International Journal of Endocrinology

International Journal of Endocrinology / 2014 / Article

Review Article | Open Access

Volume 2014 |Article ID 593982 |

Noraidatulakma Abdullah, John Attia, Christopher Oldmeadow, Rodney J. Scott, Elizabeth G. Holliday, "The Architecture of Risk for Type 2 Diabetes: Understanding Asia in the Context of Global Findings", International Journal of Endocrinology, vol. 2014, Article ID 593982, 21 pages, 2014.

The Architecture of Risk for Type 2 Diabetes: Understanding Asia in the Context of Global Findings

Academic Editor: Mario Maggi
Received24 Sep 2013
Accepted30 Jan 2014
Published13 Mar 2014


The prevalence of Type 2 diabetes is rising rapidly in both developed and developing countries. Asia is developing as the epicentre of the escalating pandemic, reflecting rapid transitions in demography, migration, diet, and lifestyle patterns. The effective management of Type 2 diabetes in Asia may be complicated by differences in prevalence, risk factor profiles, genetic risk allele frequencies, and gene-environment interactions between different Asian countries, and between Asian and other continental populations. To reduce the worldwide burden of T2D, it will be important to understand the architecture of T2D susceptibility both within and between populations. This review will provide an overview of known genetic and nongenetic risk factors for T2D, placing the results from Asian studies in the context of broader global research. Given recent evidence from large-scale genetic studies of T2D, we place special emphasis on emerging knowledge about the genetic architecture of T2D and the potential contribution of genetic effects to population differences in risk.

1. Introduction

Type 2 diabetes (T2D) is one of the top five noncommunicable diseases globally, comprising a major, growing cause of morbidity and premature death. In 2012, the International Diabetes Federation (IDF) estimated that 371 million people worldwide were living with diabetes, of which about half live in South Asia, the Western Pacific, and Eastern Mediterranean regions [1]. Asia is now the epicenter of an escalating diabetes epidemic, chiefly due to population growth and ageing in India and China. Projections suggest that by 2030, more than 60% of worldwide diabetes cases will come from Asia [2, 3], with the vast majority of these being Type 2 diabetes (T2D) [4]. T2D has an enormous economic, psychosocial, and physical impact on individuals, their families, and communities, both directly and indirectly. The direct economic burden of T2D includes both recorded expenditure by health services and unrecorded costs borne by patients and their families. Indirect costs such as loss of productivity and disability are also substantial and may match or surpass direct costs. The proportion of worldwide disability-adjusted life years (DALYs) due to T2D has soared in recent decades, rising from 43% in 1990 to 54% in 2010 [5]. Temporary and permanent disability, excess morbidity, and premature death are the consequences of T2D vascular complications, including cardiovascular disease, retinopathy (blindness), nephropathy (kidney failure), and neuropathy (nerve problems) which can lead to amputation. Intangible costs due to psychosocial effects on quality of life, diminished contribution to family tasks, and reduced income of care-giving family members are also likely substantial but difficult to assess. The enormous, growing global burden of T2D—particularly in Asia—is now viewed as a crisis by the World Health Organisation (WHO) and the United Nations (UN) [6]. There is a major worldwide push to decrease the prevalence and impact of T2D by identifying risk factors, both genetic and nongenetic. Explaining the distribution and variation of T2D susceptibility across Asia will be vital for reducing the global burden of disease, due to the demographic, cultural, and genetic heterogeneity of Asian populations, and T2D risk factor profiles between these populations [710].

2. Epidemiology

2.1. Burden of the Disease

The vast majority of T2D (about 80%) occurs in low- and middle-income countries (LMICs), with India and China providing the largest absolute contributions. The prevalence of T2D is also rising most swiftly in LMICs [6], particularly in Asian countries experiencing rapid economic growth (Figure 2). However, there are disparities in T2D prevalence among Asian populations; Asians from the Indian subcontinent (India, Pakistan and Bangladesh) have the highest prevalence (15.9% to 24.9%), with intermediate prevalence in Malays (11.4% to 16.9%) and reduced prevalence in Chinese (6.4% to 13.8%) [1113]. These risk profile differences may reflect population differences in T2D risk due to ethnicity-specific diet and lifestyle, body composition, genetic effects, or gene-environment interactions, as discussed further in the sections below.

2.2. Pathophysiology

The pathogenesis of Type 2 diabetes (T2D) involves deficient insulin secretion by pancreatic β-cells, and diminished insulin effectiveness in target tissues (insulin resistance) T2D aetiology differs from that of Type 1 diabetes (T1D), in which there is absolute insulin deficiency due to the destruction of insulin-producing β-cells [14]. T2D represents 90% of all diabetes cases worldwide [4]. Impaired insulin secretion and insulin action led to an accumulation of glucose in the blood (hyperglycaemia), with adverse effects on health. Clinical features of hyperglycaemia and T2D include excessive excretion of urine (polyuria), thirst (polydipsia), constant hunger, weight loss, vision change, and fatigue [15]. These symptoms may occur suddenly but are often less marked, and T2D patients may be unaware of their illness for several years until further complications develop.

2.2.1. Insulin Resistance

Glucose homeostasis depends upon a highly regulated feedback system comprising both insulin-secreting β-cells and insulin-sensitive target tissues. The function of either component—while accounting for the associated homeostatic response of the other—can be evaluated using Homeostasis Model Assessment (HOMA) [16]. Studies assessing insulin resistance using HOMA (HOMA-IR) report continental differences in the relative contribution of insulin deficiency versus insulin resistance to T2D. Compared to healthy European-ancestry participants matched for age and body mass index (BMI), Asian Indian individuals exhibit higher insulin resistance [17] and a greater contribution of insulin resistance—relative to insulin secretion—to T2D pathogenesis [18]. One study evaluating insulin response to a fixed glucose load also showed that Japanese-Americans displayed an insulin response more similar to native Japanese than European-Americans, in spite of sharing a highly Westernized lifestyle with their European-American counterparts [8].

There is also variation in the predisposition to insulin resistance between Asian populations [19]. For several decades, it has been recognised that the highest propensity is present in Asian Indians, in whom insulin resistance contributes substantially to T2D pathogenesis [20], potentially reflecting ancestry-related predisposition to abdominal obesity [21, 22]. A recent population based study of 4,136 Chinese, Malays, and Asian Indians living in Singapore supported these findings, reporting substantially higher insulin resistance in Asian Indians, intermediate levels in Malays, and the lowest levels in Chinese () [19]. Differences between Malays and Chinese were removed after adjusting for body mass index (BMI); the remaining additional resistance in Indians appeared to be mediated by a tendency to higher BMI and BMI-adjusted waist circumference, together with other unexplained factors [19].

Dickinson and colleagues studied postprandial hyperglycemia and insulin sensitivity after a 75 gram carbohydrate challenge in 60 lean, healthy individuals from five ethnic groups with similar age, BMI, waist circumference, and birth weight distributions. Prior to carbohydrate consumption, fasting insulin was significantly higher in South Indians and South East Asians, compared to European Caucasian, Arabic, and Chinese individuals () [23]. Following the challenge, hyperglycemia was significantly higher in South East Asian and Chinese participants compared with European Caucasians, while Indians and South East Asians showed a 2-3-fold higher insulin response than Europeans [23]. A small Singapore-based study of 30 individuals also showed significantly reduced insulin sensitivity in South Indians compared with Chinese or European individuals matched for age, BMI, and physical activity [24].

2.2.2. Insulin Secretion

Impaired insulin secretion is associated with β-cell dysfunction that results in a reduced insulin-secretion response to rises in blood glucose after eating [25]. The insulin secretion response to various foods can be quantified using the insulin index; more complex relationship between insulin secretion and insulin sensitivity can be measured using the disposition index (DI), which is assessed by an intravenous glucose tolerance-test [26]. A recent family-based study found that a high-fat, low-carbohydrate dietary pattern contributed to obesity, insulin resistance, and reduced β-cell function [27]. This finding might be explained by increased free fatty acids (FFAs) reducing the expression of β-cell—specific transcription factors and impairing the β-cells’ ability to respond to glucose with appropriate insulin secretion [28].

Similar to insulin resistance, insulin secretion also shows evidence of racial differences, being reduced in Asians compared with Europeans. The insulin index of Asians is reduced almost 70% in the progression from impaired glucose tolerance (IGT) to T2D, whereas in Europeans the corresponding reduction is only 50% [29, 30]. A population based-cohort study of insulin resistance and β-cell function during pregnancy also found a significantly lower β-cell secretory response to pregnancy-induced insulin resistance in South Asian and East Asian women, compared to European participants with a similar level of insulin resistance [31].

2.2.3. Complications

T2D complications can be life-threatening and include cardiovascular disease, nephropathy (kidney disease), retinopathy (blindness), and neuropathy (nerve impairment). Observational studies in European American and African American population report that cardiovascular disease risk in individuals with T2D is more than double the rate in the general population [32] and 50% of people with T2D die from cardiovascular disease, primarily heart disease and stroke [33].

There is evidence for population differences in the rate of T2D complications, between Asian populations and broader continental groups. A cross-sectional study of 5,707 Chinese, Indians, and Malays showed that the population attributable risk of ischaemic heart disease related to T2D was the highest in Indians (40.9%), intermediate in Malays (27.9%), and the lowest in Chinese (11%) [34]. A cohort study found that the progression of kidney dysfunction in T2D was faster in Indo-Asian (Indian, Pakistani, and Bangladeshi) subjects - with an estimated 2-3-fold increase in the mean rate of rise of serum creatinine over a constant follow-up period—compared to European-ancestry subjects [35]. The prevalence of diabetic end stage renal disease (ESRD) has also been reported as significantly higher in Asian T2D subjects (52.6%) compared to Caucasians (36.2%) [36].

Another microvascular complication of T2D, diabetic retinopathy, represents about five percent of all cases of global blindness [37]. Visual impairment occurs as a result of long-term, accumulated damage to small blood vessels in the retina. A recent cross-sectional study conducted by The Diabetic Retinopathy in Various Ethnic groups in UK (DRIVE UK) found that South Asian T2D populations have significantly higher prevalence of diabetic retinopathy (42.3% versus 38%) and sight threatening diabetic retinopathy (10.3% versus 5.5%) compared to white Europeans [38].

Combined with reduced blood flow, neuropathy (nerve damage) in the feet increases the risk of foot ulcers, infections, poor wound healing, and poor distal circulation, eventually increasing the risk of limb amputation [39]. Due to the elevated risk of these life-threatening complications, mortality risk among people with diabetes is at least double that of individuals without diabetes [40].

2.3. Conventional Risk Factors

A range of lifestyle and clinical factors contribute to risk of insulin resistance and T2D, including elevated body mass index (BMI), high waist-to-hip ratio (WHR), physical inactivity, and diet (Figure 1).

2.3.1. Body Mass Index (BMI) and Obesity

According to the World Health Organisation (WHO), body mass index (BMI) is a simple index of weight-for-height that can be widely used to classify overweight and obesity in adults [41]. It is defined as a person’s weight in kilograms divided by the square of their height in meters (kg/m2). Individuals with BMI greater than or equal to 30 kg/m2 are classified as obese for international standardised comparison. Obesity elevates serum fatty acid concentrations, reducing glucose uptake and increasing fatty acid uptake by the liver, skeletal muscle, and pancreatic β-cells. Reduced glucose uptake elevates serum glucose, stimulating further insulin secretion; it is the lack of response to this secreted insulin that induces insulin resistance [42]. Continually high insulin secretion in turn produces metabolic stress in pancreatic β-cell mitochondria, inducing the release of reactive oxygen species that damage mitochondria. Over time, mitochondria lose their ability to maintain cellular processes and β-cells undergo apoptosis, irreversibly reducing insulin secretion potential [43].

Associations between BMI, percentage of body fat, and body fat distribution differ across populations, influencing the thresholds at which T2D risk increases. Asian T2D patients have lower average BMI compared to European patients [44], which might reflect higher percentage body fat in Asians (3–5% higher) than Europeans for a given BMI [45, 46]. Similarly, for a fixed body fat percentage, Asians have a 3 to 4 unit lower BMI than Europeans [45]. The body fat percentage is also different between Asian groups; for fixed BMI, it tends to be the highest in Indians, followed by Malays and Chinese [47]. One study also showed that among Asians, Indians have the highest prevalence of obesity (35.8% (95% CI: 32.4–39.3)), followed by Malays (32.0% (95% CI: 30.6–33.4)) and Chinese (19.7 (95% CI: 17.9–21.6)) [13]. However, due to differences in body composition, recent studies have shown that waist circumference (WC) measurement or waist-to-hip ratio (WHR) is a better predictor of T2D in Asian populations than simple BMI or body fat percentage [48, 49], since these latter measures are insensitive to differences in body fat distribution.

2.3.2. Abdominal Obesity (High Waist-to-Hip Ratio/High Waist Circumference)

High waist-to-hip ratio (WHR) and waist circumference (WC), or abdominal obesity, is a major cause of insulin resistance since subcutaneous abdominal adipocytes drain their lipolytic products (free fatty acids) directly into the portal vein [50]. These free fatty acids are thought to decrease hepatic clearance of insulin and worsen systemic hyperinsulinemia [51], a precursor to T2D. Additional factors such as reduced secretion of adiponectin by adipose tissue may also contribute to the insulin-resistant state in individuals with abdominal obesity [52]. Adiponectin is an adipose tissue-specific protein that controls a number of metabolic processes, including insulin sensitivity and fatty acid oxidation [53].

The prevalence of abdominal obesity differs between ancestral groups and seems particularly marked in certain ethnic populations such as Native Americans, African-Americans, Asians, and Pacific Islanders [5456]. The Multi-Ethnicity Study of Atherosclerosis found that for a given waist circumference, Chinese have the highest diabetes incidence, followed by Hispanic, African, and European-ancestry individuals [57], a finding that may be explained by higher levels of visceral adipose tissue (VAT) in Chinese compared with Europeans, at a fixed waist circumference [58]. The same study also found that South Asians have substantially higher visceral adipose tissue compared to Europeans for given waist circumference [58]. This might explain increased lipid and insulin levels observed in South Asians compared with Europeans at the same WC and/or WHR [59]. Such differences are apparent not only between Asian and other continental populations, but also among Asian populations. Among three major Asian groups, the prevalence of abdominal obesity seems to be significantly higher among Indians (61.8% (95% CI: 58.3–65.2)) compared with Malays (45.3% (95% CI: 43.8–46.8) or Chinese (40.4% (95% CI: 38.0–42.7)) [13].

2.3.3. Diet and Physical Activity

The increasing global prevalence of T2D parallels escalating obesity rates resulting from reduced physical activity, increased intake of total calories, saturated fat (especially in fast food), and sugar-sweetened beverages (SSBs) in many societies. Asian populations are undergoing a nutrition transition in conjunction with the increasing adoption of Westernized lifestyles. In India and China, for example, caloric intake from animal fat has almost doubled in recent decades [60, 61]. High consumption of red and processed meat, SSBs, and refined grains with associated low consumption of cruciferous and yellow vegetables is strongly associated with increased in T2D [62]. At the same time, physical activity has reduced in Asian populations due to rapid urbanization and modernization [63, 64], further increasing T2D risk.

2.3.4. Metabolic Features

Metabolic features including elevated blood pressure, hyperglycaemia, and hyperlipidaemia increase T2D risk by several-fold [65]. A recent multi-ethnic population-based survey indicated population differences in the prevalence of metabolic syndrome features, irrespective of T2D status. Indians seem to have higher levels of triglycerides and hyperglycaemia and lower HDL cholesterol, compared with Malay and Chinese [66]. These findings parallel those of a case-control study in which Indians from UK and Indians from India had higher total insulin and triglycerides and lower HDL cholesterol compared to European individuals, irrespective of shared environmental influences [22].

2.3.5. Other Factors

Other factors that have been associated with T2D risk include short sleep duration [67, 68], increasing age, which may reflect reduced exercise and muscle mass [14], history of gestational diabetes, polycystic ovary syndrome, severe mental illness, and having a family history of the disease [54]. A recent randomized, crossover study found that sleep deprivation impairs peripheral metabolic pathways, thereby reducing insulin sensitivity [69]. The loss of skeletal muscle mass with age, or sarcopenia, is also related to insulin resistance, with sarcopenia thought to cause insulin resistance and thereby increase risk of diabetes [70]. In turn, insulin resistance results in further loss of muscle strength [71]. Finally, patients with severe mental illness such as schizophrenia or bipolar disorder have 3-fold higher risk of developing T2D compared to the general population; this may result from underlying lifestyle factors, adverse effects of pharmacotherapy, and possible common genetic and/or environmentally linked pathophysiologic processes [72].

2.4. Genetic Susceptibility

In addition to conventional risk factors, family, twin and genetic studies show that T2D susceptibility has a substantial genetic component [73]. Full siblings of T2D probands have a 30–60% increased risk of disease, compared with the general population [74, 75] and children with one affected parent have a 40% lifetime risk of developing T2D, which rises to almost 70% if both parents are affected [76]. Twin studies also show higher T2D concordance in monozygotic (60–70%) compared with dizygotic twins (20–30%) [7779].

The proportion of trait variance due to additive genetic effects is termed “heritability” and can be formally estimated from twin studies. Twin study heritability estimates are on the order of 30–70% for T2D and about 60% for impaired glucose tolerance (IGT) [80, 81]. Twin studies also demonstrate a substantial genetic component for quantitative phenotypes related to glucose homeostasis, with heritability estimates of 75–85% for in vivo insulin secretion, ~50% for peripheral insulin sensitivity, and ~50% for glucose metabolism [82].

Population differences in T2D pathophysiologic and risk factor profiles have been discussed in previous sections. It has been suggested that such differences may partly reflect population differences in the frequency of particular genetic risk factors and/or population-specific interactions between genetic and environmental factors [83].

2.5. Methods of Gene Identification for Common Complex Disease

Observed patterns of T2D inheritance, combined with the results of recent large-scale genetic studies, suggest that the genetic component of T2D is complex, involving multiple genetic variants of individually small effect (polygenic model) [84]. There have been three main approaches employed to identify genetic risk variants for such common complex disorders: linkage studies, candidate gene association studies (CGAS), and, more recently, genome-wide association studies (GWAS).

2.5.1. Linkage Studies

Familial linkage studies seek to identify broad genomic regions harboring disease risk variants by tracking disease and genetic marker segregation through multiple generations of families. Familial linkage studies are challenging for disorders with advanced age at onset, as parents may no longer be alive. Further challenges include difficulty in collecting accurate genealogical information and genetic (locus) heterogeneity, meaning that a particular risk locus contributes to disease in only a subset of families [85]. More broadly, this approach is limited by low power for common variants of small effect [86] and its inability to precisely localise underlying risk variants [87]. Earlier linkage studies found four (4) genetic loci linked with T2D; CAPN10 [88], ENPP1 [89], HNF4A [90], and ACDC (ADIPOQ) [91]. However, only the HNF4A locus has been confirmed by recent large-scale genome-wide association studies (GWAS) [92].

HNF4A, together with the related locus, HNF1A and also GCK also account for up to 80% of rare monogenic forms of diabetes. These diabetes cases present as familial, young onset, noninsulin dependent diabetes mellitus (maturity onset diabetes of the young or MODY) and are inherited in a Mendelian dominant pattern [75]. Unlike common polygenic forms of T2D, these monogenic forms require only one pathogenic genetic variant to produce disease.

2.5.2. Candidate Gene Association Studies (CGAS)

In contrast to linkage studies, the candidate gene approach searches for associations between common genetic variants and disease, restricting the search region to prespecified genes of interest. Candidate genes are typically selected based on a priori knowledge or hypotheses reflecting the gene’s presumed biological role in disease [93]. The most common study design is the case control study; for a particular genetic variant, this involves comparing the frequency of genetic alleles between individuals with and without disease, aiming to identify alleles that are associated with disease status [87]. Although a mainstay of the initial era of disease gene mapping, the candidate gene approach has been limited by small sample sizes, restriction of hypotheses to known biology, and an inability to replicate many results [94]. While candidate gene studies have reported numerous variants as beeing associated with T2D [75], just three loci, PPARG [95], KCNJ11 [96], and TCF7L2 [97], have been robustly confirmed by recent GWAS [74, 98, 99]. We note that the TCF7L2 association study was informed by prior genome-wide linkage study showing suggestive linkage between T2D and the 10q genomic region harboring TCF7L2 [97].

2.5.3. Genome-Wide Association Studies (GWAS)

Within the last five years, genome-wide association studies (GWAS) have emerged as the method of choice for identifying common genetic variants associated with complex disease. GWAS were facilitated by completion of the Human Genome Project in 2003, the International HapMap Project in 2005 that catalogued millions of common single nucleotide polymorphisms (SNPs), and the parallel development of high throughput genotyping arrays. Single nucleotide polymorphisms (SNPs) are DNA sequence variants in which a single nucleotide differs between individuals. SNPs have a low historical mutation rate, are amenable to high throughput genotyping, and are distributed abundantly across all 22 autosomes and the sex chromosomes. Typically several million variants are screened genome-wide; appropriate adjustment of the prespecified significance level is thus necessary to avoid an increase in false positive results. Based on patterns of human genomic correlation, Bonferroni correction for one million independent tests is the accepted approach, with variants required to reach a pointwise value < 5 × 10−8 (or 0.05/1,000,000) to be declared “genome-wide significant” [100]. Due to this stringent significance level, very large sample sizes are necessary to identify associations of modest effect, which is often achieved via international collaboration and the formation of consortia. The existence of such collaborations also facilitates rapid replication of findings in independent samples, a requirement for publication.

It has been eight years since the first notable GWAS finding in 2005, identifying a common allele of large effect associated with age-related macular degeneration. The year 2007 was coined the “Year of GWAS”, due to the explosion of GWAS publications in that year. From 2005 to September 2013, there have been more than 1,600 GWAS published reports for a range of human diseases and traits, with an online catalogue established by the National Human Genome Research Institute at the US National Institutes of Health to collate major findings ( Although complicated and costly, GWAS have successfully identified thousands of genetic loci associated with common complex diseases under the common disease common variant (CDCV) hypothesis.

2.6. Genome-Wide Association Studies of T2D

The first T2D GWAS was published in 2007 [99], and as of September 2013, there were more than 40 publications on T2D and its complications listed in the online catalogue of published genome-wide association studies ( At the time of writing, the catalogue describes over 100 individual SNPs showing genome-wide significant association ( < 5 × 10−8) with T2D and related metabolic traits across diverse populations (Table 1) and over 60 SNPs showing suggestive association ( < 1 × 10−5) (Table 2). This section will provide a review of T2D GWAS findings to date.

NumberSNP (allele)1,2Mapped gene(s)Region3Disc4 PopRep5 PopRAF6 OR (95% CI) value

1rs10923931 (T) [109]NOTCH2 1p12EUR0. (1.08–1.17)
2rs340874 (C) [155]LINC00538-PROX1 1q32.3EUR0.520.560.320.550.08NR
3rs243021 (A) [116]FLJ30838-MIR4432 2p16.1EUREURNR0.480.640.480.451.08 (1.06–1.10)
4rs7578597 (T) [109]THADA 2p21EUR0.900.881.000.830.671.15 (1.10–1.20)
5rs780094 (C) [155]GCKR 2p23.3EUR0.620.610.430.800.88NR
6rs7560163 (C) [148]RND3-FABP5P10 2q23.3AA0.861.000.850.960.891.33 (1.19–1.49)
7rs7593730 (C) [118]RBMS1 2q24.2EUR0.780.830.810.790.591.11 (1.08–1.16)
8rs3923113 (A) [92]EIF3EP3-SNORA70F 2q24.3SA0.740.590.840.760.231.09 (1.06–1.13)
9rs13389219 (C) [131] GRB14-COBLL1 2q24.3EUR0.600.560.790.151.07 (1.05–1.10)
10rs560887 (C) [155]G6PC2 2q31.1EUR0.700.670.980.851.00NR
11rs7578326 (A) [116]LOC646736 2q36.3EURNR0.650.870.850.601.11 (1.08–1.13)
12rs2943641 (C) [115]NYAP2-MIR5702 2q36.3EUREUR0.630.630.940.740.761.19 (1.13–1.25)
13rs4607103 (C) [109]ADAMTS9-AS2 3p14.1EUR0.760.810.560.480.711.09 (1.06–1.12)
14rs831571 (C) [127]PSMD6-PRICKLE2-AS1 3p14.1EA0.610.770.650.830.841.09 (1.06–1.12)
15rs11708067 (A) [155]ADCY5 3q21.1EUR0.780.770.990.770.89NR
16rs11920090 (T) [155]SLC2A2 3q26.2EUR0.870.860.980.68NR
17rs1470579 (C) [116]IGF2BP2 3q27.2EURSA, EA, PSNR0.300.240.500.861.14 (1.09–1.19)
18rs6769511 (C) [114]IGF2BP2 3q27.2EA0.320.300.240.500.841.23 (1.15–1.31)
19rs4402960 (T) [98]IGF2BP2 3q27.2EUREA0.300.300.220.490.541.14 (1.11–1.18)
20rs16861329 (G) [92]ST6GAL1 3q27.3SA0.750.880.800.760.941.09 (1.06–1.12)
21rs1801214 (T) [116]WFS1 4p16.1EURNR0. (1.08–1.18)
22rs4689388 (T) [115]WFS1 4p16.1EUREUR0.570.670.970.680.721.16 (1.10–1.21)
23rs7656416 (C) [138]CTBP1-AS1-MAEA 4p16.3EA0.680.700.901.15 (1.10–1.21)
24rs6815464 (C) [127]MAEA 4p16.3EA0.580.520.921.13 (1.10–1.16)
25rs459193 (G) [131]ANKRD55-MAP3K1 5q11.2EUR0.700.780.510.641.08 (1.05–1.11)
26rs4457053 (G) [116]ZBED3-AS1 5q13.3EURNR0.260.041.08 (1.06–1.11)
27rs1535500 (T) [127]KCNK16; KCNK17 6p21.2EA0.420.470.440.951.08 (1.05–1.11)
28rs9470794 (C) [127]ZFAND3 6p21.2EA0.270.120.340.140.191.12 (1.08–1.16)
29rs10440833 (A) [116]CDKAL1 6p22.3EUREURNR0.250.390.211.25 (1.20–1.31)
30rs4712523 (G) [144]CDKAL1 6p22.3EAEUR0.410.340.410.240.681.27 (1.21–1.33)
31rs10946398 (C)[74]CDKAL1 6p22.3EUR0.320.340.400.220.671.16 (1.10–1.22)
32rs6931514 (G) [109]CDKAL1 6p22.3EURNR0. (1.17–1.33)
33rs7754840 (C) [103]CDKAL1 6p22.3EUREA0.310.340.400.220.671.12 (1.08–1.16)
34rs7756992 (G) [170]CDKAL1 6p22.3EUR0.260.280.500.240.581.20 (1.13–1.27)
35rs7766070 (A) [171]CDKAL1 6p22.3EUR0.270.250.400.191.21 (1.14–1.28)
36rs1048886 (G) [124]C6orf57 6q13SEA (I) (1.32–1.80)
37rs4607517 (A) [155]GCK-YKT6 7p13EUR0.
38rs849134 (A) [116]JAZF1 7p15.1EURNR0.540.810.771.13 (1.09–1.18)
39rs864745 (T) [109]JAZF1 7p15.1EUR0.500.490.770.790.771.10 (1.07–1.13)
40rs2191349 (T) [155]DGKB-AGMO 7p21.2EUR0.520.480.710.58NR
41rs10229583 (G) [172] FSCN3-PAX4 7q32EA0.830.740.820.660.721.14 (1.09–1.19)
42rs6467136 (G) [127]ZNF800-GCC1 7q32.1EA0.790.500.760.570.751.11 (1.07–1.14)
43rs972283 (G) [116]KLF14-MIR29A 7q32.3EURNR0.550.670.941.07 (1.05–1.10)
44rs516946 (C) [131]ANK1 8p11.21EUR0.760.810.820.850.881.09 (1.06–1.12)
45rs515071 (G) [138]ANK1; MIR486 8p11.21EA0.790.810.790.850.851.18 (1.12–1.25)
46rs896854 (T) [116]TP53INP1 8q22.1EURNR0.440.320.400.751.06 (1.04–1.09)
47rs3802177 (G) [116]SLC30A8 8q24.11EURNR0.760.530.780.941.15 (1.10–1.21)
48rs11558471 (A) [155]SLC30A8 8q24.11EUR0.310.750.530.780.94NR
49rs13266634 (C) [144]SLC30A8 8q24.11EUREA0.570.760.530.780.941.22 (1.16–1.28)
50rs10965250 (G) [116]CDKN2B-AS1-DMRTA1 9p21.3EUREURNR0.800.581.001.20 (1.13–1.27)
51rs1333051 (A) [149]CDKN2B-AS1-DMRTA1 9p21.3HISNR0.840.850.930.911.22 (1.15–1.30)
52rs2383208 (A) [144]CDKN2B-AS1-DMRTA1 9p21.3EAEA, SA0.550.790.590.910.871.34 (1.27–1.41)
53rs10811661 (T) [98]CDKN2B-AS1-DMRTA1 9p21.3EUR0.850.800.560.910.981.20 (1.14–1.25)
54rs7018475 (G) [173]*CDKN2B-AS1-DMRTA1 9p21.3EURNR0.270.370.380.221.35 (1.18–1.56)
55rs17584499 (T) [117]PTPRD 9p24.1EA0. (1.36–1.82)
56rs10814916 (C) [146]GLIS3 9p24.2EA0.440.570.450.620.671.11 (1.08–1.15)
57rs7041847 (A) [127]GLIS3 9p24.2EA0.410.550.460.650.961.10 (1.07–1.13)
58rs7034200 (A) [155]GLIS3 9p24.2EUR0.490.540.300.60NR
59rs13292136 (C) [116]KRT18P24-CHCHD2P9 9q21.31EUREURNR0.930.910.901.11 (1.07–1.15)
60rs2796441 (G) [131]TLE1-FAM75D5 9q21.32EUR0.570.610.400.550.901.07 (1.05–1.10)
61rs10906115 (A) [120]CDC123-MIR4480 10p13EA0.570.640.640.580.761.13 (1.08–1.18)
62rs11257655 (T) [146]CDC123-MIR4480 10p13EA0.560.260.580.230.271.15 (1.10–1.20)
63rs12779790 (G) [109]CDC123-MIR4480 10p13EUR0. (1.07–1.14)
64rs1802295 (A) [92]VPS26A 10q22.1SA0.260.350.110.2901.08 (1.05–1.12)
65rs12571751 (A) [131]ZMIZ1 10q22.3EUR0.520.530.550.500.501.08 (1.05–1.10)
66rs1111875 (C) [98]HHEX-EXOC6 10q23.33EUREA0.520.580.340.410.881.13 (1.09–1.17)
67rs5015480 (C) [171]HHEX-EXOC6 10q23.33EUREA0.570.580.210.440.641.18 (1.11–1.23)
68rs10885122 (G) [155]ADRA2A-BTBD7P2 10q25.2EUR0.870.900.920.21NR
69rs4506565 (T) [104]TCF7L2 10q25.2EUR0.320.300.030.300.461.36 (1.20–1.54)
70rs7901695 (C) [74]TCF7L2 10q25.2EURNR0. (1.31–1.43)
71rs7903146 (T) [116]TCF7L2 10q25.2EUREA, SA, AA NR0. (1.34–1.46)
72rs10886471 (C) [146]GRK5 10q26.11EA0.780.480.800.650.901.12 (1.08–1.16)
73rs11605924 (A) [155]CRY2 11p11.2EUR0.490.130.700.120.94NR
74rs7944584 (A) [155]MADD 11p11.2EUR0.750.710.960.781.00NR
75rs5215 (C) [74]KCNJ11 11p15.1EURNR0.400.380.40.011.14 (1.10–1.19)
76rs5219 (T) [98]KCNJ11 11p15.1EUR0.461.14 (1.10–1.19)
77rs163182 (C) [145]KCNQ1 11p15.4EA0.340.250.370.251.28 (NR)
78rs2237895 (C) [117]KCNQ1 11p15.4EA0.331.29 (1.19–1.40)
79rs2237892 (C) [113]KCNQ1 11p15.4EAHIS, EA0.610.920.670.990.901.40 (1.34–1.47)
80rs2237897 (C) [114]KCNQ1 11p15.4EA0.340.951.33 (1.24–1.41)
81rs231362 (G) [116]KCNQ1; KCNQ1OT1 11p15.5EURNR0.520.840.861.08 (1.06–1.10)
82rs174550 (T) [155]FADS1 11q12.2EUR0.640.650.660.98NR
83rs1552224 (A) [116]ARAP1 11q13.4EURNR0.870.910.761.001.14 (1.11–1.17)
84rs1387153 (T) [116]FAT3-MTNR1B 11q14.3EUREURNR0.270.390.380.401.09 (1.06–1.11)
85rs10830963 (G) [155]MTNR1B 11q14.3EUR0.300.300.390.04NR
86rs10842994 (C) [131]KLHDC5 12p11.22EUR0.800.800.790.901.001.10 (1.06–1.13)
87rs1531343 (C) [116]RPSAP52 12q14.3EURNR0. (1.07–1.14)
88rs7961581 (C) [109]TSPAN8-LGR5 12q21.1EUR0. (1.06–1.12)
89rs35767 (G) [155]IGF1 12q23.2EUR0.850.880.650.710.55NR
90rs7957197 (T) [116]OASL 12q24.31EURNR0.851.000.861.07 (1.05–1.10)
91rs7305618 (C) [149]RPL12P33-HNF1A-AS1 12q24.31HISNR0.800.440.750.561.14 (1.09–1.20)
92rs9552911 (G) [152]SGCG 13q12.12PSPS 0.931.000.780.861.001.49 (1.30–1.72)
93rs1359790 (G) [120]NDFIP2-SPRY2 13q31.1EA0.710.730.690.840.921.15 (1.10–1.20)
94rs7403531 (T) [146]RASGRP1 15q14EA0.350.280.330.200.181.10 (1.06–1.13)
95rs7172432 (A) [119]C2CD4A-C2CD4B 15q22.2EA0.580.580.540.610.271.11 (1.08–1.14)
96rs11071657 (A) [155]C2CD4A-C2CD4B 15q22.2EUR0.630.580.690.94NR
97rs7178572 (G) [92]HMG20A 15q24.3SAEUR0.520.680.400.440.40 1.09 (1.06–1.12)
98rs7177055 (A) [131]HMG20A-LINGO1 15q24.3 EUR0.680.710.390.450.241.08 (1.05–1.10)
99rs11634397 (G) [116]ZFAND6-FAH 15q25.1EUREURNR0.640.080.550.411.06 (1.04–1.08)
100rs2028299 (C) [92]AP3S2; C15orf38-AP3S2 15q26.1SA0.310.730.120.780.401.10 (1.07–1.13)
101rs8042680 (A) [116]PRC1; LOC100507118 15q26.1EURNR0.261.000.590.981.07 (1.05–1.09)
102rs11642841 (A) [116]FTO 16q12.2EUREURNR0.470.060.041.13 (1.08–1.18)
103rs8050136 (A) [98]FTO 16q12.2EURSA0.380.460.140.250.451.17 (1.12–1.22)
104rs9939609 (A) [171]FTO 16q12.2EUR0.400.460.150.260.501.25 (1.19–1.30)
105 rs7202877 (T) [131]CTRB2-CTRB1 16q23.1EUR0.890.890.800.950.851.12 (1.07–1.16)
106rs391300 (G) [117]SRR 17p13.3EA0.620.630.750.480.421.28 (1.18–1.39)
107rs4430796 (G) [146]HNF1B 17q12EUREA0.280.510.250.310.661.19 (1.13–1.25)
108rs8090011 (G) [171]LAMA1 18p11.31EUR0.380.320.720.791.13 (1.09–1.18)
109rs12970134 (A) [131]MC4R 18q21.32EUR0. (1.05–1.11)
110rs3786897 (A) [127]PEPD 19q13.11EA0.560.610.580.810.401.10 (1.07–1.14)
111rs6017317 (G) [127]FITM2-R3HDML 20q13.12EA0.480.180.410.591.09 (1.07–1.12)
112rs4812829 (A) [92]HNF4A 20q13.12SA0.290.160.450.290.081.09 (1.06–1.12)
113rs12010175 (G) [146]FAM58A Xq28EA0.790.940.840.810.791.21 (1.14–1.28)
114rs5945326 (A) [116]KRT18P48-DUSP9 Xq28EUREANR0.780.660.841.27 (1.18–1.37)

Risk Allele and RAF not reported, but chosen based on minor allele frequency (MAF) in the population mentioned in the original publication.

NumberSNP (allele)1,2Mapped Gene(s)Region3Disc4 PopRep5 PopRAF6 OR (95% CI) value

1rs7542900 (C) [148]F3-PGBD4P7 1p21.3AA0.560.800.720.790.561.16 (1.09–1.25)
2rs11165354 (A) [151]TGFBR3 1p22.1SAAll SA0.780.620.520.850.281.17 (1.10–1.25)
3rs17045328 (G) [124]CR2 1q32.2SEA (M)0.300.030.350.070.021.38 (1.20–1.59)
4rs6426514 (A) [152]RHOU 1q42.13PS0. (1.27–1.78)
5rs12027542 (A) [124]PCNXL2 1q42.2SEA (M)0.610.930.690.950.951.41 (1.23–1.61)
6rs11677370 (T)[124]DCDC2C 2p25.3SEA (I)0.40.680.710.781.35 (1.19–1.53)
7rs6712932 (C) [174]*MRPS9-GPR45 2q12.1EURNR0.340.220.320.281.52 (1.27–1.82)
8rs6723108 (T) [151]TMEM163-MIR5590 2q21.3SAAll SA0.860.5010.9311.27 (1.17–1.39)
9rs358806 (C) [104]LRTM1-WNT5A 3p14.3EUR0.800.770.840.900.921.16 (1.03–1.33)
10rs13081389 (A) [116]SYN2-GSTM5P1 3p25.2EURNR0.950.981.001.24 (1.15–1.35)
11rs17036101 (G) [109]SYN2-GSTM5P1 3p25.2EUR0.930.950.980.981.15 (1.10–1.21)
12rs1801282 (C) [103]PPARG 3p25.2EUR0.860.900.940.911.001.14 (1.08–1.20)
13rs2063640 (A) [124]ZPLD1-NDUFA4P2 3q12.3SEA (M, C, I) (1.13–1.34)
14rs3773506 (C) [124]PLS1 3q23SEA (I) (1.39–2.35)
15rs7630877 (A) [124]PEX5L 3q26.33 SEA (C)0.170.350.180.360.311.32 (1.17–1.49)
16rs1374910 (T) [149]IGF2BP2 3q27.2HISNR0. (1.15–1.34)
17rs7659604 (T) [104]ANXA5-TMEM155 4q27EUR0.380.440.360.450.711.35 (1.19–1.54)
18rs3792615 (T) [124]36951 4q32.3 SEA (I)0.950.970.840.960.851.93 (1.45–2.59)
19rs10461617 (A) [151]RPL26P19-MAP3K1 5q11.2SA All SA0.210.180.390.260.441.17 (1.09–1.25)
20rs12518099 (C) [115]MIR3660-CETN3 5q14.3EUR0.230.230.390.310.231.16 (1.10–1.22)
21rs17053082 (T) [152]PPIGP1-SGCD 5q33.2PS0. (1.28–1.73)
22rs9472138 (T) [109]VEGFA-C6orf223 6p21.1EUR0. (1.04–1.09)
23rs3916765 (A) [171]MTCO3P1-HLA-DQA2 6p21.32EUR0. (1.12–1.31)
24rs9295474 (G) [124]CDKAL1 6p22.3SEA (M, C, I)0.360.300.410.361.16 (1.09–1.24)
25rs9465871 (C) [104]CDKAL1 6p22.3EUR0.180.160.520.210.581.18 (1.04–1.34)
26rs7769051 (A) [121]SNORA33-HMGB1P13 6q23.2AA0. (1.16–1.42)
27rs642858 (A) [124]ATP5F1P6-MIR3668 6q24.1 SEA (I)0.400.250.400.360.131.35 (1.19–1.53)
28rs6930576 (A) [121]SASH1 6q24.3AA0.280.340.180.440.241.31 (1.18–1.45)
29rs741301 (C) [175]*ELMO1 7p14.2EANR0.320.320.430.672.67 (1.71–4.16)
30rs1525739 (C) [176]*LOC100287613 7p21EURNR0.490.160.270.33NR
31rs7636 (A) [124]ACHE 7q22.1EA0.060.0400.050.331.85 (1.42–2.41)
32rs4527850 (T) [152]SLA-WISP1 8q24.22PS0.750.720.420.690.891.23 (1.13–1.34)
33rs564398 (T) [74]CDKN2B-AS1 9p21.3EUR0.560.570.920.671.001.13 (1.08–1.19)
34rs7020996 (C) [109]CDKN2B-AS1-DMRTA1 9p21.3EURNR0.810.570.741.26 (1.15–1.38)
35rs649891 (C) [150]PTPRD 9p23MA0.350.200.730.430.79NR
36rs10993738 (C) [138]SYK 9q22.2EA0.150.020.2601.16 (1.09–1.23)
37rs773506 (G) [121]SYK-AUH 9q22.31AA0.770.630.250.500.131.32 (1.18–1.49)
38rs10980508 (A) [176]*SVEP1-RPS21P5 9q31EURNR0.860.970.970.94NR
39rs1327796 (G) [138]PALM2 9q31.3EA0. (1.08–1.20)
40rs6583826 (G) [124]IDE-RPL11P4 10q23.33SEA (M, C, I)0.260.530.270.330.501.18 (1.10–1.27)
41rs10741243 (G) [124]TCERG1L 10q26.3SEA (I)0.930.950.890.491.75 (1.38–2.23)
42rs9300039 (C) [98]RPL9P23-HNRNPKP3 11p12EUR0.890.870.700.820.851.48 (1.28–1.71)
43rs2722769 (C) [148]HMGN1P22-MTND5P21 11p15.3AA0.530.560.560.760.991.35 (1.19–1.54)
44rs7107217 (C) [148]RPS27P20-TMEM45B 11q24.3AA0.910.500.340.650.541.18 (1.10–1.27)
45rs12304921 (G) [104]HIGD1C 12q13.12EUR0.150.160.500.360.152.5 (1.53–4.09)
46rs1153188 (A) [109]DCD-VDAC1P5 12q13.2EUR0.730.740.990.830.791.08 (1.05–1.11)
47rs2358944 (G) [121]PCNPP3-RPSAP52 12q14.3AA0.770.140.670.380.891.33 (1.18–1.49)
48rs1495377 (G) [104]TSPAN8-LGR5 12q21.1EUR0.500.480.240.410.151.28 (1.11–1.49)
49rs4760790 (A) [116]TSPAN8-LGR5 12q21.1EURNR0. (1.06–1.16)
50rs730570 (G) [149]BEGAIN-DLK1 14q32.2HISNR0.160.800.450.801.14 (1.08–1.21)
51rs1436953 (G) [145]C2CD4A-C2CD4B 15q22.2EA0.640.430.570.570.241.14 (NR)
52rs1436955 (C) [120]C2CD4A-C2CD4B 15q22.2EA0.730.740.740.750.651.13 (1.08–1.19)
53rs7119 (T) [124]HMG20A 15q24.3SEA (M, C, I)0.190.400.170.240.381.24 (1.14–1.34)
54rs17177078 (C) [176]*TNRC6A 16p12EURNR0.931.000.971.00NR
55rs16955379 (C) [127]CMIP 16q23.2EA0.800.980.770.961.08 (1.05–1.12)
56rs17797882 (T) [127]RPS3P7-MAF 16q23.2EA0.320. (1.05–1.12)
57rs623323 (T) [152]RNMTL1-NXN 17p13.3PS0. (1.15–1.42)
58rs10460009 (C) [124]LPIN2; LOC727896 18p11.31SEA (M)0.600.920.530.730.951.35 (1.18–1.54)
59rs472265 (G) [124]PAPL 19q13.2SEA (I) (1.20–1.61)
60rs328506 (C) [152]RBM38-HMGB1P1 20q13.31PSSA0.800.721.000.900.721.11 (1.06–1.15)
61rs2833610 (A) [124]HUNK-MIS18A 21q22.11SEA (M, C, I)0.570.300.570.510.321.17 (1.09–1.24)
62rs2106294 (T) [121]LIMK2 22q12.2AA0.940.700.860.751.001.75 (1.39–2.22)
63rs470089 (G) [176]*SULT4A1 22q13.3EURNR0.80.930.760.60NR

SNP-risk allele: SNP(s) most strongly associated with trait (risk allele).
Reference for the largest study reporting association of the SNP with T2D or fasting plasma glucose at genome-wide significance ( ).
Cytogenetic region associated with the SNP (NCBI).
Discovery population; EUR: European; SA: South Asian; EA: East Asian; SEA: Southeast Asian; AA: African-American, MA: Mexican-American; HIS: Hispanic; PS: Punjabi Sikh; M: Malay; C: Chinese; I: Indian.
Replication population: it has been confirmed in other populations; EUR: European; SA: South Asian; EA: East Asian; SEA: Southeast Asian; AA: African- American, MA: Mexican American; HIS: Hispanic; PS: Punjabi Sikh; M: Malay; C: Chinese; I: Indian.
Reported risk allele frequency (RAF) for the SNP; NR if not reported.
RAF in HapMap population for Utah residents with Northern and Western European-ancestry from the CEPH collection; “—” denotes data not listed in HapMap.
RAF in HapMap population for Han Chinese in Beijing, China; “—” denotes data not listed in HapMap.
RAF in HapMap population for Gujarati Indians in Houston, Texas; “—” denotes data not listed in HapMap.
RAF in HapMap population for Yoruban in Ibadan, Nigeria; “—” denotes data not listed in HapMap.
Risk Allele and RAF not reported, but chosen based on minor allele frequency (MAF) in the population mentioned in the original publication.

The first T2D GWAS was conducted in European-ancestry participants 2007 by Sladek and colleagues [99], using a discovery sample of 600 cases and 600 controls and a separate European replication sample of nearly 3,000 cases and 3,000 controls. This small study of early onset T2D reported T2D-associated variants in three novel susceptibility genes: TCF7L2 and HHEX/IDE which are associated with β-cell function [101] and SLC30A8, encoding a zinc transporter highly expressed in pancreatic islets [102]. Several months later, four additional European studies [74, 98, 103, 104] confirmed association of variants at these loci and identified additional associated variants in IGF2BP2, associated with β-cell dysfunction [105], and CDKN2A/CDKN2B and CDKAL1, which are both associated with β-cell development [105, 106]. During this time, variants in the FTO (fat mass and obesity associated) gene were also identified with important effects on obesity and hence, indirectly, T2D [107, 108]. Interestingly, as the effect of FTO variants on T2D is only via obesity, the FTO locus was not identified in T2D GWAS using cases and controls matched for BMI. Two of these early publications also showcased the output of international consortia: The UK-based Wellcome Trust Case Control Consortium (WTCCC) and the USA-based Diabetes Genetics Initiative (DGI), highlighting the benefits of large-scale collaboration in the GWAS era.

Since these initial GWAS had modest power to detect variants with modest effects on disease risk, follow-up studies employed meta-analysis to increase sample size and hence power to detect additional loci of similar or smaller effect. The first T2D GWAS meta-analysis was published in 2008 and was also a European study [109], representing collaboration between three different consortia; the WTCCC, DGI, and the Finland—United States Investigation of NIDDM Genetics (FUSION) which combined to form the Diabetes Genetics Replication and Meta-Analysis (DIAGRAM) consortium. This study utilised an enlarged discovery sample of 4,549 cases and 5,579 controls with replication in further 24,194 cases and 55,598 controls, all of European-ancestry. This study identified associated variants at six additional novel loci: JAZF1, CDC123, TSPAN8 and THADA which are associated with β-cell dysfunction [110, 111], ADAMTS9 which is associated with insulin action [111], and NOTCH2, associated with glucose-stimulated glucagon secretion by pancreatic islet cells [112].

These initial T2D GWAS were all restricted to populations of European-ancestry. The first two large-scale T2D GWAS conducted in Asian populations were reported in 2008, each employing a multi-stage design in East Asian groups. Both studies reported association of variants in KCNQ1, encoding the alpha subunit of a voltage-gated potassium channel expressed in the pancreas [113, 114]. These studies clearly demonstrated the utility of extending T2D GWAS to non-European populations; association of the KCNQ1 variants with T2D was not detected in previous European GWAS, due to markedly lower frequency of the risk allele in Europeans (5% versus 40%), resulting in dramatically reduced power. A European meta-analysis subsequently confirmed association of the KCNQ1 variants with T2D in Europeans but at significance levels far below thresholds usually inspiring replication or follow-up studies ().

A European study published in 2009 used multiple samples of French, Danish, and Finnish ancestry to identify association of variants in the insulin receptor substrate 1 gene (IRS1), showing that the risk allele is also associated with insulin resistance and hyperinsulinaemia in large population-based cohorts [115]. This contrasted with the apparent biology of previous associations, which principally related to impaired pancreatic β-cell function.

This first wave of T2D GWAS was succeeded by a second wave beginning in 2010, in which existing and new datasets were combined into expanded meta-analyses. The most notable was a large European study reported by Voight and colleagues, involving ~42,000 T2D cases and 100,000 controls split between discovery and replication stages and identifying twelve new associated loci. These included X-chromosomal association and an HNFA1A locus overlapping with the locus implicated in Mendelian monogenic (single gene) forms of diabetes [116]. Other studies reported at this time included three East Asian studies, one African American, and one European study, which together identified nine (9) additional loci [117121]. Three of these genes have unknown function (RBMS1, PTPRD, and SRR) [117, 118], while RPS12, LIMK2, and AUH are associated with diabetic nephropathy [121], C2CD4A is associated with β-cell dysfunction [119, 122], SPRY2 is associated with obesity and insulin resistance [120, 123], and SASH1 is associated with insulin growth factors [121].

A subsequent 2011 meta-analysis included three Southeast Asian populations: Chinese (3955 subjects), Indian (2146 subjects), and Malay (2034 subjects) and it further emphasized the value of surveying diverse ethnic groups [124]. This study was the first to include individuals from the Malay population, the largest group in Southeast Asia, with a population size of more than 300 million [124]. This study alone contributed an additional 16 novel loci, in spite of its relatively modest sample size; this partly reflected higher minor allele frequencies in Southeast Asian populations at some associated loci (e.g., rs3792615, number 18 in Table 2).

The first T2D GWAS in South Asian populations was also published in 2011, including individuals from India, Pakistan, Sri Lanka and Bangladesh. Using a relatively modest sample size (5,561 cases and 14,458 controls in the discovery step) five additional novel T2D loci were discovered [92]: HNF4A, involved in monogenic forms of diabetes and associated with β-cell development [125]; GRB14 which is associated with obesity and insulin resistance [126]; and another three loci with less clear functions; AP3S2, ST6GAL1 and VPS26A [92].

Another large meta-analysis in East Asian groups were performed in 2012 and identified 10 further novel loci [127] with mostly unknown function except for GLIS3, associated with β-cell development [128]. It is interesting that the MAEA variant discovered in this study is unique to East Asian and African populations, being monomorphic in Europeans and South Asians (Table 1; number 24) [129]. Several other variants identified in this study have substantially higher risk allele frequency (RAF) in East Asians than Europeans, for example, ZFAND3 (Table 1; number 28; 34% versus 12%), FITM2-R3HDML (Table 1; number 111; 41% versus 18%), and RPS3P7-MAF (Table 2; number 56, 18% versus 1%), enhancing their detection in East Asian samples of relatively modest size.

Reflecting the success of initial T2D GWAS and the fast pace of technology, in 2012 Voight [130] and colleagues designed the “Metabochip,” a custom genotyping array enriched for variants shown to be associated with cardiometabolic traits via GWAS. These traits include T2D, coronary heart disease, myocardial infarction, body mass index, glucose and insulin level, lipid levels, and blood pressure. This new platform offers a powerful and cost-effective approach to both the discovery and follow-up of variants associated with these related traits, due to comprehensive coverage of previously associated loci (~120,000 SNPs) [130]. Morris and colleagues used the Metabochip to discover and characterize T2D-associated variants via meta-analysis of 34,840 cases and 114,981 controls of European descent, finding ten novel loci [131] not reported in previous European studies, all of which reached genome-wide significance. Another study using the Metabochip combined newly available samples with samples from previous discovery meta-analyses, using genotype data for 66,000 follow-up SNPs. This study identified 41 novel glycaemic associations, 33 of which were also associated with T2D [132]. This study implicated new loci in the aetiology of T2D and increased the overlap between loci associated with both glycaemia and T2D. These studies highlight the Metabochip as a promising tool for identifying novel and robustly associated loci, facilitating future research into underlying biology.

Taken together, the results of T2D GWAS signify tremendous progress in our quest to understand the genetic causes of T2D. Alternatively, they also highlight the genetic complexity of this disease. Genetic variants showing replicable association with T2D uniformly exert only a modest effect on disease risk, with per-allele odds ratios typically in the range of 1.1–1.3 (Table 1). The combined effect of all variants reported to date explains only about 10% of observed familial clustering [116]. Furthermore, the functional significance of various loci remains unclear. While some appear to be associated with β-cell function and insulin resistance, the biological role of many of them remains unknown. This suggests that the findings to date represent the first stage of a long journey to understanding T2D genetic risk.

2.7. Polygenic Models of T2D

The distribution of odds ratios observed for validated T2D-associated SNPs suggests that numerous, associated loci exist with even smaller effects than those identified to date. One would not expect such loci to have reached genome-wide significance in previous GWAS due to insufficient power. The existence of such additional small effect loci is consistent with the pattern of additional associated variants being discovered as sample sizes have increased; it is also consistent with validated SNP associations explaining only a small proportion of the T2D heritability estimated from twin studies, known as the “missing heritability” problem.

Two methods have recently been developed for assessing the contribution of common SNPs not reaching genome-wide significance to the heritability of a trait. These are polygenic scoring [133] and mixed linear modelling [134]. Both methods test the combined effects of thousands (or hundreds of thousands) of SNPs upon a trait of interest. A recent study by Stahl and colleagues used polygenic analyses and linear mixed modelling to show that thousands of SNPs contribute to T2D risk, estimating that about 50% of observed variance in T2D risk could be attributed to the combined effects of all SNPs genome-wide [135]. These investigators suggested that at least 70% of T2D heritability can be attributed to common SNPs represented on GWAS arrays [135], with most having very small individual effects upon disease risk.

2.8. Population Differences in T2D Risk Alleles

The frequency of T2D risk alleles often varies between populations, producing population differences in the attributable risk due to a particular genetic risk factor or combination of risk factors. The discovery of KCNQ1 emphasized the impact of such frequency differences upon genetic discoveries [136]. Association of KCNQ1 variants was found in East Asian populations [113, 114] using a considerably smaller sample size than that required to detect the association in with European populations [116], reflecting higher allele frequency (33% in East Asian versus 8% in Europeans) and hence statistical power in Asian groups. In addition, variants at the TCF7L2 locus showed the inverse; a high risk allele frequency in Europeans (30%) compared to a low frequency in East Asians (3%) enhanced the detection in European studies [137]. Similarly, the SYK variant demonstrates a RAF of 26% in East Asians [138]and only 2% in Europeans and is monomorphic in Africans (Table 2; number 36). Further, a number of T2D risk variants are monomorphic (not variable) in some populations, preventing the detection of an association in these groups. The recently reported SCGG variant is unique to Indian Punjabi Sikh, being monomorphic in both European and African populations (Table 1; number 92). Other instances include the THADA variant, which was discovered in European populations but is monomorphic in East Asians (Table 1; number 4), RND3-FABP5P10 that was discovered in African Americans but is monomorphic in Europeans (Table 1; number 6), and G6PC2, discovered in Europeans but monomorphic in Africans (Table 1; number 10). For a set of SNPs showing association with T2D across multiple populations, Table 3 shows risk allele frequencies and odds ratios for different populations in which associations have been reported. For these 14 SNPs, risk allele frequencies commonly differ across populations; however, allelic effects upon disease seem markedly consistent in both direction and magnitude, given overlapping confidence intervals for allelic odds ratios. Taken together, these results suggest that population differences can have important effects on power to detect common genetic associations for T2D in samples of diverse ancestry but may have less impact upon disease risk within individuals carrying the identified risk alleles. Nevertheless, at the population level, the attributable risk of such genetic variants will increase with allele frequency, thus potentially influencing population disease burden.

NumberSNPsRef alleleDisc PopRAFOR (95% CI)Ref allele1st Rep Pop RAFOR (95% CI)Ref allele2nd Rep PopRAFOR (95% CI)

1rs1470579CEUR [116] 0.31.14 (1.09–1.19)CEA [177] 0.241.33 (1.20–1.48)CSA [152]0.51.06 (1.04–1.08)
2rs4402960TEUR [98]0.31.14 (1.11–1.18)TEA [144] 0.221.14 (1.08–1.21)
3rs7754840CEUR [103]0.341.12 (1.08–1.16)CEA [146] 0.41.35 (1.23–1.48)
4rs13266634CEUR [74]0.761.12 (1.07–1.16)CEA [144] 0.531.22 (1.15–1.28)
5rs2383208AEA [144]0.591.34 (1.27–1.41)ASA [151] 0.911.23 (1.13–1.34)
6rs1111875CEUR [98] 0.581.13 (1.09–1.17)CEA [144] 0.341.21 (1.15–1.28)
7rs5015480 CEUR [74]0.581.13 (1.07–1.19)CEA [120] 0.211.17 (1.11–1.24)
8rs7903146TEUR [99]0.281.65 (1.28–2.02)TEA [144] 0.031.54 (1.36–1.74)TSA [178]0.281.33 (1.19–1.49)
9rs2237892CEA [113]0.671.40 (1.34–1.47)CEA [113] 0.931.29 (1.11–1.50)CHIS [149]0.791.09 (1.06–1.12)
10rs7178572GSA [92] 0.441.09 (1.06–1.12)GEUR [171] 0.681.11 (1.07–1.15)
11rs8050136AEUR [98] 0.461.17 (1.12–1.22)ASA [151] 0.251.16 (1.09–1.24)
12rs4430796GEUR [116] 0.511.14 (1.08–1.20)GEA [146] 0.251.19 (1.13–1.25)
13rs5945326AEUR [116] 0.781.27 (1.18–1.37)AEA [146] 0.661.18 (1.13–1.23)
14rs4712523GEA [144] 0.411.27 (1.21–1.33)GEUR [115] 0.341.20 (1.14–1.26)

Population. EUR: European; SA: South Asian; EA: East Asian; HIS: Hispanic.

Significantly, a recent study assessing thousands of genetic associations showed that T2D has the most extreme population differentiation of risk allele frequencies among a broad range of complex diseases [139]. T2D risk allele frequencies demonstrated clear gradient matching paths of early human migration, suggesting potential differences in evolutionary adaptation to food availability, dietary patterns, or agricultural practices. This is consistent with “thrifty genotype” hypothesis [139, 140], which proposes that susceptibility to obesity and T2D in some populations reflects historical positive selection for genotypes promoting efficiency of metabolism, and energy and fat storage, thus providing an advantage in times of nutrient shortage [141]. This might explain the extraordinarily high prevalence of diabetes now seen among certain populations [34, 142, 143], potentially reflecting historical feast and famine cycles [62], increasing the frequency of thrifty genotypes and genetic predisposition to obesity and diabetes. While being unproven, this may partly explain higher susceptibility to abdominal obesity at lower BMI and reduced muscle mass with increased insulin resistance in Asian compared with Caucasian populations [7]. Nevertheless, pronounced population differentiation of T2D risk allele frequencies provides a strong rationale for further comprehensive genetic studies of T2D in diverse populations, expanding on the comprehensive studies in European samples.

To date, a range of non-European T2D GWAS have been conducted, including studies in Japanese [114, 119, 138, 144], Chinese [117, 145, 146], African-American [121, 147, 148], Southeast Asian [124], Hispanic [149], Mexican-American [150], South Asian [92], Indo-European [151], and Indian Punjabi Sikh [152]. These studies have led to new discoveries, including novel loci and loci that seem specific to certain populations [119, 127, 151, 152]. While many loci appear to contribute broadly to T2D risk, some loci have currently been confirmed in European populations only, including WFS1, NOTCH2, THADA, ADAMTS9, TSPAN8/LGR5, INS-IGF2, ADCY5, GCK, MTRNR1B, HMGA2, HNF1A, ZBED3, KLF14, ZFAND6, PRC1, TLES/CHCHD9, and RBMS1 [109, 116, 153155]. Other loci currently show association specifically in East Asian populations, including PTPRD, SRR, CDC123/CAMK1D, PSMD6, MAEA, ZFAND3, KCNK16, GCC1/PAX4, GLIS3, and PEPD [117, 119, 120, 127]. On the other hand, TMEM163 [151] and SGCG [152] appear unique to South Asian and Indian Punjabi Sikh, respectively. Some of these discoveries may reflect the impact of population allele frequency differences, as previously discussed. In such cases, larger studies may eventually show that some loci contribute to disease across a broader range of populations.

Seemingly population-specific genetic associations for T2D may also reflect differences in the patterns of genomic correlation, or linkage disequilibrium (LD), between associated marker loci and the underlying unobserved functional variants. Populations with different demographic histories will often display different patterns of LD reflecting population differences in evolutionary recombination [156]. Older populations such as those in Africa have lower LD and can be helpful for finely localizing a risk variant following an initial association finding. This is because the genomic distance between disease-associated markers and true risk variants is likely to be smaller in such populations [157].

Thus, the apparent population-specificity of some known T2D risk alleles may reflect population differences in risk allele frequencies or LD between tagging and causal variants, rather than actual population-specificity of the underlying functional risk loci. We note that population-specific estimates of disease variance explained by all known T2D loci—although not widely reported—do seem largely similar between European and Asian populations. In their large 2012 study, Morris and colleagues [131] estimated that known common variants explain about 5.7% of T2D disease variance in Europeans. In 2013, Tabassum and colleagues [151] estimated that known loci combined with one novel Indian-specific locus explained 7.65% of T2D risk variance in South Asian Indians. The slightly higher estimate in Indians may potentially be explained by the inclusion of additional variants discovered between publications of the two studies, together with the inclusion of the Indian-specific locus discovered in the Tabassum study. Thus, available evidence thus does not strongly suggest that differences in the cumulative variance explained by known common T2D risk alleles can explain the markedly higher T2D prevalence observed in South Asians.

Interestingly, however, very recent studies show that population differences in linkage disequilibrium (LD) and the presence of multiple independent variants within a locus can markedly influence estimates of variance explained by known risk variants [158, 159]. Detailed fine mapping of T2D susceptibility loci in diverse populations, combined with the identification of underlying functional variants, may thus reveal population differences in the contribution of known loci to disease. Future research may also show the extent to which population differences in T2D risk can be explained by rare alleles, gene-environment interactions, or epigenetic effects.

2.9. Gene-Environment Interactions in T2D

In addition to the effects of specific genetic and environmental risk factors, gene-environment interactions are likely important mediators of population differences in T2D risk and contributors to the “missing heritability” problem. Indeed, given the relative stability of DNA sequence within populations over decades, recent increases in T2D prevalence must largely reflect environmental changes. Accordingly, the single largest contributor to T2D risk is obesity, and the global T2D epidemic chronologically parallels the global obesity epidemic.

A paucity of studies has examined gene-environment interactions in the context of T2D in general, let alone in Asian populations. A study by Qi and colleagues [160] found that a high genetic risk score formed from 10 T2D-associated SNPs was further increased by the presence of a “Westernized” dietary pattern characterised by increased red and processed meat intake and reduced dietary fibre [160]. The Westernized diet was not associated with increased risk among those with a low genetic risk score. Several studies have also found evidence for interactions between T2D-associated variants in TCF7L2 and the quality and quantity of ingested carbohydrates in the context of T2D risk [161163]. These studies support a possible contribution of gene-environment interactions to T2D risk, together with a potential model where interactions between recent lifestyle transitions and genetic risk factors may be contributing to the rapidly increasing prevalence of T2D in Asian populations. However, these preliminary findings require validation. Future analyses in well-designed, well-powered studies will help to clarify the role of gene-environment interactions in population differences in T2D risk.

2.10. Epigenetics

Similar to the “thrifty genotype” hypothesis, the “thrifty phenotype” hypothesis considers the adaptive consequences of the environment in utero. The hypothesis relates to the metabolic consequences of fetal malnutrition, proposing that adaptation to a low-calorie intrauterine environment induces permanent changes in chromatin structure and gene expression that influence insulin secretion and resistance, promoting more efficient energy utilisation and thus fetal survival [164]. According to the hypothesis, such epigenetic changes may predispose to insulin dysregulation, obesity, and T2D in later life. In support, epidemiologic studies have shown associations between small birth size, a marker for fetal malnutrition, and adult-onset T2D [165, 166]. A study by van Hoek and colleagues [167] in the Dutch Famine Birth Cohort detected an interaction between an IGF2BP2 polymorphism and prenatal famine upon glucose level in the offspring. Interactions between other T2D risk variant alleles and birthweight have also been associated with increased T2D risk [168, 169].

3. Conclusions

We have discussed differences in prevalence, risk factor profiles, and genetic risk allele frequencies between different Asian countries and between Asian and other continental populations. Given these differences, continued T2D genetic studies in diverse populations are likely to contribute crucially to the broadening terrain of shared and unique population genetic effects for this disorder. Future studies will ideally include large, population-specific characterisation of risk variants, studies of gene-environment interaction, and epigenetic studies. Well-powered, well-designed studies performed in diverse Asian populations should enhance the benefits of genetic discoveries and their ultimate clinical translation for these large susceptible groups.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


Noraidatulakma Abdullah is sponsored by Ministry of Higher Education of Malaysia and Universiti Kebangsaan Malaysia. Elizabeth Holliday is funded by the NHMRC fellowship scheme.


  1. International Diabetes Federation, IDF Diabetes Atlas, 2012,
  2. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: estimates for the year 2000 and projections for 2030,” Diabetes Care, vol. 27, no. 5, pp. 1047–1053, 2004. View at: Publisher Site | Google Scholar
  3. R. Sicree, J. Shaw, and P. Zimmet, “Prevalence and projections,” in Diabetes Atlas, D. Gan, Ed., pp. 16–104, International Diabetes Federation, Brussels, Belgium, 2006. View at: Google Scholar
  4. T. Scully, “Diabetes in numbers,” Nature, vol. 485, no. 7398, pp. S2–S3, 2012. View at: Google Scholar
  5. C. J. Murray, T. Vos, R. Lozano et al., “Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010,” The Lancet, vol. 380, no. 9859, pp. 2197–2223, 2012. View at: Google Scholar
  6. U. N. H. L. A. B. I. UnitedHealth, National Heart, Lung, and Blood Institute Centers of Excellence, M. T. Cerqueira, A. Cravioto et al., “Global response to non-communicable disease,” BMJ (Clinical research ed.), vol. 342, p. d3823, 2011. View at: Publisher Site | Google Scholar
  7. J. C. N. Chan, V. Malik, W. Jia et al., “Diabetes in Asia: epidemiology, risk factors, and pathophysiology,” Journal of the American Medical Association, vol. 301, no. 20, pp. 2129–2140, 2009. View at: Publisher Site | Google Scholar
  8. S. Nakanishi, M. Okubo, M. Yoneda, K. Jitsuiki, K. Yamane, and N. Kohno, “A comparison between Japanese-Americans living in Hawaii and Los Angeles and native Japanese: the impact of lifestyle westernization on diabetes mellitus,” Biomedicine and Pharmacotherapy, vol. 58, no. 10, pp. 571–577, 2004. View at: Publisher Site | Google Scholar
  9. R. Oza-Frank, M. K. Ali, V. Vaccarino, and K. M. V. Narayan, “Asian Americans: diabetes prevalence across U.S. and World Health Organization weight classifications,” Diabetes Care, vol. 32, no. 9, pp. 1644–1646, 2009. View at: Publisher Site | Google Scholar
  10. J. Ye, G. Rust, P. Baltrus, and E. Daniels, “Cardiovascular risk factors among Asian Americans: results from a national health survey,” Annals of Epidemiology, vol. 19, no. 10, pp. 718–723, 2009. View at: Publisher Site | Google Scholar
  11. V. Bhalla, C. W. Fong, S. K. Chew, and K. Satku, “Changes in the levels of major cardiovascular risk factors in the multi-ethnic population in Singapore after 12 years of a national non-communicable disease intervention programme,” Singapore Medical Journal, vol. 47, no. 10, pp. 841–850, 2006. View at: Google Scholar
  12. Institute of Public Health, National Health Morbidity Survey III, The Ministry of Health Malaysia, Kuala Lumpur, Malaysia, 2006.
  13. Institute for Public Health, National Health and Morbidity Survey, 2011.
  14. K. G. M. M. Alberti, “The classification and diagnosis of diabetes mellitus,” in Textbook of Diabetes, C. S. Cockram, R. I. G. Holt, A. Flyvbjerg, and B. J. Goldstein, Eds., pp. 24–30, Blackwell Publishing Company, London, UK, 2010. View at: Google Scholar
  15. “World Health OrganizationDiabetes Fact Sheet,” 2013, View at: Google Scholar
  16. D. R. Matthews, J. P. Hosker, and A. S. Rudenski, “Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man,” Diabetologia, vol. 28, no. 7, pp. 412–419, 1985. View at: Google Scholar
  17. A. Raji, E. W. Seely, R. A. Arky, and D. C. Simonson, “Body fat distribution and insulin resistance in healthy Asian Indians and Caucasians,” Journal of Clinical Endocrinology and Metabolism, vol. 86, no. 11, pp. 5366–5371, 2001. View at: Publisher Site | Google Scholar
  18. A. K. Manning, M. F. Hivert, R. A. Scott et al., “A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance,” Nature Genetics, vol. 44, no. 6, pp. 659–669, 2012. View at: Google Scholar
  19. H. Gao, A. Salim, J. Lee, E. S. Tai, and R. M. van Dam, “Can body fat distribution, adiponectin levels and inflammation explain differences in insulin resistance between ethnic Chinese, Malays and Asian Indians?” International Journal of Obesity, vol. 36, no. 8, pp. 1086–1093, 2012. View at: Publisher Site | Google Scholar
  20. P. M. McKeigue, M. G. Marmot, Y. D. Syndercombe Court, D. E. Cottier, S. Rahman, and R. A. Riemersma, “Diabetes, hyperinsulinaemia, and coronary risk factors in Bangladeshis in East London,” British Heart Journal, vol. 60, no. 5, pp. 390–396, 1988. View at: Google Scholar
  21. P. M. McKeigue, B. Shah, and M. G. Marmot, “Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians,” The Lancet, vol. 337, no. 8738, pp. 382–386, 1991. View at: Publisher Site | Google Scholar
  22. J. Dhawan, C. L. Bray, R. Warburton, D. S. Ghambhir, and J. Morris, “Insulin resistance, high prevalence of diabetes, and cardiovascular risk in immigrant Asians,” British Heart Journal, vol. 72, no. 5, pp. 413–421, 1994. View at: Google Scholar
  23. S. Dickinson, S. Colagiuri, E. Faramus, P. Petocz, and J. C. Brand-Miller, “Postprandial hyperglycemia and insulin sensitivity differ among lean young adults of different ethnicities,” Journal of Nutrition, vol. 132, no. 9, pp. 2574–2579, 2002. View at: Google Scholar
  24. C.-F. Liew, E.-S. Seah, K.-P. Yeo, K.-O. Lee, and S. D. Wise, “Lean, nondiabetic Asian Indians have decreased insulin sensitivity and insulin clearance, and raised leptin compared to Caucasians and Chinese subjects,” International Journal of Obesity, vol. 27, no. 7, pp. 784–789, 2003. View at: Publisher Site | Google Scholar
  25. S. E. Kahn, “The relative contributions of insulin resistance and beta-cell dysfunction to the pathophysiology of Type 2 diabetes,” Diabetologia, vol. 46, no. 1, pp. 3–19, 2003. View at: Google Scholar
  26. B. Ahrén and H. Larsson, “Quantification of insulin secretion in relation to insulin sensitivity in nondiabetic postmenopausal women,” Diabetes, vol. 51, supplement 1, pp. S202–S211, 2002. View at: Google Scholar
  27. M. H. Black, R. M. Watanabe, E. Trigo et al., “High-fat diet is associated with obesity-mediated insulin resistance and beta-cell dysfunction in Mexican Americans,” Journal of Nutrition, vol. 143, no. 4, pp. 479–485, 2013. View at: Google Scholar
  28. K. Ohtsubo, M. Z. Chen, J. M. Olefsky, and J. D. Marth, “Pathway to diabetes through attenuation of pancreatic beta cell glycosylation and glucose transport,” Nature Medicine, vol. 17, no. 9, pp. 1067–1076, 2011. View at: Publisher Site | Google Scholar
  29. M. Fukushima, M. Usami, M. Ikeda et al., “Insulin secretion and insulin sensitivity at different stages of glucose tolerance: a cross-sectional study of Japanese type 2 diabetes,” Metabolism, vol. 53, no. 7, pp. 831–835, 2004. View at: Publisher Site | Google Scholar
  30. D. Tripathy, M. Carlsson, P. Almgren et al., “Insulin secretion and insulin sensitivity in relation to glucose tolerance: lessons from the Botnia Study,” Diabetes, vol. 49, no. 6, pp. 975–980, 2000. View at: Google Scholar
  31. K. Mørkrid, A. K. Jenum, L. Sletner et al., “Failure to increase insulin secretory capacity during pregnancy-induced insulin resistance is associated with ethnicity and gestational diabetes,” European Journal of Endocrinology, vol. 167, no. 4, pp. 579–588, 2012. View at: Google Scholar
  32. D. W. Bowden, A. J. Cox, B. I. Freedman et al., “Review of the Diabetes Heart Study (DHS) family of studies: a comprehensively examined sample for genetic and epidemiological studies of type 2 diabetes and its complications,” The Review of Diabetic Studies, vol. 7, no. 3, pp. 188–201, 2010. View at: Google Scholar
  33. N. J. Morrish, S.-L. Wang, L. K. Stevens et al., “Mortality and causes of death in the WHO multinational study of vascular disease in diabetes,” Diabetologia, vol. 44, supplement 2, pp. S14–S21, 2001. View at: Google Scholar
  34. K. K. Yeo, B. C. Tai, D. Heng et al., “Ethnicity modifies the association between diabetes mellitus and ischaemic heart disease in Chinese, Malays and Asian Indians living in Singapore,” Diabetologia, vol. 49, no. 12, pp. 2866–2873, 2006. View at: Publisher Site | Google Scholar
  35. K. A. Earle, K. K. Porter, J. Ostberg, and J. S. Yudkin, “Variation in the progression of diabetic nephropathy according to racial origin,” Nephrology Dialysis Transplantation, vol. 16, no. 2, pp. 286–290, 2001. View at: Google Scholar
  36. B. A. Young, C. Maynard, and E. J. Boyko, “Racial differences in diabetic nephropathy, cardiovascular disease, and mortality in a national population of veterans,” Diabetes Care, vol. 26, no. 8, pp. 2392–2399, 2003. View at: Publisher Site | Google Scholar
  37. D. Pascolini and S. P. Mariotti, “Global estimates of visual impairment: 2010,” British Journal of Ophthalmology, vol. 96, no. 5, pp. 614–618, 2012. View at: Publisher Site | Google Scholar
  38. S. Sivaprasad, B. Gupta, M. C. Gulliford et al., “Ethnic variations in the prevalence of diabetic retinopathy in people with diabetes attending screening in the United Kingdom (DRIVE UK),” PLoS ONE, vol. 7, no. 3, Article ID e32182, 2012. View at: Publisher Site | Google Scholar
  39. M. K. Ali, M. B. Weber, and K. M. V. Narayan, “The global burden of diabetes, in Textbook of Diabetes,” C. S. Cockram, R. I. G. Holt, A. Flyvbjerg, and B. J. Goldstein, Eds., pp. 69–84, Balckwell Publishing, USA, 2010. View at: Google Scholar
  40. G. Roglic, N. Unwin, P. H. Bennett et al., “The burden of mortality attributable to diabetes: realistic estimates for the year 2000,” Diabetes Care, vol. 28, no. 9, pp. 2130–2135, 2005. View at: Publisher Site | Google Scholar
  41. “World Health Organization Obesity and overweight Factsheet,” 2013. View at: Google Scholar
  42. R. L. Westley and F. E. May, “A twenty-first century cancer epidemic caused by obesity: the involvement of insulin, diabetes, and insulin-like growth factors,” International Journal of Endocrinology, vol. 2013, Article ID 632461, 37 pages, 2013. View at: Publisher Site | Google Scholar
  43. M. B. Weber, R. Oza-Frank, L. R. Staimez, M. K. Ali, and K. M. Narayan, “Type 2 diabetes in Asians: prevalence, risk factors, and effectiveness of behavioral intervention at individual and population levels,” Annual Review of Nutrition, vol. 32, pp. 417–439, 2012. View at: Google Scholar
  44. P. Boffetta, D. McLerran, Y. Chen et al., “Body mass index and diabetes in Asia: a cross-sectional pooled analysis of 900,000 individuals in the Asia cohort consortium,” PLoS ONE, vol. 6, no. 6, Article ID e19930, 2011. View at: Publisher Site | Google Scholar
  45. P. Deurenberg, M. Deurenberg-Yap, and S. Guricci, “Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship,” Obesity Reviews, vol. 3, no. 3, pp. 141–146, 2002. View at: Publisher Site | Google Scholar
  46. S. Gurrici, Y. Hartriyanti, J. G. A. J. Hautvast, and P. Deurenberg, “Relationship between body fat and body mass index: differences between Indonesians and Dutch Caucasians,” European Journal of Clinical Nutrition, vol. 52, no. 11, pp. 779–783, 1998. View at: Google Scholar
  47. M. Deurenberg-Yap, G. Schmidt, W. A. van Staveren, J. G. A. J. Hautvast, and P. Deurenberg, “Body fat measurement among Singaporean Chinese, Malays and Indians: a comparative study using a four-compartment model and different two-compartment models,” British Journal of Nutrition, vol. 85, no. 4, pp. 491–498, 2001. View at: Google Scholar
  48. C.-H. Cheng, C.-C. Ho, C.-F. Yang, Y.-C. Huang, C.-H. Lai, and Y.-P. Liaw, “Waist-to-hip ratio is a better anthropometric index than body mass index for predicting the risk of type 2 diabetes in Taiwanese population,” Nutrition Research, vol. 30, no. 9, pp. 585–593, 2010. View at: Publisher Site | Google Scholar
  49. Z. Xin, C. Liu, W. Y. Niu et al., “Identifying obesity indicators which best correlate with type 2 diabetes in a Chinese population,” BMC Public Health, vol. 12, p. 732, 2012. View at: Google Scholar
  50. P. Bjorntorp, “'Portal' adipose tissue as a generator of risk factors for cardiovascular disease and diabetes,” Arteriosclerosis, vol. 10, no. 4, pp. 493–496, 1990. View at: Google Scholar
  51. P. Bjorntorp, “Metabolic implications of body fat distribution,” Diabetes Care, vol. 14, no. 12, pp. 1132–1143, 1991. View at: Google Scholar
  52. J.-P. Després and I. Lemieux, “Abdominal obesity and metabolic syndrome,” Nature, vol. 444, no. 7121, pp. 881–887, 2006. View at: Publisher Site | Google Scholar
  53. J. J. Díez and P. Iglesias, “The role of the novel adipocyte-derived hormone adiponectin in human disease,” European Journal of Endocrinology, vol. 148, no. 3, pp. 293–300, 2003. View at: Publisher Site | Google Scholar
  54. R. I. G. Holt and C. W. Ronald, “Epidemiology of type 2 diabetes,” in Textbook of Diabetes, R. I. G. Holt, C. S. Cockram, A. Flyvbjerg, and B. J. Goldstein, Eds., pp. 45–68, Balckwell Publishing, Hong Kong, China, 2010. View at: Google Scholar
  55. S. A. Lear, K. H. Humphries, S. Kohli, A. Chockalingam, J. J. Frohlich, and C. L. Birmingham, “Visceral adipose tissue accumulation differs according to ethnic background: results of the Multicultural Community Health Assessment Trial (M-CHAT),” American Journal of Clinical Nutrition, vol. 86, no. 2, pp. 353–359, 2007. View at: Google Scholar
  56. S. K. Kumanyika, “Obesity in minority populations: an epidemiologic assessment,” Obesity research, vol. 2, no. 2, pp. 166–182, 1994. View at: Google Scholar
  57. P. L. Lutsey, M. A. Pereira, A. G. Bertoni, N. R. Kandula, and D. R. Jacobs Jr., “Interactions between race/ethnicity and anthropometry in risk of incident diabetes,” American Journal of Epidemiology, vol. 172, no. 2, pp. 197–204, 2010. View at: Publisher Site | Google Scholar
  58. S. A. Lear, K. H. Humphries, S. Kohli, and C. L. Birmingham, “The use of BMI and waist circumference as surrogates of body fat differs by ethnicity,” Obesity, vol. 15, no. 11, pp. 2817–2824, 2007. View at: Publisher Site | Google Scholar
  59. S. Patel, N. Unwin, R. Bhopal et al., “A comparison of proxy measures of abdominal obesity in Chinese, European and South Asian adults,” Diabetic Medicine, vol. 16, no. 10, pp. 853–860, 1999. View at: Publisher Site | Google Scholar
  60. A. Misra, R. Sharma, S. Gulati et al., “Consensus dietary guidelines for healthy living and prevention of obesity, the metabolic syndrome, diabetes, and related disorders in Asian Indians,” Diabetes Technology and Therapeutics, vol. 13, no. 6, pp. 683–694, 2011. View at: Publisher Site | Google Scholar
  61. Y. Wang, J. Mi, X.-Y. Shan, Q. J. Wang, and K.-Y. Ge, “Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China,” International Journal of Obesity, vol. 31, no. 1, pp. 177–188, 2007. View at: Publisher Site | Google Scholar
  62. F. B. Hu, “Globalization of diabetes: the role of diet, lifestyle, and genes,” Diabetes Care, vol. 34, no. 6, pp. 1249–1257, 2011. View at: Publisher Site | Google Scholar
  63. A. Ramachandran, C. Snehalatha, A. D. S. Baskar et al., “Temporal changes in prevalence of diabetes and impaired glucose tolerance associated with lifestyle transition occurring in the rural population in India,” Diabetologia, vol. 47, no. 5, pp. 860–865, 2004. View at: Publisher Site | Google Scholar
  64. S. W. Ng, E. C. Norton, and B. M. Popkin, “Why have physical activity levels declined among Chinese adults? Findings from the 1991–2006 China health and nutrition surveys,” Social Science and Medicine, vol. 68, no. 7, pp. 1305–1314, 2009. View at: Publisher Site | Google Scholar
  65. E. S. Ford, C. Li, and N. Sattar, “Metabolic syndrome and incident diabetes,” Diabetes Care, vol. 31, no. 9, pp. 1898–1904, 2008. View at: Publisher Site | Google Scholar
  66. S. Rampal, S. Mahadeva, E. Guallar et al., “Ethnic differences in the prevalence of metabolic syndrome: results from a multi-ethnic population-based survey in Malaysia,” PLoS ONE, vol. 7, no. 9, Article ID e46365, 2012. View at: Google Scholar
  67. N. T. Ayas, D. P. White, W. K. Al-Delaimy et al., “A prospective study of self-reported sleep duration and incident diabetes in women,” Diabetes Care, vol. 26, no. 2, pp. 380–384, 2003. View at: Publisher Site | Google Scholar
  68. E. G. Holliday, C. A. Magee, L. Kritharides, E. Banks, and J. Attia, “Short sleep duration is associated with risk of future diabetes but not cardiovascular disease: a prospective study and meta-analysis,” PLoS ONE, vol. 8, no. 11, Article ID e82305, 2013. View at: Google Scholar
  69. J. L. Broussard, D. A. Ehrmann, E. van Cauter, E. Tasali, and M. J. Brady, “Impaired insulin signaling in human adipocytes after experimental sleep restriction: a randomized, crossover study,” Annals of Internal Medicine, vol. 157, no. 8, pp. 549–557, 2012. View at: Google Scholar
  70. F. Landi, G. Onder, and R. Bernabei, “Sarcopenia and diabetes: two sides of the same coin,” Journal of the American Medical Directors Association, vol. 14, no. 8, pp. 540–541, 2013. View at: Google Scholar
  71. R. J. Manders, P. Jonathan, S. C. Forbes, and D. G. Candow, “Insulinotropic and muscle protein synthetic effects of branched-chain amino acids: potential therapy for type 2 diabetes and sarcopenia,” Nutrients, vol. 4, no. 11, pp. 1664–1678, 2012. View at: Google Scholar
  72. C. V. Calkin, D. M. Gardner, T. Ransom, and M. Alda, “The relationship between bipolar disorder and type 2 diabetes: more than just co-morbid disorders,” Annals of Medicine, vol. 45, no. 2, pp. 171–181, 2013. View at: Google Scholar
  73. P. R. Blackett and D. K. Sanghera, “Genetic determinants of cardiometabolic risk: a proposed model for phenotype association and interaction,” Journal of Clinical Lipidology, vol. 7, no. 1, pp. 65–81, 2013. View at: Google Scholar
  74. E. Zeggini, M. N. Weedon, C. M. Lindgren et al., “Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes,” Science, vol. 316, no. 5829, pp. 1336–1341, 2007. View at: Google Scholar
  75. A. Doria, M.-E. Patti, and C. R. Kahn, “The emerging genetic architecture of type 2 diabetes,” Cell Metabolism, vol. 8, no. 3, pp. 186–200, 2008. View at: Publisher Site | Google Scholar
  76. J. Kobberling and H. Tillil, “Empirical risk figures for first-degree relatives of non-insulin dependent diabetics,” in The Genetics of Diabetes Mellitus, J. Kobberling and R. Tattersall, Eds., pp. 201–209, Academic Press, London, UK, 1982. View at: Google Scholar
  77. B. Newman, J. V. Selby, M.-C. King, C. Slemenda, R. Fabsitz, and G. D. Friedman, “Concordance for Type 2 (non-insulin-dependent) diabetes mellitus in male twins,” Diabetologia, vol. 30, no. 10, pp. 763–768, 1987. View at: Google Scholar
  78. J. Kaprio, J. Tuomilehto, M. Koskenvuo 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, vol. 35, no. 11, pp. 1060–1067, 1992. View at: Publisher Site | Google Scholar
  79. F. Medici, M. Hawa, A. Ianari, D. A. Pyke, and R. D. G. Leslie, “Concordance rate for type II diabetes mellitus in monozygotic twins: actuarial analysis,” Diabetologia, vol. 42, no. 2, pp. 146–150, 1999. View at: Publisher Site | Google Scholar
  80. P. Poulsen, K. Ohm Kyvik, A. Vaag, and H. Beck-Nielsen, “Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance—a population-based twin study,” Diabetologia, vol. 42, no. 2, pp. 139–145, 1999. View at: Publisher Site | Google Scholar
  81. S. Carlsson, A. Ahlbom, P. Lichtenstein, and T. Andersson, “Shared genetic influence of BMI, physical activity and type 2 diabetes: a twin study,” Diabetologia, vol. 56, no. 5, pp. 1031–1035, 2013. View at: Google Scholar
  82. P. Poulsen, K. Levin, I. Petersen, K. Christensen, H. Beck-Nielsen, and A. Vaag, “Heritability of insulin secretion, peripheral and hepatic insulin action, and intracellular glucose partitioning in young and old Danish twins,” Diabetes, vol. 54, no. 1, pp. 275–283, 2005. View at: Publisher Site | Google Scholar
  83. S. N. Wulan, K. R. Westerterp, and G. Plasqui, “Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians,” Maturitas, vol. 65, no. 4, pp. 315–319, 2010. View at: Publisher Site | Google Scholar
  84. M. F. P. Vaxillare, “The genetics of type 2 diabetes: from candidate gene biology to genome-wide studies,” in Textbook of Diabetes, C. S. Cockram, R. I. G. Holt, A. Flyvbjerg, and B. J. Goldstein, Eds., pp. 191–214, Balckwell Publishing, 2010. View at: Google Scholar
  85. S. Bevan and H. S. Markus, “Genetics of common polygenic ischaemic stroke: current understanding and future challenges,” Stroke Research and Treatment, vol. 2011, Article ID 179061, 6 pages, 2011. View at: Publisher Site | Google Scholar
  86. N. Risch and K. Merikangas, “The future of genetic studies of complex human diseases,” Science, vol. 273, no. 5281, pp. 1516–1517, 1996. View at: Google Scholar
  87. K. S. Park, “The search for genetic risk factors of type 2 diabetes mellitus,” Diabetes & Metabolism, vol. 35, no. 1, pp. 12–22, 2011. View at: Google Scholar
  88. Y. Horikawa, N. Oda, N. J. Cox et al., “Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus,” Nature Genetics, vol. 26, no. 2, pp. 163–175, 2000. View at: Google Scholar
  89. D. Meyre, N. Bouatia-Naji, A. Tounian et al., “Variants of ENPP1 are associated with childhood and adult obesity and increase the risk of glucose intolerance and type 2 diabetes,” Nature Genetics, vol. 37, no. 8, pp. 863–867, 2005. View at: Publisher Site | Google Scholar
  90. K. Silander, K. L. Mohlke, L. J. Scott et al., “Genetic variation near the hepatocyte nuclear factor-4α gene predicts susceptibility to type 2 diabetes,” Diabetes, vol. 53, no. 4, pp. 1141–1149, 2004. View at: Publisher Site | Google Scholar
  91. F. Vasseur, N. Helbecque, C. Dina et al., “Single-nucleotide polymorphism haplotypes in the both proximal promoter and exon 3 of the APM1 gene modulate adipocyte-secreted adiponectin hormone levels and contribute to the genetic risk for type 2 diabetes in French Caucasians,” Human Molecular Genetics, vol. 11, no. 21, pp. 2607–2614, 2002. View at: Google Scholar
  92. J. S. Kooner, D. Saleheen, X. Sim et al., “Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci,” Nature Genetics, vol. 43, no. 10, pp. 984–989, 2011. View at: Google Scholar
  93. J. M. Kwon and A. M. Goate, “The candidate gene approach,” Alcohol Research and Health, vol. 24, no. 3, pp. 164–168, 2000. View at: Google Scholar
  94. S. Bevan, M. Traylor, P. Adib-Samii et al., “Genetic heritability of ischemic stroke and the contribution of previously reported candidate gene and genomewide associations,” Stroke, vol. 43, no. 12, pp. 3161–3167, 2012. View at: Google Scholar
  95. D. Altshuler, J. N. Hirschhorn, M. Klannemark et al., “The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes,” Nature Genetics, vol. 26, no. 1, pp. 76–80, 2000. View at: Publisher Site | Google Scholar
  96. A. L. Gloyn, M. N. Weedon, K. R. Owen et al., “Large-scale association studies of variants in genes encoding the pancreatic β-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes,” Diabetes, vol. 52, no. 2, pp. 568–572, 2003. View at: Publisher Site | Google Scholar
  97. S. F. A. Grant, G. Thorleifsson, I. Reynisdottir et al., “Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes,” Nature Genetics, vol. 38, no. 3, pp. 320–323, 2006. View at: Publisher Site | Google Scholar
  98. L. J. Scott, K. L. Mohlke, L. L. Bonnycastle et al., “A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants,” Science, vol. 316, no. 5829, pp. 1341–1345, 2007. View at: Google Scholar
  99. R. Sladek, G. Rocheleau, J. Rung et al., “A genome-wide association study identifies novel risk loci for type 2 diabetes,” Nature, vol. 445, no. 7130, pp. 881–885, 2007. View at: Publisher Site | Google Scholar
  100. I. Pe'er, R. Yelensky, D. Altshuler, and M. J. Daly, “Estimation of the multiple testing burden for genomewide association studies of nearly all common variants,” Genetic Epidemiology, vol. 32, no. 4, pp. 381–385, 2008. View at: Publisher Site | Google Scholar
  101. Q. Qi and F. B. Hu, “Genetics of type 2 diabetes in European populations,” Journal of Diabetes, vol. 4, no. 3, pp. 203–212, 2012. View at: Google Scholar
  102. F. Chimienti, S. Devergnas, A. Favier, and M. Seve, “Identification and cloning of a β-cell-specific zinc transporter, ZnT-8, localized into insulin secretory granules,” Diabetes, vol. 53, no. 9, pp. 2330–2337, 2004. View at: Publisher Site | Google Scholar
  103. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research, “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels,” Science, vol. 316, no. 5829, pp. 1331–1336, 2007. View at: Publisher Site | Google Scholar
  104. Wellcome Trust Case Control Consortium, “Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls,” Nature, vol. 447, no. 7145, pp. 661–678, 2007. View at: Publisher Site | Google Scholar
  105. M. J. Groenewoud, J. M. Dekker, A. Fritsche et al., “Variants of CDKAL1 and IGF2BP2 affect first-phase insulin secretion during hyperglycaemic clamps,” Diabetologia, vol. 51, no. 9, pp. 1659–1663, 2008. View at: Publisher Site | Google Scholar
  106. L. Pascoe, A. Tura, S. K. Patel et al., “Common variants of the novel type 2 diabetes genes CDKAL1 and HHEX/IDE are associated with decreased pancreatic β-cell function,” Diabetes, vol. 56, no. 12, pp. 3101–3104, 2007. View at: Publisher Site | Google Scholar
  107. C. Dina, D. Meyre, S. Gallina et al., “Variation in FTO contributes to childhood obesity and severe adult obesity,” Nature Genetics, vol. 39, no. 6, pp. 724–726, 2007. View at: Publisher Site | Google Scholar
  108. T. M. Frayling, N. J. Timpson, M. N. Weedon et al., “A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity,” Science, vol. 316, no. 5826, pp. 889–894, 2007. View at: Publisher Site | Google Scholar
  109. E. Zeggini, L. J. Scott, R. Saxena, and B. F. Voight, “Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes,” Nature Genetics, vol. 40, no. 5, pp. 638–645, 2008. View at: Publisher Site | Google Scholar
  110. N. Grarup, G. Andersen, N. T. Krarup et al., “Association testing of novel type 2 diabetes risk alleles in the JAZF1, CDC123/CAMK1D, TSPAN8, THADA, ADAMTS9, and NOTCH2 Loci with insulin release, insulin sensitivity, and obesity in a population-based sample of 4,516 glucose-tolerant middle-aged danes,” Diabetes, vol. 57, no. 9, pp. 2534–2540, 2008. View at: Publisher Site | Google Scholar
  111. A. M. Simonis-Bik, G. Nijpels, T. W. van Haeften et al., “Gene variants in the novel type 2 diabetes loci CDC123/CAMK1D, THADA, ADAMTS9, BCL11A, and MTNR1B affect different aspects of pancreatic β-cell function,” Diabetes, vol. 59, no. 1, pp. 293–301, 2010. View at: Publisher Site | Google Scholar
  112. A. Jonsson, C. Ladenvall, T. S. Ahluwalia et al., “Effects of common genetic variants associated with type 2 diabetes and glycemic traits on alpha- and beta-cell function and insulin action in humans,” Diabetes, vol. 62, no. 8, pp. 2978–2983, 2013. View at: Google Scholar
  113. K. Yasuda, K. Miyake, Y. Horikawa et al., “Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus,” Nature Genetics, vol. 40, no. 9, pp. 1092–1097, 2008. View at: Google Scholar
  114. H. Unoki, A. Takahashi, T. Kawaguchi et al., “SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations,” Nature Genetics, vol. 40, no. 9, pp. 1098–1102, 2008. View at: Publisher Site | Google Scholar
  115. J. Rung, S. Cauchi, A. Albrechtsen et al., “Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia,” Nature Genetics, vol. 41, no. 10, pp. 1110–1115, 2009. View at: Publisher Site | Google Scholar
  116. B. F. Voight, L. J. Scott, V. Steinthorsdottir et al., “Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis,” Nature Genetics, vol. 42, no. 7, pp. 579–589, 2010. View at: Google Scholar
  117. F.-J. Tsai, C.-F. Yang, C.-C. Chen et al., “A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese,” PLoS Genetics, vol. 6, no. 2, Article ID e1000847, 2010. View at: Publisher Site | Google Scholar
  118. L. Qi, M. C. Cornelis, P. Kraft et al., “Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes,” Human Molecular Genetics, vol. 19, no. 13, pp. 2706–2715, 2010. View at: Google Scholar
  119. T. Yamauchi, K. Hara, S. Maeda et al., “A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A-C2CD4B,” Nature Genetics, vol. 42, no. 10, pp. 864–868, 2010. View at: Publisher Site | Google Scholar
  120. X. O. Shu, J. Long, Q. Cai et al., “Identification of new genetic risk variants for Type 2 Diabetes,” PLoS Genetics, vol. 6, no. 9, Article ID e1001127, 2010. View at: Publisher Site | Google Scholar
  121. C. W. McDonough, N. D. Palmer, P. J. Hicks et al., “A genome-wide association study for diabetic nephropathy genes in African Americans,” Kidney International, vol. 79, no. 5, pp. 563–572, 2011. View at: Publisher Site | Google Scholar
  122. E. Ingelsson, C. Langenberg, M. F. Hivert et al., “Detailed physiologic characterization reveals diverse mechanisms for novel genetic Loci regulating glucose and insulin metabolism in humans,” Diabetes, vol. 59, no. 5, pp. 1266–1275, 2010. View at: Google Scholar
  123. T. O. Kilpeläinen, M. C. Zillikens, A. Stančákova et al., “Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile,” Nature Genetics, vol. 43, no. 8, pp. 753–760, 2011. View at: Google Scholar
  124. X. Sim, R. T.-H. Ong, C. Suo et al., “Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia,” PLoS Genetics, vol. 7, no. 4, Article ID e1001363, 2011. View at: Publisher Site | Google Scholar
  125. H. Wang, P. Maechler, P. A. Antinozzi, K. A. Hagenfeldt, and C. B. Wollheim, “Hepatocyte nuclear factor 4α regulates the expression of pancreatic β-cell genes implicated in glucose metabolism and nutrient-induced insulin secretion,” Journal of Biological Chemistry, vol. 275, no. 46, pp. 35953–35959, 2000. View at: Publisher Site | Google Scholar
  126. I. M. Heid, A. U. Jackson, J. C. Randall et al., “Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution,” Nature Genetics, vol. 42, no. 11, pp. 949–960, 2010. View at: Google Scholar
  127. Y. S. Cho, C. H. Chen, C. Hu et al., “Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians,” Nature Genetics, vol. 44, no. 1, pp. 67–72, 2012. View at: Google Scholar
  128. Y. Yang, B. H.-J. Chang, V. Yechoor et al., “The Krüppel-like zinc finger protein GLIS3 transactivates neurogenin 3 for proper fetal pancreatic islet differentiation in mice,” Diabetologia, vol. 54, no. 10, pp. 2595–2605, 2011. View at: Publisher Site | Google Scholar
  129. S. H. Kwak and K. S. Park, “Genetics of type 2 diabetes and potential clinical implications,” Archives of Pharmacal Research, vol. 36, no. 2, pp. 167–177, 2013. View at: Google Scholar
  130. B. F. Voight, H. M. Kang, J. Ding et al., “Correction: the metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits,” PLoS Genet, vol. 9, no. 4, 2013. View at: Google Scholar
  131. A. P. Morris, B. F. Voight, T. M. Teslovich et al., “Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes,” Nature Genetics, vol. 44, no. 9, pp. 981–990, 2012. View at: Google Scholar
  132. R. A. Scott, V. Lagou, R. P. Welch et al., “Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways,” Nature Genetics, vol. 44, no. 9, pp. 991–1005, 2012. View at: Google Scholar
  133. S. M. Purcell, N. R. Wray, J. L. Stone et al., “Common polygenic variation contributes to risk of schizophrenia and bipolar disorder,” Nature, vol. 460, no. 7256, pp. 748–752, 2009. View at: Google Scholar
  134. J. Yang, B. Benyamin, B. P. McEvoy et al., “Common SNPs explain a large proportion of the heritability for human height,” Nature Genetics, vol. 42, no. 7, pp. 565–569, 2010. View at: Publisher Site | Google Scholar
  135. E. A. Stahl, D. Wegmann, G. Trynka et al., “Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis,” Nature Genetics, vol. 44, no. 5, pp. 483–489, 2012. View at: Publisher Site | Google Scholar
  136. M. I. McCarthy, “Casting a wider net for diabetes susceptibility genes,” Nature Genetics, vol. 40, no. 9, pp. 1039–1040, 2008. View at: Publisher Site | Google Scholar
  137. M. C. Y. Ng, K. S. Park, B. Oh et al., “Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians,” Diabetes, vol. 57, no. 8, pp. 2226–2233, 2008. View at: Publisher Site | Google Scholar
  138. M. Imamura, S. Maeda, T. Yamauchi et al., “A single-nucleotide polymorphism in ANK1 is associated with susceptibility to type 2 diabetes in Japanese populations,” Human Molecular Genetics, vol. 21, no. 13, pp. 3042–3049, 2012. View at: Google Scholar
  139. R. Chen, E. Corona, M. Sikora et al., “Type 2 diabetes risk alleles demonstrate extreme directional differentiation among human populations, compared to other diseases,” PLoS Genetics, vol. 8, no. 4, Article ID e1002621, 2012. View at: Publisher Site | Google Scholar
  140. L. Carulli, S. Rondinella, S. Lombardini, I. Canedi, P. Loria, and N. Carulli, “Review article: diabetes, genetics and ethnicity,” Alimentary Pharmacology & Therapeutics, vol. 22, supplement 2, pp. 16–19, 2005. View at: Google Scholar
  141. J. V. Neel, “Diabetes mellitus: a “thrifty” genotype rendered detrimental by “progress”? 1962,” Bulletin of the World Health Organization, vol. 77, no. 8, pp. 694–693, 1999. View at: Google Scholar
  142. N. R. Sloan, “Ethnic distribution of diabetes mellitus in Hawaii,” Journal of the American Medical Association, vol. 183, pp. 419–424, 1963. View at: Google Scholar
  143. J. T. Tan, D. P. K. Ng, S. Nurbaya et al., “Polymorphisms identified through genome-wide association studies and their associations with type 2 diabetes in Chinese, Malays, and Asian-Indians in Singapore,” Journal of Clinical Endocrinology and Metabolism, vol. 95, no. 1, pp. 390–397, 2010. View at: Publisher Site | Google Scholar
  144. F. Takeuchi, M. Serizawa, K. Yamamoto 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, vol. 58, no. 7, pp. 1690–1699, 2009. View at: Publisher Site | Google Scholar
  145. B. Cui, X. Zhu, M. Xu et al., “A genome-wide association study confirms previously reported loci for type 2 diabetes in Han Chinese,” PLoS ONE, vol. 6, no. 7, Article ID e22353, 2011. View at: Publisher Site | Google Scholar
  146. H. Li, W. Gan, L. Lu et al., “A genome-wide association study identifies GRK5 and RASGRP1 as type 2 diabetes loci in Chinese Hans,” Diabetes, vol. 62, no. 1, pp. 291–298, 2013. View at: Google Scholar
  147. A. N. Kho, M. G. Hayes, L. Rasmussen-Torvik et al., “Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study,” Journal of the American Medical Informatics Association, vol. 19, no. 2, pp. 212–218, 2012. View at: Google Scholar
  148. N. D. Palmer, C. W. McDonough, P. J. Hicks et al., “A genome-wide association search for type 2 diabetes genes in African Americans,” PLoS ONE, vol. 7, no. 1, Article ID e29202, 2012. View at: Google Scholar
  149. E. J. Parra, J. E. Below, S. Krithika 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, vol. 54, no. 8, pp. 2038–2046, 2011. View at: Publisher Site | Google Scholar
  150. J. E. Below, E. R. Gamazon, J. V. Morrison et al., “Genome-wide association and meta-analysis in populations from Starr County, Texas, and Mexico City identify type 2 diabetes susceptibility loci and enrichment for expression quantitative trait loci in top signals,” Diabetologia, vol. 54, no. 8, pp. 2047–2055, 2011. View at: Publisher Site | Google Scholar
  151. R. Tabassum, G. Chauhan, O. P. Dwivedi et al., “Genome-wide association study for type 2 diabetes in Indians identifies a new susceptibility locus at 2q21,” Diabetes, vol. 62, no. 3, pp. 977–986, 2013. View at: Google Scholar
  152. R. Saxena, D. Saleheen, L. F. Been et al., “Genome-wide association study identifies a novel locus contributing to type 2 diabetes susceptibility in sikhs of punjabi origin from India,” Diabetes, vol. 62, no. 5, pp. 1746–1755, 2013. View at: Google Scholar
  153. M. S. Sandhu, M. N. Weedon, K. A. Fawcett et al., “Common variants in WFS1 confer risk of type 2 diabetes,” Nature Genetics, vol. 39, no. 8, pp. 951–953, 2007. View at: Publisher Site | Google Scholar
  154. A. Kong, V. Steinthorsdottir, G. Masson et al., “Parental origin of sequence variants associated with complex diseases,” Nature, vol. 462, no. 7275, pp. 868–874, 2009. View at: Google Scholar
  155. J. Dupuis, C. Langenberg, I. Prokopenko et al., “New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk,” Nature Genetics, vol. 42, no. 2, pp. 105–116, 2010. View at: Google Scholar
  156. L. B. Jorde, “Linkage disequilibrium and the search for complex disease genes,” Genome Research, vol. 10, no. 10, pp. 1435–1444, 2000. View at: Publisher Site | Google Scholar
  157. N. A. Rosenberg, L. Huang, E. M. Jewett, Z. A. Szpiech, I. Jankovic, and M. Boehnke, “Genome-wide association studies in diverse populations,” Nature Reviews Genetics, vol. 11, no. 5, pp. 356–366, 2010. View at: Google Scholar
  158. X. Ke, “Presence of multiple independent effects in risk loci of common complex human diseases,” The American Journal of Human Genetics, vol. 91, no. 1, pp. 185–192, 2012. View at: Google Scholar
  159. A. Gusev, G. Bhatia, N. Zaitlen et al., “Quantifying missing heritability at known GWAS loci,” PLOS Genetics, vol. 9, no. 12, Article ID e1003993, 2013. View at: Google Scholar
  160. L. Qi, M. C. Cornelis, C. Zhang, R. M. van Dam, and F. B. Hu, “Genetic predisposition, Western dietary pattern, and the risk of type 2 diabetes in men,” American Journal of Clinical Nutrition, vol. 89, no. 5, pp. 1453–1458, 2009. View at: Publisher Site | Google Scholar
  161. J. C. Florez, K. A. Jablonski, N. Bayley et al., “TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program,” New England Journal of Medicine, vol. 355, no. 3, pp. 241–250, 2006. View at: Publisher Site | Google Scholar
  162. E. Fisher, H. Boeing, A. Fritsche, F. Doering, H.-G. Joost, and M. B. Schulze, “Whole-grain consumption and transcription factor-7-like 2 (TCF7L2) rs7903146: gene-diet interaction in modulating type 2 diabetes risk,” British Journal of Nutrition, vol. 101, no. 4, pp. 478–481, 2009. View at: Publisher Site | Google Scholar
  163. L. Qi and J. Liang, “Interactions between genetic factors that predict diabetes and dietary factors that ultimately impact on risk of diabetes,” Current Opinion in Lipidology, vol. 21, no. 1, pp. 31–37, 2010. View at: Publisher Site | Google Scholar
  164. P. Shetty, “Public health: India's diabetes time bomb,” Nature, vol. 485, no. 7398, pp. S14–S16, 2012. View at: Google Scholar
  165. D. J. P. Barker, C. N. Hales, C. H. D. Fall, C. Osmond, K. Phipps, and P. M. S. Clark, “Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): relation to reduced fetal growth,” Diabetologia, vol. 36, no. 1, pp. 62–67, 1993. View at: Publisher Site | Google Scholar
  166. P. H. Whincup, S. J. Kaye, C. G. Owen et al., “Birth weight and risk of type 2 diabetes a systematic review,” Journal of the American Medical Association, vol. 300, no. 24, pp. 2886–2897, 2008. View at: Publisher Site | Google Scholar
  167. M. van Hoek, J. G. Langendonk, S. R. de Rooij, E. J. G. Sijbrands, and T. J. Roseboom, “Genetic variant in the IGF2BP2 gene may interact with fetal malnutrition to affect glucose metabolism,” Diabetes, vol. 58, no. 6, pp. 1440–1444, 2009. View at: Publisher Site | Google Scholar
  168. S. R. de Rooij, R. C. Painter, D. I. W. Phillips et al., “The effects of the Pro12Ala polymorphism of the peroxisome proliferator-activated receptor-γ2 gene on glucose/insulin metabolism interact with prenatal exposure to famine,” Diabetes Care, vol. 29, no. 5, pp. 1052–1057, 2006. View at: Publisher Site | Google Scholar
  169. N. Pulizzi, V. Lyssenko, A. Jonsson et al., “Interaction between prenatal growth and high-risk genotypes in the development of type 2 diabetes,” Diabetologia, vol. 52, no. 5, pp. 825–829, 2009. View at: Publisher Site | Google Scholar
  170. V. Steinthorsdottir, G. Thorleifsson, I. Reynisdottir et al., “A variant in CDKAL1 influences insulin response and risk of type 2 diabetes,” Nature Genetics, vol. 39, no. 6, pp. 770–775, 2007. View at: Google Scholar
  171. J. R. B. Perry, B. F. Voight, L. Yengo 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 Genetics, vol. 8, no. 5, Article ID e1002741, 2012. View at: Google Scholar
  172. R. C. Ma, C. Hu, C. H. Tam et al., “Genome-wide association study in a Chinese population identifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4,” Diabetologia, vol. 56, no. 6, pp. 1291–1305, 2013. View at: Google Scholar
  173. J. Huang, D. Ellinghaus, A. Franke, B. Howie, and Y. Li, “1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data,” European Journal of Human Genetics, vol. 20, no. 7, pp. 801–805, 2012. View at: Publisher Site | Google Scholar
  174. J. T. Salonen, P. Uimari, J.-M. Aalto et al., “Type 2 diabetes whole-genome association study in four populations: the DiaGen consortium,” American Journal of Human Genetics, vol. 81, no. 2, pp. 338–345, 2007. View at: Publisher Site | Google Scholar
  175. S. Maeda, N. Osawa, T. Hayashi, S. Tsukada, M. Kobayashi, and R. Kikkawa, “Genetic variations associated with diabetic nephropathy and type II diabetes in a Japanese population,” Kidney International, vol. 72, no. 106, pp. S43–S48, 2007. View at: Publisher Site | Google Scholar
  176. L. R. Pasquale, S. J. Loomis, H. Aschard et al., “Exploring genome-wide—dietary heme iron intake interactions and the risk of type 2 diabetes,” Frontiers in Genetics, vol. 4, article 7, 2013. View at: Google Scholar
  177. S. H. Kwak, S.-H. Kim, Y. M. Cho et al., “A genome-wide association study of gestational diabetes mellitus in Korean women,” Diabetes, vol. 61, no. 2, pp. 531–541, 2012. View at: Publisher Site | Google Scholar
  178. D. Zabaneh and D. J. Balding, “A genome-wide association study of the metabolic syndrome in Indian Asian men,” PLoS ONE, vol. 5, no. 8, Article ID e11961, 2010. View at: Publisher Site | Google Scholar

Copyright © 2014 Noraidatulakma Abdullah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.