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Open Access 01.12.2012 | Review

Identification of shared genetic susceptibility locus for coronary artery disease, type 2 diabetes and obesity: a meta-analysis of genome-wide studies

verfasst von: Chaoneng Wu, Yunguo Gong, Jie Yuan, Hui Gong, Yunzeng Zou, Junbo Ge

Erschienen in: Cardiovascular Diabetology | Ausgabe 1/2012

Abstract

Type 2 diabetes (2DM), obesity, and coronary artery disease (CAD) are frequently coexisted being as key components of metabolic syndrome. Whether there is shared genetic background underlying these diseases remained unclear. We performed a meta-analysis of 35 genome screens for 2DM, 36 for obesity or body mass index (BMI)-defined obesity, and 21 for CAD using genome search meta-analysis (GSMA), which combines linkage results to identify regions with only weak evidence and provide genetic interactions among different diseases. For each study, 120 genomic bins of approximately 30 cM were defined and ranked according to the best linkage evidence within each bin. For each disease, bin 6.2 achieved genomic significanct evidence, and bin 9.3, 10.5, 16.3 reached suggestive level for 2DM. Bin 11.2 and 16.3, and bin 10.5 and 9.3, reached suggestive evidence for obesity and CAD respectively. In pooled all three diseases, bin 9.3 and 6.5 reached genomic significant and suggestive evidence respectively, being relatively much weaker for 2DM/CAD or 2DM/obesity or CAD/obesity. Further, genomewide significant evidence was observed of bin 16.3 and 4.5 for 2DM/obesity, which is decreased when CAD was added. These findings indicated that bin 9.3 and 6.5 are most likely to be shared by 2DM, obesity and CAD. And bin 16.3 and 4.5 are potentially common regions to 2DM and obesity only. The observed shared susceptibility regions imply a partly overlapping genetic aspects of disease development. Fine scanning of these regions will definitely identify more susceptibility genes and causal variants.

Additional file 1: Table S1. Characteristics of whole genome studies of T2D [42–75,2]. Table S2. Characteristics of whole genome studies of obesity or BMI-defined obesity [76–111]. Table S3. Characteristics of whole genome studies of CAD [112–132]. (DOC 245 KB)

Authors’ original file for figure 1

Authors’ original file for figure 2

Electronic supplementary material

The online version of this article (doi:10.1186/1475-2840-11-68) contains supplementary material, which is available to authorized users.

Chaoneng Wu, Yunguo Gong contributed equally to this work.

Competing interests

The authors declare that they have no conflicts of interest.

Authors’ contributions

Chaoneng Wu and Yunguo Gong participated in designing, carried out the literature reviewing and data stastical analysis, and drafting the manuscript. Jie Yuan and Hui Gong participated in literature reviewing and data extraction. Yunzeng Zou and Junbo Ge conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

2DM

Type 2 diabetes

CAD

Coronary artery disease

GWAS

Genome-wide association studies

SNPs

Single nucleotide polymorphisms

GSMA

Genome search meta-analysis

BMI

Body mass index

NPL

Nonparametric multipoint linkage

MLS

Maximum LOD score

LOD

Logarithm of odds

ZLR

Ikelihood ratio z-score

Summed rank

CDK4

Cyclin-dependent kinase 4

ENPP1

Ectonucleotide pyrophosphate phosphodiesterase

Insulin resistance

FTO

Fat mass and obesity associated

FABP2

Fatty acid binding protein 2

APOE

Apolipoprotein E

PPARG

Peroxisome proliferator-activated receptor gamma

FOXO1

Forkhead box protein O1

UCP2

Mitochondrial uncoupling protein 2

ADRB3

Beta-3 adrenergic receptor.

Introduction

Type 2 diabetes (2DM), obesity, and coronary artery disease (CAD) are frequently coexisting disorders and major components of metabolic syndrome that cause a substantial public health and economic burden worldwide. Susceptibility to such complex diseases is strongly influenced by multiple genetic factors combined with environmental factors, and all these diseases are further characterized by a chronic inflammatory process. The strong genetic basis has been successfully revealed for each of these diseases. However, whether there are shared or interactive genetic background underlying all three diseases has remained largely unknown.

Actually, evidence has showed that some loci confer risk for more than one of the studied diseases, and most common diseases arise from interaction between multiple genetic variations. This point suggested the concept of common genetic underpinnings for common diseases[1]. For example, a Wellcome Trust Case Control Consortium study evaluated 3,000 shared controls and 2,000 cases for each of seven complex human diseases—bipolar disorder, CAD, Crohn’s disease, hypertension, rheumatoid arthritis, type 1 diabetes, and 2DM—and demonstrated common susceptibility regions for rheumatoid arthritis and type 1 diabetes[2]. The common genetic basis for 2DM and obesity has also been indicated, with the common obesity genes being found through 2DM studies[3]. Further, CAD and 2DM have also been suggested to spring from shared genetic effects, rather than CAD being a complication of diabetes. These data indicates the potentially strong common genetic aspects among 2DM, obesity and CAD.

Genome-wide association studies (GWAS) is one recent revolution, in which hundreds and thousands of single nucleotide polymorphisms (SNPs) are genotyped to capture indirectly most of the genome’s common variation[4]. Compared to the hypothesis-driven linkage and candidate-gene studies, GWAS is unbiased with regard to presumed functions or locations of causal variants. A series of GWA investigations have been reported in 2DM, obesity and CAD recently, with some of them being robustly replicated in large sample sizes. These ever fast-growing numbers of the GWA studies offer the opportunity to combine the recent findings across different investigations, so as to increase the statistical power and to identify the regions with smaller effects. Furthermore, the combination of data across differently interactive diseases might reveal the potentially underlying shared gentic mechanisms.

For this aim, we performed a meta-analysis of autosomal genome GWA scans of 35 2DM studies, 36 studies of obesity or BMI-defined obesity, and 22 CAD studies by using genome search meta-analysis (GSMA) method, an effective and convient nonparametric method to combine results across the genome[5].

Materials and methods

Eligible genome scans

The studies were identified by an extended search of the the database of GWS (http://www.genome.gov/gwastudies) and PubMed database. In PubMed, we used the following terms: (linkage AND genome-scan OR genome OR genomewide OR genome-wide OR LOD ((logarithm of odds) OR microsatellite) combined with (coronary artery disease OR coronary heart disease), or with (obesity OR body mass index OR body mass index OR BMI), or with (2DM OR type 2 diabetes OR type 2 diabetic mellitus). The limits we set included publication in English, human studies, 1998–2012, and the exclusion of reviews. Only primary GWAS were included. Excluded were “fine mapping” with additional markers or pedigrees in selected regions, as well as scans restricted to specific individual chromosome or chromosomal regions. Some studies with genetic mapping figures not available or studies whose markers were difficult to place on the current map were discarded. For a relatively homogenous phenotype, studies of coronary artery calcification or studies about obesity traits (such as leptin or other anthropometric measures) were discarded either.

Data extraction

For each eligible study, the following information was extracted: first author with year of publication, racial descent of study population, number of families and affected individuals, numbers of case–control subjects, number of markers, and types of statistical analysis. Genome scan data across each chromosome were derived from the figures provided in the published papers after digitization and from the results presented in each study.

GSMA method

GSMA is a nonparametric method for combining results from across the genome that were generated with different maps and statistical tests. It does not require individual-level genotype data. As a rank-based meta-analysis method, GSMA assesses the strongest evidence for linkage within prespecified genomic regions, termed “bins”. Briefly, the genome was divided into 120 bins of approximately 30 cM. Bins are referred to by chromosome, so “bin 1.4” indicates the fourth bin on chromosome 1. Each graph from the published GWA studies was imported into CorelDraw and overlaid with a grid dividing each chromosome equally into the required number of bins. Then the peak height within each bin was measured using a graphical ruler within the drawing program, and the maximum linkage statistic within a bin was identified (e.g., maximum LOD or NPL score (Nonparametric multipoint linkage), or minimum P-value). Bins were then ranked, and for each bin, ranks (or weighted ranks) were summed across studies, with the summed rank (SR) forming a test statistic. The significance of the SR in each bin was assessed using Monte Carlo simulations, permuting the bin location of ranks within each study to obtain an empirical P-value.

Analysis was performed using the GSMA software (http://www.kcl.ac.uk/mmg/research/gsma/), with 10,000 simulations and nominal significance. The traditional 30-cM bin definition gives a total of 120 bins on the basis of the Marshfield genetic map (http://www.bli.uzh.ch/BLI/Projects/genetics/maps/marsh.html). To control for multiple testing, we used a Bonferroni correction for the number of bins across the genome: for 30-cM bins, a P-value of 0.00042 (0.05/120 = 0.00042) was necessary for genome-wide significant evidence of linkage, and a P-value of 1/120 = 0.0083 for suggestive evidence of linkage. Corresponding P-values were calculated for other bin sizes. Considering the different number of cases for the three diseases, the number of cases of each disease was weighted, with each R study value being multiplied by its study’s weight

(\sqrt{N [affected case]})

, divided by the mean of this value over all studies, as discussed firstly by Levinson et al[6].

Results

Data included

The selection criteria and data requests provided 35 studies for 2DM comprising 378,132 samples (4,532 pedigree, 42,200 cases, 86,253 controls) (Additional file1: Table S1); 36 studies for obesity or BMI-defined obesity comprising 358,860 samples (9,973 pedigree, 348,887 subjects) (Additional file1: Table S2); and 21 CAD studies comprising 191,126 samples (2,036 pedigree, 69,828 case, 119,262 control) (Additional file1: Table S3).

Linkage evidence for each disease

For linkage evidence of 2DM, bin 6.2 reached genome-wide significance (P_SR = 0.000143), with two adjacent bins (6.4 and 6.5) showing nominal significance (Figure1, Table1). Three other bins, 9.3, 10.5, 16.3, also reached suggestive level (P_SR = 0.001767, P_SR = 0.003829 and P_SR = 0.005043, respectively) (Figure1, Table1). Bin 8.5 and 11.2 showed nominal significance (Figure1, Table1).

Table 1

Linkage evidence for each disease of 2DM, obesity and CAD

	2DM			Obesity			CAD
Pos	Bin	Cytogenetic band	P _SR	Bin	Cytogenetic band	P _SR	Bin	Cytogenetic band	P _SR
1	6.2	6p22.3-p21.1	0.000143	11.2	11p15.1-p12	0.001822	10.5	10q23.33-q26.13	0.001135
2	9.3	9p21.1-q21.32	0.001767	16.3	16q12.2-q23.1	0.004428	9.3	9p21.1-q21.32	0.007895
3	10.5	10q23.33-q26.13	0.003829	19.3	19q12-q13.33	0.008426	16.3	16q12.2-q23.1	0.009467
4	16.3	16q12.2-q23.1	0.005043	6.5	6q23.2-q25.3	0.018402	11.5	11q22.3-q24.1	0.011467
5	3.6	3q22.1-q25.31	0.009509	6.4	6q15-q23.2	0.019753	15.3	15q21.3-q26.1	0.013732
6	8.5	8q22-q24.21	0.026031	4.5	4q24-q28.3	0.025605	7.3	7p14.1-q21.11	0.026741
7	11.2	11p15.1-p12	0.034162	9.3	9p21.1-q21.32	0.036314	9.4	9q21.32-q31.1	0.032157
8	6.4	6q15-q23.2	0.035033	9.4	9q21.32-q31.1	0.042022	7.4	7q12.11-q31.1	0.046022

2DM, type 2 diabetes; CAD, coronary artery disease.

For obesity, bin 11.2 and 16.3 reached suggestive level (P_SR = 0.001822 and P_SR = 0.004428, respectively, Figure1, Table1). And six other bins (19.3, 6.3, 6.4, 4.5, 9.3 and 9.4) showed nominal significance (Figure1, Table1). For CAD, two bins, 10.5 and 9.3, reached the suggestive level (P_SR = 0.001135 and P_SR = 0.007895, respectively). And six other bins,16.3, 11.5, 15.3, 7.3, 9.4 and 7.4, showed nominal significance (Figure1, Table1).

Linkage evidence for combined each two or all three diseases

In pooled analysis, bin 9.3 and bin 6.5 were most likely to be shared by all the three diseases. Bin 9.3 reached suggestive evidence for 2DM/CAD (P_SR = 0.002362) and 2DM/obesity (P_SR = 0.000543), and normial evidence for CAD/obesity (P_SR = 0.0405) (Figure2, Table2). However, this region achieved genomewide significant evidence in pooled all the three diseases (P_SR = 0.000276). Bin 6.5 reached suggestive evidence for 2DM/obesity (P_SR = 0.003457) and CAD/obesity (P_SR = 0.006592), yet no significant evidence for 2DM/CAD (P_SR = NS) (Figure2, Table2). Still, in pooled all the three disease, this region showed suggestive evidence with a much smaller value of P_SR (P_SR =0.00205) (Figure2, Table2).

Table 2

Linkage evidence for combined each two or all three diseases of 2DM, obesity and CAD

	2DM/CAD			2DM/Obesity
Pos	Bin	Cytogenetic band	P _SR	Bin	Cytogenetic band	P _SR
1	9.3	9p21.1-q21.32	0.002362	16.3	16q12.2-q23.1	4.75E-05
2	6.2	6p22.3-p21.1	0.005861	4.5	4q24-q28.3	0.000159
3	7.4	7q12.11-q31.1	0.022381	9.3	9p21.1-q21.32	0.000543
4	16.3	16q12.2-q23.1	0.022471	6.5	6q23.2-q25.3	0.003457
5	4.1	4pter-p15.33	0.024538	2.3	2p23.2-p16.2	0.00572
6	22.2	22q12.3-pter	0.030478	4.4	4q13.3-q24	0.010083
7	17.2	17p12-q21.33	0.045176	12.3	12p11.21-q15	0.029702
	CAD/Obesity			All three diseases
Pos	Bin	Cytogenetic band	P _SR	Bin	Cytogenetic band	P _SR
1	16.3	16q12.2-q23.1	0.006016	9.3	9p21.1-q21.32	0.000276
2	6.5	6q23.2-q25.3	0.006592	16.3	16q12.2-q23.1	0.000505
3	16.2	16p13-q12.2	0.030775	6.5	6q23.2-q25.3	0.002055
4	9.3	9p21.1-q21.32	0.040512	4.5	4q24-q28.3	0.004021
5	10.5	10q23.33-q26.13	0.041678	7.3	7p14.1-q21.11	0.011893
6	11.2	11p15.1-p12	0.041838	3.6	3q22.1-q25.31	0.014341
7	4.5	4q24-q28.3	0.042347	4.4	4q13.3-q24	0.019338
8	7.3	7p14.1-q21.11	0.043952	11.3	11p12-q13.3	0.022461

2DM, type 2 diabetes; CAD, coronary artery disease.

Bin 16.3 and bin 4.5 were most likely to be shared by 2DM and obesity only. In 2DM/obesity, these two bins achieved genomewide significant evidence (P_SR = 4.75E-05 and 0.000159, respectively) (Figure2, Table2). For bin 16.3, it showed norminal evidenc for 2DM/CAD and suggestive evidence for CAD/Obesity (P_SR = 0.022471 and 0.006016, respectively) (Figure2, Table2). For bin 4.5, it showed norminal evidence for CAD/obesity (P_SR = 0.042347) and no significant evidence for 2DM/CAD (P_SR = NS) (Figure2, Table2). In pooled all three diseases, the evidence decreased to be suggestive level for either bin 16.3 or bin 4.5 (P_SR =0.000505 and 0.004021, respectively) (Figure2, Table2).

Discussion

2DM and obesity have long been recognized as major risk factors for CAD. In addition, the development of CAD can also follow or precede 2DM or obesity. Strong genetic factors for each disease have been recognized in previous studies. However, the common genetic aspects underlying all these three diseases remain undetermined. Here, we provided evidence that bin 9.3 and 6.5 were potentially common genetic regions to all the three diseases, and bin 16.3 and 4.5 were most likely to be shared by only 2DM and obesity.

Bin 9.3 (9p21.1-q21.32)

Bin 9.3 achieved much stronger evidence for pooled analysis of all the three diseases compared to each two diseases combination. The increased stastical power for this region suggested a mutually reinforcing interaction among the 2DM, obesity and CAD. Within this region, 9p21.3 is a loci approximately 22 million base pairs from the 9p telomere. This region was firstly identified to be associated with CAD across different ethinics. The genetic risk variant from this region is extremely common, with 75% of the Caucasian population having one or more risk alleles. In CAD, the locus is heterozygous in 50% of Caucasians and homozygous in 25% with increased risk of 15–20% and 30–40% respectively. A series of studies have reported that this region is associated with the risk for 2DM[7‐11]. Evidence can also be found that the variants of 9p21.3 are implicated in obesity[12].

The region of 9p21 maps two well-characterized tumor suppressor genes, CDKN2A/ CDKN2B, encoding respectively proteins p16 ^INK4a and p15 ^INK4b, both of them are involved in the regulation of cell proliferation, cell aging and apoptosis. Protein p16^INK4a inhibits cyclin-dependent kinase 4(CDK4) and is a strong regulator of pancreatic beta cell replication[13, 14]. In addition, p16^INK4a can be an important regulator for chronic inflammation, and deficiency of p16^INK4a results in decreased inflammatory signaling in murine macrophages and influences the phenotype of human adipose tissue macrophages[15, 16]. It has been suggested that chronic inflammatory process is implied in CAD, 2DM and obesity. It is therefore these data supports that defects in 9p21.3 might be the common genetic factors, indicating a chronic inflammation process that predispose to the sequelae of 2DM, obesity and CAD.

Bin 6.5 (6q23.2-q25.3)

Bin 6.5 reached much stronger evidence for the linkage of all the three diseases compared to the combination to each two diseases, implying the potentially common gentic aspects for all the three diseases of bin 6.5.

The gene ENPP1 (ectonucleotide pyrophosphate phosphodiesterase), which encodes a membrane-bound glycoprotein inhibiting the insulin-receptor tyrosine kinas activity and reducing insulin sensitivity, appears to be one of the candidate genes in the region. Evidence can be found that the variants of ENPP1 were associated with insulin resistance (IR)/atherogenic phenotypes, including earlier onset of 2DM and myocardial infarction. And it is also associated with the genetic susceptibility for metabolic syndrome in CAD patients[17, 18]. The report by Bacci S, et al., showed the variant of ENPP1 is an independent predictor of major cardiovascular events, and this effect is exacerbated by obesity in 2DM patients[19]. A set of studies have also suggested that the variant of ENPP1 were associated with only obesity-type 2DM, which indicating the substantial etiological heterogeneity of 2DM mediated by the obesity status based on the shared genetic loci[20‐22]. Elucidation of the interplay of ENPP1 in increased susceptibility to 2DM, obesity and CAD will provide recommendations for the underlying shared mechanisms of these complex common diseases.

Bin 16.3 (16q12.2-q23.1)

Bin 16.3 was the most significant region for linkage to 2DM/obesity, with much decreased evidence for pooled analysis of all the three diseases, indicating the common genetic aetiology for 2DM/obesity of bin 16.3. One most interesting gene from this region is FTO (fat mass and obesity associated), encoding 2-oxoglutarate-dependent nucleic acid demethylase. FTO harbours the strongest known obesity-susceptibility locus. In animal models, knock-out of FTO resulted in growth retardation, loss of white adipose tissue, and increase energy metabolism and systemic sympathetic activation. In contrast, FTO overexpression results in a dose-dependent increase in BMI and develop glucose intolerance on high-fat diet[23]. Amounting evidence has also suggested that FTO is associated with metabolic profiles including dyslipidemia and insulin resistance, and increased the risk for 2DM[24‐28]. Although multiple association studies as well as functional experiments have revealed that FTO is critical for obesity and 2DM, only one study with really small sample size has reported that FTO is assocaited with increased rifk for acute coronary syndrome, a severe form of CAD[29]. Our results did not support that this region was shared by 2DM/obesity and CAD. Even though, our findings suggested the underlying shared genetic region of bin 16.3, which is worthy of further investigation.

Bin 4.5 (4q24-q28.3)

Bin 4.5 was also worthy of note when deciphering the common susceptibility loci. Bin 4.5 achieved genomewide significance evidence for 2DM/obesity, which become much weaker in pooled analysis of all three diseases, indicating this loci being shared by 2DM/obesity but divergent from CAD.

FABP2 might be a potential common candidate gene from this region. FABP2 (fatty acid binding protein 2) is an intracellular proteins expressed only in the intestine and involved in the absorption and intracellular transport of dietary long-chain fatty acids. The association of FABP2 with both 2DM and obesity has been reported by several studies[30‐33]. Previous reports showed that the variants of FABP2 increased flux of dietary fatty acids into the circulation, and was also associated with increased fasting inculin concentration, fasting fatty acid oxidation and reduced glucose uptake, all are etiology of metabolic disorders[34]. In our study, this region was shared by 2DM/obesity but not CAD. The report by Georgopoulos A et al., showed that the variant of FABP2 increase the cardiovascular risk in dyslipidemic men with diabetes compared to their non-diabetic counterparts with 2 ~ 3.5-fold, which indicates the influence of the variant of FABP2 to lipid and glucose metabolic disorders and then to affect the risk to CAD[35]. Larger studies focusing on analysis of FABP2 in patients with 2DM and obesity will augment our preliminary results.

Other regions

Here, bin 19.3 reached suggestive evidence for independent obesity analysis. One most reported gene from this region is Apolipoprotein E (APOE), which influences on lipid profiles and is associated with the development of both 2DM with and without CAD, and furthermore, it increased the risk among the subjects with obesity and/or smoking, conditions that are associated with high oxidative stress[36]. The expression of APOE can be regulated by peroxisome proliferator-activated receptor gamma (PPARG, bin 3.1), a potential common gene for glucose and lipid metabolism as well as CAD development[37, 38]. In addition, PPARG interactes with another transfaction factor of forkhead box protein O1 (FOXO1, bin 13.4) to regulate the expression of mitochondrial uncoupling protein 2 (UCP2, bin11.4) and beta-3 adrenergic receptor (ADRB3, bin 8.3), all these genes being associated with metabolic disturbances such as obesity and 2DM[39‐41]. These data collectively indicated the potential complex gene interactions for these closely related disorders.

Conclusions

In summary, our results provide evidence that bin 9.3 and 6.5 are most likely to be shared by 2DM, obesity and CAD. Bin 16.3 and 4.5 were potentially common regions to 2DM and obesity only. The observed shared susceptibility regions for each two or all the three diseases suggest a common genetic cause for these diseases which implies a partly overlapping genetic aspects of disease development. Eventhough it remained unclear that one disease may have a direct causal influence on the susceptibility of the other, the potential interactive regions we identified here await for further elucidation. Fine scans of these regions will definitely identify more susceptibility genes and causal variants for these common diseases.

Electronic-database information

Accession Numbers and URLs for data in this article are as follows:

Online Mendelian Inheritance in Man (OMIM),http://www.ncbi.nlm.nih.gov/Omim

Marshfield genetic map,http://research.marshfieldclinic.org/genetics/home/index.asp

GSMA software, http://www.kcl.ac.uk/mmg/research/gsma/ [42–133].

Acknowledgements

This work was supported by an Outstanding Youth Grant from National Natural Science Foundation of China (30725036) and National Basic Research Program of China (2011CB503905).

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Competing interests

The authors declare that they have no conflicts of interest.

Authors’ contributions

Electronic supplementary material

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Freimer NB, Sabatti C: Human genetics: variants in common diseases. Nature. 2007, 445: 828-830. 10.1038/nature05568.PubMedCrossRef

Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007, 447: 661-678. 10.1038/nature05911.CrossRef

Kaiser J: Genetics. Mysterious, widespread obesity gene found through diabetes study. Science. 2007, 316-185.

Hardy J, Singleton A: Genomewide association studies and human disease. N Engl J Med. 2009, 360: 1759-1768. 10.1056/NEJMra0808700.PubMedCentralPubMedCrossRef

Pardi F, Levinson DF, Lewis CM: GSMA: software implementation of the genome search meta-analysis method. Bioinformatics. 2005, 21: 4430-4431. 10.1093/bioinformatics/bti725.PubMedCrossRef

Levinson DF, Levinson MD, Segurado R, Lewis CM: Genome scan meta-analysis of schizophrenia and bipolar disorder, part I: Methods and power analysis. Am J Hum Genet. 2003, 73: 17-33. 10.1086/376548.PubMedCentralPubMedCrossRef

Cheng X, Shi L, Nie S, Wang F, Li X, Xu C, Wang P, Yang B, Li Q, Pan Z, Li Y, Xia H, Zheng C, Ke Y, Wu Y, Tang T, Yan X, Yang Y, Xia N, Yao R, Wang B, Ma X, Zeng Q, Tu X, Liao Y, Wang QK: The same chromosome 9p21.3 locus is associated with type 2 diabetes and coronary artery disease in a Chinese Han population. Diabetes. 2011, 60: 680-684. 10.2337/db10-0185.PubMedCentralPubMedCrossRef

Parra EJ, Below JE, Krithika S, Valladares A, Barta JL, Cox NJ, Hanis CL, Wacher N, Garcia-Mena J, Hu P, Shriver MD, Kumate J, McKeigue PM, Escobedo J, Cruz M, Diabetes Genetics Replication, Consortium Meta-analysis (DIAGRAM): Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American sample from Starr County, Texas. Diabetologia. 2011, 54: 2038-2204. 10.1007/s00125-011-2172-y.PubMedCrossRef

Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, et al: Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010, 42: 579-589. 10.1038/ng.609.PubMedCentralPubMedCrossRef

10.

Takeuchi F, Serizawa M, Yamamoto K, Fujisawa T, Nakashima E, Ohnaka K, Ikegami H, Sugiyama T, Katsuya T, Miyagishi M, Nakashima N, Nawata H, Nakamura J, Kono S, Takayanagi R, Kato N: Confirmation of multiple risk Loci and genetic impacts by a genome-wide association study of type 2 diabetes in the Japanese population. Diabetes. 2009, 58: 1690-1699. 10.2337/db08-1494.PubMedCentralPubMedCrossRef

11.

Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS: Wellcome Trust Case Control Consortium (WTCCC), McCarthy MI, Hattersley AT: Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007, 316: 1336-1341. 10.1126/science.1142364.PubMedCentralPubMedCrossRef

12.

Ng MC, Park KS, Oh B, Tam CH, Cho YM, Shin HD, Lam VK, Ma RC, So WY, Cho YS, Kim HL, Lee HK, Chan JC, Cho NH: 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. 2008, 57: 2226-2233. 10.2337/db07-1583.PubMedCentralPubMedCrossRef

13.

Rane SG, Dubus P, Mettus RV, Galbreath EJ, Boden G, Reddy EP, Barbacid M: Loss of Cdk4 expression causes insulin-deficient diabetes and Cdk4 activation results in beta-islet cell hyperplasia. Nat Genet. 1999, 22: 44-52. 10.1038/8751.PubMedCrossRef

14.

Marzo N, Mora C, Fabregat ME, Martin J, Usac EF, Franco C, Barbacid M, Gomis R: Pancreatic islets from cyclin-dependent kinase 4/R24C (Cdk4) knockin mice have significantly increased beta cell mass and are physiologically functional, indicating that Cdk4 is a potential target for pancreatic beta cell mass regeneration in Type 1 diabetes. Diabetologia. 2004, 47: 686-694. 10.1007/s00125-004-1372-0.PubMedCrossRef

15.

Cudejko C, Wouters K, Fuentes L, Hannou SA, Paquet C, Bantubungi K, Bouchaert E, Vanhoutte J, Fleury S, Remy P, Tailleux A, Chinetti-Gbaguidi G, Dombrowicz D, Staels B, Paumelle R: p16INK4a deficiency promotes IL-4-induced polarization and inhibits proinflammatory signaling in macrophages. Blood. 2011, 118: 2556-2566. 10.1182/blood-2010-10-313106.PubMedCentralPubMedCrossRef

16.

Fuentes L, Wouters K, Hannou SA, Cudejko C, Rigamonti E, Mayi TH, Derudas B, Pattou F, Chinetti-Gbaguidi G, Staels B, Paumelle R: Downregulation of the tumour suppressor p16INK4A contributes to the polarisation of human macrophages toward an adipose tissue macrophage (ATM)-like phenotype. Diabetologia. 2011, 54: 3150-3156. 10.1007/s00125-011-2324-0.PubMedCentralPubMedCrossRef

17.

Lazarevic G, Milojkovic M, Tasic I, Najman S, Antic S, Stefanovic V: PC-1 (ENPP1) K121Q polymorphism in overweight and obese type 2 diabetic coronary heart disease patients. Acta Cardiol. 2008, 63: 323-330. 10.2143/AC.63.3.1020308.PubMedCrossRef

18.

Tasic I, Milojkovic M, Sunder-Plassmann R, Lazarevic G, Tasic NM, Stefanovic V: The association of PC-1 (ENPP1) K121Q polymorphism with metabolic syndrome in patients with coronary heart disease. Clin Chim Acta. 2007, 377: 237-242. 10.1016/j.cca.2006.10.003.PubMedCrossRef

19.

Bacci S, Rizza S, Prudente S, Spoto B, Powers C, Facciorusso A, Pacilli A, Lauro D, Testa A, Zhang YY, Di Stolfo G, Mallamaci F, Tripepi G, Xu R, Mangiacotti D, Aucella F, Lauro R, Gervino EV, Hauser TH, Copetti M, De Cosmo S, Pellegrini F, Zoccali C, Federici M, Doria A, Trischitta V: The ENPP1 Q121 variant predicts major cardiovascular events in high-risk individuals: evidence for interaction with obesity in diabetic patients. Diabetes. 2011, 60: 1000-1007. 10.2337/db10-1300.PubMedCentralPubMedCrossRef

20.

Valli-Jaakola K, Suviolahti E, Schalin-Jantti C, Ripatti S, Silander K, Oksanen L, Salomaa V, Peltonen L, Kontula K: Further evidence for the role of ENPP1 in obesity: association with morbid obesity in Finns. Obesity (Silver Spring). 2008, 16: 2113-2119. 10.1038/oby.2008.313.CrossRef

21.

Bottcher Y, Korner A, Reinehr T, Enigk B, Kiess W, Stumvoll M, Kovacs P: ENPP1 variants and haplotypes predispose to early onset obesity and impaired glucose and insulin metabolism in German obese children. J Clin Endocrinol Metab. 2006, 91: 4948-4952. 10.1210/jc.2006-0540.PubMedCrossRef

22.

Meyre D, Bouatia-Naji N, Vatin V, Veslot J, Samson C, Tichet J, Marre M, Balkau B, Froguel P: ENPP1 K121Q polymorphism and obesity, hyperglycaemia and type 2 diabetes in the prospective DESIR Study. Diabetologia. 2007, 50: 2090-2096. 10.1007/s00125-007-0787-9.PubMedCrossRef

23.

Church C, Moir L, McMurray F, Girard C, Banks GT, Teboul L, Wells S, Brüning JC, Nolan PM, Ashcroft FM, Cox RD: Overexpression of Fto leads to increased food intake and results in obesity. Nat Genet. 2010, 42: 1086-1092. 10.1038/ng.713.PubMedCentralPubMedCrossRef

24.

Kilpeläinen TO, Zillikens MC, Stančákova A, Finucane FM, Ried JS, Langenberg C, Zhang W, Beckmann JS, Luan J, Vandenput L, Styrkarsdottir U, Zhou Y, Smith AV, Zhao JH, Amin N, Vedantam S, Shin SY, Haritunians T, Fu M, Feitosa MF, Kumari M, Halldorsson BV, Tikkanen E, Mangino M, Hayward C, Song C, Arnold AM, Aulchenko YS, Oostra BA, Campbell H, et al: Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat Genet. 2011, 43: 753-760. 10.1038/ng.866.PubMedCentralPubMedCrossRef

25.

Hotta K, Kitamoto T, Kitamoto A, Mizusawa S, Matsuo T, Nakata Y, Kamohara S, Miyatake N, Kotani K, Komatsu R, Itoh N, Mineo I, Wada J, Yoneda M, Nakajima A, Funahashi T, Miyazaki S, Tokunaga K, Masuzaki H, Ueno T, Hamaguchi K, Tanaka K, Yamada K, Hanafusa T, Oikawa S, Yoshimatsu H, Sakata T, Matsuzawa Y, Nakao K, Sekine A: Association of variations in the FTO, SCG3 and MTMR9 genes with metabolic syndrome in a Japanese population. J Hum Genet. 2011, 56: 647-651. 10.1038/jhg.2011.74.PubMedCrossRef

26.

Freathy RM, Timpson NJ, Lawlor DA, Pouta A, Ben-Shlomo Y, Ruokonen A, Ebrahim S, Shields B, Zeggini E, Weedon MN, Lindgren CM, Lango H, Melzer D, Ferrucci L, Paolisso G, Neville MJ, Karpe F, Palmer CN, Morris AD, Elliott P, Jarvelin MR, Smith GD, McCarthy MI, Hattersley AT, Frayling TM: Common variation in the FTO gene alters diabetes-related metabolic traits to the extent expected given its effect on BMI. Diabetes. 2008, 57: 1419-1426. 10.2337/db07-1466.PubMedCentralPubMedCrossRef

27.

Li H, Kilpeläinen TO, Liu C, Zhu J, Liu Y, Hu C, Yang Z, Zhang W, Bao W, Cha S, Wu Y, Yang T, Sekine A, Choi BY, Yajnik CS, Zhou D, Takeuchi F, Yamamoto K, Chan JC, Mani KR, Been LF, Imamura M, Nakashima E, Lee N, Fujisawa T, Karasawa S, Wen W, Joglekar CV, Lu W, et al: Association of genetic variation in FTO with risk of obesity and type 2 diabetes with data from 96,551 East and South Asians. Diabetologia. 2012, 55: 981-995. 10.1007/s00125-011-2370-7.PubMedCentralPubMedCrossRef

28.

Yajnik CS, Janipalli CS, Bhaskar S, Kulkarni SR, Freathy RM, Prakash S, Mani KR, Weedon MN, Kale SD, Deshpande J, Krishnaveni GV, Veena SR, Fall CH, McCarthy MI, Frayling TM, Hattersley AT, Chandak GR: FTO gene variants are strongly associated with type 2 diabetes in South Asian Indians. Diabetologia. 2009, 52: 247-252. 10.1007/s00125-008-1186-6.PubMedCentralPubMedCrossRef

29.

Hubacek JA, Stanek V, Gebauerová M, Pilipcincová A, Dlouhá D, Poledne R, Aschermann M, Skalická H, Matousková J, Kruger A, Penicka M, Hrabáková H, Veselka J, Hájek P, Lánská V, Adámková V, Pitha J: A FTO variant and risk of acute coronary syndrome. Clin Chim Acta. 2010, 411: 1069-1072. 10.1016/j.cca.2010.03.037.PubMedCrossRef

30.

Hubacek JA, Stanek V, Gebauerová M, Pilipcincová A, Dlouhá D, Poledne R, Aschermann M, Skalická H, Matousková J, Kruger A, Penicka M, Hrabáková H, Veselka J, Hájek P, Lánská V, Adámková V, Pitha J: Preliminary evidence of FABP2 A54T polymorphism associated with reduced risk of type 2 diabetes and obesity in women from a German cohort. Horm Metab Res. 2006, 38: 341-345. 10.1055/s-2006-925400.CrossRef

31.

Albala C, Santos JL, Cifuentes M, Villarroel AC, Lera L, Liberman C, Angel B, Perez-Bravo F: Intestinal FABP2 A54T polymorphism: association with insulin resistance and obesity in women. Obes Res. 2004, 12: 340-345. 10.1038/oby.2004.42.PubMedCrossRef

32.

Okada T, Sato NF, Kuromori Y, Miyashita M, Iwata F, Hara M, Harada K, Hattori H: Thr-encoding allele homozygosity at codon 54 of FABP 2 gene may be associated with impaired delta 6 desatruase activity and reduced plasma arachidonic acid in obese children. J Atheroscler Thromb. 2006, 13: 192-196. 10.5551/jat.13.192.PubMedCrossRef

33.

Albala BC, Jimenez RB, Perez BF, Liberman GC: Fatty acid binding protein 2 (FABP-2) polymorphism, obesity and insulin resistance. Rev Med Chil. 2006, 134: 372-379.CrossRef

34.

Weiss EP, Brandauer J, Kulaputana O, Ghiu IA, Wohn CR, Phares DA, Shuldiner AR, Hagberg JM: FABP2 Ala54Thr genotype is associated with glucoregulatory function and lipid oxidation after a high-fat meal in sedentary nondiabetic men and women. Am J Clin Nutr. 2007, 85: 102-108.PubMed

35.

Georgopoulos A, Bloomfield H, Collins D, Brousseau ME, Ordovas JM, O'Connor JJ, Robins SJ, Schaefer EJ: Codon 54 polymorphism of the fatty acid binding protein (FABP) 2 gene is associated with increased cardiovascular risk in the dyslipidemic diabetic participants of the Veterans Affairs HDL intervention trial (VA-HIT). Atherosclerosis. 2007, 194: 169-74. 10.1016/j.atherosclerosis.2006.07.022.PubMedCrossRef

36.

Chaudhary R, Likidlilid A, Peerapatdit T, Tresukosol D, Srisuma S, Ratanamaneechat S, Sriratanasathavorn C: Apolipoprotein E gene polymorphism: effects on plasma lipids and risk of type 2 diabetes and coronary artery disease. Cardiovasc Diabetol. 2012, 11: 36-10.1186/1475-2840-11-36.PubMedCentralPubMedCrossRef

37.

Yue L, Mazzone T: Peroxisome proliferator-activated receptor gamma stimulation of adipocyte ApoE gene transcription mediated by the liver receptor X pathway. J Biol Chem. 2009, 284: 10453-61. 10.1074/jbc.M808482200.PubMedCentralPubMedCrossRef

38.

Ho JS, Germer S, Tam CH, So WY, Martin M, Ma RC, Chan JC, Ng MC: Association of the PPARG Pro12Ala polymorphism with type 2 diabetes and incident coronary heart disease in a Hong Kong Chinese population. Diabetes Res Clin Pract. 2012, [Epub ahead of print]

39.

Kim JJ, Li P, Huntley J, Chang JP, Arden KC, Olefsky JM: FoxO1 haploinsufficiency protects against high-fat diet-induced insulin resistance with enhanced peroxisome proliferator-activated receptor gamma activation in adipose tissue. Diabetes. 2009, 58: 1275-1282. 10.2337/db08-1001.PubMedCentralPubMedCrossRef

40.

Oktavianthi S, Trimarsanto H, Febinia CA, Suastika K, Saraswati MR, Dwipayana P, Arindrarto W, Sudoyo H, Malik SG: Uncoupling protein 2 gene polymorphisms are associated with obesity. Cardiovasc Diabetol. 2012, 11: 41-10.1186/1475-2840-11-41.PubMedCentralPubMedCrossRef

41.

Mirrakhimov AE, Kerimkulova AS, Lunegova OS, Moldokeeva CB, Zalesskaya YV, Abilova SS, Sovhozova NA, Aldashev AA, Mirrakhimov EM: An association between TRP64ARG polymorphism of the B3 adrenoreceptor gene and some metabolic disturbances. Cardiovasc Diabetol. 2011, 10: 89-10.1186/1475-2840-10-89.PubMedCentralPubMedCrossRef

42.

Palmer ND, McDonough CW, Hicks PJ, Roh BH, Wing MR, An SS, Hester JM, Cooke JN, Bostrom MA, Rudock ME, Talbert ME, Lewis JP, Ferrara A, Lu L, Ziegler JT, Sale MM, Divers J, Shriner D, Adeyemo A, Rotimi CN, Ng MC, Langefeld CD, Freedman BI, Bowden DW, Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, et al: A genome-wide association search for type 2 diabetes genes in African Americans. PLoS One. 2012, 7: e29202-10.1371/journal.pone.0029202.PubMedCentralPubMedCrossRef

43.

Cui B, Zhu X, Xu M, Guo T, Zhu D, Chen G, Li X, Xu L, Bi Y, Chen Y, Xu Y, Wang W, Wang H, Huang W, Ning G: A genome-wide association study confirms previously reported loci for type 2 diabetes in Han Chinese. PLoS One. 2011, 6: e22353-10.1371/journal.pone.0022353.PubMedCentralPubMedCrossRef

44.

Cho YS, Chen CH, Hu C, Long J, Ong RT, Sim X, Takeuchi F, Wu Y, Go MJ, Yamauchi T, Chang YC, Kwak SH, Ma RC, Yamamoto K, Adair LS, Aung T, Cai Q, Chang LC, Chen YT, Gao Y, Hu FB, Kim HL, Kim S, Kim YJ, Lee JJ, Lee NR, Li Y, Liu JJ, Lu W, et al: Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet. 2012, 44: 67-72.CrossRef

45.

Kooner JS, Saleheen D, Sim X, Sehmi J, Zhang W, Frossard P, Been LF, Chia KS, Dimas AS, Hassanali N, Jafar T, Jowett JB, Li X, Radha V, Rees SD, Takeuchi F, Young R, Aung T, Basit A, Chidambaram M, Das D, Grundberg E, Hedman AK, Hydrie ZI, Islam M, Khor CC, Kowlessur S, Kristensen MM, Liju S, Lim WY, et al: Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat Genet. 2011, 43: 984-989. 10.1038/ng.921.PubMedCentralPubMedCrossRef

46.

Below JE, Gamazon ER, Morrison JV, Konkashbaev A, Pluzhnikov A, McKeigue PM, Parra EJ, Elbein SC, Hallman DM, Nicolae DL, Bell GI, Cruz M, Cox NJ, Hanis CL: 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. 2011, 54: 2047-2055. 10.1007/s00125-011-2188-3.PubMedCentralPubMedCrossRef

47.

Tsai FJ, Yang CF, Chen CC, Chuang LM, Lu CH, Chang CT, Wang TY, Chen RH, Shiu CF, Liu YM, Chang CC, Chen P, Chen CH, Fann CS, Chen YT, Wu JY: A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet. 2010, 6: e1000847-10.1371/journal.pgen.1000847.PubMedCentralPubMedCrossRef

48.

Shu XO, Long J, Cai Q, Qi L, Xiang YB, Cho YS, Tai ES, Li X, Lin X, Chow WH, Go MJ, Seielstad M, Bao W, Li H, Cornelis MC, Yu K, Wen W, Shi J, Han BG, Sim XL, Liu L, Qi Q, Kim HL, Ng DP, Lee JY, Kim YJ, Li C, Gao YT, Zheng W, Hu FB: Identification of new genetic risk variants for type 2 diabetes. PLoS Genet. 2010, 6: e1001127-10.1371/journal.pgen.1001127. piiPubMedCentralPubMedCrossRef

49.

Yamauchi T, Hara K, Maeda S, Yasuda K, Takahashi A, Horikoshi M, Nakamura M, Fujita H, Grarup N, Cauchi S, Ng DP, Ma RC, Tsunoda T, Kubo M, Watada H, Maegawa H, Okada-Iwabu M, Iwabu M, Shojima N, Shin HD, Andersen G, Witte DR, Jorgensen T, Lauritzen T, Sandbaek A, Hansen T, Ohshige T, Omori S, Saito I, Kaku K, et al: A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A-C2CD4B. Nat Genet. 2010, 42: 864-868. 10.1038/ng.660.PubMedCrossRef

50.

Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segre AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, et al: Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010, 42: 579-589. 10.1038/ng.609.PubMedCentralPubMedCrossRef

51.

Qi L, Cornelis MC, Kraft P, Stanya KJ, Linda Kao WH, Pankow JS, Dupuis J, Florez JC, Fox CS, Pare G, Sun Q, Girman CJ, Laurie CC, Mirel DB, Manolio TA, Chasman DI, Boerwinkle E, Ridker PM, Hunter DJ, Meigs JB, Lee CH, Hu FB, van Dam RM: Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet. 2010, 19: 2706-2715. 10.1093/hmg/ddq156.PubMedCentralPubMedCrossRef

52.

Rung J, Cauchi S, Albrechtsen A, Shen L, Rocheleau G, Cavalcanti-Proenca C, Bacot F, Balkau B, Belisle A, Borch-Johnsen K, Charpentier G, Dina C, Durand E, Elliott P, Hadjadj S, Jarvelin MR, Laitinen J, Lauritzen T, Marre M, Mazur A, Meyre D, Montpetit A, Pisinger C, Posner B, Poulsen P, Pouta A, Prentki M, Ribel-Madsen R, Ruokonen A, Sandbaek A, et al: Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat Genet. 2009, 41: 1110-1115. 10.1038/ng.443.PubMedCrossRef

53.

54.

Sale MM, Lu L, Spruill IJ, Fernandes JK, Lok KH, Divers J, Langefeld CD, Garvey WT: Genome-wide linkage scan in Gullah-speaking African American families with type 2 diabetes: the Sea Islands Genetic African American Registry (Project SuGAR). Diabetes. 2009, 58: 260-267. 10.2337/db08-0198.PubMedCentralPubMedCrossRef

55.

Elbein SC, Das SK, Hallman DM, Hanis CL, Hasstedt SJ: Genome-wide linkage and admixture mapping of type 2 diabetes in African American families from the American Diabetes Association GENNID (Genetics of NIDDM) Study Cohort. Diabetes. 2009, 58: 268-274. 10.2337/db08-0931.PubMedCentralPubMedCrossRef

56.

Unoki H, Takahashi A, Kawaguchi T, Hara K, Horikoshi M, Andersen G, Ng DP, Holmkvist J, Borch-Johnsen K, Jorgensen T, Sandbaek A, Lauritzen T, Hansen T, Nurbaya S, Tsunoda T, Kubo M, Babazono T, Hirose H, Hayashi M, Iwamoto Y, Kashiwagi A, Kaku K, Kawamori R, Tai ES, Pedersen O, Kamatani N, Kadowaki T, Kikkawa R, Nakamura Y, Maeda S: SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet. 2008, 40: 1098-1102. 10.1038/ng.208.PubMedCrossRef

57.

Yasuda K, Miyake K, Horikawa Y, Hara K, Osawa H, Furuta H, Hirota Y, Mori H, Jonsson A, Sato Y, Yamagata K, Hinokio Y, Wang HY, Tanahashi T, Nakamura N, Oka Y, Iwasaki N, Iwamoto Y, Yamada Y, Seino Y, Maegawa H, Kashiwagi A, Takeda J, Maeda E, Shin HD, Cho YM, Park KS, Lee HK, Ng MC, Ma RC, et al: Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat Genet. 2008, 40: 1092-1097. 10.1038/ng.207.PubMedCrossRef

58.

Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, Styrkarsdottir U, Gretarsdottir S, Emilsson V, Ghosh S, Baker A, Snorradottir S, Bjarnason H, Ng MC, Hansen T, Bagger Y, Wilensky RL, Reilly MP, Adeyemo A, Chen Y, Zhou J, Gudnason V, Chen G, Huang H, Lashley K, Doumatey A, So WY, Ma RC, Andersen G, Borch-Johnsen K, et al: A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007, 39: 770-775. 10.1038/ng2043.PubMedCrossRef

59.

Hayes MG, Pluzhnikov A, Miyake K, Sun Y, Ng MC, Roe CA, Below JE, Nicolae RI, Konkashbaev A, Bell GI, Cox NJ, Hanis CL: Identification of type 2 diabetes genes in Mexican Americans through genome-wide association studies. Diabetes. 2007, 56: 3033-3044. 10.2337/db07-0482.PubMedCrossRef

60.

Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetroick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, et al: A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007, 316: 1341-1345. 10.1126/science.1142382.PubMedCentralPubMedCrossRef

61.

Rampersaud E, Damcott CM, Fu M, Shen H, McArdle P, Shi X, Shelton J, Yin J, Chang YP, Ott SH, Zhang L, Zhao Y, Mitchell BD, O'Connell J, Shuldiner AR: 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: 3053-3062. 10.2337/db07-0457.PubMedCrossRef

62.

Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D, Polychronakos C, Froguel P: A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007, 445: 881-885. 10.1038/nature05616.PubMedCrossRef

63.

Hanson RL, Bogardus C, Duggan D, Kobes S, Knowlton M, Infante AM, Marovich L, Benitez D, Baier LJ, Knowler WC: A search for variants associated with young-onset type 2 diabetes in American Indians in a 100K genotyping array. Diabetes. 2007, 56: 3045-3052. 10.2337/db07-0462.PubMedCrossRef

64.

Florez JC, Manning AK, Dupuis J, McAteer J, Irenze K, Gianniny L, Mirel DB, Fox CS, Cupples LA, Meigs JB: 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: 3063-3074. 10.2337/db07-0451.PubMedCrossRef

65.

Ng MC, So WY, Cox NJ, Lam VK, Cockram CS, Critchley JA, Bell GI, Chan JC: Genome-wide scan for type 2 diabetes loci in Hong Kong Chinese and confirmation of a susceptibility locus on chromosome 1q21-q25. Diabetes. 2004, 53: 1609-1613. 10.2337/diabetes.53.6.1609.PubMedCrossRef

66.

Rotimi CN, Chen G, Adeyemo AA, Furbert-Harris P, Parish-Gause D, Zhou J, Berg K, Adegoke O, Amoah A, Owusu S, Acheampong J, Agyenim-Boateng K, Eghan BA, Oli J, Okafor G, Ofoegbu E, Osotimehin B, Abbiyesuku F, Johnson T, Rufus T, Fasanmade O, Kittles R, Daniel H, Chen Y, Dunston G, Collins FS: A genome-wide search for type 2 diabetes susceptibility genes in West Africans: the Africa America Diabetes Mellitus (AADM) Study. Diabetes. 2004, 53: 838-841. 10.2337/diabetes.53.3.838.PubMedCrossRef

67.

Silander K, Scott LJ, Valle TT, Mohlke KL, Stringham HM, Wiles KR, Duren WL, Doheny KF, Pugh EW, Chines P, Narisu N, White PP, Fingerlin TE, Jackson AU, Li C, Ghosh S, Magnuson VL, Colby K, Erdos MR, Hill JE, Hollstein P, Humphreys KM, Kasad RA, Lambert J, Lazaridis KN, Lin G, Morales-Mena A, Patzkowski K, Pfahl C, Porter R, et al: A large set of Finnish affected sibling pair families with type 2 diabetes suggests susceptibility loci on chromosomes 6, 11, and 14. Diabetes. 2004, 53: 821-829. 10.2337/diabetes.53.3.821.PubMedCrossRef

68.

Xiang K, Wang Y, Zheng T, Jia W, Li J, Chen L, Shen K, Wu S, Lin X, Zhang G, Wang C, Wang S, Lu H, Fang Q, Shi Y, Zhang R, Xu J, Weng Q: Genome-wide search for type 2 diabetes/impaired glucose homeostasis susceptibility genes in the Chinese: significant linkage to chromosome 6q21-q23 and chromosome 1q21-q24. Diabetes. 2004, 53: 228-234.PubMedCrossRef

69.

Sale MM, Freedman BI, Langefeld CD, Williams AH, Hicks PJ, Colicigno CJ, Beck SR, Brown WM, Rich SS, Bowden DW: A genome-wide scan for type 2 diabetes in African-American families reveals evidence for a locus on chromosome 6q. Diabetes. 2004, 53: 830-837. 10.2337/diabetes.53.3.830.PubMedCrossRef

70.

Nawata H, Shirasawa S, Nakashima N, Araki E, Hashiguchi J, Miyake S, Yamauchi T, Hamaguchi K, Yoshimatsu H, Takeda H, Fukushima H, Sasahara T, Yamaguchi K, Sonoda N, Sonoda T, Matsumoto M, Tanaka Y, Sugimoto H, Tsubouchi H, Inoguchi T, Yanase T, Wake N, Narazaki K, Eto T, Umeda F, Nakazaki M, Ono J, Asano T, Ito Y, Akazawa S, et al: Genome-wide linkage analysis of type 2 diabetes mellitus reconfirms the susceptibility locus on 11p13-p12 in Japanese. J Hum Genet. 2004, 49: 629-634. 10.1007/s10038-004-0199-3.PubMedCrossRef

71.

Iwasaki N, Cox NJ, Wang YQ, Schwarz PE, Bell GI, Honda M, Imura M, Ogata M, Saito M, Kamatani N, Iwamoto Y: Mapping genes influencing type 2 diabetes risk and BMI in Japanese subjects. Diabetes. 2003, 52: 209-213. 10.2337/diabetes.52.1.209.PubMedCrossRef

72.

Reynisdottir I, Thorleifsson G, Benediktsson R, Sigurdsson G, Emilsson V, Einarsdottir AS, Hjorleifsdottir EE, Orlygsdottir GT, Bjornsdottir GT, Saemundsdottir J, Halldorsson S, Hrafnkelsdottir S, Sigurjonsdottir SB, Steinsdottir S, Martin M, Kochan JP, Rhees BK, Grant SF, Frigge ML, Kong A, Gudnason V, Stefansson K, Gulcher JR: Localization of a susceptibility gene for type 2 diabetes to chromosome 5q34-q35.2. Am J Hum Genet. 2003, 73: 323-335. 10.1086/377139.PubMedCentralPubMedCrossRef

73.

Busfield F, Duffy DL, Kesting JB, Walker SM, Lovelock PK, Good D, Tate H, Watego D, Marczak M, Hayman N, Shaw JT: A genomewide search for type 2 diabetes-susceptibility genes in indigenous Australians. Am J Hum Genet. 2002, 70: 349-357. 10.1086/338626.PubMedCentralPubMedCrossRef

74.

Wiltshire S, Hattersley AT, Hitman GA, Walker M, Levy JC, Sampson M, O'Rahilly S, Frayling TM, Bell JI, Lathrop GM, Bennett A, Dhillon R, Fletcher C, Groves CJ, Jones E, Prestwich P, Simecek N, Rao PV, Wishart M, Bottazzo GF, Foxon R, Howell S, Smedley D, Cardon LR, Menzel S, McCarthy MI: A genomewide scan for loci predisposing to type 2 diabetes in a U.K. population (the Diabetes UK Warren 2 Repository): analysis of 573 pedigrees provides independent replication of a susceptibility locus on chromosome 1q. Am J Hum Genet. 2001, 69: 553-569. 10.1086/323249.PubMedCentralPubMedCrossRef

75.

Ehm MG, Karnoub MC, Sakul H, Gottschalk K, Holt DC, Weber JL, Vaske D, Briley D, Briley L, Kopf J, McMillen P, Nguyen Q, Reisman M, Lai EH, Joslyn G, Shepherd NS, Bell C, Wagner MJ, Burns DK: Genomewide search for type 2 diabetes susceptibility genes in four American populations. Am J Hum Genet. 2000, 66: 1871-1881. 10.1086/302950.PubMedCentralPubMedCrossRef

76.

Okada Y, Kubo M, Ohmiya H, Takahashi A, Kumasaka N, Hosono N, Maeda S, Wen W, Dorajoo R, Go MJ, Zheng W, Kato N, Wu JY, Lu Q, Tsunoda T, Yamamoto K, Nakamura Y, Kamatani N, Tanaka T: Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. Nat Genet. 2012, 44: 302-306. 10.1038/ng.1086.PubMedCrossRef

77.

Wei WH, Hemani G, Gyenesei A, Vitart V, Navarro P, Hayward C, Cabrera CP, Huffman JE, Knott SA, Hicks AA, Rudan I, Pramstaller PP, Wild SH, Wilson JF, Campbell H, Hastie ND, Wright AF, Haley CS: Genome-wide analysis of epistasis in body mass index using multiple human populations. Eur J Hum Genet. 2012, in press

78.

Ng MC, Hester JM, Wing MR, Li J, Xu J, Hicks PJ, Roh BH, Lu L, Divers J, Langefeld CD, Freedman BI, Palmer ND, Bowden DW: Genome-wide association of BMI in African Americans. Obesity (Silver Spring). 2012, 20: 622-627. 10.1038/oby.2011.154.CrossRef

79.

Malhotra A, Kobes S, Knowler WC, Baier LJ, Bogardus C, Hanson RL: A genome-wide association study of BMI in American Indians. Obesity (Silver Spring). 2011, 19: 2102-2106. 10.1038/oby.2011.178.CrossRef

80.

Jiao H, Arner P, Hoffstedt J, Brodin D, Dubern B, Czernichow S, Van't Hooft F, Axelsson T, Pedersen O, Hansen T, Sorensen TI, Hebebrand J, Kere J, Dahlman-Wright K, Hamsten A, Clement K, Dahlman I: Genome wide association study identifies KCNMA1 contributing to human obesity. BMC Med Genomics. 201, 4: 51.CrossRef

81.

Wang K, Li WD, Zhang CK, Wang Z, Glessner JT, Grant SF, Zhao H, Hakonarson H, Price RA: A genome-wide association study on obesity and obesity-related traits. PLoS One. 2011, 6: e18939-10.1371/journal.pone.0018939.PubMedCentralPubMedCrossRef

82.

Dong C, Beecham A, Slifer S, Wang L, McClendon MS, Blanton SH, Rundek T, Sacco RL: Genome-wide linkage and peak-wide association study of obesity-related quantitative traits in Caribbean Hispanics. Hum Genet. 2011, 129: 209-219. 10.1007/s00439-010-0916-2.PubMedCentralPubMedCrossRef

83.

Croteau-Chonka DC, Marvelle AF, Lange EM, Lee NR, Adair LS, Lange LA, Mohlke KL: Genome-wide association study of anthropometric traits and evidence of interactions with age and study year in Filipino women. Obesity (Silver Spring). 2011, 19: 1019-1027. 10.1038/oby.2010.256.CrossRef

84.

Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Allen HL, Lindgren CM, Luan J, Magi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segre AV, Estrada K, Liang L, Nemesh J, Park JH, et al: Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010, 42: 937-948. 10.1038/ng.686.PubMedCentralPubMedCrossRef

85.

Liu JZ, Medland SE, Wright MJ, Henders AK, Heath AC, Madden PA, Duncan A, Montgomery GW, Martin NG, McRae AF: Genome-wide association study of height and body mass index in Australian twin families. Twin Res Hum Genet. 2010, 13: 179-193. 10.1375/twin.13.2.179.PubMedCentralPubMedCrossRef

86.

Johansson A, Marroni F, Hayward C, Franklin CS, Kirichenko AV, Jonasson I, Hicks AA, Vitart V, Isaacs A, Axenovich T, Campbell S, Floyd J, Hastie N, Knott S, Lauc G, Pichler I, Rotim K, Wild SH, Zorkoltseva IV, Wilson JF, Rudan I, Campbell H, Pattaro C, Pramstaller P, Oostra BA, Wright AF, van Duijn CM, Aulchenko YS, Gyllensten U: Linkage and genome-wide association analysis of obesity-related phenotypes: association of weight with the MGAT1 gene. Obesity (Silver Spring). 2010, 18 (4): 803-808. 10.1038/oby.2009.359.CrossRef

87.

Meyre D, Delplanque J, Chevre JC, Lecoeur C, Lobbens S, Gallina S, Durand E, Vatin V, Degraeve F, Proenca C, Gaget S, Korner A, Kovacs P, Kiess W, Tichet J, Marre M, Hartikainen AL, Horber F, Potoczna N, Hercberg S, Levy-Marchal C, Pattou F, Heude B, Tauber M, McCarthy MI, Blakemore AI, Montpetit A, Polychronakos C, Weill J, Coin LJ, et al: Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet. 2009, 41: 157-159. 10.1038/ng.301.PubMedCrossRef

88.

Sammalisto S, Hiekkalinna T, Schwander K, Kardia S, Weder AB, Rodriguez BL, Doria A, Kelly JA, Bruner GR, Harley JB, et al: Genome-wide linkage screen for stature and body mass index in 3.032 families: evidence for sex- and population-specific genetic effects. Eur J Hum Genet. 2009, 17: 258-266. 10.1038/ejhg.2008.152.PubMedCentralPubMedCrossRef

89.

Sabatti C, Service SK, Hartikainen AL, Pouta A, Ripatti S, Brodsky J, Jones CG, Zaitlen NA, Varilo T, Kaakinen M, Sovio U, Ruokonen A, Laitinen J, Jakkula E, Coin L, Hoggart C, Collins A, Turunen H, Gabriel S, Elliot P, McCarthy MI, Daly MJ, Jarvelin MR, Freimer NB, Peltonen L: Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet. 2009, 41: 35-46. 10.1038/ng.271.PubMedCentralPubMedCrossRef

90.

Liu YJ, Liu XG, Wang L, Dina C, Yan H, Liu JF, Levy S, Papasian CJ, Drees BM, Hamilton JJ, Meyre D, Delplanque J, Pei YF, Zhang L, Recker RR, Froguel P, Deng HW: Genome-wide association scans identified CTNNBL1 as a novel gene for obesity. Hum Mol Genet. 2008, 17: 1803-1813. 10.1093/hmg/ddn072.PubMedCentralPubMedCrossRef

91.

Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, Styrkarsdottir U, Gretarsdottir S, Thorlacius S, Jonsdottir I, Jonsdottir T, Olafsdottir EJ, Olafsdottir GH, Jonsson T, Jonsson F, Borch-Johnsen K, Hansen T, Andersen G, Jorgensen T, Lauritzen T, Aben KK, Verbeek AL, Roeleveld N, Kampman E, Yanek LR, Becker LC, Tryggvadottir L, et al: Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009, 41: 18-24. 10.1038/ng.274.PubMedCrossRef

92.

He LN, Liu YJ, Xiao P, Zhang L, Guo Y, Yang TL, Zhao LJ, Drees B, Hamilton J, Deng HY, Recker RR, Deng HW: Genomewide linkage scan for combined obesity phenotypes using principal component analysis. Ann Hum Genet. 2008, 72: 319-326. 10.1111/j.1469-1809.2007.00423.x.PubMedCrossRef

93.

Ciullo M, Nutile T, Dalmasso C, Sorice R, Bellenguez C, Colonna V, Persico MG, Bourgain C: Identification and replication of a novel obesity locus on chromosome 1q24 in isolated populations of Cilento. Diabetes. 2008, 57: 783-790. 10.2337/db07-0970.PubMedCrossRef

94.

Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J, Najjar S, Nagaraja R, Orru M, Usala G, Dei M, Lai S, Maschio A, Busonero F, Mulas A, Ehret GB, Fink AA, Weder AB, Cooper RS, Galan P, Chakravarti A, Schlessinger D, Cao A, Lakatta E, Abecasis GR: Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007, 3: e115-10.1371/journal.pgen.0030115.PubMedCentralPubMedCrossRef

95.

Almasy L, Goring HH, Diego V, Cole S, Laston S, Dyke B, Howard BV, Lee ET, Best LG, Devereux R, Fabsitz RR, MacCluer JW: A novel obesity locus on chromosome 4q: the Strong Heart Family Study. Obesity (Silver Spring). 2007, 15: 1741-1748. 10.1038/oby.2007.207.CrossRef

96.

Guo YF, Shen H, Liu YJ, Wang W, Xiong DH, Xiao P, Liu YZ, Zhao LJ, Recker RR, Deng HW: Assessment of genetic linkage and parent-of-origin effects on obesity. J Clin Endocrinol Metab. 2006, 91: 4001-4005. 10.1210/jc.2006-0549.PubMedCrossRef

97.

Groves CJ, Zeggini E, Walker M, Hitman GA, Levy JC, O'Rahilly S, Hattersley AT, McCarthy MI, Wiltshire S: Significant linkage of BMI to chromosome 10p in the U.K. population and evaluation of GAD2 as a positional candidate. Diabetes. 2006, 55: 1884-1889. 10.2337/db05-1674.PubMedCrossRef

98.

Herbert A, Gerry NP, McQueen MB, Heid IM, Pfeufer A, Illig T, Wichmann HE, Meitinger T, Hunter D, Hu FB, Colditz G, Hinney A, Hebebrand J, Koberwitz K, Zhu X, Cooper R, Ardlie K, Lyon H, Hirschhorn JN, Laird NM, Lenburg ME, Lange C, Christman MF: A common genetic variant is associated with adult and childhood obesity. Science. 2006, 312: 279-283. 10.1126/science.1124779.PubMedCrossRef

99.

Li WD, Dong C, Li D, Zhao H, Price RA: An obesity-related locus in chromosome region 12q23-24. Diabetes. 2004, 53: 812-820. 10.2337/diabetes.53.3.812.PubMedCrossRef

100.

Arya R, Duggirala R, Jenkinson CP, Almasy L, Blangero J, O'Connell P, Stern MP: Evidence of a novel quantitative-trait locus for obesity on chromosome 4p in Mexican Americans. Am J Hum Genet. 2004, 74: 272-282. 10.1086/381717.PubMedCentralPubMedCrossRef

101.

Heijmans BT, Beem AL, Willemsen G, Posthuma D, Slagboom PE, Boomsma D: Further evidence for a QTL influencing body mass index on chromosome 7p from a genome-wide scan in Dutch families. Twin Res. 2004, 7: 192-196.PubMedCrossRef

102.

Meyre D, Lecoeur C, Delplanque J, Francke S, Vatin V, Durand E, Weill J, Dina C, Froguel P: A genome-wide scan for childhood obesity-associated traits in French families shows significant linkage on chromosome 6q22.31–q23.2. Diabetes. 2004, 53: 803-811. 10.2337/diabetes.53.3.803.PubMedCrossRef

103.

Bell CG, Benzinou M, Siddiq A, Lecoeur C, Dina C, Lemainque A, Clement K, Basdevant A, Guy-Grand B, Mein CA, Meyre D, Froguel P: Genome-wide linkage analysis for severe obesity in french caucasians finds significant susceptibility locus on chromosome 19q. Diabetes. 2004, 53: 1857-1865. 10.2337/diabetes.53.7.1857.PubMedCrossRef

104.

Saar K, Geller F, Ruschendorf F, Reis A, Friedel S, Schauble N, Nurnberg P, Siegfried W, Goldschmidt HP, Schafer H, Ziegler A, Remschmidt H, Hinney A, Hebebrand J: Genome scan for childhood and adolescent obesity in German families. Pediatrics. 2003, 111: 321-327. 10.1542/peds.111.2.321.PubMedCrossRef

105.

Adeyemo A, Luke A, Cooper R, Wu X, Tayo B, Zhu X, Rotimi C, Bouzekri N, Ward R: A genome-wide scan for body mass index among Nigerian families. Obes Res. 2003, 11: 266-273. 10.1038/oby.2003.40.PubMedCrossRef

106.

Deng HW, Deng H, Liu YJ, Liu YZ, Xu FH, Shen H, Conway T, Li JL, Huang QY, Davies KM, Recker RR: A genomewide linkage scan for quantitative-trait loci for obesity phenotypes. Am J Hum Genet. 2002, 70: 1138-1151. 10.1086/339934.PubMedCentralPubMedCrossRef

107.

Wu X, Cooper RS, Borecki I, Hanis C, Bray M, Lewis CE, Zhu X, Kan D, Luke A, Curb D: A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program. Am J Hum Genet. 2002, 70: 1247-1256. 10.1086/340362.PubMedCentralPubMedCrossRef

108.

Feitosa MF, Borecki IB, Rich SS, Arnett DK, Sholinsky P, Myers RH, Leppert M, Province MA: Quantitative-trait loci influencing body-mass index reside on chromosomes 7 and 13: the National Heart, Lung, and Blood Institute Family Heart Study. Am J Hum Genet. 2002, 70: 72-82. 10.1086/338144.PubMedCentralPubMedCrossRef

109.

Stone S, Abkevich V, Hunt SC, Gutin A, Russell DL, Neff CD, Riley R, Frech GC, Hensel CH, Jammulapati S, Potter J, Sexton D, Tran T, Gibbs D, Iliev D, Gress R, Bloomquist B, Amatruda J, Rae PM, Adams TD, Skolnick MH, Shattuck D: A major predisposition locus for severe obesity, at 4p15-p14. Am J Hum Genet. 2002, 70: 1459-1468. 10.1086/340670.PubMedCentralPubMedCrossRef

110.

Perola M, Ohman M, Hiekkalinna T, Leppavuori J, Pajukanta P, Wessman M, Koskenvuo M, Palotie A, Lange K, Kaprio J, Peltonen L: Quantitative-trait-locus analysis of body-mass index and of stature, by combined analysis of genome scans of five finnish study groups. Am J Hum Genet. 2001, 69: 117-123. 10.1086/321286.PubMedCentralPubMedCrossRef

111.

Hsueh WC, Mitchell BD, Schneider JL, St Jean PL, Pollin TI, Ehm MG, Wagner MJ, Burns DK, Sakul H, Bell CJ, et al: Genome-wide scan of obesity in the old order amish. J Clin Endocrinol Metab. 2001, 86: 1199-1205. 10.1210/jc.86.3.1199.PubMed

112.

Davies RW, Wells GA, Stewart AF, Erdmann J, Shah SH, Ferguson JF, Hall AS, Anand SS, Burnett MS, Epstein SE, Dandona S, Chen L, Nahrstaedt J, Loley C, Konig IR, Krauss WE, Granger CB, Engert JC, Hengstenberg C, Wichmann HE, Schreiber S, Tang WH, Ellis SG, Rader DJ, Hazen SL, Reilly MP, Samani NJ, Schunkert H, Roberts R, McPherson R: A genome wide association study for coronary artery disease identifies a novel susceptibility locus in the major histocompatibility complex. Circ Cardiovasc Genet. 2012, 5: 217-225. 10.1161/CIRCGENETICS.111.961243.PubMedCentralPubMedCrossRef

113.

Takeuchi F, Yokota M, Yamamoto K, Nakashima E, Katsuya T, Asano H, Isono M, Nabika T, Sugiyama T, Fujioka A, Awata N, Ohnaka K, Nakatochi M, Kitajima H, Rakugi H, Nakamura J, Ohkubo T, Imai Y, Shimamoto K, Yamori Y, Yamaguchi S, Kobayashi S, Takayanagi R, Ogihara T, Kato N: Genome-wide association study of coronary artery disease in the Japanese. Eur J Hum Genet. 2012, 20: 333-340. 10.1038/ejhg.2011.184.PubMedCentralPubMedCrossRef

114.

Barbalic M, Reiner AP, Wu C, Hixson JE, Franceschini N, Eaton CB, Heiss G, Couper D, Mosley T, Boerwinkle E: Genome-wide association analysis of incident coronary heart disease (CHD) in African Americans: a short report. PLoS Genet. 2011, 7: e1002199-10.1371/journal.pgen.1002199.PubMedCentralPubMedCrossRef

115.

Slavin TP, Feng T, Schnell A, Zhu X, Elston RC: Two-marker association tests yield new disease associations for coronary artery disease and hypertension. Hum Genet. 2011, 130: 725-733. 10.1007/s00439-011-1009-6.PubMedCentralPubMedCrossRef

116.

Wild PS, Zeller T, Schillert A, Szymczak S, Sinning CR, Deiseroth A, Schnabel RB, Lubos E, Keller T, Eleftheriadis MS, Bickel C, Rupprecht HJ, Wilde S, Rossmann H, Diemert P, Cupples LA, Perret C, Erdmann J, Stark K, Kleber ME, Epstein SE, Voight BF, Kuulasmaa K, Li M, Schafer AS, Klopp N, Braund PS, Sager HB, Demissie S, Proust C, et al: A genome-wide association study identifies LIPA as a susceptibility gene for coronary artery disease. Circ Cardiovasc Genet. 2011, 4: 403-412. 10.1161/CIRCGENETICS.110.958728.PubMedCentralPubMedCrossRef

117.

Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, Preuss M, Stewart AF, Barbalic M, Gieger C, Absher D, Aherrahrou Z, Allayee H, Altshuler D, Anand SS, Andersen K, Anderson JL, Ardissino D, Ball SG, Balmforth AJ, Barnes TA, Becker DM, Becker LC, Berger K, Bis JC, Boekholdt SM, Boerwinkle E, Braund PS, Brown MJ, Burnett MS, et al: Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011, 43: 333-338. 10.1038/ng.784.PubMedCentralPubMedCrossRef

118.

Coronary Artery Disease (C4D) Genetics Consortium: A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat Genet. 2011, 43: 339-344. 10.1038/ng.782.CrossRef

119.

Wang F, Xu CQ, He Q, Cai JP, Li XC, Wang D, Xiong X, Liao YH, Zeng QT, Yang YZ, Cheng X, Li C, Yang R, Wang CC, Wu G, Lu QL, Bai Y, Huang YF, Yin D, Yang Q, Wang XJ, Dai DP, Zhang RF, Wan J, Ren JH, Li SS, Zhao YY, Fu FF, Huang Y, Li QX, et al: Genome-wide association identifies a susceptibility locus for coronary artery disease in the Chinese Han population. Nat Genet. 2011, 43: 345-349. 10.1038/ng.783.PubMedCrossRef

120.

Reilly MP, Li M, He J, Ferguson JF, Stylianou IM, Mehta NN, Burnett MS, Devaney JM, Knouff CW, Thompson JR, Horne BD, Stewart AF, Assimes TL, Wild PS, Allayee H, Nitschke PL, Patel RS, Martinelli N, Girelli D, Quyyumi AA, Anderson JL, Erdmann J, Hall AS, Schunkert H, Quertermous T, Blankenberg S, Hazen SL, Roberts R, Kathiresan S, Samani NJ, et al: Identification of ADAMTS7 as a novel locus for coronary atherosclerosis and association of ABO with myocardial infarction in the presence of coronary atherosclerosis: two genome-wide association studies. Lancet. 2011, 377: 383-392. 10.1016/S0140-6736(10)61996-4.PubMedCentralPubMedCrossRef

121.

Erdmann J, Willenborg C, Nahrstaedt J, Preuss M, Konig IR, Baumert J, Linsel-Nitschke P, Gieger C, Tennstedt S, Belcredi P, Aherrahrou Z, Klopp N, Loley C, Stark K, Hengstenberg C, Bruse P, Freyer J, Wagner AK, Medack A, Lieb W, Grosshennig A, Sager HB, Reinhardt A, Schafer A, Schreiber S, El Mokhtari NE, Raaz-Schrauder D, Illig T, Garlichs CD, Ekici AB, et al: Genome-wide association study identifies a new locus for coronary artery disease on chromosome 10p11.23. Eur Heart J. 2011, 32: 158-168. 10.1093/eurheartj/ehq405.PubMedCrossRef

122.

Erdmann J, Grosshennig A, Braund PS, Konig IR, Hengstenberg C, Hall AS, Linsel-Nitschke P, Kathiresan S, Wright B, Tregouet DA, Cambien F, Bruse P, Aherrahrou Z, Wagner AK, Stark K, Schwartz SM, Salomaa V, Elosua R, Melander O, Voight BF, O'Donnell CJ, Peltonen L, Siscovick DS, Altshuler D, Merlini PA, Peyvandi F, Bernardinelli L, Ardissino D, Schillert A, Blankenberg S, et al: New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nat Genet. 2009, 41: 280-282. 10.1038/ng.307.PubMedCentralPubMedCrossRef

123.

Tregouet DA, Konig IR, Erdmann J, Munteanu A, Braund PS, Hall AS, Grosshennig A, Linsel-Nitschke P, Perret C, DeSuremain M, Meitinger T, Wright BJ, Preuss M, Balmforth AJ, Ball SG, Meisinger C, Germain C, Evans A, Arveiler D, Luc G, Ruidavets JB, Morrison C, van der Harst P, Schreiber S, Neureuther K, Schafer A, Bugert P, El Mokhtari NE, Schrezenmeir J, Stark K, et al: Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease. Nat Genet. 2009, 41: 283-285. 10.1038/ng.314.PubMedCrossRef

124.

Roberts R, Stewart AF, Wells GA, Williams KA, Kavaslar N, McPherson R: Identifying genes for coronary artery disease: An idea whose time has come. Can J Cardiol. 2007, 23 (Suppl A): 7A-15A.PubMedCentralPubMedCrossRef

125.

Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, Dixon RJ, Meitinger T, Braund P, Wichmann HE, Barrett JH, Konig IR, Stevens SE, Szymczak S, Tregouet DA, Iles MM, Pahlke F, Pollard H, Lieb W, Cambien F, Fischer M, Ouwehand W, Blankenberg S, Balmforth AJ, Baessler A, Ball SG, Strom TM, Braenne I, Gieger C, Deloukas P, et al: Genomewide association analysis of coronary artery disease. N Engl J Med. 2007, 357: 443-453. 10.1056/NEJMoa072366.PubMedCentralPubMedCrossRef

126.

Farrall M, Green FR, Peden JF, Olsson PG, Clarke R, Hellenius ML, Rust S, Lagercrantz J, Franzosi MG, Schulte H, Carey A, Olsson G, Assmann G, Tognoni G, Collins R, Hamsten A, Watkins H: Genome-wide mapping of susceptibility to coronary artery disease identifies a novel replicated locus on chromosome 17. PLoS Genet. 2006, 2: e72-10.1371/journal.pgen.0020072.PubMedCentralPubMedCrossRef

127.

Samani NJ, Burton P, Mangino M, Ball SG, Balmforth AJ, Barrett J, Bishop T, Hall A: A genomewide linkage study of 1,933 families affected by premature coronary artery disease: The British Heart Foundation (BHF) Family Heart Study. Am J Hum Genet. 2005, 77: 1011-1020.PubMedCrossRef

128.

Hauser ER, Crossman DC, Granger CB, Haines JL, Jones CJ, Mooser V, McAdam B, Winkelmann BR, Wiseman AH, Muhlestein JB, Bartel AG, Dennis CA, Dowdy E, Estabrooks S, Eggleston K, Francis S, Roche K, Clevenger PW, Huang L, Pedersen B, Shah S, Schmidt S, Haynes C, West S, Asper D, Booze M, Sharma S, Sundseth S, Middleton L, Roses AD, et al: A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study. Am J Hum Genet. 2004, 75: 436-447. 10.1086/423900.PubMedCentralPubMedCrossRef

129.

Wang Q, Rao S, Shen GQ, Li L, Moliterno DJ, Newby LK, Rogers WJ, Cannata R, Zirzow E, Elston RC, Topol EJ: Premature myocardial infarction novel susceptibility locus on chromosome 1P34-36 identified by genomewide linkage analysis. Am J Hum Genet. 2004, 74: 262-271. 10.1086/381560.PubMedCentralPubMedCrossRef

130.

Pajukanta P, Cargill M, Viitanen L, Nuotio I, Kareinen A, Perola M, Terwilliger JD, Kempas E, Daly M, Lilja H, Rioux JD, Brettin T, Viikari JS, Ronnemaa T, Laakso M, Lander ES, Peltonen L: Two loci on chromosomes 2 and X for premature coronary heart disease identified in early- and late-settlement populations of Finland. Am J Hum Genet. 2000, 67: 1481-1493. 10.1086/316902.PubMedCentralPubMedCrossRef

131.

Francke S, Manraj M, Lacquemant C, Lecoeur C, Lepretre F, Passa P, Hebe A, Corset L, Yan SL, Lahmidi S, Jankee S, Gunness TK, Ramjuttun US, Balgobin V, Dina C, Froguel P: A genome-wide scan for coronary heart disease suggests in Indo-Mauritians a susceptibility locus on chromosome 16p13 and replicates linkage with the metabolic syndrome on 3q27. Hum Mol Genet. 2001, 10: 2751-2765. 10.1093/hmg/10.24.2751.PubMedCrossRef

132.

Broeckel U, Hengstenberg C, Mayer B, Holmer S, Martin LJ, Comuzzie AG, Blangero J, Nurnberg P, Reis A, Riegger GA, Jacob HJ, Schunkert H: A comprehensive linkage analysis for myocardial infarction and its related risk factors. Nat Genet. 2002, 30: 210-214. 10.1038/ng827.PubMedCrossRef

Titel: Identification of shared genetic susceptibility locus for coronary artery disease, type 2 diabetes and obesity: a meta-analysis of genome-wide studies
verfasst von: Chaoneng Wu
Yunguo Gong
Jie Yuan
Hui Gong
Yunzeng Zou
Junbo Ge
Publikationsdatum: 01.12.2012
Verlag: BioMed Central
Erschienen in: Cardiovascular Diabetology / Ausgabe 1/2012
Elektronische ISSN: 1475-2840
DOI: https://doi.org/10.1186/1475-2840-11-68

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Identification of shared genetic susceptibility locus for coronary artery disease, type 2 diabetes and obesity: a meta-analysis of genome-wide studies

Abstract

Electronic supplementary material

Competing interests

Authors’ contributions

Introduction

Materials and methods

Eligible genome scans

Data extraction

GSMA method

Results

Data included

Linkage evidence for each disease

Linkage evidence for combined each two or all three diseases

Discussion

Bin 9.3 (9p21.1-q21.32)

Bin 6.5 (6q23.2-q25.3)

Bin 16.3 (16q12.2-q23.1)

Bin 4.5 (4q24-q28.3)

Other regions

Conclusions

Electronic-database information

Acknowledgements

Competing interests

Authors’ contributions

Electronic supplementary material

Authors’ original submitted files for images

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Thrombophiliescreening – Wenn, dann richtig und nur bei therapeutischer Konsequenz

Bei Senioren mit Prostatakarzinom auf Anämie achten!

Update Innere Medizin

Springer Medizin

Abstract

Electronic supplementary material

Competing interests

Authors’ contributions

Introduction

Materials and methods

Eligible genome scans

Data extraction

GSMA method

Results

Data included

Linkage evidence for each disease

Linkage evidence for combined each two or all three diseases

Discussion

Bin 9.3 (9p21.1-q21.32)

Bin 6.5 (6q23.2-q25.3)

Bin 16.3 (16q12.2-q23.1)

Bin 4.5 (4q24-q28.3)

Other regions

Conclusions

Electronic-database information

Acknowledgements

Competing interests

Authors’ contributions

Electronic supplementary material

Authors’ original submitted files for images

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Thrombophiliescreening – Wenn, dann richtig und nur bei therapeutischer Konsequenz

Bei Senioren mit Prostatakarzinom auf Anämie achten!

Update Innere Medizin