Genetic Analysis of ADIPOR1 and ADIPOR2 Candidate Polymorphisms for Type 2 Diabetes in the Caucasian Population
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糖尿病学杂志 2006年第3期
1 Centre National de la Recherche Scientifique (CNRS) UMR 8090, Institute of Biology and Pasteur Institute, Lille, France
2 Scientific and Technical Institute of Nutrition and Food (ISTNA-CNAM), Institut National de la Santee et de la Recherche Meedicale (INSERM) U557, INRA U1125, Paris, France
3 Department of Endocrinology and Diabetology, Centre Hospitalier Sud Francilien, Corbeil-Essonnes, France
4 Section of Genomic Medicine, Imperial College Genome Centre, Hammersmith Campus, Imperial College London, London, U.K
AdipoR1, adiponectin receptor 1; AdipoR2, adiponectin receptor 2; HWE, Hardy-Weinberg equilibrium; LD, linkage disequilibrium; MAF, minor allele frequency; SNP, single nucleotide polymorphism; UTR, untranslated region
ABSTRACT
Adiponectin is a metabolic link between adipose tissue and insulin action, mediating part of obesity-associated insulin resistance and type 2 diabetes. Two adiponectin receptors have been identified, and we investigated whether sequence variations in adiponectin receptor 1 (ADIPOR1) and adiponectin receptor 2 (ADIPOR2) genes could contribute to the genetic risk for type 2 diabetes in a case-control study of 1,498 Caucasian subjects. We sequenced the putative functional regions of the two genes in 48 subjects and selected single nucleotide polymorphisms (SNPs) from the public database. Five SNPs in ADIPOR1 and 12 in ADIPOR2 were tested for association with type 2 diabetes. No SNP of ADIPOR1 showed association in any of the samples from the French population. In contrast, three SNPs of ADIPOR2 showed nominal evidence for association with type 2 diabetes before correction for multiple testing (odds ratio [OR] 1.29eC1.37, P = 0.034eC0.014); only rs767870, located in intron 6, was replicated in an additional diabetes dataset (n = 636, OR 1.29, P = 0.020) with significant allelic association from the overall meta-analysis of 2,876 subjects (adjusted OR 1.25 [95% CI 1.07eC1.45], P = 0.0051). In conclusion, our data suggest a modest contribution of ADIPOR2 variants in diabetes risk in the French population.
Although insulin resistance initiates at the early stage of obesity, severe deterioration in insulin secretion only occurs in subjects who will develop type 2 diabetes (1). In these subjects, worsening insulin resistance aggravates the altered carbohydrate and lipid metabolism, contributing to the impairment of insulin secretion through glucolipotoxic effects on -cells. Adipose tissue actively participates in such metabolic regulations through secretion of adipocytokines/peptides, among which adiponectin has insulin-sensitizing, antiatherogenic, anti-inflammatory, and antiangiogenic effects (2eC4). Adiponectin levels negatively correlate with glucose, insulin, triglyceride levels, and BMI and positively correlate with HDL cholesterol levels and insulin-stimulated glucose disposal (5,6). Furthermore, high plasma adiponectin concentration protects against type 2 diabetes (7). The adiponectin/ACDC gene is located on chromosome 3q27 at a locus linked to type 2 diabetes and also to several phenotypes related to features of the metabolic syndrome (8,9). Both case-control and prospective studies in general populations have shown that inherited variations at the ACDC locus modulate adiponectin levels and insulin sensitivity and are part of the genetic background of type 2 diabetes (10eC13). Together with animal studies, genetic epidemiology approaches suggest a causative contribution of adiponectin signaling for obesity-associated insulin resistance and type 2 diabetes.
Two putative adiponectin receptors, adiponectin receptor 1 (AdipoR1) and adiponectin receptor 1 (AdipoR2), have been cloned (14). In mice, AdipoR1 is ubiquitously expressed, with the most abundant expression occurring in skeletal muscle, whereas AdipoR2 is predominantly expressed in the liver. In humans, both receptors are significantly expressed in liver, muscle, and adipose tissue. Their expression levels in muscle correlate with distinct metabolic parameters, particularly with first-phase insulin secretion for AdipoR1 (15), but the relation with insulin sensitivity is not completely established. Expression of both receptors is lower in normal glucoseeCtolerant subjects with family history of diabetes (16). To assess a contribution of the genetic variability in the two ADIPOR1 and ADIPOR2 genes to the risk for type 2 diabetes and insulin resistance in the Caucasian population, we screened for single nucleotide polymorphisms (SNPs) the putative functional regions of the two genes by direct sequencing from 24 diabetic subjects (range of age at onset 26eC66 years) and 24 nondiabetic individuals with obesity (BMI >30 kg/m2 in adults and including five subjects with childhood obesity). The following parts of the genes were sequenced: for ADIPOR1, exons 1eC8 including the 960-bp 3'-untranslated region (UTR), exon-intron boundaries, and the proximal promoter sequences located at 1q32.1; ADIPOR2, exons 3eC9 including 5'- and 3'-UTRs and the exon-intron boundaries located at 12p13.
Thirteen SNPs in ADIPOR1 and 11 SNPs in ADIPOR2 were identified. The nucleotide changes and positions within the gene sequence are shown in Fig. 1. No nonsynonymous exonic mutations were found in these two genes, whereas three synonymous mutations were identified in exons 7 and 8 of ADIPOR2 (Fig. 1B). Two SNPs upstream of exon 1 in ADIPOR1 (rs75172865 and eC7,302 G/A) were not amenable to genotyping assay. Four SNPs in ADIPOR1 and three in ADIPOR2 present with a minor allele frequency (MAF) of <0.05 (indicated in italics on Fig. 1). A first estimate of the extent of linkage disequilibrium (LD) between pairs of SNPs with an MAF of >0.05, based on r2 values using expected maximization methods implemented in ldmax, from genotypes of the 48 subjects initially tested (data not shown) allowed us to select four SNPs in ADIPOR1 and six in ADIPOR2 for genotyping in a case-control association study. We also included additional SNPs chosen from the public dbSNP database for regions not covered by our screening (one SNP for ADIPOR1 and six for ADIPOR2 were added as indicated above the gene map in Fig. 1). These variants were selected on the basis of their location and LD structure in both gene regions evaluated by Haploview from the Hapmap Release no. 8 genotype data (June 2004). Two tag SNPs from the LD block covering ADIPOR1 and nine tag SNPs that summarize up to 80% of the common haplotypes arisen from four clusters of LD for ADIPOR2 were included in our study. From these compiled data, 5 SNPs for ADIPOR1 and 12 for ADIPOR2 were tested for association with type 2 diabetes in two French diabetic populations as previously described (Table 1) (17).
All SNPs conformed to Hardy-Weinberg equilibrium (HWE) except rs10773988 (ADR2-SNP9 in intron2), which deviated from HWE in the control groups (P = 0.0007 and P = 0.0085; the accuracy of genotyping was yet confirmed by direct sequencing). Although showing indication for allelic association with type 2 diabetes in the first (D1/C1) sample set (Table 2), this SNP did not present evidence of association in the second population or in the combined analysis of all groups. No SNP of ADIPOR1 was associated with type 2 diabetes in any sample set (Table 2). In contrast, three SNPs of ADIPOR2 (rs767870, ADR2-SNP2 [I290I], and rs1044471) showed significant association with type 2 diabetes under a dominant model in the first case-control study. The 3'UTR SNP, rs1044471, also displayed significant allelic comparison (D1 vs C1; odds ratio [OR] 1.29 [95% CI 1.04eC1.59], P = 0.022) in the first dataset but not in the second (D2/C2) case-control study (Table 2). Interestingly, when combining the two sample sets, we confirmed association with type 2 diabetes for rs767870 (ADR2-SNP1; adjusted OR 1.29 [95% CI 1.02eC1.62], P = 0.034), the I290I synonymous variant (1.32 [1.03eC1.70], P = 0.030), and also for rs2286380 (ADR2-SNP6; 1.37 [1.06eC1.76], P = 0.014) (Table 2). However, these results are no longer significant after correction for multiple testing. We estimated 80% power at P = 0.05 to detect an association with an OR 1.29 (MAF = 0.20) or 1.37 (MAF = 0.15). Therefore, we cannot rule out the possibility of a lower size effect for some variants. Pairwise LD between tested ADR2-SNPs was evaluated in the first case-control sample set using D' and r2 values showing that SNP2 and SNP6 are strongly associated (r2 > 0.9, online appendix Table 4B [available from http://diabetes.diabetesjournals.org]). The haplotype frequencies for the most common haplotypes derived from SNP1-2-6 were estimated using an expected maximization algorithm implemented in the program THESIAS (Testing Haplotype Effects in Association Studies) and compared between all case and control subjects (D1 + D2 vs. C1 + C2, Table 3). For the haplotype carrying the three at-risk alleles, we observed an OR of 1.29 (95% CI 1.02eC1.62, P = 0.032), and the at-risk alleles for each SNP1-2-6 are concentrated within one single haplotype (GAT), except for SNP1, which is present in the GCA haplotype with a frequency of >2% in the population tested. From this analysis, we cannot distinguish whether the putative effect is due to one SNP only or the combination of three SNPs.
In a replication study for ADIPOR2eCSNP1-2-6, we analyzed another group of 636 diabetic subjects (D3) compared with 742 normoglycemic control subjects (C3) of French Caucasian origin; only rs767870 (ADIPOR2-SNP1) showed significant allelic association with type 2 diabetes (OR 1.29, P = 0.020 and P = 0.0018 under recessive model). A deviation from HWE (P = 0.007) was observed in the diabetic group for this SNP; however, we obtained a 100% genotype concordance by direct sequencing of 170 duplicate samples, suggesting that this is due to chance rather than genotyping error. In the meta-analysis of the three case-control studies, including 1,380 diabetic and 1,496 control subjects, rs767870 was still significantly associated (adjusted OR 1.25 [95% CI 1.07eC1.45], P = 0.0051, and P = 0.011 under dominant model), whereas SNP2 and SNP6 did not show any significant association to type 2 diabetes. It is of note that our whole study has a statistical power of 89% to detect an OR of 1.25 (for a variant with an MAF of 0.15).
To test a role of both genes on metabolic parameters, including glucose, insulin, and adiponectin levels as well as lipid profile, we selected SNP1-4 in ADIPOR1 and SNP1-6 in ADIPOR2 for genotyping in a group of moderately obese nondiabetic subjects (mean BMI 34.26 ± 3.85 kg/m2, Table 1). Only rs730032 located in the 3'-UTR sequence of ADIPOR2 showed trends for association with total cholesterol level (corrected for age, sex, and BMI; P < 0.05 under allelic comparison). No SNP had a significant impact on the other parameters (data not shown).
Our data confirm the absence of contributions from ADIPOR1 SNPs in the genetic risk for type 2 diabetes as previously reported in two Caucasian and African-American diabetic populations (all five SNPs tested in this study were also tested in the previous study) (18). However, we observed a potentially significant effect for rs767870 in ADIPOR2 from our first combined analysis, with replicated association in the D3/C3 study, which remains significant in the overall meta-analysis of 2,876 diabetic and control subjects. SNP rs767870 was not tested in the recent study by Damcott et al. (19), which detected association between other ADIPOR2 variants and type 2 diabetes in the Old Order Amish. In contrast, we found no evidence of association in the French population for three SNPs reported to significantly increase susceptibility to type 2 diabetes in their study; these discrepancies may underlie population differences.
As long as the adiponectin receptors identity and biological function remains controversial, it is difficult to assess the functional role of these gene variants. T-cadherin was also characterized as a receptor for hexameric and higheCmolecular weight forms of adiponectin but not for the trimeric or globular species (20). The primary site of action seems to be different for globular and full-length adiponectin as suggested in mouse C2C12 myocytes (14). AdipoR1 is a high-affinity receptor for globular adiponectin, as well as a low-affinity receptor for full-length adiponectin in skeletal muscle. In contrast, AdipoR2 is an intermediate-affinity receptor for both globular and full-length adiponectin, which seems to be predominantly responsible for the effects in the liver. Although adiponectin is a modulator of insulin sensitization, its apparently complex signaling pathways complicate the identification of any contribution to type 2 diabetes risk for gene variants at the receptor level. Nevertheless, our data suggest a potential role for one ADIPOR2 variant that remains to be evaluated in different populations.
RESEARCH DESIGN AND METHODS
Gene screening for selection of SNPs in ADIPOR1 and ADIPOR2.
All putative functional regions of the two genes were screened for nucleotide changes by direct sequencing in 48 Caucasian subjects from the French family sample set ascertained by the UMR8090 unit. These were selected on the basis of elevated plasma adiponectin levels and decreased insulin sensitivity (assessed by the homeostasis model assessment). The nucleotide changes identified using Polyphred software and positions within the ADIPOR1 and ADIPOR2 gene sequences are shown in Fig. 1.
Study population.
The French Caucasian population included in the case-control association study is composed of unrelated type 2 diabetic patients diagnosed with diabetes according to World Health Organization criteria or known for being treated for diabetes: 1) 372 probands of type 2 diabetic families recruited by the Centre National de la Recherche ScientifiqueeCInstitut Pasteur Unit in Lille (D1) and 2) 372 diabetic patients examined at the Endocrinology-Diabetology Department of the Corbeil-Essonne Hospital (D2). Maturity-onset diabetes of the young and autoimmune subtypes were excluded. Two groups of healthy control subjects of French Caucasian origin were analyzed: 1) 320 normoglycemic subjects (fasting glucose <5.6 mmol/l) from the Family collection recruited in Lille and 2) 434 subjects not known for having diabetes (fasting glucose <6.1 mmol/l) from the SUVIMAX prospective population-based cohort study (21). A third dataset composed of 636 diabetic patients (D3) from the Corbeil cohort and 742 normoglycemic nonobese control subjects (C3) from the DESIR prospective population-based cohort (22) was used for replication. The clinical characteristics of all diabetic and control subjects are described in Table 1. This study includes a group of 421 moderately obese nondiabetic subjects (mean BMI 34.26 ± 3.85 kg/m2; Table 1) to assess genotype quantitative trait correlations.
Genotyping.
SNP genotyping was performed by a FRET (fluorescence resonance energy transfer)-based assay using LightTyper and LightCycler technology (Roche, Meylan, France, and Mannheim, Germany). The principle of FRET centers on the transfer of energy from one fluorescent molecule (fluorescein) to another fluorescent molecule (LC red 640), which causes an emission of light by the LC Red 640 molecule. Two sequence-specific hybridization probes were used; one is the sensor probe containing sequence complementary to the region of the SNP of interest and the second probe, called anchor probe, located 3 bp apart contains the other fluorophore. The variations of signal with the temperature are transformed in melting-curve profiles and treated through derivative peak analysis. The raw data were analyzed with the LightTyper software, allowing assignement of each individual sample to a genotype class. All genotypes have been validated by a second independent reading. Genotype data were obtained in >95% of the DNA samples (except for the C2 group, where the success rate was 85eC93%), and at least 150 samples (10%) were genotyped in duplicates with no discrepancies in the genotype scores.
Statistical analyses.
Testing genotype frequency deviation from HWE and SNP allele or genotype for case-control association were performed with the 2 test. These were examined combining the two sample sets in our study by Mantel-Haenszel meta-analysis. Correlation between SNP allele frequencies and quantitative traits was analyzed using the Wilcoxon-Kruskal-Wallis (implemented in SPSS version 10.1 for Windows). All quantitative variables were corrected by age, sex, and BMI using multivariate linear regression of log-transformed or square rooteCtransformed variables. The Mann-Whitney U test was used to test both recessive and dominant genetic models. Pairwise linkage (D' and r2) was calculated from combined case and control population data using the expectation maximization method implemented in ldmax of the GOLD software package (available from http://www.sph.umich.edu/csg/abecasis/GOLD). For the haplotype analysis, we used THESIAS, which is based on the maximum likelihood model described in Tregouet et al. (23) and is linked to the SEM algorithm. THESIAS allows the simultaneous estimation of haplotype frequencies and of their effects on the phenotype of interest through likelihood comparison under different hypotheses. A P value of <0.05 was considered significant.
ACKNOWLEDGMENTS
We gratefully acknowledge Stefan Gaget, Ceecile Lecoeur, and Christophe Waechter for their assistance in genomic annotation and database management. We are also indebted to the Roche, particularly Sylvain Kanamori and Christian Gandy in France and Monika Seller in Germany for their technical assistance and collaboration, and to Olfert Landt at Tib-Molbiol, Germany (available at www.tib-molbiol.com). We also thank all the patients and physicians for participating in our genetic study.
FOOTNOTES
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
F.L. is currently affiliated with INSERM EA2683, Centre Hospitalier Universitaire de Lille, Lille, France.
Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org.
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2 Scientific and Technical Institute of Nutrition and Food (ISTNA-CNAM), Institut National de la Santee et de la Recherche Meedicale (INSERM) U557, INRA U1125, Paris, France
3 Department of Endocrinology and Diabetology, Centre Hospitalier Sud Francilien, Corbeil-Essonnes, France
4 Section of Genomic Medicine, Imperial College Genome Centre, Hammersmith Campus, Imperial College London, London, U.K
AdipoR1, adiponectin receptor 1; AdipoR2, adiponectin receptor 2; HWE, Hardy-Weinberg equilibrium; LD, linkage disequilibrium; MAF, minor allele frequency; SNP, single nucleotide polymorphism; UTR, untranslated region
ABSTRACT
Adiponectin is a metabolic link between adipose tissue and insulin action, mediating part of obesity-associated insulin resistance and type 2 diabetes. Two adiponectin receptors have been identified, and we investigated whether sequence variations in adiponectin receptor 1 (ADIPOR1) and adiponectin receptor 2 (ADIPOR2) genes could contribute to the genetic risk for type 2 diabetes in a case-control study of 1,498 Caucasian subjects. We sequenced the putative functional regions of the two genes in 48 subjects and selected single nucleotide polymorphisms (SNPs) from the public database. Five SNPs in ADIPOR1 and 12 in ADIPOR2 were tested for association with type 2 diabetes. No SNP of ADIPOR1 showed association in any of the samples from the French population. In contrast, three SNPs of ADIPOR2 showed nominal evidence for association with type 2 diabetes before correction for multiple testing (odds ratio [OR] 1.29eC1.37, P = 0.034eC0.014); only rs767870, located in intron 6, was replicated in an additional diabetes dataset (n = 636, OR 1.29, P = 0.020) with significant allelic association from the overall meta-analysis of 2,876 subjects (adjusted OR 1.25 [95% CI 1.07eC1.45], P = 0.0051). In conclusion, our data suggest a modest contribution of ADIPOR2 variants in diabetes risk in the French population.
Although insulin resistance initiates at the early stage of obesity, severe deterioration in insulin secretion only occurs in subjects who will develop type 2 diabetes (1). In these subjects, worsening insulin resistance aggravates the altered carbohydrate and lipid metabolism, contributing to the impairment of insulin secretion through glucolipotoxic effects on -cells. Adipose tissue actively participates in such metabolic regulations through secretion of adipocytokines/peptides, among which adiponectin has insulin-sensitizing, antiatherogenic, anti-inflammatory, and antiangiogenic effects (2eC4). Adiponectin levels negatively correlate with glucose, insulin, triglyceride levels, and BMI and positively correlate with HDL cholesterol levels and insulin-stimulated glucose disposal (5,6). Furthermore, high plasma adiponectin concentration protects against type 2 diabetes (7). The adiponectin/ACDC gene is located on chromosome 3q27 at a locus linked to type 2 diabetes and also to several phenotypes related to features of the metabolic syndrome (8,9). Both case-control and prospective studies in general populations have shown that inherited variations at the ACDC locus modulate adiponectin levels and insulin sensitivity and are part of the genetic background of type 2 diabetes (10eC13). Together with animal studies, genetic epidemiology approaches suggest a causative contribution of adiponectin signaling for obesity-associated insulin resistance and type 2 diabetes.
Two putative adiponectin receptors, adiponectin receptor 1 (AdipoR1) and adiponectin receptor 1 (AdipoR2), have been cloned (14). In mice, AdipoR1 is ubiquitously expressed, with the most abundant expression occurring in skeletal muscle, whereas AdipoR2 is predominantly expressed in the liver. In humans, both receptors are significantly expressed in liver, muscle, and adipose tissue. Their expression levels in muscle correlate with distinct metabolic parameters, particularly with first-phase insulin secretion for AdipoR1 (15), but the relation with insulin sensitivity is not completely established. Expression of both receptors is lower in normal glucoseeCtolerant subjects with family history of diabetes (16). To assess a contribution of the genetic variability in the two ADIPOR1 and ADIPOR2 genes to the risk for type 2 diabetes and insulin resistance in the Caucasian population, we screened for single nucleotide polymorphisms (SNPs) the putative functional regions of the two genes by direct sequencing from 24 diabetic subjects (range of age at onset 26eC66 years) and 24 nondiabetic individuals with obesity (BMI >30 kg/m2 in adults and including five subjects with childhood obesity). The following parts of the genes were sequenced: for ADIPOR1, exons 1eC8 including the 960-bp 3'-untranslated region (UTR), exon-intron boundaries, and the proximal promoter sequences located at 1q32.1; ADIPOR2, exons 3eC9 including 5'- and 3'-UTRs and the exon-intron boundaries located at 12p13.
Thirteen SNPs in ADIPOR1 and 11 SNPs in ADIPOR2 were identified. The nucleotide changes and positions within the gene sequence are shown in Fig. 1. No nonsynonymous exonic mutations were found in these two genes, whereas three synonymous mutations were identified in exons 7 and 8 of ADIPOR2 (Fig. 1B). Two SNPs upstream of exon 1 in ADIPOR1 (rs75172865 and eC7,302 G/A) were not amenable to genotyping assay. Four SNPs in ADIPOR1 and three in ADIPOR2 present with a minor allele frequency (MAF) of <0.05 (indicated in italics on Fig. 1). A first estimate of the extent of linkage disequilibrium (LD) between pairs of SNPs with an MAF of >0.05, based on r2 values using expected maximization methods implemented in ldmax, from genotypes of the 48 subjects initially tested (data not shown) allowed us to select four SNPs in ADIPOR1 and six in ADIPOR2 for genotyping in a case-control association study. We also included additional SNPs chosen from the public dbSNP database for regions not covered by our screening (one SNP for ADIPOR1 and six for ADIPOR2 were added as indicated above the gene map in Fig. 1). These variants were selected on the basis of their location and LD structure in both gene regions evaluated by Haploview from the Hapmap Release no. 8 genotype data (June 2004). Two tag SNPs from the LD block covering ADIPOR1 and nine tag SNPs that summarize up to 80% of the common haplotypes arisen from four clusters of LD for ADIPOR2 were included in our study. From these compiled data, 5 SNPs for ADIPOR1 and 12 for ADIPOR2 were tested for association with type 2 diabetes in two French diabetic populations as previously described (Table 1) (17).
All SNPs conformed to Hardy-Weinberg equilibrium (HWE) except rs10773988 (ADR2-SNP9 in intron2), which deviated from HWE in the control groups (P = 0.0007 and P = 0.0085; the accuracy of genotyping was yet confirmed by direct sequencing). Although showing indication for allelic association with type 2 diabetes in the first (D1/C1) sample set (Table 2), this SNP did not present evidence of association in the second population or in the combined analysis of all groups. No SNP of ADIPOR1 was associated with type 2 diabetes in any sample set (Table 2). In contrast, three SNPs of ADIPOR2 (rs767870, ADR2-SNP2 [I290I], and rs1044471) showed significant association with type 2 diabetes under a dominant model in the first case-control study. The 3'UTR SNP, rs1044471, also displayed significant allelic comparison (D1 vs C1; odds ratio [OR] 1.29 [95% CI 1.04eC1.59], P = 0.022) in the first dataset but not in the second (D2/C2) case-control study (Table 2). Interestingly, when combining the two sample sets, we confirmed association with type 2 diabetes for rs767870 (ADR2-SNP1; adjusted OR 1.29 [95% CI 1.02eC1.62], P = 0.034), the I290I synonymous variant (1.32 [1.03eC1.70], P = 0.030), and also for rs2286380 (ADR2-SNP6; 1.37 [1.06eC1.76], P = 0.014) (Table 2). However, these results are no longer significant after correction for multiple testing. We estimated 80% power at P = 0.05 to detect an association with an OR 1.29 (MAF = 0.20) or 1.37 (MAF = 0.15). Therefore, we cannot rule out the possibility of a lower size effect for some variants. Pairwise LD between tested ADR2-SNPs was evaluated in the first case-control sample set using D' and r2 values showing that SNP2 and SNP6 are strongly associated (r2 > 0.9, online appendix Table 4B [available from http://diabetes.diabetesjournals.org]). The haplotype frequencies for the most common haplotypes derived from SNP1-2-6 were estimated using an expected maximization algorithm implemented in the program THESIAS (Testing Haplotype Effects in Association Studies) and compared between all case and control subjects (D1 + D2 vs. C1 + C2, Table 3). For the haplotype carrying the three at-risk alleles, we observed an OR of 1.29 (95% CI 1.02eC1.62, P = 0.032), and the at-risk alleles for each SNP1-2-6 are concentrated within one single haplotype (GAT), except for SNP1, which is present in the GCA haplotype with a frequency of >2% in the population tested. From this analysis, we cannot distinguish whether the putative effect is due to one SNP only or the combination of three SNPs.
In a replication study for ADIPOR2eCSNP1-2-6, we analyzed another group of 636 diabetic subjects (D3) compared with 742 normoglycemic control subjects (C3) of French Caucasian origin; only rs767870 (ADIPOR2-SNP1) showed significant allelic association with type 2 diabetes (OR 1.29, P = 0.020 and P = 0.0018 under recessive model). A deviation from HWE (P = 0.007) was observed in the diabetic group for this SNP; however, we obtained a 100% genotype concordance by direct sequencing of 170 duplicate samples, suggesting that this is due to chance rather than genotyping error. In the meta-analysis of the three case-control studies, including 1,380 diabetic and 1,496 control subjects, rs767870 was still significantly associated (adjusted OR 1.25 [95% CI 1.07eC1.45], P = 0.0051, and P = 0.011 under dominant model), whereas SNP2 and SNP6 did not show any significant association to type 2 diabetes. It is of note that our whole study has a statistical power of 89% to detect an OR of 1.25 (for a variant with an MAF of 0.15).
To test a role of both genes on metabolic parameters, including glucose, insulin, and adiponectin levels as well as lipid profile, we selected SNP1-4 in ADIPOR1 and SNP1-6 in ADIPOR2 for genotyping in a group of moderately obese nondiabetic subjects (mean BMI 34.26 ± 3.85 kg/m2, Table 1). Only rs730032 located in the 3'-UTR sequence of ADIPOR2 showed trends for association with total cholesterol level (corrected for age, sex, and BMI; P < 0.05 under allelic comparison). No SNP had a significant impact on the other parameters (data not shown).
Our data confirm the absence of contributions from ADIPOR1 SNPs in the genetic risk for type 2 diabetes as previously reported in two Caucasian and African-American diabetic populations (all five SNPs tested in this study were also tested in the previous study) (18). However, we observed a potentially significant effect for rs767870 in ADIPOR2 from our first combined analysis, with replicated association in the D3/C3 study, which remains significant in the overall meta-analysis of 2,876 diabetic and control subjects. SNP rs767870 was not tested in the recent study by Damcott et al. (19), which detected association between other ADIPOR2 variants and type 2 diabetes in the Old Order Amish. In contrast, we found no evidence of association in the French population for three SNPs reported to significantly increase susceptibility to type 2 diabetes in their study; these discrepancies may underlie population differences.
As long as the adiponectin receptors identity and biological function remains controversial, it is difficult to assess the functional role of these gene variants. T-cadherin was also characterized as a receptor for hexameric and higheCmolecular weight forms of adiponectin but not for the trimeric or globular species (20). The primary site of action seems to be different for globular and full-length adiponectin as suggested in mouse C2C12 myocytes (14). AdipoR1 is a high-affinity receptor for globular adiponectin, as well as a low-affinity receptor for full-length adiponectin in skeletal muscle. In contrast, AdipoR2 is an intermediate-affinity receptor for both globular and full-length adiponectin, which seems to be predominantly responsible for the effects in the liver. Although adiponectin is a modulator of insulin sensitization, its apparently complex signaling pathways complicate the identification of any contribution to type 2 diabetes risk for gene variants at the receptor level. Nevertheless, our data suggest a potential role for one ADIPOR2 variant that remains to be evaluated in different populations.
RESEARCH DESIGN AND METHODS
Gene screening for selection of SNPs in ADIPOR1 and ADIPOR2.
All putative functional regions of the two genes were screened for nucleotide changes by direct sequencing in 48 Caucasian subjects from the French family sample set ascertained by the UMR8090 unit. These were selected on the basis of elevated plasma adiponectin levels and decreased insulin sensitivity (assessed by the homeostasis model assessment). The nucleotide changes identified using Polyphred software and positions within the ADIPOR1 and ADIPOR2 gene sequences are shown in Fig. 1.
Study population.
The French Caucasian population included in the case-control association study is composed of unrelated type 2 diabetic patients diagnosed with diabetes according to World Health Organization criteria or known for being treated for diabetes: 1) 372 probands of type 2 diabetic families recruited by the Centre National de la Recherche ScientifiqueeCInstitut Pasteur Unit in Lille (D1) and 2) 372 diabetic patients examined at the Endocrinology-Diabetology Department of the Corbeil-Essonne Hospital (D2). Maturity-onset diabetes of the young and autoimmune subtypes were excluded. Two groups of healthy control subjects of French Caucasian origin were analyzed: 1) 320 normoglycemic subjects (fasting glucose <5.6 mmol/l) from the Family collection recruited in Lille and 2) 434 subjects not known for having diabetes (fasting glucose <6.1 mmol/l) from the SUVIMAX prospective population-based cohort study (21). A third dataset composed of 636 diabetic patients (D3) from the Corbeil cohort and 742 normoglycemic nonobese control subjects (C3) from the DESIR prospective population-based cohort (22) was used for replication. The clinical characteristics of all diabetic and control subjects are described in Table 1. This study includes a group of 421 moderately obese nondiabetic subjects (mean BMI 34.26 ± 3.85 kg/m2; Table 1) to assess genotype quantitative trait correlations.
Genotyping.
SNP genotyping was performed by a FRET (fluorescence resonance energy transfer)-based assay using LightTyper and LightCycler technology (Roche, Meylan, France, and Mannheim, Germany). The principle of FRET centers on the transfer of energy from one fluorescent molecule (fluorescein) to another fluorescent molecule (LC red 640), which causes an emission of light by the LC Red 640 molecule. Two sequence-specific hybridization probes were used; one is the sensor probe containing sequence complementary to the region of the SNP of interest and the second probe, called anchor probe, located 3 bp apart contains the other fluorophore. The variations of signal with the temperature are transformed in melting-curve profiles and treated through derivative peak analysis. The raw data were analyzed with the LightTyper software, allowing assignement of each individual sample to a genotype class. All genotypes have been validated by a second independent reading. Genotype data were obtained in >95% of the DNA samples (except for the C2 group, where the success rate was 85eC93%), and at least 150 samples (10%) were genotyped in duplicates with no discrepancies in the genotype scores.
Statistical analyses.
Testing genotype frequency deviation from HWE and SNP allele or genotype for case-control association were performed with the 2 test. These were examined combining the two sample sets in our study by Mantel-Haenszel meta-analysis. Correlation between SNP allele frequencies and quantitative traits was analyzed using the Wilcoxon-Kruskal-Wallis (implemented in SPSS version 10.1 for Windows). All quantitative variables were corrected by age, sex, and BMI using multivariate linear regression of log-transformed or square rooteCtransformed variables. The Mann-Whitney U test was used to test both recessive and dominant genetic models. Pairwise linkage (D' and r2) was calculated from combined case and control population data using the expectation maximization method implemented in ldmax of the GOLD software package (available from http://www.sph.umich.edu/csg/abecasis/GOLD). For the haplotype analysis, we used THESIAS, which is based on the maximum likelihood model described in Tregouet et al. (23) and is linked to the SEM algorithm. THESIAS allows the simultaneous estimation of haplotype frequencies and of their effects on the phenotype of interest through likelihood comparison under different hypotheses. A P value of <0.05 was considered significant.
ACKNOWLEDGMENTS
We gratefully acknowledge Stefan Gaget, Ceecile Lecoeur, and Christophe Waechter for their assistance in genomic annotation and database management. We are also indebted to the Roche, particularly Sylvain Kanamori and Christian Gandy in France and Monika Seller in Germany for their technical assistance and collaboration, and to Olfert Landt at Tib-Molbiol, Germany (available at www.tib-molbiol.com). We also thank all the patients and physicians for participating in our genetic study.
FOOTNOTES
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
F.L. is currently affiliated with INSERM EA2683, Centre Hospitalier Universitaire de Lille, Lille, France.
Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org.
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