Advertisement

Genetics of Diabetes and Diabetic Complications

  • Rashmi B. Prasad
  • Emma Ahlqvist
  • Leif Groop
Reference work entry
Part of the Endocrinology book series (ENDOCR)

Abstract

Diabetes is a collection of diseases characterized by defective glucose homeostasis. Different diabetes types have different etiologies and their genetic architecture ranges from highly penetrant monogenetic diseases, such as MODY and neonatal diabetes, to polygenic diseases, such as type 1 and type 2 diabetes that are caused by numerous genetic variants adding up to the individual risk. While both diabetes and diabetic complications have been known to be partly heritable for a long time, identification of risk variants was originally limited to a few variants with relatively modest effect sizes. This changed with the advent of genome-wide association studies (GWAS), which has led to the identification of hundreds of common risk variants for diabetes. Still, these variants only explain part of the heritability of complex diabetes types. Further technical development in the field, such as next-generation sequencing, has recently enabled identification of rare variants. Epigenetics, epistasis, gene-environment interactions, parent-of-origin effects, and noncoding RNAs are current research areas that provide additional layers to the genetic architecture and might reveal some of the missing heritability. In this chapter, we review the genetic basis of different diabetes types and diabetic complications and the major methodological milestones that have enabled the many success stories of the last decade.

Keywords

Type 2 diabetes (T2D) Heterogeneity of T2D Heritability Genetic association Linkage studies Candidate studies Genome-wide association studies (GWAS) Next generation sequencing (NGS) Whole genome sequencing (WGS) Whole exome sequencing (WES) Rare variants Protective variants Parent-origin Epigenetics Gene expression Gene-gene interactions Epistasis Non-coding RNA Gene-environment interactions Complications 

The Genetic Architecture of Diabetes

Diabetes is considered to result from a collision between genetic predisposition and environment but their respective roles and interactions differ between different types of diabetes and are still relatively poorly understood, especially in the case of type 2 diabetes (T2D).

The disease clusters in certain families supporting a clear heritable component. The “genetic architecture” of diabetes describes the genetic basis for differences between individuals and is defined by the number, frequencies, and effect sizes of causal alleles. The diabetes spectrum includes everything from strongly penetrant monogenic types, like MODY and neonatal diabetes, to the highly complex polygenic and multifactorial T2D, whose architecture is still under debate. One hypothesis suggests that T2D represents the extreme of a normal distribution where a large number of common variants with small additive effects contribute to the disease (the common disease common variant hypothesis – CDCV) (Plomin et al. 2009), whereas an alternative hypothesis proposes that rare alleles cause the effects observed with common variants (synthetic associations) and thereby explain most of the heritability (Lupski et al. 2011). Studies performed thus far suggest that the truth is somewhere in between, with contributions from both common and rare variants (Agarwala et al. 2013).

The genetic architecture can also vary within a diabetic subtype; especially T2D is heterogeneous and could include forms caused by rare variants with high penetrance as well as forms caused by many common risk alleles. A gene locus can also harbor different susceptibility variants in different individuals including both common and rare alleles, as has been observed at the HNF1A and HNF4A loci. These gene regions contain both rare MODY causing variants and common variants associated with T2D.

Heritability estimates how much variation in a phenotypic trait in a population is due to genetic variation. Heritability in its traditional form estimates phenotypic similarities between family members or, ideally, between monozygotic and dizygotic twins. In genetic terms, heritability can be quantified in two ways; broad-sense heritability (H2) captures the proportion of phenotypic variation due to genetic effects including dominance (allelic interactions within loci) and epistasis (interactions between loci), whereas narrow-sense heritability (h2) covers variation due to additive genetic effects only.

It is well known from both family and population studies that both type 1 diabetes (T1D) and T2D are partially heritable but the hitherto identified risk loci explain less than 20% of the heritability of T2D whereas for T1D, this number is >80% (Groop and Pociot 2014). This missing heritability could have multiple explanations, including incorrect estimations of heritability or incorrect definitions of the disease (Visscher et al. 2008). Applying an approach that considered all SNPs on the chip could explain a much larger proportion of the heritability of T2D supporting the existence of numerous yet unidentified loci with even smaller effects than those identified to date (Visscher et al. 2008). Other explanations include gene-gene interactions, also referred to as epistasis, gene-environment interactions, and epigenetics. Parent-of-origin effects, where the same allele can have different effects depending on whether it is inherited from the mother or the father, add another dimension to the genetic architecture and could play a key role in fetal programming of the disease (Kong et al. 2009; Prasad et al. 2016a). Noncoding RNAs and microRNAs add a further layer of complexity in the regulation of gene expression.

The Spectrum of Diabetes Disorders

Traditionally, diabetes has been divided into T2D and T1D. However, this is clearly an obsolete view, and it has become clear that diabetes encompasses a range of heterogeneous metabolic disorders discussed below:

Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of pancreatic beta cells and is characterized by (nearly) complete absence of insulin and presence of several autoantibodies reacting with beta-cell auto antigens leading to dependence on insulin injections. It is most often diagnosed in children, adolescents, or young adults less than 35 years old.

LADA ( latent autoimmune diabetes in adults ) is a subgroup of diabetes defined by presence of autoantibodies to glutamic acid decarboxylase (GADA) with onset after age 35. These patients may be controlled by oral antidiabetic agents during the first 6 months after diagnosis (Tuomi et al. 1993a; Groop et al. 2006), but become more T1D-like with time.

MODY (maturity-onset diabetes of the young) refers to monogenic forms of diabetes with unique mutations in more than ten different genes, a number which is still increasing. The disease is characterized by autosomal dominant transmission of early-onset (<25 years) diabetes and varying degrees of beta-cell dysfunction (Tattersall 1974).

Maternally inherited diabetes and deafness (MIDD) is due to the A3242G mutation in mitochondrial DNA (mtDNA) (van den Ouweland et al. 1992), exclusively transmitted from the mother as sperm lacks mitochondria. Symptoms also include hearing loss, and neurological problems particularly in patients with the MELAS syndrome (Mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke), which is caused by the same mutation in mtDNA.

Neonatal diabetes is defined as diabetes with onset at birth or during the first 6 months of life. Both transient and permanent forms exist (Murphy et al. 2008a).

Gestational diabetes mellitus (GDM) represents a transitory form of diabetes that manifests as hyperglycemia during pregnancy and usually resolves postpartum .

Secondary diabetes is caused by pancreatic diseas e (pancreatitis or cancer) or other endocrine disorders.

Type 2 diabetes is the most prevalent form of diabetes comprising today >80% of all reported patients with diabetes. T2D develops when pancreatic beta cells can no longer produce enough insulin to compensate for the insulin resistance imposed by increasing obesity. There is no formal definition of T2D; individuals with diabetes who do not fulfill criteria for any of the diabetes forms mentioned above are considered to have T2D. While T2D is mostly diagnosed in elderly people (Groop and Pociot 2014), it is increasingly reported already in adolescents from India and China (WHO 2014) but also in Hispanics and African Americans. The diabetes subtypes represent a diverse range of genetic etiologies and manifestations and usually require separate therapeutic strategies.

The ANDIS (All New Diabetics in Scania) project in Southern Sweden represents a new attempt to reclassify diabetes into subgroups based upon phenotypic indications, genetic markers, and other biomarkers (Fig. 1). The aim of ANDIS is to register all new cases of diabetes in Scania and improve diagnosis and treatment strategies. At the time of registration, blood samples are drawn to determine the presence of GAD-antibodies, measure C-peptide, biobanking, and for genetic analysis. The data is used to classify the disease into subtypes and to study genetic causes of diabetes, diabetic complications, and other disorders related to diabetes (http://andis.ludc.med.lu.se/all-new-diabetics-in-scania-andis/). A similar project has been initiated in Uppsala with the same goal (ANDIU – All new diabetics in Uppsala; http://www.andiu.se/english/). Recently these cohorts were used to subgroup patients using unsupervised clustering, based on age at diagnosis, BMI, HbA1c at diagnosis, presence of GAD antibodies and measures of insulin secretion and insulin resistance (c-peptide based HOMA2-B and HOMA2-IR respectively; Fig. 1). Using this strategy patients could be divided into five subtypes, of which one, referred to as severe autoimmune diabetes (SAID) corresponded to T1D and LADA. Of the four type 2 diabetes subgroups, one was characterized by low insulin secretion and poor metabolic control (severe insulin-deficient diabetes; SIDD) and had an increased risk of retinopathy, whereas a subtype defined by strong insulin resistance (severe insulin-resistant diabetes; SIRD) had increased risk of kidney disease (Ahlqvist et al. 2018).
Fig. 1

Distributions of diabetes patient in the Swedish ANDIS cohort where type 2 diabetes patients have been subclassified into four new subgroups: severe insulin-deficient diabetes (SIDD; 17.5%), severe insulin-resistant diabetes (SIRD; 15.3%), moderate obesity-related diabetes (MOD; 21.6%), and moderate age-related diabetes (MARD; 39.1%)

Development of the Field of Complex Genetics

The field of genetics has been revolutionized in the last decade driven by technical advances in sequencing and genotyping techniques (Fig. 2).
Fig. 2

Major landmarks in the history of genomics and diabetes genetics

Linkage Analysis

Many genetic diseases have been mapped to disease causing genes using data from affected families. Family-based linkage analysis is a method that takes advantage of the long stretches of chromosomes in linkage within a family that stem from the genetic recombination process during meiosis. Finding that affected family members share a certain genetic region that is identical by decent (i.e., identical because it was inherited from the same parent) more often than expected by chance is evidence that a disease causing variant is in linkage with that marker. Thanks to the long linked regions, disease loci could be mapped on a genome-wide level without any prior hypothesis by genotyping only 400–500 genetic markers (microsatellites). However, the low number of genetic recombinations also results in very low resolution, making it difficult to go from locus to disease causing gene. This strategy is very successful in mapping diseases like MODY that have a strong penetrance and a known mode of inheritance, but much less fruitful for complex diseases such as T1D and T2D.

A modern application of linkage analysis uses a dense GWAS (see below) with about 2.5 M SNPs to identify the shortest, transmitted haplotype followed by sequencing of the most informative individuals to identify the causal variant on the haplotype. The resolution of this approach is much better than traditional linkage studies, but still it sometimes remains a challenge to identify the functional causal variant .

Candidate Genes, Haplotypes, and Association Studies

The common disease/common variant hypothesis suggests that common disorders are caused by aggregation of common risk alleles with small individual effects (Lupski et al. 2011). This hypothesis stimulated the development of novel tools for genetic association studies. Given the high cost of genotyping, genetic association studies were first restricted to testing a single nucleotide polymorphism (SNP) in a functional candidate gene, e.g., PPARγ (the Pro12Ala variant) for association with a phenotype, insulin resistance, and/or T2D (Deeb et al. 1998)

An important step in the development of association studies was the realization that we inherit short stretches of the chromosomes where variants are in linkage disequilibrium (LD) with another so-called haplotypes. The Human Genome Project (Collins et al. 2003) pioneered and identified >100,000 SNPs spread all over the genome, thereby providing a first catalogue of markers for genetic studies. This allowed studies of larger cohorts with improved statistical power and resolution of observed association signals. A drawback was the need for very large numbers of genetic markers to cover a region, and association studies were therefore, in the early days primarily performed on small regions known to harbor genes that were known or expected to be involved in pathogenesis of the disease.

The next important step came with the the HapMap project which provided a catalogue of haplotypes across the genome and demonstrated that genotyping of 500,000 SNPs was enough to cover about 75% of common variants with minor allele frequency > 5% in the genome (International HapMap Consortium 2003).

Genome-Wide Association Studies (GWAS)

The rapid improvement in high throughput technology for SNP genotyping, allowing simultaneous genotyping of hundreds of thousands of SNPs, as well as the HapMap project, opened new possibilities for performing association studies on the genome-wide level, so-called Genome-Wide Association Studies (GWAS). In 2007, the first GWASes in T2D were published describing a modest list of about ten variants associated with T2D. This list has continuously grown and include today (2017) >140 SNPs showing association with T2D or glycemic traits like glucose and/or insulin. A state-of-the-art GWAS today interrogates over 10 million variants across the genome. This has been made possible not only by development of better genotyping technology but also by the development of reference genomes (usually based on sequencing of thousands of full genomes) that allows inference of SNPs without genotyping, so-called imputation, which takes advantage of the known correlation (LD) between markers in the population.

The huge number of variants that can be tested requires strict correction for multiple testing. A commonly adopted thresholds for genome-wide statistical significance is p < 5 × 10−8, which is equivalent to Bonferroni correction for a million independent tests. The GWAS approach has proven highly effective in identifying robustly associated loci for many complex traits and diseases. In recent years, we have seen many international collaborations joining efforts to combine GWAS studies in ever-larger meta-analyses resulting in the identification of genetic loci with smaller and smaller effects.

Next-Generation Sequencing

Since natural selection usually removes deleterious variants, rare risk alleles are often more recent and likely to have arisen in extended pedigrees in isolated populations. Their term “Clan genomics” has been used to describe the concept of rare variant combinations in families and their role in disease etiology (Lupski et al. 2011).

Techniques for large-scale genomic analysis have continued to evolve thereby making detection of rare variants feasible. One important advancement was the advent of whole exome sequencing (WES) and whole genome-sequencing (WGS) made possible by the continuously improved next-generation sequencing technologies, allowing affordable high - throughput sequencing of entire genomes. While the Human Genome Project, using capillary electrophoresis-based Sanger sequencing, took over 10 years and cost several billion US dollars, the current figures for a full genome is in the range of a few days and 1,000$. WES is a highly effective method to capture more than 90% of the coding DNA of an individual. This is accomplished by applying various “exome capture” techniques that extract the protein-coding portion of the genome using specific DNA probes. The exome represents only 1–1.5% of the genome, but since many disease-causing mutations are located in coding regions, this is a cost-effective approach to identify such rare variants and WES has proven to be very successful in identifying novel genes and disease pathways.

Gene-Gene Interactions

Epistasis is a well-known phenomenon in genetics and refers to interactions between genetic loci resulting in greater effects on a phenotype than expected from the sum of the effects of the involved loci. While epistasis was described more than 100 years ago and has been demonstrated many times in model organisms, there is relatively little evidence for substantial amounts of statistical epistasis in human populations or most natural populations of other organisms, which does not mean that it is not important (Sackton and Hartl 2016). The study of epistasis in complex diseases is severely hampered by the huge sample sizes needed to discover small or medium interaction effect variants with statistical significance. Exhaustively evaluating all of the possible combinations of SNPs is not computationally feasible. A genome-wide data set including one million SNPs generates 5 × 1011 possible two-SNP models, which requires extensive computing resources and p-values below 10–11 to claim statistical significance after correction for the number of tests. Models including three or more SNPs would of course be even more problematic. Numerous methods have been developed to make whole genome epistasis analysis more computationally tractable (Wei et al. 2014). Still, the power is limited by the sample size. A number of filtering approaches have been suggested to overcome these problems. One is to include only SNPs shown to have an independent effect that is below a certain p-value. While this has been shown to have high power (Evans et al. 2006) and has identified significant interactions for some diseases, SNPs that have effects only through their interactions with other genes would be missed. Another strategy is to use biological knowledge, such as genes belonging to the same pathway or having similar functions, to filter SNPs and then evaluate multi-marker combinations based on biological criteria (Carlson et al. 2004). However, this will bias the analysis in favor of models with an already known biological foundation and miss new potentially more interesting interactions .

Gene-Environment Interactions

It is well known that most forms of diabetes result from a complex interplay between genes and environment. The T2D epidemic is quite recent, dating back ~50 years, and it is evident that during this period, there has been a substantial change in the environment and lifestyle. In contrast, it takes much longer to change our genetic architecture, which determines how we respond to the effects of the environment and which is therefore an important aspect in determining diabetes etiology. For instance, genetic variation affecting metabolic processes could render an individual more susceptible to the effects of a poor diet, while variants affecting personality traits could influence the individual’s risk to over-consume food.

A common argument against models that includes genetic variants with strong effects is that if alleles are associated with negative health effects they should have been removed from the population by natural selection (Diamond 2003). However, in the case of diabetes, it is important to remember that the penetrance of the genetic effect depends on interactions with the environment, which has dramatically changed in the recent years .

Epigenetics

The environment can also influence the manifestation of a trait through epigenetic effects on the genome. Epigenetics is defined as a heritable change in gene expression that can be passed on from one cell generation to another through mitotic inheritance or between generations of species (meiotic inheritance) without changing the DNA sequence (Chong and Whitelaw 2004; Chong et al. 2007; Bird 2007). This can be in the form of epigenetic modifications, such as addition of methyl group to the DNA sequence, or post-translational modification of histones or microRNAs. The addition of a methyl group often occurs at CpG sites, whereby cytosine is converted into a 5-methylcytosine. If occurring at the promoter, this is usually associated with reduced transcriptional activity and silencing of gene expression, imprinting. Methylation can be studied by bisulfate sequencing. Treatment of DNA with bisulfite converts cytosine residues to uracil, but leaves 5-methylcytosine residues unaffected. Bisulfate sequencing can be targeted or global, i.e., the entire genome. There is emerging evidence that environmental factors such as diet and exercise can change the degree of DNA methylation and thereby cause changes in gene expression. It has been shown that poor physical fitness and activity and a low VO2max increase risk of developing T2D. Obesity and insulin resistance, mitochondrial dysfunction, and changes in muscle fiber – type composition are potential mechanisms linking poor physical fitness with an increased risk for disease Exercise is also a pot ential environmental factor which could exert effects on gene expression by methylation (White et al. 2013).

Noncoding RNAs: microRNAs

Noncoding RNAs are important regulators of gene expression and function. MicroRNAs (miRNAs) piRNAs (PIWI-interacting RNAs), snoRNAs (small nucleolar RNAs), lincRNAs (long intergenic noncoding RNAs), and lncRNAs (long noncoding RNAs) represent different forms of noncoding RNAs that can regulate gene expression and eventually contribute to the development of diabetes. For instance, the efficiency of miRNAs binding to target transcripts depends on both the sequence and the intra-molecular structure of the transcript. SNPs can contribute to alterations in the structure of regions flanking them or may alter the target sequence, thereby influencing the accessibility for miRNA binding (http://200.12.130.109/nrdr/) (Fernandez-Valverde et al. 2011; Hariharan et al. 2009). Manipulation of specific miRNAs is now being explored as novel therapeutic modalities (Davidson and McCray Jr. 2011).

Parent-of-Origin Effects

The risk of developing T2D is higher if the mother has T2D than the father, whereas the opposite is seen for T1D. These phenomena conflict with the classical Mendelian inheritance patterns, which assume functional equivalence of maternal and paternal alleles (Groop et al. 1996; Hemminki et al. 2010). Sex specific parental effects have also been reported for glucose stimulated insulin secretion and HDL concentrations (Groop et al. 1996). A potential explanation for this could be preferential parental transmission of causative alleles to offspring, which is often associated with DNA methylation and imprinting. Certain epigenetic modifications have the potential to be stable and heritable across cell divisions and manifest as parent-of-origin effects (Chong and Whitelaw 2004).

The conflict hypothesis, or the kinship theory of genomic imprinting, suggests that inequality between the parental genomes results from a genomic tug-of-war between mothers and fathers over the use of maternal resources for the fetus. The paternal imprinting maximizes the utilization of intrauterine resources to the offspring to increase his evolutionary fitness whereas the maternal imprinting tries to minimize utilization of these resources to conserve them for her own survival and for her future offspring (Moore and Haig 1991). In contrast, the co-adaptation hypothesis suggests that imprinted genes coevolve to improve fetal development and maternal provisioning of nutrition and care (Wolf and Hager 2006). While there is insufficient evidence to favor either theory over the other, imprinting nevertheless plays a key role in defining paternal and maternal effects on the offspring.

Parent-of-origin effects (POE) can also be caused by intrauterine effects, which could play a role in fetal programming. Poor nutrition can affect fetal growth and produce permanent changes in glucose-insulin metabolism, often associated with low birth weight (Hales and Barker 2001). Low birth weight can induce permanent changes in metabolism and increase susceptibility to chronic diseases as diabetes as proposed by the Developmental Origins of Health and Disease (DoHAD) hypothesis (Barker 2007). If intrauterine programming results in a reduced β-cell mass, it could predispose to diabetes later in life if insufficient to increase insulin secretion to meet increased demands imposed by insulin resistance. Gestational diabetes in the mother can lead to a hyperglycemic environment, which, in turn, is associated with both macrosomia and low birth weight (Young and Ecker 2013; Group HSCR et al. 2008). A “U” shaped curve has been observed for the association of low and high birth weight and risk of T2D and obesity (Harder et al. 2007).

Investigation of parent-of-origin effects requires family-based cohorts with pedigree information. Long-range phasing and imputation methods allow for predicting genotypes, thereby assigning “surrogate” parents despite availability of DNA from only a few family members. Novel POE methods allow detection of imprinting effects from differences in the phenotypic variance of heterozygotes in very large case-control studies (Hoggart et al. 2014). Parent-of-origin effects could explain part of the missing heritability and must be taken into consideration in investigations of etiology of diabetes.

A large family-based study on Iceland showed that variants in the KCNQ1, KLF14, and MOB2 genes show higher risk of T2D when the risk allele is transmitted from the mother than from the father (Kong et al. 2009; Small et al. 2011); these findings have subsequently been replicated in our own studies (Hanson et al. 2013). We have also provided evidence for excess maternal transmission of variants in the THADA gene to offspring with T2D (Prasad et al. 2015). The KCNQ1 gene is an example of fetal programming showing monoallelic expression in fetal islets but biallelic expression in adult islets (Travers et al. 2013) Moreover, paternal mutations at this locus show reduced pancreatic beta cell mass (Asahara et al. 2015).

Genetics of Specific Diabetes Types

Type 1 Diabetes

T1D accounts for 5–10% of diabetes cases worldwide. It is a chronic disease characterized by an autoimmune reaction to the pancreatic beta cells and presence of autoantibodies, leading to nearly complete absence of insulin secretion and dependence on insulin injections. T1D is usually diagnosed in children or adults younger than 35 years. Incidence (cases per year) varies depending on geography, age, and family history, with the highest incidence rates observed in Finland and Sardinia and the lowest in China and Venezuela (Karvonen et al. 2000). The first sign of disease is the appearance of beta-cell autoantibodies, which can occur very early in childhood. The first antibodies are usually directed against either insulin or glutamic acid decarboxylase (GAD), but additional antibodies against ZnT8A and islet antigen-2 (IA2) are common; IA2 are especially frequent in young children. The appearance of auto-antibodies can be followed by a period of slight elevation of blood glucose until overt symptomatic diabetes develops (Pociot and Lernmark 2016).

Heritability

The genetic component in T1D is strong. The average prevalence risk is 0.4% for children with no family history of T1D, but ~6% when one parent has T1D, and >30% when both parents are affected. There is also a great difference in concordance rates between dizygotic (7–11%) and monozygotic (40–50%) twins (Kyvik et al. 1995; Hyttinen et al. 2003). Interestingly, as previously mentioned, the risk of inheriting T1D differs depending on which parent is affected with approximately double risk if inherited from the father (5–8%) than from the mother (2–4%) (Kyvik et al. 1995; Pociot et al. 1993). The sibling relative risk of T1D is about 15 (Patterson et al. 2009; Dahlquist et al. 1989) as compared to 3 for T2D.

Genetic Risk Loci

The main susceptibility locus for T1D is the Human Leukocyte Antigen (HLA) gene complex encoding the major histocompatibility complex (MHC) in humans. This locus accounts for up to 50% of genetic T1D risk and was identified already in the 1970s (Singal and Blajchman 1973; Nerup et al. 1974).

HLA molecules are cell-surface proteins that bind and present peptide antigens to T-lymphocytes. HLA is categorized into two classes. Class I molecules (A, B, and C) consist of a polypeptide chain that form a heterodimer with β-2 microglobulin which is not encoded by the HLA complex. Class II molecules (DR, DQ, and DP) consist of a heterodimer created from two polypeptides (α and β). Class I molecules present peptides from inside the cells and activate cytotoxic T-cells, whereas class II molecules present extra-cellular antigens to T-helper cells that stimulate B-cells to produce antibodies. Peptide binding, and thus antigen presentation, is determined by the shape and electrical charges of the peptide binding groove and the ability of the T-cell receptor to bind to the HLA-peptide complex.

The HLA region exhibits strong linkage disequilibrium, so that within a population individual alleles are usually found in only one or a few haplotype combinations. The highest T1D risk is attributable to class II loci HLA-DR3-DQ2 and HLA-DR4-DQ8. Nearly 90% of children diagnosed with T1D in Scandinavia have either HLA-DR3-DQ2 or HLA-DR4-DQ8 haplotypes (Sanjeevi et al. 1995). The association between HLA and diabetes seems to be related to risk of developing the first auto-antibody, so that children homozygous for HLA-DR3-DQ2 are more likely to have GADA antibodies as their first antibody and children with HLA-DR4-DQ8 haplotype more likely to have insulin autoantibodies first (Ilonen et al. 2013). Other class II haplotypes have also been associated with risk of T1D with smaller effects, e.g., the DPB1 locus is associated with both protection (DPB1*04:02) and susceptibility (DPB1*03:01 and DPB1*02:02) (Noble 2015). HLA risk alleles also differ between populations. The HLA-DR7 haplotype including DRB1*07:01 is protective in the European population but confers risk in Africans (Erlich et al. 2008). Similarly, an African specific DR3 haplotype (DRB1*03:02-DQA1*04:01-DQB1*04:02) protects from T1D (Erlich et al. 2008).

Multiple non-HLA loci contribute to disease risk with smaller effects. The first, and strongest (OR 2.4), non-HLA locus, in the insulin gene (INS), was identified already in 1984 (Bell et al. 1984). The promotor region was found to have a variable number of repeats (VNTR) marking alleles with different expression of the INS gene, which is postulated to affect susceptibility by modulating thymic expression of insulin and affecting T-cell education (Pugliese 2005). Susceptibility loci in the CTLA4, PTPN22, and IL2RA regions were all identified in candidate gene studies. Since the introduction of GWAS more than 50 loci have been identified, explaining ~80% of the narrow sense heritability of T1D (Pociot et al. 2010). One of the largest efforts was the type 1 Diabetes Genetics Consortium (T1DGC), an international collaboration through which >14,000 samples were collected and genotyped. Of the identified loci only PTPN22 and IL2RA have ORs greater than 1.5; most are in the range of 1.1–1.3, underscoring the importance of the HLA region (Pociot et al. 2010).

Recognition of a specific antigen and HLA by the T-cell receptor may result in autoimmune attack, which could be further potentiated by gene variants that impair antigen presentation or T-cell signaling. Functional insights into the role of T1D susceptibility loci has revealed that many candidate genes are involved in functions related to T-cell-mediated adaptive immune response and tolerance mechanisms and also to innate immunity involved in recognition of β-cell antigens (Zhernakova et al. 2009). Many genetic associations are also shared with other autoimmune diseases (Zhernakova et al. 2009). For example, a common loss-of-function allele in the tyrosine phosphatase PTPN22 locus decreases the risk of Crohn disease but increases the risk of rheumatoid arthritis and T1D. Interestingly, at least 50% of the identified candidate genes, including CTRB1/2, IFIH1, GLIS3, and PTPN2 are also expressed in beta-cells supporting the concept that genetic susceptibility to T1D influences both the im mune system and beta-cell function (Bergholdt et al. 2012). Post-GWAS fine mapping and functional characterization remain to be performed for most loci.

Gene-Gene and Gene-Environment Interactions

A number of gene-gene interactions have been identified for T1D, primarily between HLA and non-HLA loci, e.g., an interaction between the PTPN22 locus and DR3/DR4-DQ302 where the effect of PTPN22 is stronger with low risk HLA (Smyth et al. 2008).

As for gene-environment interactions, T1D is most likely triggered by an environmental factor but the initiating events that lead to the presentation of beta-cell antigens to T cells for their activation are yet to be elucidated.

Epigenetics

Many processes involved in T1D could be influenced by epigenetic mechanisms, including beta-cell development, metabolism, and regeneration. Immune responses, including activation of T cells and induction of regulatory T-cells, rely on epigenetic regulation. The pattern of four CpG sites proximal to the transcription start site of the INS gene has been shown to differ between T1D patients and controls, with three sites being less methylated and one more methylated (Fradin et al. 2012). Similarly, CpG sites in the promoter of IL2 were more densely methylated in T1D patients than in controls (Belot et al. 2013).

Histone modifications may also be relevant for T1D, For example, case-control studies have revealed different levels of acetylated histone H4 or of H3K9 acetylation in T1D patients compared with controls (Miao et al. 2012), and increased levels of H3K9me2 in T1D-related genes, including CTLA4, in lymphocytes from T1D patients compared with controls (Miao et al. 2008).

A growing number of observations suggest that miRNAs can also contribute to the development of T1D. Experimental studies in animal models and cultured cells have provided convincing evidence that miRNA can participate in controlling autoimmune damage of β-cells, regulation of insulin synthesis and secretion (Zheng et al. 2017). The expression of specific miRNAs in blood and lymphocytes has also been shown to differ between T1D patients and controls and to be correlated with disease severity (Zullo et al. 2017). Measurement of these miRNAs may therefore be useful for identifying people at risk of developing T1D and for disease prevention.

Type 2 Diabetes

Heritability

Heritability estimates for T2D have varied between 25% and 80% in different studies; the highest estimates seen in those with the longest follow-up period. The lifetime risk of developing T2D is 40% for individuals who have one parent with T2D and almost 70% with two affected parents (Köbberling and Tillil 1982). The concordance rate of T2D in monozygotic twins is ~70%, while the concordance in dizygotic twins is only 20–30% (Kaprio et al. 1992; Newman et al. 1987; Poulsen et al. 1999; Medici et al. 1999).

The relative risk for first-degree relatives is approximately 3 and ~6 if both parents are affected (Meigs et al. 2000). The prevalence of T2D varies from a few percent among Caucasians in Europe to 50% among Pima Indians in Arizona (Diamond 2003). Thus, there is no doubt that the risk of T2D is partly determined by genetic factors. However, the genetic factors discovered thus far, mostly by GWAS, explain only 10–15% of the heritability of T2D.

Genetic Risk Loci

Linkage studies identified the first T2D gene CAPN10 on chromosome 10 encoding calpain 10, a cysteine protease with largely unknown functions in glucose metabolism (Horikawa et al. 2000). However, this finding has been difficult to replicate. The TCF7L2 gene variant, which shows the strongest association with T2D, was originally identified in a region showing modest linkage with T2D on chromosome 10q. Luckily, fine-mapping identified the TCF7L2 intronic rs7903146 SNP contributing to, but not fully explaining, the original linkage (Duggirala et al. 1999; Reynisdottir et al. 2003; Grant et al. 2006). This association has since been confirmed in various populations world-wide rendering it the most consistent association with T2D to date, conferring a relative risk of ~1.4 (Tong et al. 2009).

Candidate gene studies have robustly associated two loci, PPARG and KCNJ11, with T2D (Deeb et al. 1998; Hani et al. 1998; Gloyn et al. 2003). The KCNJ11 E23K and PPARG P12A polymorphisms act in an additive manner to increase T2D risk (Hansen et al. 2005). PPARG encodes the nuclear receptor PPAR-γ which is a molecular target for thiazolidinediones , a class of insulin sensitizing drugs used to treat T2D. This variant was associated with increased transcriptional activity, increased insulin sensitivity, and protection against T2D (Deeb et al. 1998). KCNJ11 encodes four out of eight subunits of the ATP-sensitive potassium (K-ATP) channel in pancreatic beta-cells, the other four coded by ABCC8 (SUR1). In pancreatic beta cells, ATP-potassium channels are crucial for the regulation of glucose stimulated insulin secretion and are targets for the antidiabetic drugs sulfonylureas, which act by stimulating insulin secretion. Activating mutations in this gene also cause neonatal diabetes while loss-of-function mutations in KCNJ11 and ABCC8 cause hyperinsulinemia associated with hypoglycemia in infancy (Gloyn et al. 2004).

Genome-wide association studies have been successful in identifying numerous loci associated with T2D and related traits. The first four GWASes for T2D were published in 2007, also by the Science magazine coined “Breakthrough of the Year” (Diabetes Genetics Initiative of Broad Institute of H et al. 2007; Scott et al. 2007; Wellcome Trust Case Control C 2007; Sladek et al. 2007). Unforeseen in genetics of T2D, three of the studies reported the same top findings!

Association studies require large study populations for sufficient power. A second wave of GWAS combined existing or new GWAS to meta-analyze >50,000 individuals (Voight et al. 2010). Many research groups worked together in consortia like DIAGRAM (DIAbetes Genetics Replication and Meta-analysis Consortium) and MAGIC (Meta-Analyses of Glucose-and Insulin-related traits Consortium) to facilitate this. Since the most strongly associated SNPs are often only markers for the functional variant responsible for the observed genetic effect, additional fine mapping of the loci is necessary .

Impact of ethnicity: A number of GWAS and meta-analysis studies have also been performed in non-European cohorts, adding several new loci to the list of variants associated with T2D (Cho et al. 2012; Imamura et al. 2012; Kooner et al. 2011; Li et al. 2013; Palmer et al. 2012; Parra et al. 2011; Shu et al. 2010; Replication et al. 2014). Interestingly, most associations found in one ethnic group also show some evidence of association in populations of other ethnicities.

In total, GWAS performed until date have identified ~248 variants for T2D mapping to >150 loci as well as numerous loci for glucose or insulin-related traits and more are likely to come (online table with references – Tables 1 and 2) (Fig. 3).
Table 1

Genetic loci associated with T2D risk and glycemic traits

N

T2D risk SNP

GENE/nearest Gene

Gene location

Chr

RA

OR

TRAIT

References

1

rs17106184

FAF1

Intron

1

G

1.1

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

2

rs2296172

MACF1

Coding – missense

1

G

1.1

2D

(Albrechtsen et al. 2013)

3

rs10923931

NOTCH2

Intron

1

T

1.13

T2D

(Zeggini et al. 2008; Lyssenko et al. 2008)

4

rs340874

PROX1

Intergenic

1

C

1.07

Fasting glucose/HOMA B/T2D

(Dupuis et al. 2010)

5

rs243021

BCL11A

Intergenic

2

A

1.08

T2D

(Voight et al. 2010)

6

rs243088

BCL11A

Intergenic

2

T

1.07

T2D

(Morris et al. 2012a)

7

rs2975760

CAPN10

Intron

2

C

1.17

T2D

(Horikawa et al. 2000; Weedon et al. 2003)

8

rs3792267

CAPN10

Intron

2

G

1.17

T2D

(Horikawa et al. 2000; Weedon et al. 2003)

9

rs7607980

COBLL1

Coding – missense

2

T

1.14

T2D

(Albrechtsen et al. 2013)

10

rs560887

G6PC2/ABCB11

Intron

2

T

1.03

Fasting glucose/T2D/HOMA B

(Dupuis et al. 2010)

11

rs780094

GCKR

Intron

2

C

1.06

T2D/fasting glucose/beta cell function/triglycerides/fasting insulin

(Dupuis et al. 2010)

12

rs3923113

GRB14

Intergenic

2

A

1.07

T2D

(Morris et al. 2012a; Kooner et al. 2011)

13

rs13389219

GRB14

Intergenic

2

C

1.07

T2D

(Morris et al. 2012a)

14

rs2943641

IRS1

Intergenic

2

C

1.19

Fasting glucose/T2D/HOMAB, HOMA IR/AUC ins/AUC ratio/ISI

(Rung et al. 2009)

15

rs7578326

KIAA1486/IRS1

Intron of uncharacterized LOC646736

2

A

1.11

T2D

(Voight et al. 2010)

16

rs7593730

RBMS1/ITGB6

Intronic

2

C

1.11

T2D

(Qi et al. 2010)

17

rs7560163

RND3

Intergenic

2

G

1.33

T2D

(Palmer et al. 2012)

18

rs7578597

THADA

Coding – missense

2

T

1.15

T2D

(Zeggini et al. 2008; Lyssenko et al. 2008)

19

rs10200833

THADA

Intron

2

G

1.06

T2D

(Zeggini et al. 2008; Saxena et al. 2012)

20

rs6723108

TMEM163

Intergenic

2

T

1.31

Decreased fasting plasma insulin/HOMA-IR/T2D

(Tabassum et al. 2013)

21

rs998451

TMEM163

Intron

2

G

1.56

Decreased fasting plasma insulin/HOMA-IR/T2D

(Tabassum et al. 2013)

22

rs4607103

ADAMTS9-AS2

Intron

3

C

1.09

T2D

(Zeggini et al. 2008; Lyssenko et al. 2008)

23

rs6795735

ADAMTS9-AS2

Intron

3

C

1.09

T2D

(Zeggini et al. 2008; Lyssenko et al. 2008)

24

rs11708067

ADCY5

Intron

3

A

1.12

T2D/2hr glucose/HOMA B

(Dupuis et al. 2010; Saxena et al. 2010)

25

rs2877716

ADCY5

Intron

3

C

1.12

2 hr insulin adjusted for 2 hr glucose/2 hr glucose/T2D

(Dupuis et al. 2010; Saxena et al. 2010)

26

rs11071657

FAM148B

Intergenic

3

A

1.03

Fasting glucose/T2D/HOMA B

(Dupuis et al. 2010)

27

rs4402960

IGF2BP2

Intron

3

T

1.11

T2D

(Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007)

28

rs1470579

IGF2BP2

Intron

3

C

1.15

T2D

(Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Scott et al. 2012; Zeggini et al. 2007; Unoki et al. 2008)

29

rs6808574

LPP

Intergenic

3

C

1.07

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

30

rs1801282

PPARG

Coding – missense

3

C

1.09

T2D

(Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007)

31

rs13081389

PPARG

Intergenic

3

A

1.24

T2D

(Voight et al. 2010; Zeggini et al. 2007; Deeb et al. 1998; Saxena et al. 2007)

32

rs17036160

PPARG

Intron

3

C

1.11

T2D

(Saxena et al. 2012)

33

rs1797912

PPARG

Intron

3

A

1.06

T2D

(Saxena et al. 2012)

34

rs831571

PSMD6

Intergenic

3

C

1.09

T2D

(Cho et al. 2012a)

35

rs7647305

SFRS10

Intergenic

3

C

1.08

BMI/obesity T2D

(Thorleifsson et al. 2009)

36

rs16861329

ST6GAL1

Intron

3

G

1.09

T2D

(Kooner et al. 2011)

37

rs6780569

UBE2E2

Intergenic

3

G

1.21

T2D

(Yamauchi et al. 2010)

38

rs6815464

MAEA

Intron

4

C

1.13

T2D

(Cho et al. 2012a)

39

rs7656416

MAEA

Intron

4

C

1.15

T2D

(Cho et al. 2012a; Imamura et al. 2012)

40

rs6813195

TMEM154

Intergenic

4

C

1.08

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

41

rs10010131

WFS1

Intron

4

G

1.14

T2D

(Lyssenko et al. 2008; Sandhu et al. 2007)

42

rs4689388

WFS1

NearGene-5

4

T

1.16

T2D

(Rung et al. 2009)

43

rs6446482

WFS1

Intron

4

G

1.11

T2D

(Voight et al. 2010; Sandhu et al. 2007; Minton et al. 2002)

44

rs1801214

WFS1

Coding – missense

4

T

1.13

T2D

(Voight et al. 2010; Sandhu et al. 2007; Minton et al. 2002)

45

rs459193

ANKRD55

Intergenic

5

G

1.08

T2D

(Morris et al. 2012a)

46

rs702634

ARL15

Intron

5

A

1.06

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

47

rs4457053

ZBED3

Intron of ZBED3-AS1

5

G

1.08

T2D

(Voight et al. 2010)

48

rs1048886

C6orf57

Coding – missense

6

G

1.54

T2D

(Sim et al. 2011)

49

rs7754840

CDKAL1

Intron

6

C

1.17

T2D

(Voight et al. 2010; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012a)

50

rs7756992

CDKAL1

Intron

6

G

1.2

T2D

(Steinthorsdottir et al. 2007)

51

rs2206734

CDKAL1

Intron

6

T

1.2

T2D

(Voight et al. 2010; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012a)

52

rs4712523

CDKAL1

Intron

6

G

1.27

T2D

(Voight et al. 2010; Rung et al. 2009; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012a)

53

rs10946398

CDKAL1

Intron

6

C

1.12

T2D

(Voight et al. 2010; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012a)

54

rs7766070

CDKAL1

Intron

6

A

1.23

T2D

(Voight et al. 2010; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012a)

55

rs2244020 (rs9266650)

HLA-B

Intergenic

6

G

1.09

T2D

(Ng et al. 2014)

56

rs1535500

KCNK16

Coding – missense

6

T

1.08

T2D

(Cho et al. 2012a)

57

rs3130501

POU5F1-TCF19

NearGene-5

6

G

1.07

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

58

rs9505118

SSR1-RREB1

Intron

6

A

1.06

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

59

rs9470794

ZFAND3

Intron

6

C

1.12

T2D

(Cho et al. 2012a)

60

rs17168486

DGKB

Intergenic

7

T

1.15

T2D

(Morris et al. 2012b)

61

rs2191349

DGKB/TMEM195

Intergenic

7

T

1.06

Fasting glucose, Homa B/T2D

(Dupuis et al. 2010)

62

rs6467136

GCC1-PAX4

Intergenic

7

G

1.11

T2D

(Cho et al. 2012a)

63

rs4607517

GCK

Intergenic

7

A

1.07

Fasting glucose/T2D/HOMA B

(Dupuis et al. 2010)

64

rs864745

JAZF1

Intron

7

T

1.1

T2D

(Zeggini et al. 2008; Lyssenko et al. 2008)

65

rs849134

JAZF1

Intron

7

A

1.13

T2D

(Zeggini et al. 2008; Voight et al. 2010)

66

rs12113122

JAZF1

Intron

7

G

1.55

T2D

(Saxena et al. 2012)

67

rs972283

KLF14

Intergenic

7

G

1.07

Reduced insulin sensitivity T2D

(Voight et al. 2010)

68

rs516946

ANK1

Intron

8

C

1.09

T2D

(Morris et al. 2012b)

69

rs515071

ANK1

Intron

8

G

1.18

T2D reduced beta-cell function

(Imamura et al. 2012; Morris et al. 2012b)

70

rs13266634

SLC30A8

Coding – missense

8

C

1.19

T2D

(Sladek et al. 2007)

71

rs11558471

SLC30A8

UTR-3

8

A

1.15

Fasting glucose, HOMA B T2D

(Dupuis et al. 2010; Voight et al. 2010; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Zeggini et al. 2007; Takeuchi et al. 2009; Perry et al. 2012a; Sladek et al. 2007)

72

rs3802177

SLC30A8

UTR-3

8

G

1.26

T2D

(Dupuis et al. 2010; Voight et al. 2010; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Zeggini et al. 2007; Takeuchi et al. 2009; Perry et al. 2012a; Sladek et al. 2007)

73

rs896854

TP53INP1

Intron

8

T

1.06

T2D

(Voight et al. 2010)

74

rs10965250

CDKN2A/2B

Intergenic

9

G

1.2

T2D

(Voight et al. 2010; Saxena et al. 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012b)

75

rs2383208

CDKN2A/2B

Intergenic

9

A

1.19

T2D

(Voight et al. 2010; Saxena et al. 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012b)

76

rs7018475

CDKN2A/2B

Intergenic

9

G

1.35

T2D

(Voight et al. 2010; Saxena et al. 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012b)

77

rs564398

CDKN2A/2B

Intergenic

9

T

1.12

T2D

(Voight et al. 2010; Saxena et al. 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012b)

78

rs10757282

CDKN2A/2B

Intergenic

9

C

1.14

T2D

(Voight et al. 2010; Saxena et al. 2007; Yamauchi et al. 2010; Steinthorsdottir et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Perry et al. 2012b)

79

rs10811661

CDKN2B

Intergenic

9

T

1.2

T2D

(Zeggini et al. 2008; Voight et al. 2010; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Zeggini et al. 2007; Saxena et al. 2007; Scott et al. 2007; Li et al. 2013; Takeuchi et al. 2009; Parra et al. 2011)

80

rs7034200

GLIS3

Intron

9

A

1.03

Fasting glucose/T2D/HOMA B

(Dupuis et al. 2010)

81

rs7041847

GLIS3

Intron

9

A

1.1

T2D

(Cho et al. 2012a; Li et al. 2013)

82

rs10814916

GLIS3

Intron

9

C

1.11

T2D

(Dupuis et al. 2010; Cho et al. 2012a; Li et al. 2013)

83

rs17584499

PTPRD

Intron

9

T

1.57

T2D

(Tsai et al. 2010)

84

rs2796441

TLE1

Intergenic

9

G

1.07

T2D

(Morris et al. 2012b)

85

rs13292136

TLE4 (CHCHD9)

Intergenic

9

C

1.11

T2D

(Voight et al. 2010)

86

rs553668

ADRA2A

UTR-3

10

A

1.42

T2D

(Rosengren et al. 2010)

87

rs10885122

ADRA2A

Intergenic

10

G

1.04

Fasting glucose/HOMA B/T2D

(Dupuis et al. 2010)

88

rs12779790

CDC123,CAMK1D

Intergenic

10

G

1.11

T2D

(Zeggini et al. 2008; Lyssenko et al. 2008)

89

rs11257655

CDC123/CAMK1D

Intergenic

10

C

1.15

T2D

(Zeggini et al. 2008; Li et al. 2013; Shu et al. 2010)

90

rs10906115

CDC123/CAMK1D

Intergenic

10

A

1.13

T2D

(Zeggini et al. 2008; Li et al. 2013; Shu et al. 2010)

91

rs10886471

GRK5

Intron

10

C

1.12

T2D

(Li et al. 2013)

92

rs5015480

HHEX

Intergenic

10

C

1.13

T2D

(Voight et al. 2010; Saxena et al. 2007; Takeuchi et al. 2009; Sladek et al. 2007; Perry et al. 2012b)

93

rs1111875

HHEX/IDE

Intergenic

10

C

1.13

T2D

(Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007)

94

rs7903146

TCF7L2

Intronic/promoter

10

T

1.35

T2D, fasting glucose, 2 hr glucose

(Grant et al. 2006)

95

rs4506565

TCF7L2

Intron

10

T

1.34

Fasting glucose, HOMA B T2D

(Zeggini et al. 2008; Voight et al. 2010; Zeggini et al. 2007; Saxena et al. 2007; Steinthorsdottir et al. 2007; Scott et al. 2007; Takeuchi et al. 2009; Sladek et al. 2007; Perry et al. 2012b; Grant et al. 2006; Saxena et al. 2006; Salonen et al. 2007; Timpson et al. 2009; Wellcome Trust Case Control Consortium 2007)

96

rs7901695

TCF7L2

Intron

10

C

1.37

T2D

(Zeggini et al. 2008; Voight et al. 2010; Zeggini et al. 2007; Saxena et al. 2007; Steinthorsdottir et al. 2007; Scott et al. 2007; Takeuchi et al. 2009; Sladek et al. 2007; Perry et al. 2012b; Grant et al. 2006; Saxena et al. 2006; Salonen et al. 2007; Timpson et al. 2009; Wellcome Trust Case Control Consortium 2007)

97

rs1802295

VPS26A

UTR-3

10

A

1.08

T2D

(Kooner et al. 2011)

98

rs12571751

ZMIZ1

Intron

10

A

1.08

T2D

(Morris et al. 2012b)

99

rs11603334

ARAP1

UTR-5

11

G

1.13

T2D fasting proinsulin levels/fasting glucose/

(Strawbridge et al. 2011)

100

rs1552224

CENTD2

Intergenic

11

A

1.14

T2D

(Voight et al. 2010)

101

rs11605924

CRY2

Intron

11

A

1.04

Fasting glucose/HOMA B/T2D

(Dupuis et al. 2010)

102

rs174550

FADS1

Intron

11

T

1.04

Fasting glucose/T2D/HOMA B

(Dupuis et al. 2010)

103

rs2334499

HCCA2

Intergenic

11

T

1.35

T2D

(Kong et al. 2009)

104

rs3842770

INS-IGF2

Intron

11

A

1.18

T2D – African American

(Ng et al. 2014)

105

rs5219

KCNJ11

Coding – missense

11

T

1.14

T2D

(Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Zeggini et al. 2007; Scott et al. 2007; Timpson et al. 2009; Hani et al. 1998)

106

rs5215

KCNJ11

Coding – missense

11

C

1.14

T2D

(Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007; Zeggini et al. 2007; Scott et al. 2007; Timpson et al. 2009; Hani et al. 1998)

107

rs2237895

KCNQ1

Intron

11

C

1.45

T2D

(Yasuda et al. 2008)

108

rs231362

KCNQ1

Intron

11

G

1.08

T2D

(Voight et al. 2010)

109

rs163184

KCNQ1

Intron

11

G

1.22

T2D

(Morris et al. 2012a; Yasuda et al. 2008)

110

rs2237892

KCNQ1

Intron

11

C

1.25

Reduced beta-cell function T2D

(Voight et al. 2010; Unoki et al. 2008; Takeuchi et al. 2009; Tsai et al. 2010; Yasuda et al. 2008)

111

rs10501320

MADD

Intron

11

G

1.01

T2D fasting proinsulin levels/fasting glucose

(Strawbridge et al. 2011)

112

rs10830963

MTNR1B

Intron

11

G

1.09

T2D

(Prokopenko et al. 2009)

113

rs1387153

MTNR1B

Intergenic

11

T

1.09

Reduced beta-cell function T2D

(Voight et al. 2010; Tsai et al. 2010; Prokopenko et al. 2009)

114

rs7138803

BCDIN3D/FAIM2

Intergenic

12

A

1.11

BMI/obesity T2D

(Thorleifsson et al. 2009; Willer et al. 2008)

115

rs11063069

CCND2

Intergenic

12

G

1.12

T2D

(Morris et al. 2012b)

116

rs1153188

DCD

Intergenic

12

A

1.08

T2D

(Zeggini et al. 2008)

117

rs1531343

HMGA2

Intron of pseudogene

12

C

1.1

T2D

(Voight et al. 2010)

118

rs9668162

HMGA2

Intron

12

G

1.26

T2D

(Saxena et al. 2012)

119

rs7305618

HNF1A

Intergenic

12

C

1.14

T2D

(Voight et al. 2010; Parra et al. 2011)

120

rs35767

IGF1

NearGene-5

12

G

1.04

Fasting insulin/T2D/HOMA IR

(Dupuis et al. 2010)

121

rs10842994

KLHDC5

Intergenic

12

C

1.1

T2D

(Morris et al. 2012b)

122

rs4275659

MPHOSPH9

Intron

12

C

1.06

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

123

rs7957197

OASL/TCF1/HNF1A

Intron of OASL

12

T

1.07

T2D

(Voight et al. 2010)

124

rs7961581

TSPAN8,LGR5

Intergenic

12

C

1.09

T2D

(Zeggini et al. 2008; Lyssenko et al. 2008)

125

rs9552911

SGCG

Intron

13

G

1.63

T2D

(Saxena et al. 2013)

126

rs1359790

SPRY2

Intergenic

13

G

1.15

T2D

(Shu et al. 2010)

127

rs2028299

AP3S2

UTR-3

15

C

1.1

T2D

(Kooner et al. 2011)

128

rs7172432

C2CD4A/B

Intergenic

15

A

1.14

Reduced beta-cell function, T2D

(Yamauchi et al. 2010)

129

rs7178572

HMG20A

Intergenic

15

A

1.09

Lean T2D

(Kooner et al. 2011; Perry et al. 2012a)

130

rs7177055

HMG20A

Intergenic

15

A

1.08

T2D

(Morris et al. 2012b)

131

rs8042680

PRC1

Intron

15

A

1.07

T2D

(Voight et al. 2010)

132

rs7403531

RASGRP1

Intron

15

T

1.1

T2D

(Li et al. 2013)

133

rs4502156

VPS13C/C2CD4A/B

Intergenic

15

T

1.07

Fasting proinsulin levels T2D

(Strawbridge et al. 2011)

134

rs11634397

ZFAND6

Intergenic

15

G

1.06

T2D

(Voight et al. 2010)

135

rs7202877

BCAR1

Intergenic

16

T

1.12

T2D

(Morris et al. 2012b)

136

rs8050136

FTO

Intron

16

A

1.17

Increased BMI, reduced insulin sensitivity, T2D

(Zeggini et al. 2008; Voight et al. 2010; Zeggini et al. 2007; Scott et al. 2007; Perry et al. 2012a; Timpson et al. 2009; Wellcome Trust Case Control Consortium 2007; Frayling et al. 2007)

137

rs9939609

FTO

Intron

16

A

1.25

T2D (obese)

(Zeggini et al. 2008; Voight et al. 2010; Zeggini et al. 2007; Scott et al. 2007; Perry et al. 2012a; Timpson et al. 2009; Wellcome Trust Case Control Consortium 2007; Frayling et al. 2007)

138

rs11642841

FTO

Intron

16

A

1.13

T2D

(Zeggini et al. 2008; Voight et al. 2010; Zeggini et al. 2007; Scott et al. 2007; Perry et al. 2012a; Timpson et al. 2009; Wellcome Trust Case Control Consortium 2007; Frayling et al. 2007)

139

rs4430796

HNF1B

Intron

17

G

1.19

Reduced beta-cell function T2D

(Li et al. 2013; Gudmundsson et al. 2007; Winckler et al. 2005a; Winckler et al. 2005b)

140

rs7501939

HNF1B

Intron

17

T

1.09

T2D

(Gudmundsson et al. 2007)

141

rs391300

SRR

Intron

17

G

1.28

T2D

(Tsai et al. 2010)

142

rs4523957

SRR

NearGene-5

17

T

1.27

T2D

(Tsai et al. 2010)

143

rs8090011

LAMA1

Intron

18

G

1.13

Lean T2D

(Perry et al. 2012a)

144

rs17782313

MC4R

Intergenic

18

C

1.06

BMI/T2D

(Thorleifsson et al. 2009; Willer et al. 2008)

145

rs12970134

MC4R

Intergenic

18

A

1.08

T2D/BMI/waist circumference/insulin resistance

(Morris et al. 2012a; Chambers et al. 2008)

146

rs3794991

GATAD2A/CILP2

Intron, intergenic

19

T

1.12

T2D

(Morris et al. 2012a; Saxena et al. 2012)

147

rs8108269

GIPR

Intergenic

19

G

1.05

T2D

(Morris et al. 2012a)

148

rs3786897

PEPD

Intron

19

A

1.1

T2D

(Cho et al. 2012b)

149

rs10401969

SUGP1/CILP2

Intron

19

C

1.13

T2D

(Morris et al. 2012a; Saxena et al. 2012)

150

rs6017317

FITM2-R3HDML-HNF4A

Intergenic

20

G

1.09

T2D

(Cho et al. 2012a)

151

rs4812829

HNF4A

Intron

20

A

1.09

T2D

(Kooner et al. 2011)

152

rs5945326

DUSP9

Intergenic

X

A

1.27

T2D

(Voight et al. 2010)

153

rs12010175

FAM58A

Intron

X

G

1.21

T2D

(Li et al. 2013)

154

rs8181588

KCNQ1

Intron

11

A

1.3

T2D

(Hanson et al. 2014)

155

rs10229583

FSCN3 – PAX4

Downstream_gene_variant

7

G

1.14

T2D

(Ma et al. 2013)

156

rs9936385

FTO

Intron

16

C

1.13

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

157

rs9502570

LOC105374905

Regulatory_region_variant

6

A

1.06

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

158

rs849135

JAZF1

Intron_variant

7

G

1.12

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

159

rs4458523

WFS1

Intron_variant

4

G

1.09

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

160

rs3132524

POU5F1

Intron_variant

6

G

1.07

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

161

rs2943640

LOC646736 – LOC105373913

Intergenic_variant

2

C

1.09

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

162

rs7612463

UBE2E2

Intron_variant

3

C

1.1

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

163

rs1727313

MPHOSPH9

3_prime_UTR_variant

12

C

1.06

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

164

rs11717195

ADCY5

Intron_variant

3

T

1.09

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

165

rs17791513

LOC101927450 – CHCHD2P9

Intergenic_variant

9

A

1.21

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

166

rs2261181

RPSAP52

Intron_variant

12

T

1.16

T2D

(DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 2014)

167

rs6931514

CDKAL1

Intron_variant

6

G

1.25

T2D

(Zeggini et al. 2008)

168

rs791595

LOC101928423

Regulatory_region_variant

7

A

1.17

T2D

(Hara et al. 2014)

169

rs312457

SLC16A13

Intron_variant

17

G

1.2

T2D

(Hara et al. 2014)

170

rs11787792

GPSM1

Intron_variant

9

A

1.15

T2D

(Hara et al. 2014)

171

rs163182

KCNQ1

Intron_variant

11

C

1.28

T2D

(Cui et al. 2011)

172

rs4712524

CDKAL1

Intron_variant

6

G

1.22

T2D

(Unoki et al. 2008)

173

rs6769511

IGF2BP2

Intron_variant

3

C

1.23

T2D

(Unoki et al. 2008)

174

rs2244020

DHFRP2 – LOC101929072

Upstream_gene_variant

6

G

1.09

T2D

(Ng et al. 2014)

175

rs231356

LOC105376523, KCNQ1, KCNQ1OT1

Non_coding_transcript_exon_variant

11

T

1.09

T2D

(Ng et al. 2014)

176

rs9348440

CDKAL1

Intron_variant

6

A

0.246

T2D

(Go et al. 2013)

177

rs12229654

LOC105369980 – LOC105369981

Intergenic_variant

12

G

0.277

T2D

(Go et al. 2013)

178

rs11066453

OAS1

Intron_variant

12

G

0.242

T2D

(Go et al. 2013)

179

rs75493593

SLC16A11

Missense_variant

17

?

1.25

T2D

(SIGMA Type 2 Diabetes Consortium 2014a)

180

rs1333051

CDKN2B-AS1 – DMRTA1

Regulatory_region_variant

9

A

1.22

T2D

(Parra et al. 2011)

181

rs10440833

CDKAL1

Intron_variant

6

A

1.25

T2D

(Voight et al. 2010)

182

rs60780116/rs1996546

ACSL1

Coding

4

T/G

1.09

T2D

(Scott et al. 2017)

183

rs9271774/rs9271775

HLA-DQA1

Coding

6

C/T

1.09

T2D

(Scott et al. 2017)

184

rs6918311/rs4407733

SLC35D3

Intergenic

6

A/A

1.06

T2D

(Scott et al. 2017)

185

rs1182436/rs1182397

MNX1

Coding

7

C/G

1.08

T2D

(Scott et al. 2017)

186

rs635634/rs495828

ABO

Intergenic

9

T/T

1.08

T2D

(Scott et al. 2017)

187

rs2292626/rs2421016

PLEKHA1

Coding

10

C/C

1.07

T2D

(Scott et al. 2017)

188

rs1061810/rs3736505

HSD17B12

Coding

11

A/G

1.07

T2D

(Scott et al. 2017)

189

rs111669836/rs11227234

MAP3K11

Coding

11

A/T

1.06

T2D

(Scott et al. 2017)

190

rs10146997/rs17109256

NRXN3

Coding

14

G/A

1.07

T2D

(Scott et al. 2017)

191

rs2925979/rs2925979

CMIP

Coding

16

T/T

1.07

T2D

(Scott et al. 2017)

192

rs7224685/rs8068804

ZZEF1

Coding

17

T/A

1.07

T2D

(Scott et al. 2017)

193

rs78761021/rs17676067

GLP2R

Coding

17

G/C

1.06

T2D

(Scott et al. 2017)

194

rs79349575/rs15563

GIP

Coding

17

A/G

1.06

T2D

(Scott et al. 2017)

195

rs78124264

IRS1

Intergenic

2

  

T2D

(Fuchsberger et al. 2016)

196

rs79856023

PPARG

Intergenic

3

  

T2D

(Fuchsberger et al. 2016)

197

rs9727115

SNX7

Intron

1

G

0.0133

Fasting proinsulin levels adjusted for fasting glucose

(Strawbridge et al. 2011)

198

rs2785980

LYPLAL1

Intergenic

1

T

0.017

Fasting insulin

(Manning et al. 2012)

199

rs4675095

IRS1

Intron

2

A

0.003

Fasting glucose/HOMA-IR

(Dupuis et al. 2010)

200

rs2943634

IRS1

Intergenic

2

C

0.025

Fasting insulin, CAD

(Manning et al. 2012)

201

rs1371614

DPYSL5

Intron

2

T

0.022

Fasting glucose

(Manning et al. 2012)

202

rs11920090

SLC2A2

Intron

3

T

0.02

Fasting glucose/HOMA B/HBA1C

(Dupuis et al. 2010)

203

rs17046216

MSMO1

Intron

4

A

0.18; 0.19

Fasting insulin; insulin resistance

(Chen et al. 2012)

204

rs4691380

PDGFC

Intron

4

C

0.021

Fasting insulin

(Manning et al. 2012)

205

rs6235

PCSK1

Coding – missense

5

G

0.0394/−0.014

Fasting proinsulin levels/fasting glucose

(Strawbridge et al. 2011)

206

rs13179048

PCSK1

Intergenic

5

C

0.018

Fasting glucose

(Manning et al. 2012)

207

rs4646949

TAF11

NearGene-3

6

T

0.02

Fasting insulin

(Manning et al. 2012)

208

rs6943153

GRB10

Intron

7

C

0.0154

FG, FI

(Scott et al. 2012)

209

rs4841132

PPP1R3B

Intergenic

8

A

0.03

Fasting glucose

(Manning et al. 2012)

210

rs7077836

TCERG1L

Intergenic

10

T

0.28; 0.34

Fasting insulin; insulin resistance

(Chen et al. 2012)

211

rs7944584

MADD

Intron

11

A

0.021

Fasting proinsulin/fasting glucose/Homa B

(Dupuis et al. 2010)

212

rs10838687

MADD

Intron

11

T

0.0253

Fasting proinsulin levels

(Strawbridge et al. 2011)

213

rs1483121

OR4S1

Intergenic

11

G

0.015

Fasting glucose

(Manning et al. 2012)

214

rs2074356

HECTD4/C12orf51

Intron

12

  

1 hr plasma glucose

(Go et al. 2013)

215

rs2293941

PDX1 – AS1

Intron

13

A

0.016

Fasting glucose

(Manning et al. 2012)

216

rs17271305

VPS13C

Intron

15

G

0.07

2 hr glucose/2 hr insulin, adjusted for 2 hr glucose

(Saxena et al. 2010)

217

rs1549318

LARP6

Iintergenic

15

T

0.0192

Fasting proinsulin levels

(Strawbridge et al. 2011)

218

rs4790333

SGSM2

Intron

17

T

0.0154

Fasting proinsulin levels

(Strawbridge et al. 2011)

219

rs10423928

GIPR

Intron

19

A

 

2 hr glucose/Insulinogenic index/AUCins/gluc/2 hr insulin, adjusted for 2 hr glucose/T2D

(Saxena et al. 2010)

220

rs6048205

FOXA2/LINC00261

Intergenic/nearGene-5

20

A

0.,029

Fasting glucose

(Manning et al. 2012)

Table 2

Rare risk and protective loci associated with T2D and glycemic traits

N

SNPs

GENE/nearest gene

Gene location

Chr

References

1

rs35658696

PAM

Coding – missense

5

(Huyghe et al. 2013)

2

rs78408340

PAM

Coding – missense

5

(Huyghe et al. 2013)

3

rs36046591

PPIP5K2

Coding – missense

5

(Huyghe et al. 2013)

4

p.Lys34Serfs*50

SLC30A8

Coding – missense

8

(Flannick et al. 2014)

5

p.Arg138*

SLC30A8

Coding – missense

8

(Flannick et al. 2014)

6

rs3824420

KANK1

Coding – missense

9

(Huyghe et al. 2013)

7

rs505922

ABO

Intronic

9

(Huyghe et al. 2013)

8

rs60980157

GPSM1

Coding – missense

9

(Huyghe et al. 2013)

9

p.Leu5Val (20)

ATG13

Coding – missense

11

(Huyghe et al. 2013)

10

p.Ile131Val (1)

ATG13

Coding – missense

11

(Huyghe et al. 2013)

11

p.Gln249Pro (3)

ATG13

Coding – missense

11

(Huyghe et al. 2013)

12

p.Arg392Trp (1)

ATG13

Coding – missense

11

(Huyghe et al. 2013)

13

p.Leu427Gln (3)

ATG13

Coding – missense

11

(Huyghe et al. 2013)

14

p.Gly434Arg (488)

ATG13

Coding – missense

11

(Huyghe et al. 2013)

15

p.X406Gly (200)

ATG13

Coding – missense

11

(Huyghe et al. 2013)

16

rs35233100

MADD

Coding – missense

11

(Huyghe et al. 2013)

17

p.Arg279Cys (324)

TBC1D30

Coding – missense

12

(Huyghe et al. 2013)

18

p.Pro746Leu (427)

TBC1D30

Coding – missense

12

(Huyghe et al. 2013)

19

c.1522G>A[p.E508K

HNF1A

Coding – missense

12

(SIGMA Type 2 Diabetes Consortium 2014b)

20

rs76895963

CCND2

Intergenic

12

(Steinthorsdottir et al. 2014)

21

rs75615236

CCND2

Intergenic

12

(Steinthorsdottir et al. 2014)

22

rs150781447

TBC1D30

Coding – missense

12

(Huyghe et al. 2013)

23

rs2650000

HNF1A

Intergenic

12

(Huyghe et al. 2013)

24

Chr. 13: g.27396636delT

PDX1

Coding – missense

13

(Flannick et al. 2014)

25

p.Tyr416Cys (78)

SGSM2

Coding – missense

17

(Huyghe et al. 2013)

26

p.Thr789Pro (3),

SGSM2

Coding – missense

17

(Huyghe et al. 2013)

27

p.Val996Ile (236)

SGSM2

Coding – missense

17

(Huyghe et al. 2013)

28

rs61741902

SGSM2

Coding – missense

17

(Huyghe et al. 2013)

Fig. 3

Loci associated with T2D and glycemic traits. The variants are represented by gene names, which could indicate the location, either in or near the gene. The black circle contains loci associated with T2D. The large number of loci overlapping with insulin secretion traits illustrates the importance of beta cell function in T2D

Rare and Protective Variants

A study applying WGS and imputation in an Icelandic population with follow-up in Danish and Iranian populations identified rare variants in the PAM and PDX1 genes associated with T2D (Steinthorsdottir et al. 2014). WES and GWAS of a small founder population in Greenland identified the p.Arg684Ter variant (allele frequency of 17%) in the TBC1D4 gene associated with glucose and insulin concentrations and muscle insulin resistance (Moltke et al. 2014).

Very often is the common variant the risk variant. In fact, the average T2D risk variant frequency in the population is ~54% which raises the question whether T2D is the default condition? If so, do rare protective variants make any difference in disease susceptibility? The ideal population to identify protective variants is a population that despite having a clustering of risk factors for T2D have escaped the disease. A rare loss of function mutation (R138X) was detected in the SLC30A8 gene in the Botnia region from Finland and subsequently replicated applying the Exome chip in>150,000 individuals from other European countries. Another protective loss-of-function frameshift mutation in the same gene was identified on Iceland. The SLC30A8 gene encodes the islet zinc transporter 8 with a putative effect on insulin secretion. Notably, a common variant in the same gene increases susceptibility to T2D whereas autoantibodies to T1D predispose to T1D .

Collectively, carriers of these protein-truncating mutations have a 65% lower risk of T2D (Flannick et al. 2014). Other studies based on Icelandic, Danish, and Iranian populations identified a low frequency variant in the CCND2 gene which reduced T2D risk by half (Steinthorsdottir et al. 2014). Moreover, variants in TCF2 were found to be protective against T2D (Gudmundsson et al. 2007). It is likely that more recent variants could be detected by sequencing large families.

In addition to SNPs, structural variants could also contribute to T2D risk. A common copy number variation (CNV), CNVR5583.1 in the TSPAN8 gene has been repeatedly shown to be associated with T2D (Wellcome Trust Case Control C et al. 2010; Zeggini et al. 2008).

Gene-Gene and Gene-Environment Interaction s

Few studies have reported significant gene-gene interactions on risk of T2D, and all of them have been based on previously established T2D risk loci. Further studies in large populations using unbiased and novel approaches will most likely be necessary to identify such effects.

A number of studies have suggested an interaction between Pro12Ala in the PPARG gene with intake of dietary fatty acids and exercise for risk of T2D (Luan et al. 2001; Kahara et al. 2003). Physical exercise has been shown to modify the effect of the FTO variant rs9939609 on BMI (Andreasen et al. 2008; Franks et al. 2008). A recent study reported interactions between the FTO variant and frequency of alcohol consumption, sleep duration, salt intake, and physical activity (Young et al. 2016). The HNF1B rs4430796 variant has been shown to interact with self-reported physical activity (Brito et al. 2009). Further, patients with HNF1A mutations respond better to sulfonylureas than to metformin (Pearson et al. 2003). The effect of the GIPR rs10423928 variant on incident diabetes risk was modified by dietary fat and carbohydrate intake (Sonestedt et al. 2012).

Epigenetics

Epigenome-wide association studies (EWAS) in blood have reported hypomethylation of a CpG site at the FTO locus and at least three studies have reported differential methylation at a CpG site in the TXNIP gene (Toperoff et al. 2012; Chambers et al. 2015; Florath et al. 2016; Kulkarni et al. 2015). Further, CpG sites in TXNIP, ABCG1, PHOSPHO1, SOCS3, and SREBF1 have been associated with risk of developing future T2D (Chambers et al. 2015). Studies in human pancreatic islets have reported differential methylation of CpGs in TCF7L2, FTO, and KCNQ1; an additional 102 genes showed differential methylation in the EWAS, but the role for pathogenesis of T2D remains unclear (Chambers et al. 2015). EWAS on human pancreatic islets revealed many differentially expressed genes between type 2 diabetic and non-diabetic donors including CDKN1A and SEPT9 (Dayeh et al. 2014). Aging associated with increased DNA methylation in multiple loci including KLF14 and some of this associated with impaired insulin secretion (Bacos et al. 2016). One of the earliest studies on whole genome bisulfite sequencing of human pancreatic islets showed >25,000 differentially methylated regions (DMRs) in islets from type 2 diabetics including those with known islet function, e.g., PDX1, TCF7L2, and ADCY5 (Volkov et al. 2017).

Further, the CDKN2A/B region on chromosome 9 is associated with T2D, as well as cardiovascular disease and a number of other disorders. This region harbors an lncRNA, ANRIL (non-protein coding CDKN2B-AS1 CDKN2B antisense RNA 1), which can potentially modify and explain some of these association s (Broadbent et al. 2008).

Gene Expression in Pancreatic Islets

Elucidating the molecular mechanisms underlying complex diseases requires understanding of gene expression in relevant cell types and tissues. Multiple novel genes with a potential role in glucose metabolism and insulin secretion have been discovered through global expression studies using microarrays (Taneera et al. 2015). Later, rapid technological advances in next generation sequencing facilitated the identification and precise quantification of all transcripts in the cell through RNA sequencing. This allowed investigation of genetic effects on gene expression, e.g., expression quantitative trait loci (eQTLs), splicing (splice QTLs), allelic imbalance, (Fadista et al. 2014), cis-regulatory networks (Pasquali et al. 2014), and noncoding RNAs (Moran et al. 2012).

In a heterogeneous tissue like pancreatic islet, containing diverse cell types with myriad functions, distinct expression data from each cell type further facilitates dissection of unique cell functions. Single cell sequencing allows investigation of gene expression in individual cellular subsets. RNA sequencing of α, β, γ, δ, and ε cells from adult and fetal pancreas have generated distinct expression profiles for these specific cell types (Blodgett et al. 2015; Wang et al. 2016) and identified genes that were differentially expressed between T2D and nondiabetic donors (Segerstolpe et al. 2016; Xin et al. 2016). Interestingly, some of the key genes reported in previous studies were missing in data from single cells .

LADA

LADA (latent autoimmune diabetes in adults) accounts for 4–14% of diabetic patients in Europe with the highest prevalence in northern Europe (Laugesen et al. 2015). LADA was originally defined by presence of diabetes-associated autoantibodies, especially GADA, age at onset more than 35 years and no requirement of insulin treatment during the first 6 months (Tuomi et al. 1993b), but the exact criteria remain controversial and different thresholds and cutoffs have been used in different studies. Phenotypically, LADA is an intermediary form between T1D and T2D, where LADA with high antibody titers are more similar to T1D, whereas LADA with lower titers are closer to T2D.

Heritability

A family history of any form of diabetes is a risk factor for LADA (Carlsson et al. 2007). A few genetic studies on LADA have focused on candidate genes associated with T1D and T2D and found LADA to be associated with both T1D (HLA) and T2D susceptibility (e.g., TCF7L2) (Andersen et al. 2014). Whether this is due to an admixture of T1D and T2D patients within the LADA group or a disease etiology including both autoimmune and metabolic pathways is unclear. GWAS are still missing and therefore we know nothing about the possible existence of LADA specific loci .

Genetic Risk Loci

A number of studies have investigated the association between LADA and the HLA locus and found an age-at-diagnosis-dependent association with a higher frequency of HLA-DRB1*03 (DR3) and HLA-DRB1*04 (DR4) in younger patients (Horton et al. 1999). DR3 and DR4 were associated with GADA and IA–2A positivity, respectively. The strongest associations outside the HLA region have been found for the T1D loci INS and PTPN22 (Howson et al. 2011). Other T1D loci found to be associated with adult onset autoimmune diabetes in the same direction as for T1D include STAT4, CTLA4, IL2RA, ERBB3, SH2B3, and CLEC16A (Howson et al. 2011). Common variants in the TCF7L2 gene help to differentiate autoimmune from non-autoimmune diabetes in young (15–34 years) but not in middle-aged (40–59 years) diabetic patients (Cervin et al. 2008).

MODY

Maturity-onset diabetes of the young (MODY) is a collection of monogenic forms of diabetes often stated to comprise ~1% of diabetes patients; however, the exact prevalence is unclear (Kleinberger and Pollin 2015). MODY is characterized by early-onset, usually before 25 years of age, insulin secretion defects and an autosomal dominant inheritance pattern. However, the penetrance and expression of disease can vary and not all MODY patients have a family history of diabetes. The diagnosis of MODY is a challenge as the phenotype can be quite varying and diagnosis requires sequencing, a tool that has not yet received widespread acceptance in the clinic.

Since the different types of MODY differ from each other and from other diabetes types with regard to severity, course of disease, risk of complications, and response to different therapies, a correct diagnosis is of great importance. Many MODY-patients are treated with insulin even though for some MODY types (MODY 1 and 4) treatment with sulfonylureas is a better alternative.

So far, mutations in at least 14 genes are known to cause MODY. Most of them are encoding transcription factors. The most common MODY types are MODY2 (32% of U.K. MODY cases) caused by mutations in the glucokinase gene (GCK) an MODY3 (52% of U.K. cases), caused by mutations in hepatocyte nuclear factor-1alpha (HNF1A) (Shields et al. 2010). Within these genes, a large variety of mutations can cause disease (there are more than 200 mutations described in the GCK and HNF1A genes) and MODY mutations are often unique to a given family (Murphy et al. 2008b). Because of the extreme allelic heterogeneity, the appropriate diagnosis requires sequencing to identify the causal mutation. With the advent of next-generation sequencing, this is becoming much more feasible but still a large proportion of patients with MODY is undiagnosed.

A correct diagnosis of MODY is important for the correct management of the disease. MODY2 is associated with a mild increase in glucose concentrations, the disease does not progress and patients do not develop complications such as diabetic retinopathy or kidney disease and usually does not require pharmacological treatment (Ajjan and Owen 2014). MODY1 and 3 are often misdiagnosed as T1D and treated with insulin injections but can be better managed with low doses of sulfonylurea (Shepherd et al. 2009). A genetic diagnosis of MODY should prompt genetic screening of other family members to identify undiagnosed cases of MODY.

In addition to the severe MODY causing mutations, many MODY genes also harbor common variants that increase risk of T2D, including HNF1A, HNF4A, HNF1B, GCK, and PDX1 (Voight et al. 2010; Scott et al. 2012; Steinthorsdottir et al. 2007).

Neonatal Diabetes

Neonatal diabetes mellitus (NDM) presents as uncontrolled hyperglycemia within the first 6 months of life with an estimated prevalence of one case per 300,000–500,000 live births (Polak and Cave 2007). NDM often presents with intra - uterine growth retardation (IUGR), failure to thrive, decreased subcutaneous fat, and very low C-peptide levels. NDM is usually subdivided into permanent (PNDM) and relapsing transient (TNDM) forms; the latter form often develops T2D later (von Muhlendahl and Herkenhoff 1995).

The most common form of TNDM is due to imprinting in a locus on chromosome 6q24. Two genes at this locus are exclusively expressed from the paternal copy; PLAGL1 (Pleiomorphic Adenoma Gene-Like 1), a zinc-finger transcription factor and HYMAI (Hydatiform Mole-Associated Imprinted), an untranslated RNA of unknown function (Temple et al. 1995, 1996; Arima et al. 2001). The disease manifests as a consequence of a double dose of either or both genes, which can occur due to (i) paternal isodisomy (both copies of paternal origin), (ii) duplication, which is inherited from the father, and (iii) methylation abnormality, wherein the maternal copy is silenced by methylation (Temple et al. 1995, 1996; Arima et al. 2001).

The etiology of PNMD is much more diverse with most cases being sporadic, but both dominant and recessive autosomal inheritance has also been reported. The most common forms of PNMD are caused by mutations in the KCNJ11 (K+ Channel inwardly rectifying family J, member 11) or ABCC8 genes (ATP-Binding Cassette, subfamily C, member8). Some KCNJ11 mutations can cause both TNDM and PNDM (Gloyn et al. 2004; Babenko et al. 2006). PNDM can also be due to dominant mutations in the insulin gene (Stoy et al. 2007). At least two MODY genes, GCK (Glucokinase, MODY2) and IPF (insulin promoter factor, necessary for pancreatic development, mutated in MODY4), can cause recessively inherited PNDM when both parents transmit a mutated allele (Njolstad et al. 2003; Gloyn 2003; Stoffers et al. 1997). Other forms of PNMD are associated with mutations in the FOXP3, EIF2AK3, PDX1, RFX6, and GLIS3 genes (Greeley et al. 2010).

Gestational Diabetes

Gestational diabetes mellitus (GDM) is a transitory form of diabetes that manifests as hyperglycemia during pregnancy and often reso lves postpartum. Insulin resistance begins to develop during mid-pregnancy and escalates until third trimester and requires compensation by increased insulin secretion. If this is not possible, GDM develops (Kuhl 1975). High age, obesity, history of macrosomia, multiparity, and history of polycystic ovary syndrome (PCOS) all increase risk of GDM. The risk is particularly high in women of South Asian, Middle Eastern, or Hispanic (Guariguata et al. 2014; Shaat et al. 2004).

There is emerging evidence that GDM, like T2D, has a genetic basis. GDM is more frequent in women whose mothers had GDM, as well as in women with a maternal family history of T2D (Williams et al. 2003; Martin et al. 1985; Harder et al. 2001). Women with parental history of T2D had a 2.3-fold increased risk of GDM (Williams et al. 2003), and those with a diabetic sibling had an 8.4-fold higher risk of GDM compared to women with no diabetic siblings (Robitaille and Grant 2008). Of note, changes in the diagnostic criteria complicate comparison of studies from different time points.

Many of the GDM-associated genetic risk variants overlap with T2D risk variants (Robitaille and Grant 2008; Cho et al. 2009; Lauenborg et al. 2009; O’Sullivan 1991; Kim et al. 2002). These include variants in the TCF7L2, GCK, KCNJ11, KCNQ1, SLC30A8, HHEX/IDE, CDKAL1, IGF2BP2, FTO, PPARG, MTNR1B, and IRS1 genes (Cho et al. 2009; Lauenborg et al. 2009; Huopio et al. 2013; Kwak et al. 2012). Further, the TCF7L2 rs7903146 and FTO rs8050136 SNPs have been shown to predict diabetes after GDM (Ekelund et al. 2012).

Rare MODY mutations have also been observed in GDM, i.e., mutations in the GCK (MODY-2), HNF1A (MODY-3) and PDX1 (MODY-4) genes which account for less than 10% of reported GDM cases (Buchanan and Xiang 2005; Ellard et al. 2000). Defects in β-cell function due to autoimmune destruction of pancreatic β-cells, as in T1D, can also cause GDM characterized by circulating autoantibodies reacting with β-cell antigens (GAD, or insulin autoantibodies, IAA). These patients appear to have evolving T1D, and they rapidly develop overt diabetes after pregnancy. This situation is seen in about 10% of GDM women (Buchanan and Xiang 2005; Catalano et al. 1990).

The role of epigenetics as a trigger of GDM is still unclear but there is some evidence that GDM can result in altered methylation in blood (Enquobahrie et al. 2015; Wu et al. 2018). There are also some epigenetic markers like H3K27 and H3K4 that can predict progression from GDM to overt T2D (Michalczyk et al. 2016).

Women with GDM are at increased risk for adverse pregnancy outcomes including fetal hyperinsulinism and macrosomia (Young and Ecker 2013; Group HSCR et al. 2008). Glucose from the mother passes freely across the placenta to the fetus while insulin cannot cross this barrier. Therefore, the fetal pancreas is stimulated to produce additional insulin which acts as a growth hormone promoting growth and adiposity (Silverman et al. 1991). GDM can also have consequences for the offspring later in life, such as increased predisposition to obesity and T2D (Silverman et al. 1991; Pettitt et al. 1993). Altered placental DNA methylation of CpG sites in the Adiponectin and Leptin genes has been reported in GDM (Bouchard et al. 2012; Lesseur et al. 2014); the expression of Leptin can be mediated by methylation of the PPARGC1α gene (Cote et al. 2016). Altered methylation patterns in response to maternal metabolic status, e.g., HDL-C levels and glucose have also been reported in placenta and cord blood (Finer et al. 2015) of the ABCA1 gen e (Houde et al. 2013).

Genetics of Diabetic Complications

A major concern in all types of diabetes is the risk of developing complications, including diabetic kidney disease (DKD), diabetic retinopathy (DR), cardiovascular disease (CVD), diabetic neuropathy, and peripheral vascular disease (PVD). DKD and other complications are responsible for most of the morbidity and mortality associated with diabetes (Alberti and Zimmet 2013; American 2008). Diabetes is the leading cause of end-stage renal disease (ESRD) in many countries and a major contributor to blindness, lower limb amputation, and CVD (Centers for Disease Control and Prevention 2011; Gilg et al. 2013).

Diabetic Kidney Disease

D iabetic kidney disease affects as many as ~30% of patients with chronic diabetes (Ritz et al. 2011; Krolewski et al. 1985). It can often be characterized by an early phase of glomerular hyperfiltration, followed by a progressive increase in protein leakage through the glomerular basement membrane. Overt DKD is often preceded by a stage of microalbuminuria (urinary albumin excretion rate [AER] 20–199 μg/min) that will often but not always progress to macroalbuminuria (AER ≥ 200 μg/min). In parallel, the glomerular filtration rate (eGFR) decreases, leading first to chronic kidney disease (CKD, eGFR <60 mL/min/1.73 m2) and subsequently to ESRD (eGFR <15 mL/min/1.73 m2) when the patient will need dialysis or a kidney transplantation to survive.

The pathologic processes involved in the development of DN are complex and only partly known, but multiple pathways seem to be involved. Hyperglycemia is known to be a major risk factor for all diabetic complications, affecting various kidney structures including podocytes, tubular, mesangial, endothelial, and inflammatory cells but the underlying mechanisms have remained elusive (Forbes and Cooper 2013). Several candidate pathways have been implied, including protein glycation, formation of reactive oxygen species, and increased flux through the polyol pathway. In T2D, insulin resistance seems to be an important driver of DKD.

Heritability

The risk of developing DKD also depends on genetic factors as evidenced by familial aggregation. The estimated heritability of AER is ~20–40% and a sibling of an affected individual has the double risk of developing DKD (Harjutsalo et al. 2004; Langefeld et al. 2004; Krolewski et al. 2006; Forsblom et al. 1999). Prevalence of DKD also differs between ethnic groups with different genetic backgrounds. Despite this compelling evidence for genetic effects, the search for the specific variants conferring DKD predisposition has been rather unrewarding and only a few robust associations have been found.

Genetic Risk Loci

One of the most plausible and best-supported candidate loci is an insertion/deletion (I/D) variant in the gene encoding ACE. ACE inhibitors confer protection against DKD by decreasing glomerular hypertension and permeability, and the ACE I/D variant is associated with a twofold increase in ACE activity, making this a highly credible biological candidate (Nikzamir et al. 2008; Rigat et al. 1990). This locus has been studied in numerous association studies but a role in DKD susceptibility has still not been proven. A meta-analysis, including over 26,000 individuals from 63 studies, observed some modest evidence for this locus in DKD, but the association was mainly observed in Asian populations with T2D and did not reach genome-wide significance (Wang et al. 2012).

Compared to diabetes and many other traits, there have been relatively few GWAS studies performed on DKD. The GENIE consortium performed the first study to yield a genome-wide significant finding, identifying one locus in the AFF3 gene and another between the RGMA and MCTP2 gene loci associated with risk of ESRD in patients with T1D (Sandholm et al. 2012). A follow-up study identified a third locus, near SP3, that was significantly associated with ESRD only in women (Sandholm et al. 2013). However, these loci still need to be replicated in independent cohorts before they can be universally accepted as DKD risk loci.

The situation for genetics of DKD in T2D is even less rewarding. While a few studies, including family-based linkage analysis and GWAS have been performed and produced interesting candidates; none of the loci have reached genome-wide significance (Ahlqvist et al. 2015).

The limited success of genetic studies in DKD compared to many other diseases is probably due to a combination of factors, including inadequate sample sizes and phenotypic imprecision. DKD typically develops after more than 15 years, which reduces the number of available patients with duration of diabetes sufficient to identify control patients who will escape DKD or at least have a clearly later onset than the case group. Another problem is the high prevalence of other types of kidney disease in T2D patients. These can only be clearly distinguished from DKD by kidney biopsies, which are not routinely taken in most countries (Ruggenenti and Remuzzi 2000).

In order to increase sample sizes, many studies have included patients with early signs of DKD, such as microalbuminuria, as well as cases with macroalbuminuria or ESRD. However, albuminuria is a relatively poor predictor of DKD, especially in the early stages, adding further uncertainty to the classification (Perkins et al. 2010; Boger and Sedor 2012). Reduced kidney function, as revealed by eGFR and ESRD, and dysfunction of the glomerular filtration barrier, reflected by albuminuria, can develop independently suggesting that these processes result from partly different disease mechanisms, with distinct genetic determinants (Perkins et al. 2010; Steinke et al. 2005; Ellis et al. 2012). In spite of these obstacles, ongoing efforts combining large cohorts (IMI studies SUMMIT and Beat-DKD, the JDRF-funded GENIE consortium, etc.) in meta-analyses will hopefully reach adequate sample sizes to identify robust associations also for DKD in T2D .

Diabetic Retinopathy

Diabetic retinopathy is characterized by microva scular changes in the retina, increasing vascular permeability and capillary degeneration with resulting microaneurysms, exudates, and neovascularization (Forbes and Cooper 2013). The main clinical risk factors for DR are duration of diabetes, chronic hyperglycemia, hypertension, and lipids (Yau et al. 2012).

The prevalence of DR is double for patients with microalbuminuria and sixfold increase in patients with macroalbuminuria compared to patients with no signs of renal dysfunction suggesting both common and unique mechanisms (Rani et al. 2011; Drury et al. 2011; Groop et al. 2009). Clinical data support at least two, potentially distinct, pathological processes for DR, resulting in proliferative retinopathy and macular edema, respectively (Viswanath and McGavin 2003).

The heritability of DR has been estimated to 18–57%, which is consistent with a substantial genetic component but might also reflect challenges in defining the phenotype consistently (Arar et al. 2008; Hietala et al. 2008; Looker et al. 2007).

As for DKD, robust genetic findings are very sparse. Candidate gene studies have produced a plethora of suggestive associations but none has reached genome-wide significance (Cho and Sobrin 2014). One of the best-studied candidate genes is the gene encoding vascular endothelial growth factor A (VEGFA). VEGF inhibition is used clinically to treat DR making this a highly credible biological candidate. In spite of this, variants that influence VEGF expression show only marginal association to DR (Qiu et al. 2013; Zhao and Zhao 2010; Abhary et al. 2009). A second biologically credible candidate gene is aldose reductase (AKR1B1). Aldose reductase is the rate-limiting enzyme in the polyol pathway, which has been widely implicated in glucose-related tissue damage. However, association data for AKR1B1 also remains unconvincing (Abhary et al. 2009, 2010). A third locus suggested to contribute to both DKD and DR is variants in the promoter of the gene encoding EPO (Tong et al. 2008), but again these have not been consistently replicated (Williams et al. 2012).

Only a few GWAS studies of DR have been performed so far and none of them have yielded significant reproducible associations (Sheu et al. 2013; Huang et al. 2011; Grassi et al. 2011). This is not so surprising given the small sample sizes. The largest reanalysis of the GoKIND and EDIC data sets included only 973 cases and 1856 controls (Cho and Sobrin 2014). Much larger sample sizes will likely be needed given the heterogeneity of the phenotype and progressive nature of the disease. Such studies are currently being assembled by large international consortia such as SUMMIT (Cho and Sobrin 2014).

Diabetic Neuropathy

Diabetic neuropathy is also a highly heterogeneous complication affecting ~10% of individuals with chronic diabetes. The mechanisms leading to nerve damage are poorly understood but likely include both vascular ischemic mechanisms and damage caused by advanced glycation end products that stimulate proinflammatory pathways and matrix-metalloproteinases (Said 2007).

There have been few genetic studies on diabetic neuropathy and no GWAS. A study in T1D suggested that an AKR1B1 polymorphism was involved in decline of nerve function but unfortunately even this study was severely restrained by small sample sizes (Thamotharampillai et al. 2006).

Cardiovascular Complications

Diabetes accelerates the process of atherosclerosis, leading to coronary artery disease (CAD), ischemic stroke (IS) and peripheral arterial disease (PAD). For example, hyperglycemia promotes vascular dysfunction by inhibiting nitric oxide production in endothelial cells and platelets, impairing endothelium-dependent vasodilation and increasing production of reactive oxygen species (ROS). Thereby atherosclerotic plaques are destabilized and rendered more vulnerable to rupture while a hypercoagulable state increases the formation and persistence of thrombi (Beckman et al. 2002). The heritability for CAD in the general population is estimated to be 40–50% and more than 60 variants, many of which are involved in cholesterol and lipid metabolism, have been robustly associated with disease risk (Khera and Kathiresan 2017). These loci are likely to play a role also in diabetic individuals, but additional loci might influence risk of macrovascular disease as a consequence of hyperglycemia. A locus near the glutamate-ammonia ligase (GLUL) gene has been associated with coronary heart disease specifically in T2D patients (Qi et al. 2013), but overall, diabetes specific association have not yet been sufficiently studied.

Epigenetics and Diabetic Complications

Epigenetic changes affecting the development of complications have been suggested to explain the observation of “metabolic memory”; a concept coined to describe the observation that early poor metabolic control can be memorized by target tissues and promote diabetic complications despite intensified treatment later in the disease. For example, the UKPDS and DCCT studies showed that an initial good metabolic control was associated with reduced frequency of diabetic complications decades later (DCCT 1993; Writing Team for the Diabetes C et al. 2002, 2003; Holman et al. 2008). The thioredoxin-interacting protein (TXNIP) gene is extremely sensitive to increases in glucose and has been ascribed proinflammatory roles in many tissues as well as promoting insulin resistance and glucose increased TXNIP expression histone acetylation in mice could promote diabetic kidney disease (De Marinis et al. 2016).

Summary

While the HLA plays a central role in the genetics of T1D, the role of genetics in T2D and diabetic complications has been more difficult to dissect. There can be several explanations for these shortcomings as discussed in this review, not at least the poorly defined phenotypes. Work is though in progress to refine phenotypes and subgroups of T2D and diabetic complications. Also the rapid improvement of genetic tools will hopefully accelerate the search for these missing genes. Even with the limitations of only partly explained heritability, and small effect loci, genetic studies still provide valuable information about disease mechanisms and identifies new potential therapeutic target. In monogenic diabetes, identification of the underlying variants has already enabled personalized treatment. With refined phenotypes and improved patient stratification this will hopefully be in our near future also for complex diabetes types.

References

  1. Abhary S, Hewitt AW, Burdon KP, Craig JE. A systematic meta-analysis of genetic association studies for diabetic retinopathy. Diabetes. 2009;58(9):2137–47. Epub 2009/07/10PubMedCentralCrossRefPubMedGoogle Scholar
  2. Abhary S, Burdon KP, Laurie KJ, Thorpe S, Landers J, Goold L, et al. Aldose reductase gene polymorphisms and diabetic retinopathy susceptibility. Diabetes Care. 2010;33(8):1834–6. Epub 2010/04/29PubMedCentralCrossRefPubMedGoogle Scholar
  3. Agarwala V, Flannick J, Sunyaev S, Go TDC, Altshuler D. Evaluating empirical bounds on complex disease genetic architecture. Nat Genet. 2013;45(12):1418–27. Epub 2013/10/22PubMedCentralCrossRefPubMedGoogle Scholar
  4. Ahlqvist E, van Zuydam NR, Groop LC, McCarthy MI. The genetics of diabetic complications. Nat Rev Nephrol. 2015;11(5):277–87. Epub 2015/04/01CrossRefGoogle Scholar
  5. Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, Vikman P, Prasad RB, Aly DM, Almgren P, Wessman Y, Shaat N, Spégel P, Mulder H, Lindholm E, Melander O, Hansson O, Malmqvist U, Lernmark Å, Lahti K, Forsén T, Tuomi T, Rosengren AH, Groop L (2018) Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. The Lancet Diabetes & Endocrinology.Google Scholar
  6. Ajjan RA, Owen KR. Glucokinase MODY and implications for treatment goals of common forms of diabetes. Curr Diab Rep. 2014;14(12):559. Epub 2014/10/27CrossRefGoogle Scholar
  7. Alberti KG, Zimmet P. Global burden of disease – where does diabetes mellitus fit in? Nat Rev Endocrinol. 2013;9(5):258–60. Epub 2013/03/13CrossRefGoogle Scholar
  8. Albrechtsen A, et al. Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia. 2013;56(2):298–310.CrossRefGoogle Scholar
  9. American DA. Economic costs of diabetes in the U.S. in 2007. Diabetes Care. 2008;31(3):596–615. Epub 2008/03/01CrossRefGoogle Scholar
  10. Andersen MK, Sterner M, Forsen T, Karajamaki A, Rolandsson O, Forsblom C, et al. Type 2 diabetes susceptibility gene variants predispose to adult-onset autoimmune diabetes. Diabetologia. 2014;57(9):1859–68. Epub 2014/06/08CrossRefGoogle Scholar
  11. Andreasen CH, Stender-Petersen KL, Mogensen MS, Torekov SS, Wegner L, Andersen G, et al. Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes. 2008;57(1):95–101.CrossRefGoogle Scholar
  12. Arar NH, Freedman BI, Adler SG, Iyengar SK, Chew EY, Davis MD, et al. Heritability of the severity of diabetic retinopathy: the FIND-Eye study. Investig Ophthalmol Vis Sci. 2008;49(9):3839–45. Epub 2008/09/04CrossRefGoogle Scholar
  13. Arima T, Drewell RA, Arney KL, Inoue J, Makita Y, Hata A, et al. A conserved imprinting control region at the HYMAI/ZAC domain is implicated in transient neonatal diabetes mellitus. Hum Mol Genet. 2001;10(14):1475–83.CrossRefGoogle Scholar
  14. Asahara S, Etoh H, Inoue H, Teruyama K, Shibutani Y, Ihara Y, et al. Paternal allelic mutation at the Kcnq1 locus reduces pancreatic beta-cell mass by epigenetic modification of Cdkn1c. Proc Natl Acad Sci U S A. 2015;112(27):8332–7.PubMedCentralCrossRefPubMedGoogle Scholar
  15. Babenko AP, Polak M, Cave H, Busiah K, Czernichow P, Scharfmann R, et al. Activating mutations in the ABCC8 gene in neonatal diabetes mellitus. N Engl J Med. 2006;355(5):456–66.CrossRefGoogle Scholar
  16. Bacos K, Gillberg L, Volkov P, Olsson AH, Hansen T, Pedersen O, et al. Blood-based biomarkers of age-associated epigenetic changes in human islets associate with insulin secretion and diabetes. Nat Commun. 2016;7:11089.PubMedCentralCrossRefPubMedGoogle Scholar
  17. Barker DJ. The origins of the developmental origins theory. J Intern Med. 2007;261(5):412–7.CrossRefGoogle Scholar
  18. Beckman JA, Creager MA, Libby P. Diabetes and atherosclerosis: epidemiology, pathophysiology, and management. JAMA. 2002;287(19):2570–81.CrossRefGoogle Scholar
  19. Bell GI, Horita S, Karam JH. A polymorphic locus near the human insulin gene is associated with insulin-dependent diabetes mellitus. Diabetes. 1984;33(2):176–83. Epub 1984/02/01CrossRefGoogle Scholar
  20. Belot MP, Fradin D, Mai N, Le Fur S, Zelenika D, Kerr-Conte J, et al. CpG methylation changes within the IL2RA promoter in type 1 diabetes of childhood onset. PLoS One. 2013;8(7):e68093.PubMedCentralCrossRefPubMedGoogle Scholar
  21. Bergholdt R, Brorsson C, Palleja A, Berchtold LA, Floyel T, Bang-Berthelsen CH, et al. Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression. Diabetes. 2012;61(4):954–62. Epub 2012/02/22PubMedCentralCrossRefPubMedGoogle Scholar
  22. Bird A. Perceptions of epigenetics. Nature. 2007;447(7143):396–8. Epub 2007/05/25CrossRefGoogle Scholar
  23. Blodgett DM, Nowosielska A, Afik S, Pechhold S, Cura AJ, Kennedy NJ, et al. Novel observations from next-generation RNA sequencing of highly purified human adult and fetal islet cell subsets. Diabetes. 2015;64(9):3172–81.PubMedCentralCrossRefPubMedGoogle Scholar
  24. Boger CA, Sedor JR. GWAS of diabetic nephropathy: is the GENIE out of the bottle? PLoS Genet. 2012;8(9):e1002989. Epub 2012/10/03PubMedCentralCrossRefPubMedGoogle Scholar
  25. Bouchard L, Hivert MF, Guay SP, St-Pierre J, Perron P, Brisson D. Placental adiponectin gene DNA methylation levels are associated with mothers’ blood glucose concentration. Diabetes. 2012;61(5):1272–80.PubMedCentralCrossRefPubMedGoogle Scholar
  26. Brito EC, Lyssenko V, Renstrom F, Berglund G, Nilsson PM, Groop L, et al. Previously associated type 2 diabetes variants may interact with physical activity to modify the risk of impaired glucose regulation and type 2 diabetes: a study of 16,003 Swedish adults. Diabetes. 2009;58(6):1411–8.PubMedCentralCrossRefPubMedGoogle Scholar
  27. Broadbent HM, Peden JF, Lorkowski S, Goel A, Ongen H, Green F, et al. Susceptibility to coronary artery disease and diabetes is encoded by distinct, tightly linked SNPs in the ANRIL locus on chromosome 9p. Hum Mol Genet. 2008;17(6):806–14. Epub 2007/12/01CrossRefGoogle Scholar
  28. Buchanan TA, Xiang AH. Gestational diabetes mellitus. J Clin Invest. 2005;115(3):485–91.PubMedCentralCrossRefPubMedGoogle Scholar
  29. Carlson CS, Eberle MA, Kruglyak L, Nickerson DA. Mapping complex disease loci in whole-genome association studies. Nature. 2004;429(6990):446–52. Epub 2004/05/28CrossRefGoogle Scholar
  30. Carlsson S, Midthjell K, Grill V. Influence of family history of diabetes on incidence and prevalence of latent autoimmune diabetes of the adult: results from the Nord-Trondelag Health Study. Diabetes Care. 2007;30(12):3040–5. Epub 2007/09/20CrossRefGoogle Scholar
  31. Catalano PM, Tyzbir ED, Sims EA. Incidence and significance of islet cell antibodies in women with previous gestational diabetes. Diabetes Care. 1990;13(5):478–82.CrossRefGoogle Scholar
  32. Centers for Disease Control and Prevention. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta: U.S. Department of Health and Human; 2011.Google Scholar
  33. Cervin C, Lyssenko V, Bakhtadze E, Lindholm E, Nilsson P, Tuomi T, et al. Genetic similarities between latent autoimmune diabetes in adults, type 1 diabetes, and type 2 diabetes. Diabetes. 2008;57(5):1433–7. Epub 2008/03/04CrossRefGoogle Scholar
  34. Chambers JC, et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet. 2008;40(6):716–8.CrossRefGoogle Scholar
  35. Chambers JC, Loh M, Lehne B, Drong A, Kriebel J, Motta V, et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol. 2015;3(7):526–34. Epub 2015/06/23PubMedCentralCrossRefPubMedGoogle Scholar
  36. Chen G, et al. Genome-wide association study identifies novel loci association with fasting insulin and insulin resistance in African Americans. Hum Mol Genet. 2012;21(20):4530–6.PubMedCentralCrossRefPubMedGoogle Scholar
  37. Cho YS, et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet. 2012a;44(1):67–72.CrossRefGoogle Scholar
  38. Cho YS, et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet. 2012b;44(1):67–72.CrossRefGoogle Scholar
  39. Cho H, Sobrin L. Genetics of diabetic retinopathy. Curr Diab Rep. 2014;14(8):515.PubMedCentralCrossRefPubMedGoogle Scholar
  40. Cho YM, Kim TH, Lim S, Choi SH, Shin HD, Lee HK, et al. Type 2 diabetes-associated genetic variants discovered in the recent genome-wide association studies are related to gestational diabetes mellitus in the Korean population. Diabetologia. 2009;52(2):253–61.CrossRefGoogle Scholar
  41. Cho YS, Chen CH, Hu C, Long J, Ong RT, Sim X, et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet. 2012;44(1):67–72. Epub 2011/12/14CrossRefGoogle Scholar
  42. Chong S, Whitelaw E. Epigenetic germline inheritance. Curr Opin Genet Dev. 2004;14(6):692–6.CrossRefGoogle Scholar
  43. Chong S, Youngson NA, Whitelaw E. Heritable germline epimutation is not the same as transgenerational epigenetic inheritance. Nat Genet. 2007;39(5):574–5; author reply 5–6CrossRefGoogle Scholar
  44. Collins FS, Green ED, Guttmacher AE, Guyer MS. A vision for the future of genomics research. Nature. 2003;422(6934):835–47. Epub 2003/04/16CrossRefGoogle Scholar
  45. Cote S, Gagne-Ouellet V, Guay SP, Allard C, Houde AA, Perron P, et al. PPARGC1alpha gene DNA methylation variations in human placenta mediate the link between maternal hyperglycemia and leptin levels in newborns. Clin Epigenetics. 2016;8:72.PubMedCentralCrossRefPubMedGoogle Scholar
  46. Cui B, et al. A genome-wide association study confirms previously reported loci for type 2 diabetes in Han Chinese. PLoS One. 2011;6(7):e22353.PubMedCentralCrossRefPubMedGoogle Scholar
  47. Dahlquist G, Blom L, Tuvemo T, Nystrom L, Sandstrom A, Wall S. The Swedish childhood diabetes study–results from a nine year case register and a one year case-referent study indicating that type 1 (insulin-dependent) diabetes mellitus is associated with both type 2 (non-insulin-dependent) diabetes mellitus and autoimmune disorders. Diabetologia. 1989;32(1):2–6. Epub 1989/01/01CrossRefGoogle Scholar
  48. Davidson BL, McCray PB Jr. Current prospects for RNA interference-based therapies. Nat Rev Genet. 2011;12(5):329–40. Epub 2011/04/19CrossRefGoogle Scholar
  49. Dayeh T, Volkov P, Salo S, Hall E, Nilsson E, Olsson AH, et al. Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLoS Genet. 2014;10(3):e1004160.PubMedCentralCrossRefPubMedGoogle Scholar
  50. DCCT. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group. N Engl J Med. 1993;329(14):977–86.CrossRefGoogle Scholar
  51. De Marinis Y, Cai M, Bompada P, Atac D, Kotova O, Johansson ME, et al. Epigenetic regulation of the thioredoxin-interacting protein (TXNIP) gene by hyperglycemia in kidney. Kidney Int. 2016;89(2):342–53.CrossRefGoogle Scholar
  52. Deeb SS, Fajas L, Nemoto M, Pihlajamaki J, Mykkanen L, Kuusisto J, et al. A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet. 1998;20(3):284–7.CrossRefGoogle Scholar
  53. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316(5829):1331–6.Google Scholar
  54. DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46(3):234–44.CrossRefGoogle Scholar
  55. Diamond J. The double puzzle of diabetes. Nature. 2003;423(6940):599–602. Epub 2003/06/06CrossRefGoogle Scholar
  56. Drury PL, Ting R, Zannino D, Ehnholm C, Flack J, Whiting M, et al. Estimated glomerular filtration rate and albuminuria are independent predictors of cardiovascular events and death in type 2 diabetes mellitus: the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study. Diabetologia. 2011;54(1):32–43. Epub 2010/07/30PubMedCentralCrossRefPubMedGoogle Scholar
  57. Duggirala R, Blangero J, Almasy L, Dyer TD, Williams KL, Leach RJ, et al. Linkage of type 2 diabetes mellitus and of age at onset to a genetic location on chromosome 10q in Mexican Americans. Am J Hum Genet. 1999;64(4):1127–40. Epub 1999/03/26PubMedCentralCrossRefPubMedGoogle Scholar
  58. Dupuis J, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42(2):105–16.PubMedCentralCrossRefPubMedGoogle Scholar
  59. Ekelund M, Shaat N, Almgren P, Anderberg E, Landin-Olsson M, Lyssenko V, et al. Genetic prediction of postpartum diabetes in women with gestational diabetes mellitus. Diabetes Res Clin Pract. 2012;97(3):394–8.CrossRefGoogle Scholar
  60. Ellard S, Beards F, Allen LI, Shepherd M, Ballantyne E, Harvey R, et al. A high prevalence of glucokinase mutations in gestational diabetic subjects selected by clinical criteria. Diabetologia. 2000;43(2):250–3.CrossRefGoogle Scholar
  61. Ellis JW, Chen MH, Foster MC, Liu CT, Larson MG, de Boer I, et al. Validated SNPs for eGFR and their associations with albuminuria. Hum Mol Genet. 2012;21(14):3293–8. Epub 2012/04/12PubMedCentralCrossRefPubMedGoogle Scholar
  62. Enquobahrie DA, Moore A, Muhie S, Tadesse MG, Lin S, Williams MA. Early pregnancy maternal blood DNA methylation in repeat pregnancies and change in gestational diabetes mellitus status – a pilot study. Reprod Sci. 2015;22(7):904–10.PubMedCentralCrossRefPubMedGoogle Scholar
  63. Erlich H, Valdes AM, Noble J, Carlson JA, Varney M, Concannon P, et al. HLA DR-DQ haplotypes and genotypes and type 1 diabetes risk. Analysis of the type 1 diabetes genetics consortium families. Diabetes. 2008;57(4):1084–92.PubMedCentralCrossRefPubMedGoogle Scholar
  64. Evans DM, Marchini J, Morris AP, Cardon LR. Two-stage two-locus models in genome-wide association. PLoS Genet. 2006;2(9):e157. Epub 2006/09/28PubMedCentralCrossRefPubMedGoogle Scholar
  65. Fadista J, Vikman P, Laakso EO, Mollet IG, Esguerra JL, Taneera J, et al. Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism. Proc Natl Acad Sci U S A. 2014;111(38):13924–9.PubMedCentralCrossRefPubMedGoogle Scholar
  66. Fernandez-Valverde SL, Taft RJ, Mattick JS. MicroRNAs in beta-cell biology, insulin resistance, diabetes and its complications. Diabetes. 2011;60(7):1825–31. Epub 2011/06/29PubMedCentralCrossRefPubMedGoogle Scholar
  67. Finer S, Mathews C, Lowe R, Smart M, Hillman S, Foo L, et al. Maternal gestational diabetes is associated with genome-wide DNA methylation variation in placenta and cord blood of exposed offspring. Hum Mol Genet. 2015;24(11):3021–9.CrossRefGoogle Scholar
  68. Flannick J, Thorleifsson G, Beer NL, Jacobs SB, Grarup N, Burtt NP, et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat Genet. 2014;46(4):357–63.PubMedCentralCrossRefPubMedGoogle Scholar
  69. Florath I, Butterbach K, Heiss J, Bewerunge-Hudler M, Zhang Y, Schottker B, et al. Type 2 diabetes and leucocyte DNA methylation: an epigenome-wide association study in over 1,500 older adults. Diabetologia. 2016;59(1):130–8. Epub 2015/10/05CrossRefGoogle Scholar
  70. Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol Rev. 2013;93(1):137–88. Epub 2013/01/11CrossRefGoogle Scholar
  71. Forsblom CM, Kanninen T, Lehtovirta M, Saloranta C, Groop LC. Heritability of albumin excretion rate in families of patients with type II diabetes. Diabetologia. 1999;42(11):1359–66. Epub 1999/11/07CrossRefGoogle Scholar
  72. Fradin D, Le Fur S, Mille C, Naoui N, Groves C, Zelenika D, et al. Association of the CpG methylation pattern of the proximal insulin gene promoter with type 1 diabetes. PLoS One. 2012;7(5):e36278.PubMedCentralCrossRefPubMedGoogle Scholar
  73. Franks PW, Jablonski KA, Delahanty LM, McAteer JB, Kahn SE, Knowler WC, et al. Assessing gene-treatment interactions at the FTO and INSIG2 loci on obesity-related traits in the Diabetes Prevention Program. Diabetologia. 2008;51(12):2214–23.PubMedCentralCrossRefPubMedGoogle Scholar
  74. Frayling TM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–94.PubMedCentralCrossRefPubMedGoogle Scholar
  75. Fuchsberger C, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41–7.PubMedCentralCrossRefPubMedGoogle Scholar
  76. Gilg J, Rao A, Fogarty D. UK Renal Registry 16th annual report: chapter 1 UK renal replacement therapy incidence in 2012: national and centre-specific analyses. Nephron Clin Pract. 2013;125(1–4):1–27. Epub 2013/01/01Google Scholar
  77. Gloyn AL. Glucokinase (GCK) mutations in hyper- and hypoglycemia: maturity-onset diabetes of the young, permanent neonatal diabetes, and hyperinsulinemia of infancy. Hum Mutat. 2003;22(5):353–62.CrossRefGoogle Scholar
  78. Gloyn AL, Weedon MN, Owen KR, Turner MJ, Knight BA, Hitman G, et al. Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes. 2003;52(2):568–72. Epub 2003/01/24CrossRefGoogle Scholar
  79. Gloyn AL, Pearson ER, Antcliff JF, Proks P, Bruining GJ, Slingerland AS, et al. Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit Kir6.2 and permanent neonatal diabetes. N Engl J Med. 2004;350(18):1838–49. Epub 2004/04/30CrossRefGoogle Scholar
  80. Go MJ, et al. New susceptibility loci in MYL2, C12orf51 and OAS1 associated with 1-h plasma glucose as predisposing risk factors for type 2 diabetes in the Korean population. J Hum Genet. 2013;58(6):362–5.CrossRefGoogle Scholar
  81. Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006;38(3):320–3.CrossRefGoogle Scholar
  82. Grassi MA, Tikhomirov A, Ramalingam S, Below JE, Cox NJ, Nicolae DL. Genome-wide meta-analysis for severe diabetic retinopathy. Hum Mol Genet. 2011;20(12):2472–81. Epub 2011/03/29PubMedCentralCrossRefPubMedGoogle Scholar
  83. Greeley SA, Tucker SE, Naylor RN, Bell GI, Philipson LH. Neonatal diabetes mellitus: a model for personalized medicine. Trends Endocrinol Metab. 2010;21(8):464–72.PubMedCentralCrossRefPubMedGoogle Scholar
  84. Groop L, Pociot F. Genetics of diabetes–are we missing the genes or the disease? Mol Cell Endocrinol. 2014;382(1):726–39. Epub 2013/04/17CrossRefGoogle Scholar
  85. Groop L, Forsblom C, Lehtovirta M, Tuomi T, Karanko S, Nissen M, et al. Metabolic consequences of a family history of NIDDM (the Botnia study): evidence for sex-specific parental effects. Diabetes. 1996;45(11):1585–93. Epub 1996/11/01CrossRefGoogle Scholar
  86. Groop L, Tuomi T, Rowley M, Zimmet P, Mackay IR. Latent autoimmune diabetes in adults (LADA)–more than a name. Diabetologia. 2006;49(9):1996–8. Epub 2006/07/05CrossRefGoogle Scholar
  87. Groop PH, Thomas MC, Moran JL, Waden J, Thorn LM, Makinen VP, et al. The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes. Diabetes. 2009;58(7):1651–8. Epub 2009/04/30PubMedCentralCrossRefPubMedGoogle Scholar
  88. Group HSCR, Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008;358(19):1991–2002.CrossRefGoogle Scholar
  89. Guariguata L, Linnenkamp U, Beagley J, Whiting DR, Cho NH. Global estimates of the prevalence of hyperglycaemia in pregnancy. Diabetes Res Clin Pract. 2014;103(2):176–85.CrossRefGoogle Scholar
  90. Gudmundsson J, Sulem P, Steinthorsdottir V, Bergthorsson JT, Thorleifsson G, Manolescu A, et al. Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet. 2007;39(8):977–83.CrossRefGoogle Scholar
  91. Hales CN, Barker DJ. The thrifty phenotype hypothesis. Br Med Bull. 2001;60:5–20.CrossRefGoogle Scholar
  92. Hani EH, Boutin P, Durand E, Inoue H, Permutt MA, Velho G, et al. Missense mutations in the pancreatic islet beta cell inwardly rectifying K+ channel gene (KIR6.2/BIR): a meta-analysis suggests a role in the polygenic basis of type II diabetes mellitus in Caucasians. Diabetologia. 1998;41(12):1511–5.CrossRefGoogle Scholar
  93. Hansen SK, Nielsen EM, Ek J, Andersen G, Glumer C, Carstensen B, et al. Analysis of separate and combined effects of common variation in KCNJ11 and PPARG on risk of type 2 diabetes. J Clin Endocrinol Metab. 2005;90(6):3629–37.CrossRefGoogle Scholar
  94. Hanson RL, Guo T, Muller YL, Fleming J, Knowler WC, Kobes S, et al. Strong parent-of-origin effects in the association of KCNQ1 variants with type 2 diabetes in American Indians. Diabetes. 2013;62(8):2984–91.PubMedCentralCrossRefPubMedGoogle Scholar
  95. Hanson RL, et al. A genome-wide association study in American Indians implicates DNER as a susceptibility locus for type 2 diabetes. Diabetes. 2014;63(1):369–76.CrossRefGoogle Scholar
  96. Hara K, et al. Genome-wide association study identifies three novel loci for type 2 diabetes. Hum Mol Genet. 2014;23(1):239–46.CrossRefGoogle Scholar
  97. Harder T, Franke K, Kohlhoff R, Plagemann A. Maternal and paternal family history of diabetes in women with gestational diabetes or insulin-dependent diabetes mellitus type I. Gynecol Obstet Investig. 2001;51(3):160–4.CrossRefGoogle Scholar
  98. Harder T, Rodekamp E, Schellong K, Dudenhausen JW, Plagemann A. Birth weight and subsequent risk of type 2 diabetes: a meta-analysis. Am J Epidemiol. 2007;165(8):849–57.CrossRefGoogle Scholar
  99. Hariharan M, Scaria V, Brahmachari SK. dbSMR: a novel resource of genome-wide SNPs affecting microRNA mediated regulation. BMC Bioinf. 2009;10:108. Epub 2009/04/18CrossRefGoogle Scholar
  100. Harjutsalo V, Katoh S, Sarti C, Tajima N, Tuomilehto J. Population-based assessment of familial clustering of diabetic nephropathy in type 1 diabetes. Diabetes. 2004;53(9):2449–54. Epub 2004/08/28CrossRefGoogle Scholar
  101. Hemminki K, Li X, Sundquist K, Sundquist J. Familial risks for type 2 diabetes in Sweden. Diabetes Care. 2010;33(2):293–7. Epub 2009/11/12CrossRefGoogle Scholar
  102. Hietala K, Forsblom C, Summanen P, Groop PH, FinnDiane Study G. Heritability of proliferative diabetic retinopathy. Diabetes. 2008;57(8):2176–80. Epub 2008/04/30PubMedCentralCrossRefPubMedGoogle Scholar
  103. Hoggart CJ, Venturini G, Mangino M, Gomez F, Ascari G, Zhao JH, et al. Novel approach identifies SNPs in SLC2A10 and KCNK9 with evidence for parent-of-origin effect on body mass index. PLoS Genet. 2014;10(7):e1004508.PubMedCentralCrossRefPubMedGoogle Scholar
  104. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577–89. Epub 2008/09/12CrossRefGoogle Scholar
  105. Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M, et al. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet. 2000;26(2):163–75.PubMedCentralCrossRefPubMedGoogle Scholar
  106. Horton V, Stratton I, Bottazzo GF, Shattock M, Mackay I, Zimmet P, et al. Genetic heterogeneity of autoimmune diabetes: age of presentation in adults is influenced by HLA DRB1 and DQB1 genotypes (UKPDS 43). UK Prospective Diabetes Study (UKPDS) Group. Diabetologia. 1999;42(5):608–16. Epub 1999/05/20CrossRefGoogle Scholar
  107. Houde AA, Guay SP, Desgagne V, Hivert MF, Baillargeon JP, St-Pierre J, et al. Adaptations of placental and cord blood ABCA1 DNA methylation profile to maternal metabolic status. Epigenetics. 2013;8(12):1289–302.PubMedCentralCrossRefPubMedGoogle Scholar
  108. Howson JM, Rosinger S, Smyth DJ, Boehm BO, Todd JA. Genetic analysis of adult-onset autoimmune diabetes. Diabetes. 2011;60(10):2645–53. Epub 2011/08/30PubMedCentralCrossRefPubMedGoogle Scholar
  109. Huang YC, Lin JM, Lin HJ, Chen CC, Chen SY, Tsai CH, et al. Genome-wide association study of diabetic retinopathy in a Taiwanese population. Ophthalmology. 2011;118(4):642–8. Epub 2011/02/12CrossRefGoogle Scholar
  110. Huopio H, Cederberg H, Vangipurapu J, Hakkarainen H, Paakkonen M, Kuulasmaa T, et al. Association of risk variants for type 2 diabetes and hyperglycemia with gestational diabetes. Eur J Endocrinol. 2013;169(3):291–7.CrossRefGoogle Scholar
  111. Huyghe JR, et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet. 2013;45(2):197–201.CrossRefGoogle Scholar
  112. Hyttinen V, Kaprio J, Kinnunen L, Koskenvuo M, Tuomilehto J. Genetic liability of type 1 diabetes and the onset age among 22,650 young Finnish twin pairs: a nationwide follow-up study. Diabetes. 2003;52(4):1052–5.CrossRefGoogle Scholar
  113. Ilonen J, Hammais A, Laine A-P, Lempainen J, Vaarala O, Veijola R, et al. Patterns of β-cell autoantibody appearance and genetic associations during the first years of life. Diabetes. 2013;62(10):3636–40.PubMedCentralCrossRefPubMedGoogle Scholar
  114. Imamura M, Maeda S, Yamauchi T, Hara K, Yasuda K, Morizono T, et al. A single-nucleotide polymorphism in ANK1 is associated with susceptibility to type 2 diabetes in Japanese populations. Hum Mol Genet. 2012;21(13):3042–9.CrossRefGoogle Scholar
  115. International HapMap Consortium. The International HapMap Project. Nature. 2003;426(6968):789–96. Epub 2003/12/20CrossRefGoogle Scholar
  116. Kahara T, Takamura T, Hayakawa T, Nagai Y, Yamaguchi H, Katsuki T, et al. PPARgamma gene polymorphism is associated with exercise-mediated changes of insulin resistance in healthy men. Metabolism. 2003;52(2):209–12.CrossRefGoogle Scholar
  117. Kaprio J, Tuomilehto J, Koskenvuo M, Romanov K, Reunanen A, Eriksson J, et al. Concordance for type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia. 1992;35(11):1060–7. Epub 1992/11/01CrossRefGoogle Scholar
  118. Karvonen M, Viik-Kajander M, Moltchanova E, Libman I, LaPorte R, Tuomilehto J. Incidence of childhood type 1 diabetes worldwide. Diabetes Mondiale (DiaMond) Project Group. Diabetes Care. 2000;23(10):1516–26. Epub 2000/10/07CrossRefGoogle Scholar
  119. Khera AV, Kathiresan S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat Rev Genet. 2017;18(6):331–44. Epub 2017/03/14PubMedCentralCrossRefPubMedGoogle Scholar
  120. Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care. 2002;25(10):1862–8.CrossRefGoogle Scholar
  121. Kleinberger JW, Pollin TI. Undiagnosed MODY: time for action. Curr Diab Rep. 2015;15(12):110.PubMedCentralCrossRefPubMedGoogle Scholar
  122. Köbberling J, Tillil H. Empirical risk figures for first-degree relatives of non-insulin dependent diabetics. In: Köbberling J, Tattersall R, editors. The genetics of diabetes mellitus. London: Academic; 1982. p. 201–9.Google Scholar
  123. Kong A, Steinthorsdottir V, Masson G, Thorleifsson G, Sulem P, Besenbacher S, et al. Parental origin of sequence variants associated with complex diseases. Nature. 2009;462(7275):868–74.PubMedCentralCrossRefPubMedGoogle Scholar
  124. Kooner JS, Saleheen D, Sim X, Sehmi J, Zhang W, Frossard P, et al. Genome-wide association study in individuals of south Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat Genet. 2011;43(10):984–9.PubMedCentralCrossRefPubMedGoogle Scholar
  125. Krolewski AS, Warram JH, Christlieb AR, Busick EJ, Kahn CR. The changing natural history of nephropathy in type I diabetes. Am J Med. 1985;78(5):785–94. Epub 1985/05/01CrossRefGoogle Scholar
  126. Krolewski AS, Poznik GD, Placha G, Canani L, Dunn J, Walker W, et al. A genome-wide linkage scan for genes controlling variation in urinary albumin excretion in type II diabetes. Kidney Int. 2006;69(1):129–36. Epub 2005/12/24CrossRefGoogle Scholar
  127. Kuhl C. Glucose metabolism during and after pregnancy in normal and gestational diabetic women. 1. Influence of normal pregnancy on serum glucose and insulin concentration during basal fasting conditions and after a challenge with glucose. Acta Endocrinol. 1975;79(4):709–19.CrossRefGoogle Scholar
  128. Kulkarni H, Kos MZ, Neary J, Dyer TD, Kent JW Jr, Goring HH, et al. Novel epigenetic determinants of type 2 diabetes in Mexican-American families. Hum Mol Genet. 2015;24(18):5330–44. Epub 2015/06/24PubMedCentralCrossRefPubMedGoogle Scholar
  129. Kwak SH, Kim SH, Cho YM, Go MJ, Cho YS, Choi SH, et al. A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes. 2012;61(2):531–41.PubMedCentralCrossRefPubMedGoogle Scholar
  130. Kyvik KO, Green A, Beck-Nielsen H. Concordance rates of insulin dependent diabetes mellitus: a population based study of young Danish twins. BMJ. 1995;311(7010):913–7. Epub 1995/10/07PubMedCentralCrossRefPubMedGoogle Scholar
  131. Langefeld CD, Beck SR, Bowden DW, Rich SS, Wagenknecht LE, Freedman BI. Heritability of GFR and albuminuria in Caucasians with type 2 diabetes mellitus. Am J Kidney Dis. 2004;43(5):796–800. Epub 2004/04/28CrossRefGoogle Scholar
  132. Lauenborg J, Grarup N, Damm P, Borch-Johnsen K, Jorgensen T, Pedersen O, et al. Common type 2 diabetes risk gene variants associate with gestational diabetes. J Clin Endocrinol Metab. 2009;94(1):145–50.CrossRefGoogle Scholar
  133. Laugesen E, Ostergaard JA, Leslie RD, Danish Diabetes Academy Workshop and Workshop Speakers. Latent autoimmune diabetes of the adult: current knowledge and uncertainty. Diabet Med. 2015;32(7):843–52. Epub 2015/01/21PubMedCentralCrossRefPubMedGoogle Scholar
  134. Lesseur C, Armstrong DA, Paquette AG, Li Z, Padbury JF, Marsit CJ. Maternal obesity and gestational diabetes are associated with placental leptin DNA methylation. Am J Obstet Gynecol. 2014;211(6):654 e1–9.CrossRefGoogle Scholar
  135. Li H, Gan W, Lu L, Dong X, Han X, Hu C, et al. A genome-wide association study identifies GRK5 and RASGRP1 as type 2 diabetes loci in Chinese Hans. Diabetes. 2013;62(1):291–8.CrossRefGoogle Scholar
  136. Looker HC, Nelson RG, Chew E, Klein R, Klein BE, Knowler WC, et al. Genome-wide linkage analyses to identify Loci for diabetic retinopathy. Diabetes. 2007;56(4):1160–6. Epub 2007/03/31CrossRefGoogle Scholar
  137. Luan J, Browne PO, Harding AH, Halsall DJ, O’Rahilly S, Chatterjee VK, et al. Evidence for gene-nutrient interaction at the PPARgamma locus. Diabetes. 2001;50(3):686–9.CrossRefGoogle Scholar
  138. Lupski JR, Belmont JW, Boerwinkle E, Gibbs RA. Clan genomics and the complex architecture of human disease. Cell. 2011;147(1):32–43. Epub 2011/10/04PubMedCentralCrossRefPubMedGoogle Scholar
  139. Lyssenko V, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008;359(21):2220–32.CrossRefGoogle Scholar
  140. Ma RC, et al. Genome-wide association study in a Chinese population identifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4. Diabetologia. 2013;56(6):1291–305.PubMedCentralCrossRefPubMedGoogle Scholar
  141. Manning AK, et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet. 2012;44(6):659–69.PubMedCentralCrossRefPubMedGoogle Scholar
  142. Martin AO, Simpson JL, Ober C, Freinkel N. Frequency of diabetes mellitus in mothers of probands with gestational diabetes: possible maternal influence on the predisposition to gestational diabetes. Am J Obstet Gynecol. 1985;151(4):471–5.CrossRefGoogle Scholar
  143. Medici F, Hawa M, Ianari A, Pyke DA, Leslie RD. Concordance rate for type II diabetes mellitus in monozygotic twins: actuarial analysis. Diabetologia. 1999;42(2):146–50. Epub 1999/03/04CrossRefGoogle Scholar
  144. Meigs JB, Cupples LA, Wilson PW. Parental transmission of type 2 diabetes: the Framingham Offspring Study. Diabetes. 2000;49(12):2201–7. Epub 2000/12/16CrossRefGoogle Scholar
  145. Miao F, Smith DD, Zhang L, Min A, Feng W, Natarajan R. Lymphocytes from patients with type 1 diabetes display a distinct profile of chromatin histone H3 lysine 9 dimethylation: an epigenetic study in diabetes. Diabetes. 2008;57(12):3189–98.PubMedCentralCrossRefPubMedGoogle Scholar
  146. Miao F, Chen Z, Zhang L, Liu Z, Wu X, Yuan YC, et al. Profiles of epigenetic histone post-translational modifications at type 1 diabetes susceptible genes. J Biol Chem. 2012;287(20):16335–45.PubMedCentralCrossRefPubMedGoogle Scholar
  147. Michalczyk AA, Dunbar JA, Janus ED, Best JD, Ebeling PR, Ackland MJ, et al. Epigenetic markers to predict conversion from gestational diabetes to type 2 diabetes. J Clin Endocrinol Metab. 2016;101(6):2396–404.CrossRefGoogle Scholar
  148. Minton JA, et al. Association studies of genetic variation in the WFS1 gene and type 2 diabetes in U.K. populations. Diabetes. 2002;51(4):1287–90.CrossRefGoogle Scholar
  149. Moltke I, Grarup N, Jorgensen ME, Bjerregaard P, Treebak JT, Fumagalli M, et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature. 2014;512(7513):190–3.CrossRefGoogle Scholar
  150. Moore T, Haig D. Genomic imprinting in mammalian development: a parental tug-of-war. Trends Genet. 1991;7(2):45–9.CrossRefGoogle Scholar
  151. Moran VA, Perera RJ, Khalil AM. Emerging functional and mechanistic paradigms of mammalian long non-coding RNAs. Nucleic Acids Res. 2012;40(14):6391–400.PubMedCentralCrossRefPubMedGoogle Scholar
  152. Morris AP, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012a;44(9):981–90.PubMedCentralCrossRefPubMedGoogle Scholar
  153. Morris AP, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012b;44(9):981–90.PubMedCentralCrossRefPubMedGoogle Scholar
  154. 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. 2008a;25(4):383–99.CrossRefGoogle Scholar
  155. Murphy R, Ellard S, Hattersley AT. Clinical implications of a molecular genetic classification of monogenic beta-cell diabetes. Nat Clin Pract Endocrinol Metab. 2008b;4(4):200–13. Epub 2008/02/28CrossRefGoogle Scholar
  156. Nerup J, Platz P, Andersen OO, Christy M, Lyngse J, Poulsen JE, et al. HL-A antigens and diabetes mellitus. Lancet. 1974;304(7885):864–6.CrossRefGoogle Scholar
  157. Newman B, Selby JV, King MC, Slemenda C, Fabsitz R, Friedman GD. Concordance for type 2 (non-insulin-dependent) diabetes mellitus in male twins. Diabetologia. 1987;30(10):763–8. Epub 1987/10/01CrossRefGoogle Scholar
  158. Ng MC, et al. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet. 2014;10(8):e1004517.PubMedCentralCrossRefPubMedGoogle Scholar
  159. Nikzamir A, Nakhjavani M, Esteghamati A, Rashidi A. Correlates of ACE activity in macroalbuminuric type 2 diabetic patients treated with chronic ACE inhibition. Nephrol Dial Transplant. 2008;23(4):1274–7.CrossRefGoogle Scholar
  160. Njolstad PR, Sagen JV, Bjorkhaug L, Odili S, Shehadeh N, Bakry D, et al. Permanent neonatal diabetes caused by glucokinase deficiency: inborn error of the glucose-insulin signaling pathway. Diabetes. 2003;52(11):2854–60.CrossRefGoogle Scholar
  161. Noble JA. Immunogenetics of type 1 diabetes: a comprehensive review. J Autoimmun. 2015;64:101–12.CrossRefGoogle Scholar
  162. O’Sullivan JB. Diabetes mellitus after GDM. Diabetes. 1991;40(Suppl 2):131–5.CrossRefGoogle Scholar
  163. Palmer ND, McDonough CW, Hicks PJ, Roh BH, Wing MR, An SS, et al. A genome-wide association search for type 2 diabetes genes in African Americans. PLoS One. 2012;7(1):e29202.PubMedCentralCrossRefPubMedGoogle Scholar
  164. Parra EJ, Below JE, Krithika S, Valladares A, Barta JL, Cox NJ, et al. Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American sample from Starr County, Texas. Diabetologia. 2011;54(8):2038–46.CrossRefGoogle Scholar
  165. Pasquali L, Gaulton KJ, Rodriguez-Segui SA, Mularoni L, Miguel-Escalada I, Akerman I, et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet. 2014;46(2):136–43.PubMedCentralCrossRefPubMedGoogle Scholar
  166. Patterson CC, Dahlquist GG, Gyurus E, Green A, Soltesz G, Group ES. Incidence trends for childhood type 1 diabetes in Europe during 1989–2003 and predicted new cases 2005–20: a multicentre prospective registration study. Lancet. 2009;373(9680):2027–33. Epub 2009/06/02CrossRefPubMedGoogle Scholar
  167. Pearson ER, Starkey BJ, Powell RJ, Gribble FM, Clark PM, Hattersley AT. Genetic cause of hyperglycaemia and response to treatment in diabetes. Lancet. 2003;362(9392):1275–81.CrossRefGoogle Scholar
  168. Perkins BA, Ficociello LH, Roshan B, Warram JH, Krolewski AS. In patients with type 1 diabetes and new-onset microalbuminuria the development of advanced chronic kidney disease may not require progression to proteinuria. Kidney Int. 2010;77(1):57–64. Epub 2009/10/23PubMedCentralCrossRefPubMedGoogle Scholar
  169. Perry JR, et al. Stratifying type 2 diabetes cases by BMI identifies genetic risk variants in LAMA1 and enrichment for risk variants in lean compared to obese cases. PLoS Genet. 2012a;8(5):e1002741.PubMedCentralCrossRefPubMedGoogle Scholar
  170. Perry JR, et al. Stratifying type 2 diabetes cases by BMI identifies genetic risk variants in LAMA1 and enrichment for risk variants in lean compared to obese cases. PLoS Genet. 2012b;8(5):e1002741.PubMedCentralCrossRefPubMedGoogle Scholar
  171. Pettitt DJ, Nelson RG, Saad MF, Bennett PH, Knowler WC. Diabetes and obesity in the offspring of Pima Indian women with diabetes during pregnancy. Diabetes Care. 1993;16(1):310–4.CrossRefGoogle Scholar
  172. Plomin R, Haworth CM, Davis OS. Common disorders are quantitative traits. Nat Rev Genet. 2009;10(12):872–8.CrossRefGoogle Scholar
  173. Pociot F, Lernmark A. Genetic risk factors for type 1 diabetes. Lancet. 2016;387(10035):2331–9. Epub 2016/06/16CrossRefPubMedGoogle Scholar
  174. Pociot F, Norgaard K, Hobolth N, Andersen O, Nerup J. A nationwide population-based study of the familial aggregation of type 1 (insulin-dependent) diabetes mellitus in Denmark. Danish Study Group of Diabetes in Childhood. Diabetologia. 1993;36(9):870–5. Epub 1993/09/01CrossRefGoogle Scholar
  175. Pociot F, Akolkar B, Concannon P, Erlich HA, Julier C, Morahan G, et al. Genetics of type 1 diabetes: what’s next? Diabetes. 2010;59(7):1561–71. Epub 2010/07/01PubMedCentralCrossRefPubMedGoogle Scholar
  176. Polak M, Cave H. Neonatal diabetes mellitus: a disease linked to multiple mechanisms. Orphanet J Rare Dis. 2007;2:12. Epub 2007/03/14PubMedCentralCrossRefPubMedGoogle Scholar
  177. Poulsen P, Kyvik KO, Vaag A, Beck-Nielsen H. Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance–a population-based twin study. Diabetologia. 1999;42(2):139–45. Epub 1999/03/04CrossRefGoogle Scholar
  178. Prasad RB, Lessmark A, Almgren P, Kovacs G, Hansson O, Oskolkov N, et al. Excess maternal transmission of variants in the THADA gene to offspring with type 2 diabetes. Diabetologia. 2016a;59(8):1702–13. Epub 2016/05/09CrossRefGoogle Scholar
  179. Prasad RB, Lessmark A, Almgren A, Kovacs G, Oskolkov, N, Vitai M, Ladenvall C, Kovacs P, Fadista J, Lachmann M, Zhou Y, Hansson O, Sonestedt E, Poon W, Wolheim C, Orho-Melander M, Stumvoll M, Tuomi T, Pääbo S, Koranyi L, Groop L. Genetics of type 2 diabetes—Pitfalls and possibilities. Genes (Basel). 2015;6(1):87–123.Google Scholar
  180. Prokopenko I, et al. Variants in MTNR1B influence fasting glucose levels. Nat Genet. 2009;41(1):77–81.CrossRefGoogle Scholar
  181. Pugliese A. The insulin gene in type 1 diabetes. IUBMB Life. 2005;57(7):463–8. Epub 2005/08/06CrossRefGoogle Scholar
  182. Qi L, et al. Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet. 2010;19(13):2706–15.PubMedCentralCrossRefPubMedGoogle Scholar
  183. Qi L, Qi Q, Prudente S, Mendonca C, Andreozzi F, di Pietro N, et al. Association between a genetic variant related to glutamic acid metabolism and coronary heart disease in individuals with type 2 diabetes. JAMA. 2013;310(8):821–8.CrossRefGoogle Scholar
  184. Qiu M, Xiong W, Liao H, Li F. VEGF -634G>C polymorphism and diabetic retinopathy risk: a meta-analysis. Gene. 2013;518(2):310–5. Epub 2013/01/29CrossRefGoogle Scholar
  185. Rani PK, Raman R, Gupta A, Pal SS, Kulothungan V, Sharma T. Albuminuria and diabetic retinopathy in type 2 diabetes mellitus sankara nethralaya diabetic retinopathy epidemiology and molecular genetic study (SN-DREAMS, report 12). Diabetol Metab Syndr. 2011;3(1):9. Epub 2011/05/27PubMedCentralCrossRefPubMedGoogle Scholar
  186. Replication DIG, Meta-analysis C, Asian Genetic Epidemiology Network Type 2 Diabetes C, South Asian Type 2 Diabetes C, Mexican American Type 2 Diabetes C, Type 2 Diabetes Genetic Exploration by Nex-generation sequencing in muylti-Ethnic Samples C, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46(3):234–44. Epub 2014/02/11CrossRefGoogle Scholar
  187. Reynisdottir I, Thorleifsson G, Benediktsson R, Sigurdsson G, Emilsson V, Einarsdottir AS, et al. Localization of a susceptibility gene for type 2 diabetes to chromosome 5q34-q35.2. Am J Hum Genet. 2003;73(2):323–35. Epub 2003/07/10PubMedCentralCrossRefPubMedGoogle Scholar
  188. Rigat B, Hubert C, Alhenc-Gelas F, Cambien F, Corvol P, Soubrier F. An insertion/deletion polymorphism in the angiotensin I-converting enzyme gene accounting for half the variance of serum enzyme levels. J Clin Invest. 1990;86(4):1343–6.PubMedCentralCrossRefPubMedGoogle Scholar
  189. Ritz E, Zeng XX, Rychlik I. Clinical manifestation and natural history of diabetic nephropathy. Contrib Nephrol. 2011;170:19–27. Epub 2011/06/11CrossRefGoogle Scholar
  190. Robitaille J, Grant AM. The genetics of gestational diabetes mellitus: evidence for relationship with type 2 diabetes mellitus. Genet Med. 2008;10(4):240–50.CrossRefGoogle Scholar
  191. Rosengren AH, et al. Overexpression of alpha2A-adrenergic receptors contributes to type 2 diabetes. Science. 2010;327(5962):217–20.CrossRefGoogle Scholar
  192. Ruggenenti P, Remuzzi G. Nephropathy of type 1 and type 2 diabetes: diverse pathophysiology, same treatment? Nephrol Dial Transplant. 2000;15(12):1900–2. Epub 2000/11/30CrossRefGoogle Scholar
  193. Rung J, et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat Genet. 2009;41(10):1110–5.CrossRefGoogle Scholar
  194. Sackton TB, Hartl DL. Genotypic context and epistasis in individuals and populations. Cell. 2016;166(2):279–87. Epub 2016/07/16PubMedCentralCrossRefPubMedGoogle Scholar
  195. Said G. Diabetic neuropathy – a review. Nat Clin Pract Neurol. 2007;3(6):331–40.CrossRefGoogle Scholar
  196. Salonen JT, et al. Type 2 diabetes whole-genome association study in four populations: the DiaGen consortium. Am J Hum Genet. 2007;81(2):338–45.PubMedCentralCrossRefPubMedGoogle Scholar
  197. Sandholm N, Salem RM, McKnight AJ, Brennan EP, Forsblom C, Isakova T, et al. New susceptibility Loci associated with kidney disease in type 1 diabetes. PLoS Genet. 2012;8(9):e1002921. Epub 2012/10/03PubMedCentralCrossRefPubMedGoogle Scholar
  198. Sandholm N, McKnight AJ, Salem RM, Brennan EP, Forsblom C, Harjutsalo V, et al. Chromosome 2q31.1 associates with ESRD in women with type 1 diabetes. J Am Soc Nephrol. 2013;24(10):1537–43. Epub 2013/09/14PubMedCentralCrossRefPubMedGoogle Scholar
  199. Sandhu MS, et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet. 2007;39(8):951–3.PubMedCentralCrossRefPubMedGoogle Scholar
  200. Sanjeevi CB, Lybrand TP, DeWeese C, Landin-Olsson M, Kockum I, Dahlquist G, et al. Polymorphic amino acid variations in HLA-DQ are associated with systematic physical property changes and occurrence of IDDM. Members of the Swedish Childhood Diabetes Study. Diabetes. 1995;44(1):125–31. Epub 1995/01/01CrossRefGoogle Scholar
  201. Saxena R, et al. Common single nucleotide polymorphisms in TCF7L2 are reproducibly associated with type 2 diabetes and reduce the insulin response to glucose in nondiabetic individuals. Diabetes. 2006;55(10):2890–5.CrossRefGoogle Scholar
  202. Saxena R, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316(5829):1331–6.CrossRefGoogle Scholar
  203. Saxena R, et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet. 2010;42(2):142–8.PubMedCentralCrossRefPubMedGoogle Scholar
  204. Saxena R, et al. Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci. Am J Hum Genet. 2012;90(3):410–25.PubMedCentralCrossRefPubMedGoogle Scholar
  205. Saxena R, et al. Genome-wide association study identifies a novel locus contributing to type 2 diabetes susceptibility in Sikhs of Punjabi origin from India. Diabetes. 2013;62(5):1746–55.PubMedCentralCrossRefPubMedGoogle Scholar
  206. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316(5829):1341–5.PubMedCentralCrossRefPubMedGoogle Scholar
  207. Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, Luan J, et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet. 2012;44(9):991–1005.PubMedCentralCrossRefPubMedGoogle Scholar
  208. Scott RA, et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes. 2017;66(11):2888–902.PubMedCentralCrossRefPubMedGoogle Scholar
  209. Segerstolpe A, Palasantza A, Eliasson P, Andersson EM, Andreasson AC, Sun X, et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 2016;24(4):593–607.PubMedCentralCrossRefPubMedGoogle Scholar
  210. Shaat N, Ekelund M, Lernmark A, Ivarsson S, Nilsson A, Perfekt R, et al. Genotypic and phenotypic differences between Arabian and Scandinavian women with gestational diabetes mellitus. Diabetologia. 2004;47(5):878–84.CrossRefGoogle Scholar
  211. Shepherd M, Shields B, Ellard S, Rubio-Cabezas O, Hattersley AT. A genetic diagnosis of HNF1A diabetes alters treatment and improves glycaemic control in the majority of insulin-treated patients. Diabet Med. 2009;26(4):437–41. Epub 2009/04/25CrossRefGoogle Scholar
  212. Sheu WH, Kuo JZ, Lee IT, Hung YJ, Lee WJ, Tsai HY, et al. Genome-wide association study in a Chinese population with diabetic retinopathy. Hum Mol Genet. 2013;22(15):3165–73.PubMedCentralCrossRefPubMedGoogle Scholar
  213. Shields BM, Hicks S, Shepherd MH, Colclough K, Hattersley AT, Ellard S. Maturity-onset diabetes of the young (MODY): how many cases are we missing? Diabetologia. 2010;53(12):2504–8. Epub 2010/05/26CrossRefGoogle Scholar
  214. Shu XO, Long J, Cai Q, Qi L, Xiang YB, Cho YS, et al. Identification of new genetic risk variants for type 2 diabetes. PLoS Genet. 2010;6(9):e1001127.PubMedCentralCrossRefPubMedGoogle Scholar
  215. SIGMA Type 2 Diabetes Consortium, et al. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature. 2014a;506(7486):97–101.CrossRefGoogle Scholar
  216. SIGMA Type 2 Diabetes Consortium, et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA. 2014b;311(22):2305–14.CrossRefGoogle Scholar
  217. Silverman BL, Rizzo T, Green OC, Cho NH, Winter RJ, Ogata ES, et al. Long-term prospective evaluation of offspring of diabetic mothers. Diabetes. 1991;40(Suppl 2):121–5.CrossRefGoogle Scholar
  218. Sim X, et al. Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia. PLoS Genet. 2011;7(4):e1001363.PubMedCentralCrossRefPubMedGoogle Scholar
  219. Singal DP, Blajchman MA. Histocompatibility (HL-A) antigens, lymphocytotoxic antibodies and tissue antibodies in patients with diabetes mellitus. Diabetes. 1973;22(6):429–32. Epub 1973/06/01CrossRefGoogle Scholar
  220. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445(7130):881–5.CrossRefGoogle Scholar
  221. Small KS, Hedman AK, Grundberg E, Nica AC, Thorleifsson G, Kong A, et al. Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nat Genet. 2011;43(6):561–4. Epub 2011/05/17PubMedCentralCrossRefPubMedGoogle Scholar
  222. Smyth DJ, Cooper JD, Howson JM, Walker NM, Plagnol V, Stevens H, et al. PTPN22 Trp620 explains the association of chromosome 1p13 with type 1 diabetes and shows a statistical interaction with HLA class II genotypes. Diabetes. 2008;57(6):1730–7. Epub 2008/02/29CrossRefGoogle Scholar
  223. Sonestedt E, Lyssenko V, Ericson U, Gullberg B, Wirfalt E, Groop L, et al. Genetic variation in the glucose-dependent insulinotropic polypeptide receptor modifies the association between carbohydrate and fat intake and risk of type 2 diabetes in the Malmo Diet and Cancer cohort. J Clin Endocrinol Metab. 2012;97(5):E810–8.CrossRefGoogle Scholar
  224. Steinke JM, Sinaiko AR, Kramer MS, Suissa S, Chavers BM, Mauer M, et al. The early natural history of nephropathy in Type 1 Diabetes: III. Predictors of 5-year urinary albumin excretion rate patterns in initially normoalbuminuric patients. Diabetes. 2005;54(7):2164–71. Epub 2005/06/29PubMedCentralCrossRefPubMedGoogle Scholar
  225. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007;39(6):770–5.CrossRefGoogle Scholar
  226. Steinthorsdottir V, Thorleifsson G, Sulem P, Helgason H, Grarup N, Sigurdsson A, et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat Genet. 2014;46(3):294–8.CrossRefGoogle Scholar
  227. Stoffers DA, Zinkin NT, Stanojevic V, Clarke WL, Habener JF. Pancreatic agenesis attributable to a single nucleotide deletion in the human IPF1 gene coding sequence. Nat Genet. 1997;15:106–10.CrossRefGoogle Scholar
  228. Stoy J, Edghill EL, Flanagan SE, Ye H, Paz VP, Pluzhnikov A, et al. Insulin gene mutations as a cause of permanent neonatal diabetes. Proc Natl Acad Sci U S A. 2007;104(38):15040–4.PubMedCentralCrossRefPubMedGoogle Scholar
  229. Strawbridge RJ, et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes. 2011;60(10):2624–34.PubMedCentralCrossRefPubMedGoogle Scholar
  230. Tabassum R, et al. Genome-wide association study for type 2 diabetes in Indians identifies a new susceptibility locus at 2q21. Diabetes. 2013;62(3):977–86.PubMedCentralCrossRefPubMedGoogle Scholar
  231. Takeuchi F, et al. Confirmation of multiple risk Loci and genetic impacts by a genome-wide association study of type 2 diabetes in the Japanese population. Diabetes. 2009;58(7):1690–9.PubMedCentralCrossRefPubMedGoogle Scholar
  232. Taneera J, Fadista J, Ahlqvist E, Atac D, Ottosson-Laakso E, Wollheim CB, et al. Identification of novel genes for glucose metabolism based upon expression pattern in human islets and effect on insulin secretion and glycemia. Hum Mol Genet. 2015;24(7):1945–55. Epub 2014/12/10CrossRefGoogle Scholar
  233. Tattersall RB. Mild familial diabetes with dominant inheritance. Q J Med. 1974;43(170):339–57.Google Scholar
  234. Temple IK, James RS, Crolla JA, Sitch FL, Jacobs PA, Howell WM, et al. An imprinted gene(s) for diabetes? Nat Genet. 1995;9(2):110–2.CrossRefGoogle Scholar
  235. Temple IK, Gardner RJ, Robinson DO, Kibirige MS, Ferguson AW, Baum JD, et al. Further evidence for an imprinted gene for neonatal diabetes localised to chromosome 6q22-q23. Hum Mol Genet. 1996;5(8):1117–21.CrossRefGoogle Scholar
  236. Thamotharampillai K, Chan AK, Bennetts B, Craig ME, Cusumano J, Silink M, et al. Decline in neurophysiological function after 7 years in an adolescent diabetic cohort and the role of aldose reductase gene polymorphisms. Diabetes Care. 2006;29(9):2053–7.CrossRefGoogle Scholar
  237. Thorleifsson G, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009;41(1):18–24.CrossRefGoogle Scholar
  238. Timpson NJ, et al. Adiposity-related heterogeneity in patterns of type 2 diabetes susceptibility observed in genome-wide association data. Diabetes. 2009;58(2):505–10.PubMedCentralCrossRefPubMedGoogle Scholar
  239. Tong Z, Yang Z, Patel S, Chen H, Gibbs D, Yang X, et al. Promoter polymorphism of the erythropoietin gene in severe diabetic eye and kidney complications. Proc Natl Acad Sci U S A. 2008;105(19):6998–7003. Epub 2008/05/07PubMedCentralCrossRefPubMedGoogle Scholar
  240. Tong Y, Lin Y, Zhang Y, Yang J, Liu H, Zhang B. Association between TCF7L2 gene polymorphisms and susceptibility to type 2 diabetes mellitus: a large Human Genome Epidemiology (HuGE) review and meta-analysis. BMC Med Genet. 2009;10:15. Epub 2009/02/21PubMedCentralCrossRefPubMedGoogle Scholar
  241. Toperoff G, Aran D, Kark JD, Rosenberg M, Dubnikov T, Nissan B, et al. Genome-wide survey reveals predisposing diabetes type 2-related DNA methylation variations in human peripheral blood. Hum Mol Genet. 2012;21(2):371–83. Epub 2011/10/14CrossRefGoogle Scholar
  242. Travers ME, Mackay DJ, Dekker Nitert M, Morris AP, Lindgren CM, Berry A, et al. Insights into the molecular mechanism for type 2 diabetes susceptibility at the KCNQ1 locus from temporal changes in imprinting status in human islets. Diabetes. 2013;62(3):987–92. Epub 2012/11/10PubMedCentralCrossRefPubMedGoogle Scholar
  243. Tsai FJ, et al. A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet. 2010;6(2):e1000847.PubMedCentralCrossRefPubMedGoogle Scholar
  244. Tuomi T, Groop LC, Zimmet PZ, Rowley MJ, Knowles W, Mackay IR. Antibodies to glutamic acid decarboxylase reveal latent autoimmune diabetes mellitus in adults with a non-insulin-dependent onset of disease. Diabetes. 1993a;42(2):359–62. Epub 1993/02/01CrossRefGoogle Scholar
  245. Tuomi T, Groop LC, Zimmet PZ, Rowley MJ, Knowles W, Mackay IR. Antibodies to glutamic acid decarboxylase reveal latent autoimmune diabetes mellitus in adults with a non-insulin-dependent onset of disease. Diabetes. 1993b;42(2):359–62.CrossRefGoogle Scholar
  246. Unoki H, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet. 2008;40(9):1098–102.CrossRefGoogle Scholar
  247. van den Ouweland JM, Lemkes HH, Ruitenbeek W, Sandkuijl LA, de Vijlder MF, Struyvenberg PA, et al. Mutation in mitochondrial tRNA(Leu)(UUR) gene in a large pedigree with maternally transmitted type II diabetes mellitus and deafness. Nat Genet. 1992;1(5):368–71.CrossRefGoogle Scholar
  248. Visscher PM, Hill WG, Wray NR. Heritability in the genomics era–concepts and misconceptions. Nat Rev Genet. 2008;9(4):255–66. Epub 2008/03/06CrossRefPubMedGoogle Scholar
  249. Viswanath K, McGavin DD. Diabetic retinopathy: clinical findings and management. Community Eye Health/Int Centre Eye Health. 2003;16(46):21–4.Google Scholar
  250. Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010;42(7):579–89.PubMedCentralCrossRefPubMedGoogle Scholar
  251. Volkov P, Bacos K, Ofori JK, Esguerra JL, Eliasson L, Ronn T, et al. Whole-genome bisulfite sequencing of human pancreatic islets reveals novel differentially methylated regions in type 2 diabetes pathogenesis. Diabetes. 2017;66(4):1074–85.CrossRefGoogle Scholar
  252. von Muhlendahl KE, Herkenhoff H. Long-term course of neonatal diabetes. N Engl J Med. 1995;333(11):704–8.CrossRefGoogle Scholar
  253. Wang F, Fang Q, Yu N, Zhao D, Zhang Y, Wang J, et al. Association between genetic polymorphism of the angiotensin-converting enzyme and diabetic nephropathy: a meta-analysis comprising 26,580 subjects. J Renin-Angiotensin-Aldosterone Syst. 2012;13(1):161–74. Epub 2011/08/04CrossRefGoogle Scholar
  254. Wang YJ, Schug J, Won KJ, Liu C, Naji A, Avrahami D, et al. Single-cell transcriptomics of the human endocrine pancreas. Diabetes. 2016;65(10):3028–38.PubMedCentralCrossRefPubMedGoogle Scholar
  255. Weedon MN, et al. Meta-analysis and a large association study confirm a role for calpain-10 variation in type 2 diabetes susceptibility. Am J Hum Genet. 2003;73(5):1208–12.PubMedCentralCrossRefPubMedGoogle Scholar
  256. Wei WH, Hemani G, Haley CS. Detecting epistasis in human complex traits. Nat Rev Genet. 2014;15(11):722–33. Epub 2014/09/10CrossRefGoogle Scholar
  257. Wellcome Trust Case Control C. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–78.CrossRefGoogle Scholar
  258. Wellcome Trust Case Control C, Craddock N, Hurles ME, Cardin N, Pearson RD, Plagnol V, et al. Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature. 2010;464(7289):713–20.CrossRefGoogle Scholar
  259. White AJ, Sandler DP, Bolick SC, Xu Z, Taylor JA, DeRoo LA. Recreational and household physical activity at different time points and DNA global methylation. Eur J Cancer. 2013;49(9):2199–206.PubMedCentralCrossRefPubMedGoogle Scholar
  260. Willer CJ, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40(2):161–9.PubMedCentralCrossRefPubMedGoogle Scholar
  261. Williams MA, Qiu C, Dempsey JC, Luthy DA. Familial aggregation of type 2 diabetes and chronic hypertension in women with gestational diabetes mellitus. J Reprod Med. 2003;48(12):955–62.Google Scholar
  262. Williams WW, Salem RM, McKnight AJ, Sandholm N, Forsblom C, Taylor A, et al. Association testing of previously reported variants in a large case-control meta-analysis of diabetic nephropathy. Diabetes. 2012;61(8):2187–94. Epub 2012/06/23PubMedCentralCrossRefPubMedGoogle Scholar
  263. Winckler W, et al. Association of common variation in the HNF1alpha gene region with risk of type 2 diabetes. Diabetes. 2005a;54(8):2336–42.CrossRefGoogle Scholar
  264. Winckler W, et al. Association testing of variants in the hepatocyte nuclear factor 4alpha gene with risk of type 2 diabetes in 7,883 people. Diabetes. 2005b;54(3):886–92.CrossRefGoogle Scholar
  265. Wolf JB, Hager R. A maternal-offspring coadaptation theory for the evolution of genomic imprinting. PLoS Biol. 2006;4(12):e380.PubMedCentralCrossRefPubMedGoogle Scholar
  266. Writing Team for the Diabetes C, Complications Trial/Epidemiology of Diabetes I, Complications Research G. Effect of intensive therapy on the microvascular complications of type 1 diabetes mellitus. JAMA. 2002;287(19):2563–9. Epub 2002/05/22CrossRefGoogle Scholar
  267. Writing Team for the Diabetes C, Complications Trial/Epidemiology of Diabetes I, Complications Research G. Sustained effect of intensive treatment of type 1 diabetes mellitus on development and progression of diabetic nephropathy: the Epidemiology of Diabetes Interventions and Complications (EDIC) study. JAMA. 2003;290(16):2159–67. Epub 2003/10/23CrossRefGoogle Scholar
  268. Wu P, Farrell WE, Haworth KE, Emes RD, Kitchen MO, Glossop JR, et al. Maternal genome-wide DNA methylation profiling in gestational diabetes shows distinctive disease-associated changes relative to matched healthy pregnancies. Epigenetics. 2018;13(2):122–128.  https://doi.org/10.1080/15592294.2016.1166321.CrossRefGoogle Scholar
  269. Xin Y, Kim J, Okamoto H, Ni M, Wei Y, Adler C, et al. RNA sequencing of single human islet cells reveals type 2 diabetes genes. Cell Metab. 2016;24(4):608–15.CrossRefGoogle Scholar
  270. Yamauchi T, et al. A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A-C2CD4B. Nat Genet. 2010;42(10):864–8.CrossRefGoogle Scholar
  271. Yasuda K, et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat Genet. 2008;40(9):1092–7.CrossRefGoogle Scholar
  272. Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35(3):556–64. Epub 2012/02/04PubMedCentralCrossRefPubMedGoogle Scholar
  273. Young BC, Ecker JL. Fetal macrosomia and shoulder dystocia in women with gestational diabetes: risks amenable to treatment? Curr Diab Rep. 2013;13(1):12–8.CrossRefGoogle Scholar
  274. Young AI, Wauthier F, Donnelly P. Multiple novel gene-by-environment interactions modify the effect of FTO variants on body mass index. Nat Commun. 2016;7:12724.PubMedCentralCrossRefPubMedGoogle Scholar
  275. Zeggini E, et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007;316(5829):1336–41.PubMedCentralCrossRefPubMedGoogle Scholar
  276. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40(5):638–45.PubMedCentralCrossRefPubMedGoogle Scholar
  277. Zhao T, Zhao J. Association between the -634C/G polymorphisms of the vascular endothelial growth factor and retinopathy in type 2 diabetes: a meta-analysis. Diabetes Res Clin Pract. 2010;90(1):45–53. Epub 2010/07/02CrossRefGoogle Scholar
  278. Zheng Y, Wang Z, Zhou Z. miRNAs: novel regulators of autoimmunity-mediated pancreatic beta-cell destruction in type 1 diabetes. Cell Mol Immunol. 2017;14(6):488–96.PubMedCentralCrossRefPubMedGoogle Scholar
  279. Zhernakova A, van Diemen CC, Wijmenga C. Detecting shared pathogenesis from the shared genetics of immune-related diseases. Nat Rev Genet. 2009;10(1):43–55. Epub 2008/12/19CrossRefGoogle Scholar
  280. Zullo A, Sommese L, Nicoletti G, Donatelli F, Mancini FP, Napoli C. Epigenetics and type 1 diabetes: mechanisms and translational applications. Transl Res. 2017;185:85–93. Epub 2017/05/30CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Clinical Sciences, Diabetes and Endocrinology UnitLund University Diabetes CentreMalmoSweden
  2. 2.Finnish Institute of Molecular MedicineHelsinki UniversityHelsinkiFinland

Personalised recommendations