- Research article
- Open Access
- Open Peer Review
Aryl hydrocarbon receptor nuclear translocator (ARNT) gene as a positional and functional candidate for type 2 diabetes and prediabetic intermediate traits: Mutation detection, case-control studies, and gene expression analysis
© Das et al; licensee BioMed Central Ltd. 2008
- Received: 21 August 2007
- Accepted: 17 March 2008
- Published: 17 March 2008
ARNT, a member of the basic helix-loop-helix family of transcription factors, is located on human chromosome 1q21–q24, a region which showed well replicated linkage to type 2 diabetes. We hypothesized that common polymorphisms in the ARNT gene might increase the susceptibility to type 2 diabetes through impaired glucose-stimulated insulin secretion.
We selected 9 single nucleotide polymorphisms to tag common variation across the ARNT gene. Additionally we searched for novel variants in functional coding domains in European American and African American samples. Case-control studies were performed in 191 European American individuals with type 2 diabetes and 187 nondiabetic European American control individuals, and in 372 African American individuals with type 2 diabetes and 194 African American control individuals. Metabolic effects of ARNT variants were examined in 122 members of 26 European American families from Utah and in 225 unrelated individuals from Arkansas. Gene expression was tested in 8 sibling pairs discordant for type 2 diabetes.
No nonsynonymous variants or novel polymorphisms were identified. No SNP was associated with type 2 diabetes in either African Americans or European Americans, but among nondiabetic European American individuals, ARNT SNPs rs188970 and rs11204735 were associated with acute insulin response (AIRg; p =< 0.005). SNP rs2134688 interacted with body mass index to alter β-cell compensation to insulin resistance (disposition index; p = 0.004). No significant difference in ARNT mRNA levels was observed in transformed lymphocytes from sibling pairs discordant for type 2 diabetes.
Common ARNT variants are unlikely to explain the linkage signal on chromosome 1q, but may alter insulin secretion in nondiabetic subjects. Our studies cannot exclude a role for rare variants or variants of small (< 1.6) effect size.
- Insulin Secretion
- Minor Allele Frequency
- Disposition Index
- Acute Insulin Response
- Aryl Hydrocarbon Receptor Nuclear Translocator
Type 2 diabetes mellitus is a complex heterogeneous group of metabolic conditions characterized by elevated levels of serum glucose. Impairment in both hepatic and peripheral insulin action, and defective pancreatic insulin secretion characterize fully developed type 2 diabetes. The interaction between genetic risk factors and environmental or lifestyle factors likely account for the current rising prevalence of type 2 diabetes among many populations, including United States European Americans and African Americans .
Aryl Hydrocarbon Receptor Nuclear Translocator (ARNT, also known as hypoxia inducible factor-1β) is a member of the basic helix-loop-helix Per/AhR/ARNT/Sim (bHLH-PAS) family of transcription factors. ARNT and its interacting partners HIF1α, HIF2α, and AhR form heterodimeric complexes which are required for cellular responses to hypoxia and environmental toxins such as dioxin. ARNT complexes also directly regulate the expression of genes involved in glucose transport and glucose metabolism . Recently, Gunton et al  implicated ARNT directly in impaired insulin secretion in type 2 diabetes by showing a 90% decrease ARNT messenger RNA in islets from diabetic individuals when compared to normal islets. Down-regulation of ARNT expression decreased the glucose stimulated insulin secretion in the Min 6 β-cell line, and mice lacking β-cell ARNT expression exhibited abnormal glucose tolerance, impaired glucose stimulated insulin secretion, and changes in islet gene expression similar to human type 2 diabetes . These studies have implicated ARNT in the transcriptional regulation of genes required for optimal glucose-responsive insulin secretion, and thus suggest ARNT as a strong candidate gene for diabetes. A further role of ARNT in type 2 diabetes is suggested by its role as an obligate partner of several transcription factors involved in the response to toxins and hypoxic stress . Hence, ARNT may integrate the genetic predisposition to β-cell failure  with environmental insults leading to diabetes pathogenesis.
The ARNT gene is located on human chromosome 1q21.2, a region with well replicated linkage to type 2 diabetes by us in European American and African American populations, and by others in European American, Chinese, and Pima Indian populations . Thus, in addition to being a strong functional candidate, ARNT is a strong positional candidate gene for type 2 diabetes. We hypothesized that common polymorphisms in the ARNT gene would alter its function or expression, and in turn increase the susceptibility to diabetes by affecting glucose-stimulated insulin secretion. We evaluated the association of common ARNT sequence variants with type 2 diabetes by using HapMap phase2 linkage disequilibrium information  as well as by screening putative functional regions of the gene for sequence variants in European American and African American subjects, and by testing these variants for an association with type 2 diabetes. To examine the physiologic impact of ARNT variants, we studied 352 non-diabetic subjects who had undergone frequently sampled intravenous glucose tolerance tests, including members of families ascertained in Utah and unrelated European American subjects ascertained in Arkansas. We also examined ARNT expression in transformed lymphocytes as a surrogate tissue using a discordant sib pair design, and we searched for additional evidence for cis-acting regulatory sequence polymorphisms that would alter the mRNA expression ratio between ARNT alleles.
Summary of Study Populations
Utah Caucasian case/control cohort1
51.0 ± 15.3
61.7 ± 10.7
51.5 ± 12.1
AR Caucasian case/control cohort2
28.1 (19.1, 41.2)
41.0 ± 14.4
31.78 (21.7, 46.5)
60.5 ± 12.2
49.9 ± 12.8
African American case/control cohort
42.8 ± 13.3
32.0 (20.8, 49.4)
54.8 ± 12.5
42.8 ± 11.9
Utah Caucasian metabolic
Family based non-diabetic
39.3 ± 10.5
AR Caucasian metabolic
Population based non-diabetic
29.4 (19.8, 43.7)
37.2 ± 9.5
Metabolic effects of ARNT sequence polymorphisms were studied in two nondiabetic populations: a family based cohort of 122 non-diabetic members from 26 families of Northern European descent ascertained in Utah [10, 11], and a European American population of 225 unrelated nondiabetic subjects ascertained in Arkansas with age under 60 years. All Utah family members underwent a tolbutamide – modified, frequently sampled intravenous glucose tolerance test (FSIGT). Because tolbutamide became unavailable during the study, 100 subjects had tolbutamide modified tests, whereas the remainder had an insulin modified (0.04 unit/kg) FSIGT . All subjects provided written, informed consent under a protocol approved by the Institutional Review Board of either the University of Utah Health Sciences Center or the University of Arkansas for Medical Sciences.
Screening for novel variants
Given the large size of the ARNT gene, we focused the screening for novel variants on the known major functional domains: the DNA binding basic helix loop helix (bHLH) domain (amino acid residues 103–143); the dimerization (PAS) domain (amino acid residues 161–235 and 349–419); and the transactivation domain at the carboxyl terminal. Primers were designed for amplicons of under 600 bp using sequence NM_001668 and the University of California Santa Cruz (genome.ucsc.edu) genome and proteome browsers for exons 1 (amino terminal), 6 (bHLH domain), 7 (PAS domain 1), 12–13 (PAS domain 2), and 22 (transactivation domain). Additionally, the flanking intronic sequences, 5' untranslated region, and 272 bp of the 3' untranslated region were screened. Fragments were screened using denaturing high-performance liquid chromatography (DHPLC) on a Transgenomic WAVE HT DNA fragment analysis system (Transgenomic Inc., Omaha, NE) in 24 European American and 24 African American individuals, each population comprising 16 diabetic and 8 nondiabetic participants. Variant chromatographic patterns were characterized by dideoxy DNA sequencing. The 2 kb region 5' to the transcription start site, which was non amenable to DHPLC screening, was sequenced directly in the same 48 individuals (Polymorphic DNA Technologies, Inc.; Alameda, CA).
Transformed lymphocytes from 8 sibling pairs discordant for type 2 diabetes were grown under normoglycemic (5.6 mM glucose) conditions, then exposed to either 5.6 mM glucose, or 28 mM glucose and 5 nM insulin for 8 hours before harvest. Total RNA was extracted using RNEasy mini kit (Qiagene Inc., Valancia, CA), the quantity and quality checked using an Agilent 2100 Bioanalyzer, and 600 ng reverse transcribed using random hexamer primers (TaqMan Reverse Transcription Reagents, Applied Biosystems, Inc). ARNT expression was measured by real time PCR on a Rotorgene 2000 Real time-PCR system (Corbett Life Science, Sydney Australia), with 18S ribosomal RNA as a normalization standard. Primer sequences were as follows: 18S forward: ATCAACTTTCGATGG TAGTCG, 18S reverse: TCCTTGGATGTGGTAGCCG, ARNT – Forward: TTCATCCCATACTCAAAATACCC, ARNT – Reverse: AAAGCAAAACCCAATCTCAA.
Allelic expression imbalance
Unequal expression of ARNT alleles was sought as evidence for cis acting regulatory variants by comparing peak heights in individuals heterozygous for the synonymous coding SNP rs2228099 or 3' untranslated SNP rs10847, using methods described elsewhere . Briefly, total RNA was reverse transcribed using random hexamers. Allelic specific quantitation of both cDNA and genomic DNA samples was determined using the same assay for pyrosequencing on a PSQ 96 Pyrosequencer (Biotage, Inc, Uppsala, Sweden), with peak height quantified using Allele Quantification software (Biotage, Inc).
Allele frequencies in case and control populations were compared using Fisher Exact and Cochran-Armitage Trend tests. Hardy-Weinberg equilibrium was tested using the online DeFinetti program . Linkage Disequilibrium and haplotype association analyses were performed by Haploview v. 3.32 . Insulin secretion was calculated as the acute insulin response to glucose (AIRg), determined either as the mean excursion over baseline from 2–10 min (Utah population) or the 2 min – 10 min Area Under Curve (AUC; Arkansas population). Although they give different values, the two methods of computing AIRgare highly correlated. Insulin sensitivity (SI) was calculated from the FSIGT using either the MinMod (Utah sample) or MinMod Millenium (Arkansas Sample) programs [15, 16]. The two programs use the same algorithms and provide nearly identical estimates of SI. The ability of the β-cell to compensate for insulin sensitivity was determined by the disposition index (DI = SI *AIRg) [17, 18]. Genotypic effects on glucose homeostasis traits (SI, AIRg, DI) were tested using mixed effect, general linear regression models implemented in SPSS v.12 for Windows (SPSS Inc., Chicago, IL). Skewed variables were ln-transformed to normality prior to analysis, and age, body mass index (BMI), gender, genotype, protocol (tolbutamide or insulin), and diagnosis (IGT or glucose tolerant) were included as factors and covariates, as appropriate. Pedigree membership was included as a random factor in analyses of family-based Utah samples.
ARNT expression was normalized to 18S RNA and both pair-wise and group-wise differences compared using the nonparametric Mann-Whitney U and Wilcoxan signed rank tests. For allelic expression imbalance, we computed the 95% CI for the assay from DNA (expected ratio 1:1). We considered allelic expression imbalance to be present if the ARNT RNA ratio fell outside the 95% CI for DNA in heterozygous individuals. The significance for the number of samples falling outside the 95% CI was determined using a chi-square goodness of fit test.
We calculated the power assuming p < 0.05 and for odds ratios over 1.5 (approximately that of TCF7L2) using allelic association (approximately the same as the Cochran Armitage trend test). We considered a control minor allele frequency range of 0.1 to 0.4. Among our primary European American population, we had 70% power at an odds ratio of 1.7 for a minor allele frequency of 0.1, or over 70% power for an odds ratio of 1.6 or greater for a minor allele frequency of 0.15 – 0.25, and over 70% for a minor allele frequency over 0.25 at an odds ratio of 1.5. Among African Americans, our power exceeded 70% for an odds ratio over 1.5 at minor allele frequencies over 0.15, but would have required an odds ratio of 1.6 for 70% power at a minor allele frequency of 0.1.
Association with type 2 diabetes
Summary of single nucleotide polymorphisms and allele frequencies
Caucasian Case/Control Frequency
African – American Case/Control Frequency
Exon 7 (Val to Val)
3' Flanking/Intron 1 of CTSK
Raw counts of cases and controls by genotype for ARNT region SNPs in Caucasians and African Americans
Type 2 Diabetes
Screening of the ARNT gene for additional variants identified a single synonymous SNP in exon 7, previously reported as rs2228099. Our selected tagSNP rs1889740, which showed no association with type 2 diabetes in European American or African American populations, was a perfect proxy (r2 = 1) in both HapMap CEPH and Yoruban populations. We confirmed the strong linkage disequilibrium in 120 European American individuals. We confirmed also the presence of common SNPs rs7517566 in the 5' flanking region, rs35756904 in the 5' untranslated region, rs10305650 in intron 1, rs2256355 and rs1027699 in intron 6, rs3738483 and rs3768017 in intron 12,, and rs10305749 in the 3' untranslated region. Each common variant was typed directly or tagged by a typed SNP at r2 > 0.9, and neither the typed SNPs nor excellent proxies were associated with type 2 diabetes. We also identified a very rare SNP (rs7515228; 1 heterozygous individual in 48 samples) in the 5' flanking region, which was not typed due to low power in our population.
Haplotype Frequencies for ARNT SNPs in Utah Caucasian Cases and Controls
Haplotype frequencies for ARNT SNPs in African American cases and controls
ARNTSNPs and insulin secretion
Marginal means for intravenous glucose tolerance measures
Utah Caucasian Family samples
Arkansas Caucasian population
4.85 (4.11, 5.71)
5.45 (4.47, 6.63)
5.42 (3.58, 8.18)
5.68 (5.07, 6.37)
5.40 (4.85, 6.00)
5.70 (4.68, 6.93)
2664 (2052, 3468)
5.38 (4.43, 6.52)
164 (134, 200)
132 (88, 197)
5.60 (4.88, 6.45)
5.22 (4.70, 5.78)
5.52 (4.68, 6.52)
ARNT mRNA levels in transformed lymphocytes did not differ between 8 European American control individuals and cell lines from their 8 type 2 diabetic siblings, either when grown under normoglycemic conditions (ARNT to 18 S RNA ratio 0.958, range 0.611–1.285 in controls vs 1.067, range 0.446 – 1.555 in diabetic cell lines; p = 0.40) or when cultured in 28 mM glucose and 5 nmol/l insulin (0.8131.067, range 0.449–1.164 in controls; 0.946, range 0.400 – 2.329 in type 2 diabetes; p = 0.67).
ARNT (or HIF1β) is the obligate heterodimer for a family of bHLH-PAS transcription factors that include HIF1α, HIF2α and AhR. These proteins in turn mediate signal transduction in response to both hypoxia and environmental toxins, including dioxin and polycyclic aromatic hydrocarbons . Recent data have also implicated ARNT in regulation of important downstream genes in the β-cell, including HNF4α, IRS2, AKT2, and enzymes of the glycolytic pathway . Indeed, putative ARNT binding sites may exceed 13,000 in the genome , and may include genes involved in glucose metabolism, vascular function, and oxygen transport (erythropoietin). Genes downstream of ARNT such as HNF4α may in turn bind to large numbers of promoters . In addition to data supporting ARNT as a strong functional candidate gene for type 2 diabetes, the ARNT gene is located on human chromosome 1q21, a region with replicated linkage to type 2 diabetes in diverse populations. These facts suggested that genetic variants altering ARNT function or regulation would impact glucose homeostasis and diabetes risk. To test this hypothesis we searched for indirect evidence that SNPs marking common haplotypes were associated with type 2 diabetes or altered insulin secretion. We also searched directly for coding or highly conserved variants that might directly impact ARNT function.
In the current study, we found no nonsynonymous SNPs that were likely to alter ARNT function. Based on HapMap data and our own genotyping, the selected tagSNPs should have adequately covered all common genetic variants. No SNP showed an allelic association with type 2 diabetes, either European American or African American individuals. Although we found associations of SNPs rs2134688 (intron 4) and rs4379678 (3' flanking region) under a recessive model, these findings could not be confirmed in a larger population and we believe the original observations, which were also out of Hardy Weinberg Equilibrium, were likely spurious and due to small sample size. We anticipated that variation in ARNT would alter insulin secretion, and we indeed found evidence for an association of SNPs rs188970 (a perfect proxy for synonymous SNP rs2228099) and rs11204735, with altered AIRg among members of high risk families. On the other hand, these findings were not replicated in a separate and differently ascertained population from Arkansas. The lack of replication may reflect differences in the two populations – one cohort of related individuals from high risk families, the other a more heterogeneous and unrelated population from Arkansas with more obese subjects and a very different environment. Alternatively, the observation in high risk families may have been spurious, as we tested 3 traits and 4 SNPs. However, the suspected biology of ARNT would suggest that environmental interactions may differ across populations. The same arguments apply for the interaction of SNP rs2134688 with gender to determine DI. Additional studies are needed to determine the role of these variants in insulin secretion.
Replication of genetic associations, both with dichotomous traits such as type 2 diabetes and quantitative traits such as AIRg has been difficult with sample sizes that are practical for individual laboratories. We have sought alternative methods based on comparison of ARNT expression among siblings discordant for type 2 diabetes, and using allelic expression imbalance for transcribed SNPs to find evidence for regulatory variants . In the current study, we examined two transcribed SNPs. In both cases, a few heterozygous individuals showed allelic imbalance outside of the range observed for genomic DNA (Figure 2), suggesting a possible regulatory mutation. Whether these results in transformed lymphocytes reflect expression in pancreatic β-cells is unknown, and the two SNPs appear to give different results. Nonetheless, SNP rs2228099 is in strong linkage disequilibrium with the SNP rs188970 that was associated with altered insulin secretion.
The strengths of this study are the use of multiple approaches to examine the ARNT gene, including association studies in two populations, quantitative trait studies for insulin secretion, and gene expression studies in transformed lymphocytes as a surrogate tissue for pancreatic β-cells. Nonetheless, this study had several limitations. First, the association study was of modest size, and would have been unable to detect a role for variants with small effect. Recent genome wide association scans in type 2 diabetes [20–23] have suggested that most if not all loci had odds ratios (ORs) below 1.5 and generally near 1.2 or less. Whereas we had 70% power to detect an association for common SNPs with an OR in the range of 1.5–1.7, or the approximate effect of the TCF7L2 gene , we had very limited power to detect effects with an OR of 1.2 or less. Although data for most published genome wide association scans are not yet publicly available, the Diabetes Genome Initiative scan  data are publicly available, and show 3 SNPs in the ARNT gene that can be estimated to capture 50% of the genetic variation. As with our study, no SNP showed any trend to association in over 1400 cases and 1400 controls. Based on allelic association and p < 0.05, the two SNPs at 35% minor allele frequency had 87% power to exclude an odds ratio of 1.2 or greater. That effect size would be comparable to the role of known diabetes genes including the potassium channel gene KCNJ11 E23K variant, or peroxisome proliferator activating receptor γ (PPARG) P12A variant, or more recently described variants that have required very large populations to confirm [20–23, 25]. Hence, our failure to detect an association with type 2 diabetes in our more modest sample size (albeit with two separately examined study populations) probably did not result from SNPs of small effect size.
Small effects on insulin secretion similarly would not have been detectable in populations of a size that could be examined with detailed phenotyping, as was done in our study. Thus, additional ARNT variants might have a small effect on insulin secretion that we did not detect, or might have an effect on aspects of insulin secretion that we did not measure. Finally, we did not exhaustively screen the ARNT gene for variants, but instead used a combination of screening conserved regions and indirect screening based on linkage disequilibrium relationships to test the common variants. Although unlikely, a causative SNP outside of the region screened and not in linkage disequilibrium with our tagSNPs might have gone undetected.
Gunton et al.  observed a 90% decrease in ARNT expression in pancreatic islets from type 2 diabetic individuals when compared with nondiabetic islets. In contrast, we found no difference in ARNT message in transformed lymphocytes from siblings with type diabetes and nondiabetic siblings. Our results may reflect the lack of correspondence of gene expression in transformed lymphocytes and β-cells, or Gunton et al may have found expression changes that were secondary to hyperglycemia. Our data provide some evidence that ARNT variants may alter insulin secretion and that ARNT gene variants may alter transcript levels, but our findings are not compatible with the dramatic differences in transcript levels observed by Gunton et al. We suggest that such striking reductions in ARNT transcript levels in the diabetic pancreas are unlikely to be caused by genetic variants and thus not the direct cause of the insulin secretory defect that occurs early in type 2 diabetes and progresses throughout the disease.
This work was supported by grants from NIH/NIDDK (DK039311 and DK54366), by the Research Service of the Department of Veterans Affairs, and by the American Diabetes Association. Insulin secretion studies in nondiabetic subjects were supported by grant M01RR14288 from National Center for Research Resources (NIH). We thank the Clinical Research Center nursing staff and laboratory for invaluable support in conducting these studies.
- Das SK, Elbein SC: The Genetic Basis of Type 2 Diabetes. Cellscience. 2006, 2: 100-131.PubMedPubMed CentralGoogle Scholar
- Semenza GL: Regulation of mammalian O2 homeostasis by hypoxia-inducible factor 1. Annu Rev Cell Dev Biol. 1999, 15: 551-578. 10.1146/annurev.cellbio.15.1.551.View ArticlePubMedGoogle Scholar
- Gunton JE, Kulkarni RN, Yim S, Okada T, Hawthorne WJ, Tseng YH, Roberson RS, Ricordi C, O'Connell PJ, Gonzalez FJ, Kahn CR: Loss of ARNT/HIF1beta mediates altered gene expression and pancreatic-islet dysfunction in human type 2 diabetes. Cell. 2005, 122: 337-349. 10.1016/j.cell.2005.05.027.View ArticlePubMedGoogle Scholar
- Czech MP: ARNT misbehavin' in diabetic beta cells. Nat Med. 2006, 12: 39-40. 10.1038/nm0106-39.View ArticlePubMedGoogle Scholar
- Polonsky KS, Sturis J, Bell GI: Non-insulin-dependent diabetes mellitus - a genetically programmed failure of the beta cell to compensate for insulin resistance. N Engl J Med. 1996, 334: 777-783. 10.1056/NEJM199603213341207.View ArticlePubMedGoogle Scholar
- Das SK, Elbein SC: The search for type 2 diabetes susceptibility loci: the chromosome 1q story. Curr Diab Rep. 2007, 7: 154-164. 10.1007/s11892-007-0025-3.View ArticlePubMedGoogle Scholar
- Gibbs RA, Belmont JW, Hardenbol P, Willis TD, Yu F, Yang H, Ch'ang LY, Huang W, Liu B, Shen Y, Tam PK, Tsui LC, Waye MM, Wong JT, Zeng C, Zhang Q, Chee MS, Galver LM, Kruglyak S, Murray SS, Oliphant AR, Montpetit A, Hudson TJ, Chagnon F, Ferretti V, Leboeuf M, Phillips MS, Verner A, Kwok PY, Duan S, Lind DL, Miller RD, Rice JP, Saccone NL, Taillon-Miller P, Xiao M, Nakamura Y, Sekine A, Sorimachi K, Tanaka T, Tanaka Y, Tsunoda T, Yoshino E, Bentley DR, Deloukas P, Hunt S, Powell D, Altshuler D, Gabriel SB, Zhang H, Matsuda I, Fukushima Y, Macer DR, Suda E, Rotimi CN, Adebamowo CA, Aniagwu T, Marshall PA, Matthew O, Nkwodimmah C, Royal CD, Leppert MF, Dixon M, Stein LD, Cunningham F, Kanani A, Thorisson GA, Chakravarti A, Chen PE, Cutler DJ, Kashuk CS, Donnelly P, Marchini J, McVean GA, Myers SR, Cardon LR, Abecasis GR, Morris A, Weir BS, Mullikin JC, Sherry ST, Feolo M, Altshuler D, Daly MJ, Schaffner SF, Qiu R, Kent A, Dunston GM, Kato K, Niikawa N, Knoppers BM, Foster MW, Clayton EW, Wang VO, Watkin J, Gibbs RA, Belmont JW, Sodergren E, Weinstock GM, Wilson RK, Fulton LL, Rogers J, Birren BW, Han H, Wang H, Godbout M, Wallenburg JC, L'Archeveque P, Bellemare G, Todani K, Fujita T, Tanaka S, Holden AL, Lai EH, Collins FS, Brooks LD, McEwen JE, Guyer MS, Jordan E, Peterson JL, Spiegel J, Sung LM, Zacharia LF, Kennedy K, Dunn MG, Seabrook R, Shillito M, Skene B, Stewart JG, Valle DL, Jorde LB, Belmont JW, Chakravarti A, Cho MK, Duster T, Foster MW, Jasperse M, Knoppers BM, Kwok PY, Licinio J, Long JC, Marshall PA, Ossorio PN, Wang VO, Rotimi CN, Royal CD, Spallone P, Terry SF, Lander ES, Lai EH, Nickerson DA, Altshuler D, Bentley DR, Boehnke M, Cardon LR, Daly MJ, Deloukas P, Douglas JA, Gabriel SB, Hudson RR, Hudson TJ, Kruglyak L, Kwok PY, Nakamura Y, Nussbaum RL, Royal CD, Schaffner SF, Sherry ST, Stein LD, Tanaka T: The International HapMap Project. Nature. 2003, 426: 789-796. 10.1038/nature02168.View ArticleGoogle Scholar
- Das SK, Hasstedt SJ, Zhang Z, Elbein SC: Linkage and Association Mapping of a Chromosome 1q21-q24 Type 2 Diabetes Susceptibility Locus in Northern European Caucasians. Diabetes. 2004, 53: 492-499. 10.2337/diabetes.53.2.492.View ArticlePubMedGoogle Scholar
- Das SK, Chu W, Zhang Z, Hasstedt SJ, Elbein SC: Calsquestrin 1 (CASQ1) gene polymorphisms under chromosome 1q21 linkage peak are associated with type 2 diabetes in Northern European Caucasians. Diabetes. 2004, 53: 3300-3306. 10.2337/diabetes.53.12.3300.View ArticlePubMedGoogle Scholar
- Elbein SC, Hasstedt SJ, Wegner K, Kahn SE: Heritability of pancreatic beta-cell function among nondiabetic members of Caucasian familial type 2 diabetic kindreds. J Clin Endocrinol Metab. 1999, 84: 1398-1403. 10.1210/jc.84.4.1398.PubMedGoogle Scholar
- Chu WS, Das SK, Wang H, Chan JC, Deloukas P, Froguel P, Baier LJ, Jia W, McCarthy MI, Ng MC, Damcott C, Shuldiner AR, Zeggini E, Elbein SC: Activating Transcription Factor 6 (ATF6) Sequence Polymorphisms in Type 2 Diabetes and Pre-Diabetic Traits. Diabetes. 2007, 56: 856-862. 10.2337/db06-1305.View ArticlePubMedPubMed CentralGoogle Scholar
- Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-265. 10.1093/bioinformatics/bth457.View ArticlePubMedGoogle Scholar
- Wang H, Elbein SC: Pyrosequencing Protocols. Edited by: Marsh S. 2007, Totowa, NJ, Humana Press, 373: 157-176. Detection of Allelic Imbalance Using Pyrosequencing.Pyrosequencing Methods in Molecular Biology.View ArticleGoogle Scholar
- Strom TM, Wienker TF: DiFinetti Program: Tests for deviation from Hardy-Weinberg equilibrium and tests for association. 2004, [http://ihg.gsf.de/cgi-bin/hw/hwa1.pl]Google Scholar
- Pacini G, Bergman RN: MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test. Comput Methods Programs Biomed. 1986, 23: 113-122. 10.1016/0169-2607(86)90106-9.View ArticlePubMedGoogle Scholar
- Boston RC, Stefanovski D, Moate PJ, Sumner AE, Watanabe RM, Bergman RN: MINMOD Millennium: a computer program to calculate glucose effectiveness and insulin sensitivity from the frequently sampled intravenous glucose tolerance test. Diabetes Technol Ther. 2003, 5: 1003-1015. 10.1089/152091503322641060.View ArticlePubMedGoogle Scholar
- Bergman RN: Toward physiological understanding of glucose tolerance: minimal model approach. Diabetes. 1989, 38 (12): 1512-1527. 10.2337/diabetes.38.12.1512.View ArticlePubMedGoogle Scholar
- Kahn SE, Prigeon RL, McCulloch DK, Boyko EJ, Bergman RN, Schwartz MW, Neifing JL, Ward WK, Beard JC, Palmer JP, Porte Jr. D: Quantification of the relationship between insulin sensitivity and beta-cell function in human subjects: evidence for a hyperbolic function. Diabetes. 1993, 42: 1663-1672. 10.2337/diabetes.42.11.1663.View ArticlePubMedGoogle Scholar
- Odom DT, Zizlsperger N, Gordon DB, Bell GW, Rinaldi NJ, Murray HL, Volkert TL, Schreiber J, Rolfe PA, Gifford DK, Fraenkel E, Bell GI, Young RA: Control of pancreas and liver gene expression by HNF transcription factors. Science. 2004, 303: 1378-1381. 10.1126/science.1089769.View ArticlePubMedPubMed CentralGoogle Scholar
- 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.View ArticlePubMedGoogle Scholar
- 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, Burton PR, Clayton DG, Craddock N, Deloukas P, Duncanson A, Kwiatkowski DP, Ouwehand WH, Samani NJ, Todd JA, Donnelly P, Davison D, Easton D, Evans D, Leung HT, Spencer CC, Tobin MD, Attwood AP, Boorman JP, Cant B, Everson U, Hussey JM, Jolley JD, Knight AS, Koch K, Meech E, Nutland S, Prowse CV, Stevens HE, Taylor NC, Walters GR, Walker NM, Watkins NA, Winzer T, Jones RW, McArdle WL, Ring SM, Strachan DP, Pembrey M, Breen G, St Clair D, Caesar S, Gordon-Smith K, Jones L, Fraser C, Green EK, Grozeva D, Hamshere ML, Holmans PA, Jones IR, Kirov G, Moskvina V, Nikolov I, O'Donovan MC, Owen MJ, Collier DA, Elkin A, Farmer A, Williamson R, McGuffin P, Young AH, Ferrier IN, Ball SG, Balmforth AJ, Barrett JH, Bishop DT, Iles MM, Maqbool A, Yuldasheva N, Hall AS, Braund PS, Dixon RJ, Mangino M, Stevens S, Thompson JR, Bredin F, Tremelling M, Parkes M, Drummond H, Lees CW, Nimmo ER, Satsangi J, Fisher SA, Forbes A, Lewis CM, Onnie CM, Prescott NJ, Sanderson J, Mathew CG, Barbour J, Mohiuddin MK, Todhunter CE, Mansfield JC, Ahmad T, Cummings FR, Jewell DP, Webster J, Brown MJ, Lathrop GM, Connell J, Dominiczak A, Braga Marcano CA, Burke B, Dobson R, Gungadoo J, Lee KL, Munroe PB, Newhouse SJ, Onipinla A, Wallace C, Xue M, Caulfield M, Farrall M, Barton A, Bruce IN, Donovan H, Eyre S, Gilbert PD, Hider SL, Hinks AM, John SL, Potter C, Silman AJ, Symmons DP, Thomson W, Worthington J, Dunger DB, Widmer B, Newport M, Sirugo G, Lyons E, Vannberg F, Hill AV, Bradbury LA, Farrar C, Pointon JJ, Wordsworth P, Brown MA, Franklyn JA, Heward JM, Simmonds MJ, Gough SC, Seal S, Stratton MR, Rahman N, Ban M, Goris A, Sawcer SJ, Compston A, Conway D, Jallow M, Rockett KA, Bumpstead SJ, Chaney A, Downes K, Ghori MJ, Gwilliam R, Hunt SE, Inouye M, Keniry A, King E, McGinnis R, Potter S, Ravindrarajah R, Whittaker P, Widden C, Withers D, Cardin NJ, Ferreira T, Pereira-Gale J, Hallgrimsdottir IB, Howie BN, Su Z, Teo YY, Vukcevic D, Bentley D, Compston A, Ouwehand NJ, Samani MR, Isaacs JD, Morgan AW, Wilson GD, Ardern-Jones A, Berg J, Brady A, Bradshaw N, Brewer C, Brice G, Bullman B, Campbell J, Castle B, Cetnarsryj R, Chapman C, Chu C, Coates N, Cole T, Davidson R, Donaldson A, Dorkins H, Douglas F, Eccles D, Eeles R, Elmslie F, Evans DG, Goff S, Goodman S, Goudie D, Gray J, Greenhalgh L, Gregory H, Hodgson SV, Homfray T, Houlston RS, Izatt L, Jackson L, Jeffers L, Johnson-Roffey V, Kavalier F, Kirk C, Lalloo F, Langman C, Locke I, Longmuir M, Mackay J, Magee A, Mansour S, Miedzybrodzka Z, Miller J, Morrison P, Murday V, Paterson J, Pichert G: 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.View ArticlePubMedPubMed CentralGoogle Scholar
- 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, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M: A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007, 316: 1341-1345. 10.1126/science.1142382.View ArticlePubMedPubMed CentralGoogle Scholar
- Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson BK, Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR, Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R, Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson M, Svensson M, Tewhey R, Blumenstiel B, Parkin M, DeFelice M, Barry R, Brodeur W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B, Healy C, Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel SB, Chirn GW, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S: Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007, 316: 1331-1336. 10.1126/science.1142358.View ArticlePubMedGoogle Scholar
- Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, Helgason A, Stefansson H, Emilsson V, Helgadottir A, Styrkarsdottir U, Magnusson KP, Walters GB, Palsdottir E, Jonsdottir T, Gudmundsdottir T, Gylfason A, Saemundsdottir J, Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson G, Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K: Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006, 38: 320-323. 10.1038/ng1732.View ArticlePubMedGoogle Scholar
- 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, Jorgensen T, Vliet-Ostaptchouk JV, Hofker MH, Wijmenga C, Christiansen C, Rader DJ, Rotimi C, Gurney M, Chan JC, Pedersen O, Sigurdsson G, Gulcher JR, Thorsteinsdottir U, Kong A, Stefansson K: A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007, 39: 770-775. 10.1038/ng2043.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/9/16/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.