About diabetes

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Introduction

Type 2 diabetes encompasses an heterogeneous group of metabolic disorders characterized by varying degrees of insulin resistance, impaired insulin secretion and increased glucose production (1).

According to the World Confederation for combating diabetes, there has been a dramatic increase in the prevalence of the disease over the last two decades, while the number of patients is expected to rise worldwide from 171 million in 2000 to 336 million in 2030 (2). The increasing prevalence of type 2 diabetes worldwide is attributed to population growth, aging, urbanization, and the increasing prevalence of obesity and physical inactivity (2). In addition, there is compelling evidence that genetic factors make a major contribution to the development of T2D (3, 4). Indeed, nowadays, type 2 diabetes is best described as a multifactorial trait in which multiple genetic and environmental factors interplay in complex and non-linear ways to produce the common phenotype of hyperglycemia (3, 4). Understanding the genetic component of T2D pathogenesis will facilitate its treatment, diagnosis and prevention (3, 4).

Over the past two decades, extensive research efforts have taken place in order to identify the genetic variants that contribute to individual differences in predisposition to T2D. However, until recently, these efforts were characterized by slow progress and limited success (5). Until 2006, the main approaches used to identify common genetic variants influencing common dichotomous traits, such as T2D, were linkage analysis and candidate gene studies. Both approaches suffered from certain limitations and neither of them proved particularly successful in identifying robustly replicating T2D susceptibility loci. The first approach suffered from being underpowered because linkage analysis is best placed to detect variants with high penetrance. Thus far, there is no evidence that common variants with high penetrance make a substantial contribution to the risk of common forms of T2D (5). The second approach had difficulties mainly associated with choosing credible gene candidates. For the purposes of a candidate gene study, the selection of genes was typically based on hypothesis about probable biological mechanisms involved in T2D pathogenesis. However, the poor characterisation of the function of much of the genome rendered the selection of candidate genes difficult. In addition, poor understanding of the architecture of genetic variation, low-throughput genotyping platforms available at the time, the small sample sizes deployed and the use of liberal thresholds for declaring significance were some of the factors that hindered candidate-gene approaches from identifying reproducibly associated T2D variants.

Hence, until 2006, only two of the many T2D-associated variants reported by candidate gene studies had been convincingly replicated: the Pro12Ala and Glu23Lys variants in PPARG and KCNJ11 genes respectively ().The protein encoded by the PPARG (peroxisome proliferator-activated receptor gamma) gene is a regulator of adipocyte differentiation and represents the target for the thiazolidinedione class of drugs used to treat T2D. The KCNJ11 (potassium inwardly-rectifying channel, subfamily J, member 11) gene encodes a subunit of an inwardly rectifying ATP-sensitive potassium channel. In pancreatic beta cells, these channels are crucial for the regulation of glucose-induced insulin secretion and are the target for the sulfonylureas. The strongest, thus far, known association to T2D was identified in 2006 through linkage fine-mapping study on chromosome 10 (). It resides within TCF7L2 (transcription factor 7 like 2) gene and has been replicated in multiple populations ().

Over the past three years, the advent of genome wide association (GWA) scans has ushered in a new era regarding the capacity of identifying common genetic variants that contribute to predisposition to complex multifactorial phenotypes such as type 2 diabetes. The implementation of the GWA approach was the result of three components: 1) the development of high-throughput genotyping platforms that enabled the conduct of massive SNP typing at high accuracy and low cost, 2) the availability of large sample collections which increased power in association studies and 3) a better understanding of patterns of sequence variation resulting from international efforts such as the Human Genome Sequencing Project and the HapMap.

GWA scans for type 2 diabetes

For the purposes of this review, we examined GWA scans that have genotyped over 150,000 SNPs. We found seven such GWA studies, six conducted in European populations and one conducted in Japanese population (Table 1). Moreover, a meta-analysis of three GWA scans, which is in fact the first of its kind, is reported in this paper. Five GWA scans that have genotyped less than 150,000 SNPs were not included (Table 2).

Common features of most of the studies reported in this paper are the case-control study design and the employment of commercially available, fixed content genotyping platforms. Other aspects such as the specific populations examined, sample size and ascertainment, the extent to which cases and controls were matched and follow-up strategies, differ among studies. These factors underlie some of the heterogeneity in the findings.

The first published GWA study for T2D was conducted by Sladek et al (). In particular, the Diabetes Gene Discovery Group undertook a two-stage GWA scan, the fist one performed by Sladek et al. and the second by Rung et al. At the first stage of the study, 392,935 SNPs were genotyped in 679 T2D cases and 697 controls from France. Genotypes for each study subject were obtained using two platforms: illumina 100k to assay SNPs chosen using a gene-centred design and illumina 300k to assay SNPs chosen to tag haplotype blocks identified by the Phase 1 Hap Map. Selection criteria such as family history of at least one affected first degree relative, age at onset under 45 years and body mass index (BMI) 30 Kg/m2 were chosen for the diabetic subjects in order to decrease phenotypic heterogeneity and enrich for variants that determine insulin resistance and ß-cell dysfunction through mechanisms other than severe obesity. Because of unequal male/female ratios in cases and controls, 12,666 X-linked SNPs were separately analyzed for each gender, however none of them attained significance. In total, 59 autosomal SNPs showing significant association at stage 1, were genotyped in a bigger sample of 2.617 T2D cases and 2.894 controls. The study confirmed the previously known association of TCF7L2

Four further GWA studies were published shortly afterwards (). Steinthorsdottir et al. (), using illumina 300k, genotyped 313,179 SNPs in a sample of 1.399 T2D cases and 5.275 controls from Iceland. In addition, 339,846 two-marker haplotypes were tested. The previously identified SNP, rs7903146, in TCF7L2 gave the most significant results with OR=1.38 and P=1.82x10-10 in all individuals with type 2 diabetes, whereas no other SNP or haplotype was significant after adjustment for the number of tests performed. In total, 51 SNPs (single SNPs and two-marker haplotypes with P0.00005) were selected for replication in a sample of 1.110 T2D cases and 2.272 controls from Denmark. In the Danish group of T2D cases, two SNPs were significantly associated: rs7756992 and rs13266634 (OR=1.24, P=0.00013 and OR=1.20, P=0.0012 respectively). These SNPs were additionally genotyped in three other T2D case-control groups of European ancestry from Denmark, the Netherlands and Philadelphia (of total sample size 3.836 cases and 12.562 controls) as well as in case control groups from Hong Kong (1.457 cases/986 controls) and West Africa (865 cases/1.106 controls). The SNP rs7756992 in the CDKAL1 gene was associated with T2D in individuals of European ancestry (OR=1.20, P=7.7x10-9) and individuals from Hong Kong of Han Chinese ancestry (OR=1.25, P=0.00018). In conclusion, the deCODE study confirmed the previously known association of TCF7L2 gene with T2D and identified CDKAL1 as new T2D-susceptibility locus.

The CDKAL1 locus was independently found by three further contemporaneous studies performed by the Wellcome Trust Case Control Consortium (WTCCC), Diabetes Genetics Initiative (DGI) and Finland-United States Investigation of NIDDM genetics (FUSION) (). WTCCC () is a joint GWA scan of 7 common diseases: bipolar disorder, coronary artery disease, hypertension, Crohn's disease, rheumatoid arthritis, type 1 diabetes and type 2 diabetes. The study was conducted in the British population using groups of ~2.000 cases for each of the 7 examined diseases and a shared group of ~3.000 controls. The genotyping platform, Affymetrix 500k, was used to genotype a total number of 469,557 SNPs. The group of T2D cases was comprised of 1.924 individuals.... In order to decrease the phenotypic heterogeneity of T2D the researchers employed selection criteria such as: British/Irish ancestry, family history of type 2 diabetes and age at onset under 65 years. The study identified 24 independent association signals for the seven diseases that were examined. The

The DGI study employed the same genotyping platform, Affymetrix 500k, and successfully genotyped 386,731 SNPs in 1.464 T2D cases and 1.467 controls from Finland and Sweden. Additionally, 284,968 haplotypes were examined.

Salonen et al (), using the genotyping platform illumina 300k, genotyped almost 300,000 SNPs in 500 cases of T2D and 497 controls originated from four different populations. In particular, the study was conducted in 200 cases and 200 controls from Eastern Finland, 200 cases and 197 controls from Israel and finally 99 cases and 100 controls from Germany and England. The phenotypic heterogeneity of T2D cases was restricted using selection criteria such as: family history of T2D and age at onset under 60 years. Initially, 315,917 SNPs were genotyped in the sample mentioned above, whereas in the replication study, 10 SNPs showing the strongest statistically significant association where chosen to be genotyped in a sample of 2.573 T2D cases and 2.776 controls. It should be noted that this replication study sample was the same one used for replication by Sladek et al. The study confirmed the previously known association of the rs7903146 SNP in TCF7L2 gene but, primarily for reasons of restricted power, failed to reveal any novel T2D susceptibility loci.

Unoki et al (), performed the first GWA scan in a population of non European descent. In particular, 268,068 SNPs that cover approximately 56% of common SNPs in the Japanese, were genotyped in 194 cases with T2D and diabetic retinopathy and in 1.558 controls. Subsequently, 8,323 SNPs with the highest levels of significance were genotyped in 1.367 individuals with T2D and diabetic retinopathy and in 1.266 controls. Out of 6,731 SNPs for which data were obtained successfully, the researchers selected 9 SNPs (p0.0001) and genotyped them in a third set of cases and controls comprised of 3.557 Japanese individuals with T2D and 1.352 controls. The study confirmed the association of the CDKAL1 and IGF2BP2 loci and additionally identified KCNQ1 (rs2283228, OR=1.26, 95% CI=1.18-1.34) as a novel T2D susceptibility locus. KCNQ1 encodes a protein for a voltage-gated potassium channel required for the repolarization phase of the cardiac action potential. The gene product can form heteromultimers with two other potassium channel proteins, KCNE1 and KCNE3. Mutations in this gene are associated with hereditary long QT syndrome, Romano-Ward syndrome, Jervell and Lange-Nielsen syndrome and familial atrial fibrillation. The gene is located in a region of chromosome 11 that contains a large number of contiguous genes that are abnormally imprinted in cancer and the Beckwith-Wiedemann syndrome ().

The second stage of Diabetes Genes Discovery study, performed by Rung et al () (the first one was performed by Sladek et al), was recently published. Out of 392,365 SNPs that were initially genotyped in a total sample of 1.376 individuals from France, the researchers selected 16,273 SNPs and genotyped them in 2.245 T2D cases and in 2.732 controls from France. Subsequently, 28 SNPs were chosen for replication study in 7.698 Danish individuals comprised of 3.334 T2D cases and 4.364 controls. The study confirmed the association of the previously reported loci TCF7L2 (rs7903146), CDKAL1 (rs4712523) and WFS1 (rs4689388), and identified one SNP (rs2943641, OR=1.19, CI=1.13-1.25) located adjacent to IRS1 gene as a novel T2D-susceptibility loci. IRS1 gene encodes a protein which is phosphorylated by insulin receptor tyrosine kinase (). Mutations in this gene are associated with type II diabetes and susceptibility to insulin resistance ().

The DIAGRAM Consortium (Diabetes Genetics, Replication And Meta-Analysis) is the first complex disease GWA scan meta-analysis conducted by Zeggini et al (). DIAGRAM combined data across 4.549 T2D cases and 5.579 controls, all of European descent, from the studies WTCCC, DGI and FUSION. The genotyped autosomal SNPs that passed quality control filters in each study were: a) 393,143 SNPs from the Affymetrix 500k in WTCCC (MAF>0.01, 1.924 T2D cases and 2.938 controls), b) 378,860 SNPs from the Affymetrix 500k in DGI (ΜΑF>0.01, 1.464 T2D cases and 1.476 controls) and c) 306,222 SNPs from the Illumina 300k in FUSION (MAF>0.01, 1.161 T2D cases and 1.174 controls). There were 44.700 SNPs directly genotyped in all three studies across the two platforms. In addition, 1,570,311 SNPs were imputed in each sample resulting in the final examination of 2,202,892 variants across a total sample of 10.128 individuals. Sixty nine SNPs showing the strongest associations (meta-analysis p value <10−4 and meta-analysis heterogeneity p value >10−4), were selected for replication in 22.426 individuals of European descent. The top 11 signals (p<10−5) emerging from this second stage were further genotyped in 57.366 individuals of European descent in stage 3. After integrating data from all three stages of the meta-analysis, six signals reached genome-wide significance levels (p≤ 5x10−8) for T2D association: JAZF1 (OR=1.1, CI=1.07-1.13), CDC123/CAMK1D (OR=1.11, CI=1.07-1.14), TSPAN8/LGR5 (OR=1.09, CI=1.06-1.12), THADA (OR=1.15, CI=1.10-1.20), ADAMTS9 (OR=1.09, CI=1.06-1.12), και NOTCH2 (OR=1.13, CI=1,08-1.17). The JAZF1 (juxtaposed with another zing finger gene 1) gene encodes a nuclear protein with three C2H2-type zinc fingers that functions as a transcriptional repressor of NR2C2 (nuclear receptor subfamily 2,group C, member 2). Chromosomal aberrations involving this gene are associated with endometrial stromal tumors ().The protein encoded by this gene is a member of the transmembrane 4 superfamily, also known as the tetraspanin family. Most of these members are cell-surface proteins that are characterized by the presence of four hydrophobic domains. The proteins mediate signal transduction events that play a role in the regulation of cell development, activation, growth and motility. This encoded protein is a cell surface glycoprotein that is known to complex with integrins. This gene is expressed in different carcinomas. This gene encodes a member of the ADAMTS (a disintegrin and metalloproteinase with thrombospondin motifs) protein family. Members of the family share several distinct protein modules, including a propeptide region, a metalloproteinase domain, a disintegrin-like domain, and a thrombospondin type 1 (TS) motif. Individual members of this family differ in the number of C-terminal TS motifs, and some have unique C-terminal domains. Members of the ADAMTS family have been implicated in the cleavage of proteoglycans, the control of organ shape during development, and the inhibition of angiogenesis. This gene is localized to chromosome 3p14.3-p14.2, an area known to be lost in hereditary renal tumors. This gene encodes a member of the Notch family. Members of this Type 1 transmembrane protein family share structural characteristics including an extracellular domain consisting of multiple epidermal growth factor-like (EGF) repeats, and an intracellular domain consisting of multiple, different domain types. Notch family members play a role in a variety of developmental processes by controlling cell fate decisions. The Notch signaling network is an evolutionarily conserved intercellular signaling pathway which regulates interactions between physically adjacent cells. In Drosophilia, notch interaction with its cell-bound ligands (delta, serrate) establishes an intercellular signaling pathway that plays a key role in development. Homologues of the notch-ligands have also been identified in human, but precise interactions between these ligands and the human notch homologues remain to be determined. This protein is cleaved in the trans-Golgi network, and presented on the cell surface as a heterodimer. This protein functions as a receptor for membrane bound ligands, and may play a role in vascular, renal and hepatic development.

Table 1. GWA scans included

Study

Year

published

Sample

source

Number of

cases

/

controls

Genotyping platform

[SNPs passing QC]

Refs

Diabetes

Gene Discovery Group

1st stage by Sladek et al.

2nd stage by Rung et al.

2007

2009

France

694/645

Illumina 300k + Illumina 100k

()

()

deCODE Genetics

2007

Iceland

1399/5275

Illumina 300k

()

Wellcome Trust Case Control Consortium

(WTCCC)

2007

UK

1924/2938

Affymetrix 500k

()

Diabetes Genetics Initiative

(DGI)

2007

Finland

Sweden

1464

/

1467

Affymetrix 500k

()

Finland-US investigation of NIDDM Genetics (FUSION)

2007

Finland

1161/1174

Illumina 300k

()

DiaGen

2007

East Finland, Germany, UK, Ashkenazi

500/497

Illumina 300k

()

BioBank Japan

2008

Japanese

194/1556

Custom set of 268k SNPs

()


Locus/

gene

Chr

Index

SNP

Position

Effect size

Risk-

allele

frequency

Year association

‘proven'

Study type

Probable mechanism

PPARG

3

rs1801282

12368125

1.14

0.87

2000

Candidate gene

Insulin action

KCNJ11

11

rs5215

*

1.14

0.35

2003

Candidate gene

β-cell dysfunction

TCF7L2

10

rs7901695

114744078

1.37

0.31

2006

Large-scale association

β-cell dysfunction

HHEX/IDE

10

rs1111875

*

1.15

0.65

2007

GWA

β-cell dysfunction

SLC30A8

8

rs13266634

118253964

1.15

0.65

2007

GWA

β-cell dysfunction

CDKAL1

6

rs10946398

20769013

1.14

0.32

2007

GWA

β-cell dysfunction

FTO

16

rs8050136

52373776

1.17

0.40

2007

GWA

Altered BMI

CDKN2A/B

9

rs10811661

22124094

1.20

0.83

2007

GWA

β-cell dysfunction

IGF2BP2

3

rs4402960

186994381

1.14

0.32

2007

GWA

β-cell dysfunction

HNF1B

17

rs4430796

*

1.10

0.47

2007

Large-scale association

β-cell dysfunction

WFS1

4

rs10010131

6343816

1.12

0.60

2007

Large-scale association

Unknown

JAZF1

7

rs864745

28147081

1.10

0.50

2008

GWA

meta-analysis

β-cell dysfunction

CDC123/CAMK1D

10

rs12779790

12368016

1.11

0.18

2008

GWA

meta-analysis

Unknown

TSPAN8/LGR5

12

rs7961581

69949369

1.09

0.27

2008

GWA

meta-analysis

Unknown

THADA

2

rs7578597

43586327

1.15

0.90

2008

GWA

meta-analysis

Unknown

ADAMTS9

3

rs4607103

64686944

1.09

0.76

2008

GWA

meta-analysis

Unknown

NOTCH2

1

rs10923931

120319482

1.13

0.10

2008

GWA

meta-analysis

Unknown

KCNQ1

11

rs2237892

2796327

1.29

0.93

2008

β-cell dysfunction


Table 2. GWA scans not included

Study

Year

published

Sample source

Number of

cases/controls

G

enotyping platform

Refs

Hansonet al.

2007

Pima Indians

300/334

Affymetrix 100k

()

Hayes et al.

2007

Mexican Americans

281/280

Affymetrix 100k

()

Rampersaud et al

2007

Amish

124/295

Affymetrix 100k

()

Florez et al.

2007

Massachusetts

91/1087

Affymetrix 100k

()

Yasuda et al.

2008

Japanese

187/1504

JSNP 100k SNPs

()

PI. Tsai FJ, Yang CF, Chen CC, Chuang LM, Lu CH, Chang CT et al. A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. Plos Genet 2010;6(2):e1000847.

NI. Florez JC, Manning AK, Dupuis J, McAteer J, Irenze K, Gianniny L et al. A 100k genome-wide association scan for diabetes and related traits in the Framingham Heart Study: replication and integration with other genome-wide datasets. Diabetes 2007;56(12):3063-74.

NI. Hanson RL, Bogardus C, Duggan D, Kobes S, Knowlton M, Infante AM et al. A search for variants associated with young-onset type 2 diabetes in American Indians in a 100k genotyping array. Diabetes 2007;56(12):3045-52.

NI. Hayes MG, Pluzhnikov A, Miyake K, Sun Y, Ng MC, Roe CA, et al. Identification of type 2 diabetes genes in Mexican Americans through genome-wide association studies. Diabetes 2007;56(12):3033-44.

NI. Rampersaud E, Damcott CM, Fu M, Shen H, McArdle P, Shi X, et al. Identification of novel candidate genes for type 2 diabetes from a genome-wide association scan in the Old Order Amish: evidence for replication from diabetes related quantitative traits and from independent populations. Diabetes 2007; 56(12): 3053-62.

NI. Yasuda K, Miyake K, Horikawa Y, Hara K, Osawa H, Furuta H, et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008; 40(9):1092-7.


Study

Year

published

Sample source

Number of

cases/controls

Genotyping platform

Refs

Diabetes Gene Discovery Group

1st stage by Sladek et al.

2007

France

694 cases

645 controls

Illumina 300k + Illumina 100k

()

deCODE Genetics

2007

Iceland

1399 cases

5275 controls

Illumina 300k

()

Wellcome Trust Case Control Consortium

(WTCCC)

2007

UK

1924 cases

2938 controls

Affymetrix 500k

()

Diabetes Genetics Initiative (DGI)

2007

Finland

Sweden

1464 cases

1467 controls

Affymetrix 500k

()

Finland-US investigation of NIDDM Genetics (FUSION)

2007

Finland

1161 cases

1174 controls

Illumina 300k

()

DiaGen

2007

East Finland, Germany,

UK, Ashkenazi

500 cases

497 controls

Illumina 300k

()

BioBank Japan

2008

Japanese

194 cases

1556 controls

Custom set of 268k SNPs

()

Takeuchi et al.

2009

Japanese

519 cases

503 controls

Illumina

()

Diabetes Gene Discovery Group

2nd stage by Rung et al.

2009

France

Illumina

()

HanChinese

2010

Chinese

995 cases

894 controls

Illumina 550k

()

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