Genetic Variation in Adiponectin Receptor 1 and Adiponectin Receptor 2 Is Associated With Type 2 Diabetes in the Old Order Amish
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糖尿病学杂志 2005年第7期
1 Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland
2 Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland
ABSTRACT
Adiponectin receptor 1 (ADIPOR1) and adiponectin receptor 2 (ADIPOR2) are newly identified receptors for adiponectin, an adipocytokine with anti-inflammatory and insulin-sensitizing properties. We screened for polymorphisms by performing sequence analysis on all eight exons, splice junctions, and 2 kb of the 5' flanking regions of each receptor. We detected 5 single nucleotide polymorphisms (SNPs) in ADIPOR1 and 16 SNPs in ADIPOR2. We genotyped these SNPs in Amish subjects with type 2 diabetes (n = 137), impaired glucose tolerance (IGT) (n = 139), and normal glucose tolerance (n = 342) to test for association with type 2 diabetes. Three intronic SNPs in ADIPOR1 were significantly associated with type 2 diabetes (P = 0.014eC0.007; odds ratio [OR] 1.61eC1.65) and in high linkage disequilibrium (r2 = 0.97eC1.0). In ADIPOR2, we found that five SNPs delineated one large haplotype block (r2 = 0.9eC1.0) spanning >98 kb of the gene and promoter region, which was strongly associated with the combined type 2 diabetes/IGT trait (P 0.001; OR 1.64eC1.71). To our knowledge, these data provide the first evidence for association between variation in the adiponectin receptors and type 2 diabetes.
Adiponectin is an adipocytokine that is involved in lipid and glucose metabolism, energy homeostasis, and inflammatory pathways (1eC5). Circulating concentrations of adiponectin are reduced in subjects with type 2 diabetes (6eC9). Low plasma adiponectin levels are observed in rodent models of obesity and insulin resistance (2,10), and adiponectin-deficient mice exhibit diet-induced insulin resistance (11,12). The insulin-sensitizing effects of adiponectin appear to be mediated by increased fatty acid oxidation and glucose uptake (10,13) and suppression of gluconeogenesis (14,15) through activation of AMP-activated protein kinase (16,17) and peroxisome proliferatoreCactivated receptor- (18).
Recently, two receptors, adiponectin receptor 1 (ADIPOR1) and adiponectin receptor 2 (ADIPOR2), were cloned, each consisting of eight exons that are located on chromosomes 1q32.1 and 12p13.33, respectively. In humans, ADIPOR1 is ubiquitously expressed, with highest levels in the skeletal muscle, while ADIPOR2 is predominantly expressed in skeletal muscle and liver (19). Yamauchi et al. (19) showed that activation of AMP-activated protein kinase and peroxisome proliferatoreCactivated receptor- by adiponectin is mediated by ADIPOR1 and ADIPOR2. In addition, normal glucose-tolerant Mexican-American subjects with a family history of type 2 diabetes exhibit significantly lower levels of ADIPOR1 and ADIPOR2 mRNA in skeletal muscle compared with those without a family history (20). Civitarese et al. (20) also observed that mRNA expression of both receptors was positively correlated with glucose disposal, while ADIPOR2 expression was positively correlated with plasma adiponectin levels. These studies suggest that downregulation or altered function of ADIPOR1 and ADIPOR2 may be responsible for development of insulin resistance in peripheral tissues. Therefore, we hypothesized that genetic variation in ADIPOR1 and ADIPOR2 may contribute to increased susceptibility to type 2 diabetes. We addressed this question by screening ADIPOR1 and ADIPOR2 for polymorphisms and testing for association between the observed sequence variation and type 2 diabetes in the Old Order Amish.
In ADIPOR1, DNA sequence analysis in 24 Amish individuals detected five SNPs previously reported in dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/). Figure 1A shows the ADIPOR1 gene structure, SNPs identified, and pairwise linkage disequilibrium (LD) (r2). We genotyped all five SNPs in 618 individuals enrolled in the Amish Family Diabetes Study (AFDS) (137 subjects with type 2 diabetes, 139 subjects with impaired glucose tolerance [IGT], and 342 control subjects with normal glucose tolerance [NGT]). Two of the SNPs, rs2275738 and rs2275737, are located 4 bp apart in intron 1 and were in perfect LD (r2 = 1); therefore, only one of them (rs2275737) was included in the analyses. Genotype frequencies of the two intronic SNPs, rs2275737 and rs1342387, differed significantly between subjects with type 2 diabetes and those with NGT (P = 0.007 and P = 0.014, respectively), with the minor allele being the risk allele for type 2 diabetes (odds ratio [OR] 1.65 and 1.61, respectively) (Table 1 and online appendix Fig. 1 [available at http://diabetes.diabetesjournals.org] for genotype frequencies in type 2 diabetes and IGT cases and NGT control subjects). The intronic SNPs span 6 kb of the gene from intron 1 to intron 4 and were in high LD (r2 0.97) (Fig. 1A). The two SNPs flanking the intronic SNP cluster, rs6666089 and rs7539542, were not significantly associated with type 2 diabetes (P = 0.17 and P = 0.35, respectively) nor were they in LD with each other or the intronic SNPs, suggesting that the intronic SNPs mark a type 2 diabetes susceptibility allele or haplotype. We used Haploscore to estimate four SNP haplotype frequencies and to identify haplotypes associated with type 2 diabetes. None of the haplotypes provided stronger evidence for association with type 2 diabetes than the single SNPs (online appendix Table 1). In addition to the case/control analysis, we examined the effects of ADIPOR1 SNP genotype on BMI and plasma glucose and insulin during a 3-h oral glucose tolerance test (OGTT) in an expanded set of 698 nondiabetic AFDS subjects, but found no association (data not shown).
We previously reported evidence for linkage to type 2 diabetes on chromosome 1q21-q24 in the Amish (21), which raises the question of whether ADIPOR1 contributes to this linkage. While we cannot rule out that possibility, ADIPOR1 resides 44 Mb downstream from the 1-LOD (logarithm of odds) support interval for our peak linkage to chromosome 1 and is thus unlikely to be the cause of the linkage.
We identified 16 SNPs by sequence analysis in ADIPOR2 (Fig. 1B) and genotyped 12 of the SNPs in 618 AFDS subjects. Four SNPs (AR2p3, AR2pID, rs10735003, and rs9805042) were eliminated due to difficulties with genotyping assays. AR2p3, AR2pID, and rs9805042 had minor allele frequencies (MAFs) <0.015, and rs10735003 had an r2 = 1 with rs11061971 in the extended sequencing set (n = 96 alleles); thus, these SNPs were not pursued further. Three SNPs were in perfect LD (r2 = 1), including rs10773980, rs11061971, and rs10735003 (estimated from 96 alleles), so only rs11061971 was included in the analyses. One large haplotype block spanning 65 kb of the promoter, exon 1 through exon 7, and 1.8 kb of exon 8 (>98 kb total) contains three common SNPs (rs1029629, rs11061971, and rs12342) that are in high LD (r2 0.90) and show strong association with the combined type 2 diabetes/IGT trait (P 0.001; OR 1.64eC1.71), with the minor allele being the risk allele (Table 2 and Fig. 1B; see online appendix Fig. 2 for genotype frequencies in type 2 diabetes and IGT cases and NGT control subjects). This block is interrupted by one SNP (rs730032) that is neither in LD with the large haplotype block (r2 = 0.21eC0.24) nor associated with the type 2 diabetes/IGT trait (P = 0.23; OR 1.30). Two additional SNPs located 3' of the large haplotype block (rs1044471 and AR2eC8f) were significantly associated with type 2 diabetes/IGT (P = 0.006 and P = 0.012, respectively), but not in high LD with each other or the SNPs in the large haplotype block. The haplotype analysis revealed one haplotype (GTGTT) that was significantly associated with the combined type 2 diabetes/IGT trait (P = 0.004; global P value = 0.015) (Fig. 2). This haplotype contains the risk alleles for each of the single type 2 diabetes/IGT-associated SNPs. These results should be interpreted with caution since Haploscore does not account for relatedness between subjects.
Quantitative trait analysis of ADIPOR2 SNPs in an expanded set of 698 nondiabetic subjects revealed significant associations between rs1029629 and mean plasma glucose levels and total glucose area under the curve during the OGTT (P = 0.005eC0.016 and P = 0.021, respectively, for recessive model). Carriers of the G risk allele for rs1029629 exhibited significantly higher plasma glucose levels at each 30-min interval from 90 to 180 min and total glucose area under the curve during the OGTT (online appendix Fig. 3), providing additional evidence that the haplotype containing the G allele of rs1029629 influences glucose homeostasis and type 2 diabetes risk. There was no association between SNPs in ADIPOR2 and BMI or plasma insulin during the OGTT.
Although these type 2 diabetes/IGT-associated SNPs do not disrupt any obvious transcription factoreCbinding sites (22) or splice sites, these variants could possibly alter expression or mRNA stability. We explored the possibility of epistasis between polymorphisms in the receptors but found no evidence for interaction between SNPs in ADIPOR1 and ADIPOR2.
In a recent report, Wang et al. (23) screened ADIPOR1 (but not ADIPOR2) for sequence variation in Caucasian and African-American subjects but found no association between the SNPs they detected (including the five SNPs we examined) and type 2 diabetes, insulin sensitivity, or insulin secretion. The discrepancy between their study and ours may be a result of low power or underlying population differences. Alternatively, it is possible that we have detected a false-positive. We did not correct for multiple comparisons in our analyses. If the highly conservative Bonferroni correction were used in our case/control analysis, the significantly associated ADIPOR1 SNPs would become borderline to nonsignificant whereas the ADIPOR2 SNPs would remain significant at the P = 0.01 level. This observation strengthens the evidence that variation in ADIPOR2 contributes to increased susceptibility to type 2 diabetes.
In summary, we have extensively screened the two adiponectin receptors to search for polymorphisms associated with type 2 diabetes in the Old Order Amish. We identified multiple polymorphisms in both ADIPOR1 and ADIPOR2 that were significantly associated with type 2 diabetes and related traits, providing evidence that genetic variation in the receptors, particularly ADIPOR2, plays a role in type 2 diabetes susceptibility. Alternatively, these SNPs may be in LD with an undetected functional SNP in the receptors or a neighboring gene. Further analysis in other populations, as well as functional studies, will be necessary to further elucidate the role of polymorphism in ADIPOR1 and ADIPOR2 in the pathogenesis of type 2 diabetes.
RESEARCH DESIGN AND METHODS
The AFDS was initiated in 1995 with the goal of identifying susceptibility genes for type 2 diabetes in the Old Order Amish. Details of the AFDS design, recruitment, phenotyping, and pedigree structure have been described previously (24). Briefly, probands with previously diagnosed type 2 diabetes (onset age 35eC65 years) and, where possible, all first- and second-degree relatives of probands and spouses >18 years of age were recruited. Phenotypic characterization of study participants included medical and family history, anthropometry, and an OGTT with insulin levels. Total glucose and insulin areas under the curve during the OGTT were calculated using the trapezoid method. BMI was calculated as weight in kilograms divided by the square of height (in meters). This study includes additional subjects recruited subsequent to the report by Hsueh et al. (24) and excludes all subjects with NGT <38 years of age in order to increase the probability of their capacity for diabetes resistance. Informed consent was obtained from all study subjects, and the Institutional Review Board at the University of Maryland School of Medicine approved the study protocol.
Sequence analysis and genotyping.
Direct sequencing was used to screen the eight exons with splice junctions (100 bp of the intronic flanking sequence) of both ADIPOR1 and ADIPOR2 and 1,712 bp and 1,976 bp of the region 5' of the initiation codons of ADIPOR1 and ADIPOR2, respectively, for genetic variation in 24 participants in the AFDS (eight subjects each with type 2 diabetes, IGT, and NGT). This sequencing set provides 91% power to detect at least one copy of the minor allele for SNPs with allele frequencies 0.05. Subsequent to PCR amplification both strands were sequenced on an ABI 3730 DNA sequencer and analyzed using Sequence Analysis 3.2 software (Applied Biosystems, Foster City, CA). All identified variants were genotyped in 618 individuals enrolled in the AFDS (137 subjects with type 2 diabetes, 139 with IGT, and 342 with NGT) with the exception of AR2p3, AR2pID, rs10735003, and rs9805042, which were not genotyped due to technical difficulties. To obtain better estimates of MAFs and LD for these four SNPs, an additional 24 subjects were sequenced. Genotyping for the other SNPs was completed using the SNPstream Ultra High Throughput genotyping platform (Beckman Coulter, Fullerton, CA) (25) and the Pyrosequencing PSQ HS 96A system (Pyrosequencing, Uppsula, Sweden). The error rate, based on blind replicates for the SNPs examined, was 0eC2%.
Statistical analysis.
Before analysis, genotypes were checked for Mendelian consistency using the PedCheck software program (26) in the extended Amish pedigree. Mendelian errors were resolved or removed before analysis. Allele frequencies were calculated by gene counting. All SNPs conformed to Hardy-Weinberg expectations. We evaluated the association between SNP genotype and disease status (type 2 diabetes versus NGT and type 2 diabetes/IGT versus NGT) under the additive genetic model using a variance component approach, in which we modeled the probability that the subject was a case or control as a function of the individual’s age, sex, and genotype, conditional on the correlations in phenotype among relative pairs. Statistical testing was accomplished using the likelihood ratio test (27). The OR was computed by comparing the odds of disease between subjects carrying one copy of the minor allele and subjects not carrying any copies of the minor allele. The association analyses were carried out using the SOLAR software program (28).
In similar fashion, we also evaluated the effect of genotype on BMI and plasma glucose and insulin levels during an OGTT in an expanded set of 698 nondiabetic NGT (n = 568) and IGT (n = 130) AFDS subjects. We compared the likelihood of a model in which the trait values were allowed to vary by genotype (unconstrained model) to that in which the genotype effects were constrained to be zero using the likelihood ratio test. Within each model, we simultaneously estimated the effects of age, sex, and family relationship (27). Insulin values were transformed by their natural logarithms (ln) to reduce skewness. Quantitative trait analyses were conducted using the SOLAR program (28).
Pairwise LD (r2) was computed using the ZAPLO software program (29). Haploscore was used to estimate and compare haplotype frequencies between case and control subjects (30).
ACKNOWLEDGMENTS
This work was supported by research grants T32-AG00219, R01-DK54261, K24-DK02673, U01-DK58026, K07-CA67960, the University of Maryland General Clinical Research Center, grant M01 RR 16500, the General Clinical Research Centers Program, the National Center for Research Resources, the National Institutes of Health, and the Baltimore Veterans Administration Geriatric Research and Education Clinical Center.
We gratefully acknowledge our Amish liaisons and field workers and the extraordinary cooperation and support of the Amish community, without which these studies would not be possible.
FOOTNOTES
Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org.
ADIPOR1, adiponectin receptor 1; ADIPOR2, adiponectin receptor 2; AFDS, Amish Family Diabetes Study; IGT, impaired glucose tolerance; LD, linkage disequilibrium; MAF, minor allele frequency; NGT, normal glucose tolerance; OGTT, oral glucose tolerance test; SNP, single nucleotide polymorphism
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2 Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland
ABSTRACT
Adiponectin receptor 1 (ADIPOR1) and adiponectin receptor 2 (ADIPOR2) are newly identified receptors for adiponectin, an adipocytokine with anti-inflammatory and insulin-sensitizing properties. We screened for polymorphisms by performing sequence analysis on all eight exons, splice junctions, and 2 kb of the 5' flanking regions of each receptor. We detected 5 single nucleotide polymorphisms (SNPs) in ADIPOR1 and 16 SNPs in ADIPOR2. We genotyped these SNPs in Amish subjects with type 2 diabetes (n = 137), impaired glucose tolerance (IGT) (n = 139), and normal glucose tolerance (n = 342) to test for association with type 2 diabetes. Three intronic SNPs in ADIPOR1 were significantly associated with type 2 diabetes (P = 0.014eC0.007; odds ratio [OR] 1.61eC1.65) and in high linkage disequilibrium (r2 = 0.97eC1.0). In ADIPOR2, we found that five SNPs delineated one large haplotype block (r2 = 0.9eC1.0) spanning >98 kb of the gene and promoter region, which was strongly associated with the combined type 2 diabetes/IGT trait (P 0.001; OR 1.64eC1.71). To our knowledge, these data provide the first evidence for association between variation in the adiponectin receptors and type 2 diabetes.
Adiponectin is an adipocytokine that is involved in lipid and glucose metabolism, energy homeostasis, and inflammatory pathways (1eC5). Circulating concentrations of adiponectin are reduced in subjects with type 2 diabetes (6eC9). Low plasma adiponectin levels are observed in rodent models of obesity and insulin resistance (2,10), and adiponectin-deficient mice exhibit diet-induced insulin resistance (11,12). The insulin-sensitizing effects of adiponectin appear to be mediated by increased fatty acid oxidation and glucose uptake (10,13) and suppression of gluconeogenesis (14,15) through activation of AMP-activated protein kinase (16,17) and peroxisome proliferatoreCactivated receptor- (18).
Recently, two receptors, adiponectin receptor 1 (ADIPOR1) and adiponectin receptor 2 (ADIPOR2), were cloned, each consisting of eight exons that are located on chromosomes 1q32.1 and 12p13.33, respectively. In humans, ADIPOR1 is ubiquitously expressed, with highest levels in the skeletal muscle, while ADIPOR2 is predominantly expressed in skeletal muscle and liver (19). Yamauchi et al. (19) showed that activation of AMP-activated protein kinase and peroxisome proliferatoreCactivated receptor- by adiponectin is mediated by ADIPOR1 and ADIPOR2. In addition, normal glucose-tolerant Mexican-American subjects with a family history of type 2 diabetes exhibit significantly lower levels of ADIPOR1 and ADIPOR2 mRNA in skeletal muscle compared with those without a family history (20). Civitarese et al. (20) also observed that mRNA expression of both receptors was positively correlated with glucose disposal, while ADIPOR2 expression was positively correlated with plasma adiponectin levels. These studies suggest that downregulation or altered function of ADIPOR1 and ADIPOR2 may be responsible for development of insulin resistance in peripheral tissues. Therefore, we hypothesized that genetic variation in ADIPOR1 and ADIPOR2 may contribute to increased susceptibility to type 2 diabetes. We addressed this question by screening ADIPOR1 and ADIPOR2 for polymorphisms and testing for association between the observed sequence variation and type 2 diabetes in the Old Order Amish.
In ADIPOR1, DNA sequence analysis in 24 Amish individuals detected five SNPs previously reported in dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/). Figure 1A shows the ADIPOR1 gene structure, SNPs identified, and pairwise linkage disequilibrium (LD) (r2). We genotyped all five SNPs in 618 individuals enrolled in the Amish Family Diabetes Study (AFDS) (137 subjects with type 2 diabetes, 139 subjects with impaired glucose tolerance [IGT], and 342 control subjects with normal glucose tolerance [NGT]). Two of the SNPs, rs2275738 and rs2275737, are located 4 bp apart in intron 1 and were in perfect LD (r2 = 1); therefore, only one of them (rs2275737) was included in the analyses. Genotype frequencies of the two intronic SNPs, rs2275737 and rs1342387, differed significantly between subjects with type 2 diabetes and those with NGT (P = 0.007 and P = 0.014, respectively), with the minor allele being the risk allele for type 2 diabetes (odds ratio [OR] 1.65 and 1.61, respectively) (Table 1 and online appendix Fig. 1 [available at http://diabetes.diabetesjournals.org] for genotype frequencies in type 2 diabetes and IGT cases and NGT control subjects). The intronic SNPs span 6 kb of the gene from intron 1 to intron 4 and were in high LD (r2 0.97) (Fig. 1A). The two SNPs flanking the intronic SNP cluster, rs6666089 and rs7539542, were not significantly associated with type 2 diabetes (P = 0.17 and P = 0.35, respectively) nor were they in LD with each other or the intronic SNPs, suggesting that the intronic SNPs mark a type 2 diabetes susceptibility allele or haplotype. We used Haploscore to estimate four SNP haplotype frequencies and to identify haplotypes associated with type 2 diabetes. None of the haplotypes provided stronger evidence for association with type 2 diabetes than the single SNPs (online appendix Table 1). In addition to the case/control analysis, we examined the effects of ADIPOR1 SNP genotype on BMI and plasma glucose and insulin during a 3-h oral glucose tolerance test (OGTT) in an expanded set of 698 nondiabetic AFDS subjects, but found no association (data not shown).
We previously reported evidence for linkage to type 2 diabetes on chromosome 1q21-q24 in the Amish (21), which raises the question of whether ADIPOR1 contributes to this linkage. While we cannot rule out that possibility, ADIPOR1 resides 44 Mb downstream from the 1-LOD (logarithm of odds) support interval for our peak linkage to chromosome 1 and is thus unlikely to be the cause of the linkage.
We identified 16 SNPs by sequence analysis in ADIPOR2 (Fig. 1B) and genotyped 12 of the SNPs in 618 AFDS subjects. Four SNPs (AR2p3, AR2pID, rs10735003, and rs9805042) were eliminated due to difficulties with genotyping assays. AR2p3, AR2pID, and rs9805042 had minor allele frequencies (MAFs) <0.015, and rs10735003 had an r2 = 1 with rs11061971 in the extended sequencing set (n = 96 alleles); thus, these SNPs were not pursued further. Three SNPs were in perfect LD (r2 = 1), including rs10773980, rs11061971, and rs10735003 (estimated from 96 alleles), so only rs11061971 was included in the analyses. One large haplotype block spanning 65 kb of the promoter, exon 1 through exon 7, and 1.8 kb of exon 8 (>98 kb total) contains three common SNPs (rs1029629, rs11061971, and rs12342) that are in high LD (r2 0.90) and show strong association with the combined type 2 diabetes/IGT trait (P 0.001; OR 1.64eC1.71), with the minor allele being the risk allele (Table 2 and Fig. 1B; see online appendix Fig. 2 for genotype frequencies in type 2 diabetes and IGT cases and NGT control subjects). This block is interrupted by one SNP (rs730032) that is neither in LD with the large haplotype block (r2 = 0.21eC0.24) nor associated with the type 2 diabetes/IGT trait (P = 0.23; OR 1.30). Two additional SNPs located 3' of the large haplotype block (rs1044471 and AR2eC8f) were significantly associated with type 2 diabetes/IGT (P = 0.006 and P = 0.012, respectively), but not in high LD with each other or the SNPs in the large haplotype block. The haplotype analysis revealed one haplotype (GTGTT) that was significantly associated with the combined type 2 diabetes/IGT trait (P = 0.004; global P value = 0.015) (Fig. 2). This haplotype contains the risk alleles for each of the single type 2 diabetes/IGT-associated SNPs. These results should be interpreted with caution since Haploscore does not account for relatedness between subjects.
Quantitative trait analysis of ADIPOR2 SNPs in an expanded set of 698 nondiabetic subjects revealed significant associations between rs1029629 and mean plasma glucose levels and total glucose area under the curve during the OGTT (P = 0.005eC0.016 and P = 0.021, respectively, for recessive model). Carriers of the G risk allele for rs1029629 exhibited significantly higher plasma glucose levels at each 30-min interval from 90 to 180 min and total glucose area under the curve during the OGTT (online appendix Fig. 3), providing additional evidence that the haplotype containing the G allele of rs1029629 influences glucose homeostasis and type 2 diabetes risk. There was no association between SNPs in ADIPOR2 and BMI or plasma insulin during the OGTT.
Although these type 2 diabetes/IGT-associated SNPs do not disrupt any obvious transcription factoreCbinding sites (22) or splice sites, these variants could possibly alter expression or mRNA stability. We explored the possibility of epistasis between polymorphisms in the receptors but found no evidence for interaction between SNPs in ADIPOR1 and ADIPOR2.
In a recent report, Wang et al. (23) screened ADIPOR1 (but not ADIPOR2) for sequence variation in Caucasian and African-American subjects but found no association between the SNPs they detected (including the five SNPs we examined) and type 2 diabetes, insulin sensitivity, or insulin secretion. The discrepancy between their study and ours may be a result of low power or underlying population differences. Alternatively, it is possible that we have detected a false-positive. We did not correct for multiple comparisons in our analyses. If the highly conservative Bonferroni correction were used in our case/control analysis, the significantly associated ADIPOR1 SNPs would become borderline to nonsignificant whereas the ADIPOR2 SNPs would remain significant at the P = 0.01 level. This observation strengthens the evidence that variation in ADIPOR2 contributes to increased susceptibility to type 2 diabetes.
In summary, we have extensively screened the two adiponectin receptors to search for polymorphisms associated with type 2 diabetes in the Old Order Amish. We identified multiple polymorphisms in both ADIPOR1 and ADIPOR2 that were significantly associated with type 2 diabetes and related traits, providing evidence that genetic variation in the receptors, particularly ADIPOR2, plays a role in type 2 diabetes susceptibility. Alternatively, these SNPs may be in LD with an undetected functional SNP in the receptors or a neighboring gene. Further analysis in other populations, as well as functional studies, will be necessary to further elucidate the role of polymorphism in ADIPOR1 and ADIPOR2 in the pathogenesis of type 2 diabetes.
RESEARCH DESIGN AND METHODS
The AFDS was initiated in 1995 with the goal of identifying susceptibility genes for type 2 diabetes in the Old Order Amish. Details of the AFDS design, recruitment, phenotyping, and pedigree structure have been described previously (24). Briefly, probands with previously diagnosed type 2 diabetes (onset age 35eC65 years) and, where possible, all first- and second-degree relatives of probands and spouses >18 years of age were recruited. Phenotypic characterization of study participants included medical and family history, anthropometry, and an OGTT with insulin levels. Total glucose and insulin areas under the curve during the OGTT were calculated using the trapezoid method. BMI was calculated as weight in kilograms divided by the square of height (in meters). This study includes additional subjects recruited subsequent to the report by Hsueh et al. (24) and excludes all subjects with NGT <38 years of age in order to increase the probability of their capacity for diabetes resistance. Informed consent was obtained from all study subjects, and the Institutional Review Board at the University of Maryland School of Medicine approved the study protocol.
Sequence analysis and genotyping.
Direct sequencing was used to screen the eight exons with splice junctions (100 bp of the intronic flanking sequence) of both ADIPOR1 and ADIPOR2 and 1,712 bp and 1,976 bp of the region 5' of the initiation codons of ADIPOR1 and ADIPOR2, respectively, for genetic variation in 24 participants in the AFDS (eight subjects each with type 2 diabetes, IGT, and NGT). This sequencing set provides 91% power to detect at least one copy of the minor allele for SNPs with allele frequencies 0.05. Subsequent to PCR amplification both strands were sequenced on an ABI 3730 DNA sequencer and analyzed using Sequence Analysis 3.2 software (Applied Biosystems, Foster City, CA). All identified variants were genotyped in 618 individuals enrolled in the AFDS (137 subjects with type 2 diabetes, 139 with IGT, and 342 with NGT) with the exception of AR2p3, AR2pID, rs10735003, and rs9805042, which were not genotyped due to technical difficulties. To obtain better estimates of MAFs and LD for these four SNPs, an additional 24 subjects were sequenced. Genotyping for the other SNPs was completed using the SNPstream Ultra High Throughput genotyping platform (Beckman Coulter, Fullerton, CA) (25) and the Pyrosequencing PSQ HS 96A system (Pyrosequencing, Uppsula, Sweden). The error rate, based on blind replicates for the SNPs examined, was 0eC2%.
Statistical analysis.
Before analysis, genotypes were checked for Mendelian consistency using the PedCheck software program (26) in the extended Amish pedigree. Mendelian errors were resolved or removed before analysis. Allele frequencies were calculated by gene counting. All SNPs conformed to Hardy-Weinberg expectations. We evaluated the association between SNP genotype and disease status (type 2 diabetes versus NGT and type 2 diabetes/IGT versus NGT) under the additive genetic model using a variance component approach, in which we modeled the probability that the subject was a case or control as a function of the individual’s age, sex, and genotype, conditional on the correlations in phenotype among relative pairs. Statistical testing was accomplished using the likelihood ratio test (27). The OR was computed by comparing the odds of disease between subjects carrying one copy of the minor allele and subjects not carrying any copies of the minor allele. The association analyses were carried out using the SOLAR software program (28).
In similar fashion, we also evaluated the effect of genotype on BMI and plasma glucose and insulin levels during an OGTT in an expanded set of 698 nondiabetic NGT (n = 568) and IGT (n = 130) AFDS subjects. We compared the likelihood of a model in which the trait values were allowed to vary by genotype (unconstrained model) to that in which the genotype effects were constrained to be zero using the likelihood ratio test. Within each model, we simultaneously estimated the effects of age, sex, and family relationship (27). Insulin values were transformed by their natural logarithms (ln) to reduce skewness. Quantitative trait analyses were conducted using the SOLAR program (28).
Pairwise LD (r2) was computed using the ZAPLO software program (29). Haploscore was used to estimate and compare haplotype frequencies between case and control subjects (30).
ACKNOWLEDGMENTS
This work was supported by research grants T32-AG00219, R01-DK54261, K24-DK02673, U01-DK58026, K07-CA67960, the University of Maryland General Clinical Research Center, grant M01 RR 16500, the General Clinical Research Centers Program, the National Center for Research Resources, the National Institutes of Health, and the Baltimore Veterans Administration Geriatric Research and Education Clinical Center.
We gratefully acknowledge our Amish liaisons and field workers and the extraordinary cooperation and support of the Amish community, without which these studies would not be possible.
FOOTNOTES
Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org.
ADIPOR1, adiponectin receptor 1; ADIPOR2, adiponectin receptor 2; AFDS, Amish Family Diabetes Study; IGT, impaired glucose tolerance; LD, linkage disequilibrium; MAF, minor allele frequency; NGT, normal glucose tolerance; OGTT, oral glucose tolerance test; SNP, single nucleotide polymorphism
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