Words: Type 2 diabetes, drug targets, genetics, personalized medicine.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.     

The genetics of childhood obesity and interaction with dietary macronutrients

The genes contributing to childhood obesity are categorized into three different types based on distinct genetic and phenotypic characteristics. These types of childhood obesity are represented by rare monogenic forms of syndromic or non-syndromic childhood obesity, and common polygenic childhood obesity. In some cases, genetic susceptibility to these forms of childhood obesity may result from different variations of the same gene. Although the prevalence for rare monogenic forms of childhood obesity has not increased in recent times, the prevalence of common childhood obesity has increased in the United States and developing countries throughout the world during the past few decades. A number of recent genome-wide association studies and mouse model studies have established the identification of susceptibility genes contributing to common childhood obesity. Accumulating evidence suggests that this type of childhood obesity represents a complex metabolic disease resulting from an interaction with environmental factors, including dietary macronutrients. The objective of this article is to provide a review on the origins, mechanisms, and health consequences of obesity susceptibility genes and interaction with dietary macronutrients that predispose to childhood obesity. It is proposed that increased knowledge of these obesity susceptibility genes and interaction with dietary macronutrients will provide valuable insight for individual, family, and community preventative lifestyle intervention, and eventually targeted nutritional and medicinal therapies.     

Genes, environment, and interactions in prevention of type 2 diabetes: a focus on physical activity and lifestyle changes

Type 2 diabetes is one of the fastest growing public health problems worldwide. Both environmental (e.g. physical activity, obesity, and diet) and genetic factors are involved in the development of type 2 diabetes. The associations between physical activity and diabetes risk have been assessed by a number of prospective studies and clinical trials. The results from these studies consistently indicate that the regular physical activity during occupation, commuting, leisure time or daily life reduces the risk of type 2 diabetes by 15-60%; and lifestyle intervention, including counselling for physical activity, nutrition, and body weight, can reduce the risk of type 2 diabetes by 40-60% among adults with impaired glucose tolerance and by about 20% among general individuals. In the past decade, studies using traditional linkage analysis and candidate-gene association approach have found dozens of genes harboring common variants that were related to the common-form type 2 diabetes. However, most reported associations are lack of reproducibility, except TCF7L2, PPARG, CAPN10, and KCNJ11. Since 2007, seven genome-wide association (GWA) studies emerged to generate a list of new diabetes genes. The genetic effects are largely of moderate size. These findings provide novel insight into the diabetes etiology and pave new avenue for predicting the disease risk using genetic information. In addition, data especially those from intervention trials display preliminary but promising evidence that the genetic variants might interact with physical activity in predisposing to type 2 diabetes. The gene-environment interactions merit extensive exploration in large, prospective studies.     

Génétique du syndrome métabolique

Metabolic syndrome (MetS) is a common phenotype, affecting about 24 % of the US population. It is associated with an increased risk for type 2 diabetes and cardiovascular disease. Although there is no universally accepted definition for MetS, affected individuals commonly have a cluster of features, including abdominal obesity, hypertension, dyslipidemia, and dysglycemia. Recently, there has been extensive interest in potential genetic contributions to MetS. At present, no single gene or cluster of genes has been consistently replicated for MetS among different populations, likely due to the complex interplay between gene and environment necessary for expression of this phenotype. We review recent studies regarding the genetic contributions to MetS.     

A study of features of the inheritance of latent autoimmune diabetes of adults (LADA)

A study of the genetic determination of latent autoimmune diabetes of adults (LADA) is conducted on data consisting of clinical and genealogical data on 51 patients with LADA, 400 patients with insulin-dependent diabetes mellitus (type 1 diabetes mellitus), and 504 patients with insulin-independent diabetes mellitus (type 2 diabetes mellitus), along with relatives of these patients (first degree of consanguinity). Testing of the Smith model revealed the genetic independence of LADA and both type 1 and type 2 diabetes mellitus. A study of genetic heterogeneity in accordance with the Smith model showed that LADA shares roughly the same number of common genes with type 1 diabetes mellitus and with type 2 diabetes mellitus, which also determines the clinical course of this form of diabetes. The inheritance of LADA is described by parameters of a polygenic threshold model. Within the framework of this model, it is found that genetic factors are responsible for 60.4% of the development of the disease.     

Genetic association, post-translational modification, and protein-protein interactions in Type 2 diabetes mellitus

Type 2 diabetes mellitus is a complex disorder with a strong genetic component. Inherited complex disease susceptibility in humans is most commonly associated with single nucleotide polymorphisms. The mechanisms by which this occurs are still poorly understood. Here we focus on analyzing the effect of a set of disease-causing missense variations of the monogenetic form of Type 2 diabetes mellitus and a set of disease-associated nonsynonymous variations in comparison with that of nonsynonymous variations without any experimental evidence for association with any disease. Analysis of different properties such as evolutionary conservation status, solvent accessibility, secondary structure, etc. suggests that disease-causing variations are associated with extreme changes in the value of the parameters relating to evolutionary conservation and/or protein stability. Disease-associated variations are rather moderately conserved and have a milder effect on protein function and stability. The majority of the genes harboring these variations are clustered in or near the insulin signaling network. Most of these variations are identified as potential sites for post-translational modifications; certain predictions have already reported experimental evidence. Overall our results indicate that Type 2 diabetes mellitus may result from a large number of single nucleotide polymorphisms that impair modular domain function and post-translational modifications involved in signaling. Our emphasis is more on conserved corresponding residues than the variation alone. We believe that the approach of considering a stretch of peptide sequence involving a polymorphism would be a better method of defining the role of the polymorphism in the manifestation of this disease. Because most of the variations associated with the disease are rare, we hypothesize that this disease is a "mosaic model" of interaction between a large number of rare alleles and a small number of common alleles along with the environment, which is little contrary to the existing common disease common variant model.     

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.     

Genome-wide searching of rare genetic variants in WTCCC data

Although they have demonstrated success in searching for common variants for complex diseases, genome-wide association (GWA) studies are less successful in detecting rare genetic variants because of the poor statistical power of most of current methods. We developed a two-stage method that can apply to GWA studies for detecting rare variants. Here we report the results of applying this two-stage method to the Wellcome Trust Case Control Consortium (WTCCC) dataset that include seven complex diseases: bipolar disorder, cardiovascular disease, hypertension (HT), rheumatoid arthritis, Crohn’s disease, type 1 diabetes and type 2 diabetes (T2D). We identified 24 genes or regions that reach genome wide significance. Eight of them are novel and were not reported in the WTCCC study. The cumulative risk (or protective) haplotype frequency for each of the 8 genes or regions is small, being at most 11%. For each of the novel genes, the risk (or protective) haplotype set cannot be tagged by the common SNPs available in chips (r ² < 0.32). The gene identified in HT was further replicated in the Framingham Heart Study, and is also significantly associated with T2D. Our analysis suggests that searching for rare genetic variants is feasible in current GWA studies and candidate gene studies, and the results can severe as guides to future resequencing studies to identify the underlying rare functional variants.     

Mouse models and the interpretation of human GWAS in type 2 diabetes and obesity

Within the last 3 years, genome-wide association studies (GWAS) have had unprecedented success in identifying loci that are involved in common diseases. For example, more than 35 susceptibility loci have been identified for type 2 diabetes and 32 for obesity thus far. However, the causal gene and variant at a specific linkage disequilibrium block is often unclear. Using a combination of different mouse alleles, we can greatly facilitate the understanding of which candidate gene at a particular disease locus is associated with the disease in humans, and also provide functional analysis of variants through an allelic series, including analysis of hypomorph and hypermorph point mutations, and knockout and overexpression alleles. The phenotyping of these alleles for specific traits of interest, in combination with the functional analysis of the genetic variants, may reveal the molecular and cellular mechanism of action of these disease variants, and ultimately lead to the identification of novel therapeutic strategies for common human diseases. In this Commentary, we discuss the progress of GWAS in identifying common disease loci for metabolic disease, and the use of the mouse as a model to confirm candidate genes and provide mechanistic insights.     

Genome-wide association studies provide new insights into type 2 diabetes aetiology

Human geneticists are currently in the middle of a race. Thanks to a new technology in the form of 'genome-wide chips', investigators can potentially find many novel disease genes in one large experiment. Type 2 diabetes has been hot out of the blocks with six recent publications that together provide convincing evidence for six new gene regions involved in the condition. Together with candidate approaches, these studies have identified 11 confirmed genomic regions that alter the risk of type 2 diabetes in the European population. One of these regions, the fat mass and obesity associated gene (FTO), represents by far the best example of an association between common variation and fat mass in the general population.     

Thirty years of Alzheimer's disease genetics: the implications of systematic meta-analyses

The genetic underpinnings of Alzheimer's disease (AD) remain largely elusive despite early successes in identifying three genes that cause early-onset familial AD (those that encode amyloid precursor protein (APP) and the presenilins (PSEN1 and PSEN2)), and one genetic risk factor for late-onset AD (the gene that encodes apolipoprotein E (APOE)). A large number of studies that aimed to help uncover the remaining disease-related loci have been published in recent decades, collectively proposing or refuting the involvement of over 500 different gene candidates. Systematic meta-analyses of these studies currently highlight more than 20 loci that have modest but significant effects on AD risk. This Review discusses the putative pathogenetic roles and common biochemical pathways of some of the most genetically and biologically compelling of these potential AD risk factors.     

Transmitted beta-cell dysfunction as a cause for type 2-diabetes

The pathways that control insulin release and regulate pancreatic beta-cell mass are crucial on the development of type 2 diabetes mellitus. Maturity-onset diabetes of the young comprises a number of single-gene disorders affecting beta-cell development and/or function. A genetic basis for the more common forms of type 2 diabetes which affect adults in developed as well as many developing countries is less clear cut. It is also characterized by abnormal beta-cell function. Appropriate inbred rodent models are an essential tool for the identification of genes and environmental factors that increase the risk of type 2 diabetes. The informations available from studies in the Goto-Kakizaki (GK) rat are here reviewed in such a perspective. This model was obtained by selective breeding of individuals with mild glucose intolerance from a non-diabetic Wistar rat colony. Heritability of defective beta-mass and beta-cell function in GK model is proposed to reflect the complex interactions of three pathogenic players: (1) three independent loci containing genes causating impaired insulin secretion; (2) gestational metabolic (hyperglycaemic) impairment inducing a programming of endocrine pancreas (decreased beta-cell mass) which is transmitted to the next generation; (3) secondary (acquired) loss of beta-cell differentiation due to chronic exposure to hyperglycaemia (glucotoxicity). A better understanding of the mechanisms involved in the failure of beta-cell function in the GK model will lead to identification of new therapeutic targets for both the prevention and treatment of type 2 diabetes.     

Association of chromosome 9p21 SNPs with cardiovascular phenotypes in morbid obesity using electronic health record data

Genomic medicine research requires substantial time and resources to obtain phenotype data. The electronic health record offers potential efficiencies in addressing these temporal and economic challenges, but few studies have explored the feasibility of using such data for genetics research. The main objective of this study was to determine the association of two genetic variants located on chromosome 9p21 conferring susceptibility to coronary heart disease and type 2 diabetes with a variety of clinical phenotypes derived from the electronic health record in a population of morbidly obese patients. Data on more than 100 clinical measures including diagnoses, laboratory values, and medications were extracted from the electronic health records of a total of 709 morbidly obese (body mass index (BMI) >/= 40 kg/m(2)) patients. Two common single nucleotide polymorphisms located at chromosome 9p21 recently linked to coronary heart disease and type 2 diabetes (McPherson et al. Science 316:1488-1491, 2007; Saxena et al. Science 316:1331-1336, 2007; Scott et al. Science 316:1341-1345, 2007) were genotyped to assess statistical association with clinical phenotypes. Neither the type 2 diabetes variant nor the coronary heart disease variant was related to any expected clinical phenotype, although high-risk type 2 diabetes/coronary heart disease compound genotypes were associated with several coronary heart disease phenotypes. Electronic health records may be efficient sources of data for validation studies of genetic associations.