A Synonymous Coding Polymorphism in the α2-Heremans-Schmid Glycoprotein Gene Is Associated With Type 2 Diabetes in French Caucasians

RESEARCH DESIGN AND METHODS

American Diabetes Association 2003 criteria (13) for the classification of subjects as diabetic or normoglycemic were applied. The subset of 55 French families with early-onset type 2 diabetes (age at diagnosis <46 years) that produced the linkage result at chromosome 3q27 was described previously (3). The type 2 diabetes case-control study was carried out with a cohort of 682 unrelated type 2 diabetic subjects (age 60 ± 12 years, BMI 27.0 ± 3.5 kg/m², men/women 55/45%) and 918 unrelated normoglycemic subjects (age 54 ± 11 years, BMI 24.6 ± 3.9 kg/m², men/women, 41/59%). The type 2 diabetic cases were composed of 310 probands from type 2 diabetic families, recruited by P. Froguel’s CNRS Institute Pasteur Unit in Lille, and 372 singleton patients with a family history of type 2 diabetes recruited at the Endocrinology-Diabetology Department of the Corbeil-Essonne Hospital. The control subjects were composed of 372 normoglycemic husbands or wives from type 2 diabetic families, and 560 normoglycemic subjects from the SUVIMAX prospective population-based cohort study (14). The morbidly obese case-control study was carried out with a cohort of 575 unrelated morbidly obese patients (age 46 ± 12 years, BMI 47.3 ± 7.4 kg/m², men/women 23/77%) (15) using the same control subjects as for the type 2 diabetes case-control study. Subjects were all of French Caucasian ancestry. With the exception of plasma insulin levels, a number of quantitative clinical phenotypes (including plasma levels of glucose, triglycerides, and cholesterol) were assayed in >95% of our normoglycemic cohort. Plasma insulin levels were only available for the 372 normoglycemic husbands or wives. The homeostasis model assessment of insulin resistance (HOMA-IR) index was calculated as fasting insulin (μU/ml) × fasting glucose (mmol/l)/22.5 (16). Informed consent was obtained from all subjects, and the study was approved by the local ethics committees.

Screening the AHSG gene for SNPs.

We carried out SNP detection by PCR followed by direct sequencing of the PCR products. PCR primer sequences are available on request. Genomic DNA sequence spanning the AHSG gene was obtained from the National Center for Biotechnology Information (NCBI) website (available from http://www.ncbi.nlm.nih.gov/; Entrez GeneID, 197). PCRs were performed with 100 ng human genomic DNA and Taq Gold polymerase (Applied Biosystems, Foster City, CA) using standard PCR conditions in a total volume of 25 μl. Sequencing reactions were performed with the BigDye Terminator v.3 cycle sequencing kit (Applied Biosystems) and electrophoresed on the Applied Biosystems 3700 Genetic Analyzer according to the manufacturer’s instructions. Sequence analysis and SNP identification were carried out using the Phred/Phrap/Consed system (17,18). We sequenced ∼1 kb upstream of the ATG codon, each exon together with ∼200 bp flanking intronic sequence and 500 bp of the 3′ untranslated region (UTR) in 24 unrelated probands (48 chromosomes) taken from the 24 type 2 diabetic families with the strongest evidence of linkage at 3q27 (a nonparametric linkage score ≥0.816). SNPs identified in two or less heterozygote subjects of 24 (allele frequency ≤4%) were designated rare SNPs.

SNP genotyping.

Genotyping was performed with the Sequenom MassARRAY system, as previously described (19).

Statistical analyses.

Comparisons of SNP allele and haplotype frequencies in case and control groups were performed using the χ² test, with P values presented uncorrected for multiple testing. The SNPspD method (20) for correction of multiple SNP testing was employed. The basis of this method is the use of spectral decomposition of matrices of pairwise SNP linkage disequilibrium to generate an adjusted significance threshold. Testing SNP alleles for association with quantitative traits was carried out with the Wilcoxon–Kruskal-Wallis. Pairwise SNP linkage disequilibrium was calculated with the GOLD software package (21) from the haplotype counts output by PHASE (22). The Haplotype Trend Regression program (23) was used to test inferred haplotypes for association with quantitative phenotypes. Associations with the evidence for linkage analyses were carried out as described previously (24). Briefly, this involved comparing the expected and observed allele-sharing proportions for affected sibpairs (ASPs) with concordant 1/x–1/x genotypes, where one is the major allele and x is either the major or minor allele, conditional on the identical-by-descent (IBD) allele-sharing distribution of the original genome scan dataset (3).

RESULTS

The AHSG gene, consisting of seven exons and spanning 8.2 kb of genomic DNA, was screened for SNPs by direct sequencing. We identified nine common SNPs within the AHSG gene with a minor allele frequency ≥5% and six rare SNPs (Table 1). The same common SNPs were identified in a recent study of the effect of AHSG gene variation on obesity and insulin action in the Swedish population (12). Since the aim of this study was to evaluate common variation in the AHSG gene, the rare SNPs were not subjected to any further analysis. In addition, no further analysis was carried out on SNP rs4831 due to failure to successfully genotype it using either the Sequenom (19) or Taqman methods (25).

The other eight common AHSG SNPs were genotyped in our type 2 diabetic case-control cohort with an average success rate of 88%. The genotype distribution was in accordance with Hardy-Weinberg equilibrium for all SNPs (data not shown). We first evaluated the extent of pairwise linkage disequilibrium, as quantified by the metrics D′ and Δ² (Fig. 1). The average D′ and Δ² values across the AHSG gene were 0.92 and 0.28, respectively. SNPs rs4917 and rs4918 exhibited almost complete linkage disequilibrium (Δ² = 0.95), whereas SNPs rs2248690 and rs1071592, at opposite ends of the gene, were also in strong linkage disequilibrium (Δ² = 0.86). The results of evaluating each of the common SNPs for association with type 2 diabetes are presented in Table 2. The major allele of SNP rs1071592, a synonymous SNP in exon 7, presented evidence of association with type 2 diabetes (P = 0.008, odds ration [OR] 1.27 [95% CI 1.06–1.52]). We also observed borderline association for SNPs rs2248690 (P = 0.06) and rs4918 (P = 0.09). These results are consistent with the high level of linkage disequilibrium between these three SNPs, as measured by Δ². Note that P values are presented uncorrected for multiple testing. Using the SNPSpD correction method of Nyholt (20), which accounts for intermarker linkage disequilibrium, a significance threshold of 0.009 was obtained for the eight AHSG SNPs tested. The rs1071592 association result fell just under this threshold.

SNP rs1071592 was evaluated for association with the evidence for linkage of type 2 diabetes to 3q27, using the linkage partitioning method described previously (24). We found that the observed IBD-sharing proportion of concordant-genotype ASPs carrying the major allele was no higher than that expected under the null hypothesis of no association with the evidence for linkage. The observed IBD-sharing proportion was 0.69 (from a total of 72 ASPs), whereas the expected IBD-sharing proportion for an allele with a frequency equal to that of the major allele of SNP rs1071592 was 0.75 (a total of 69 ASPs). This result argued against the hypothesis that SNP rs1071592 is significantly associated with the evidence for linkage and was corroborated using a simulation-based approach (15) (data not shown).

There were no significant differences in SNP allele or haplotype frequencies between males and females, and no additional type 2 diabetes associations were uncovered by stratifying for sex (data not shown). None of the AHSG SNPs were associated with BMI, HOMA-IR, or with plasma levels of glucose, triglycerides, or cholesterol in our nondiabetic cohort (data not shown).

The AHSG haplotype frequency distribution of type 2 diabetic case and control subjects is shown in Table 3. A total of six common AHSG haplotypes were identified, accounting for 95% of control chromosomes. The average probability of the most likely haplotype pair across the dataset was 0.95 ± 0.13. Haplotype 21122221 was associated with normoglycemia (P = 0.014), indicating a protective effect against type 2 diabetes. This finding is consistent with the SNP association results, since this haplotype is the only one carrying the minor (protective) allele of SNP rs1071592.

The observation that AHSG-null mice are resistant to diet-induced obesity (10) prompted us to examine a possible role for AHSG gene variation in susceptibility to human obesity. An obesity case-control study was carried out in which we genotyped AHSG SNPs in 575 morbidly obese (BMI >40 kg/m²) subjects, using the previously genotyped normoglycemic control subjects as the control set. However, we found no evidence for association of AHSG SNPs or haplotypes with morbid obesity (data not shown).

DISCUSSION

Significant evidence of association with type 2 diabetes was observed for a synonymous coding SNP rs1071592 in exon 7. The data presented here suggests that rs1071592 is a novel candidate polymorphism for modulating susceptibility to type 2 diabetes. However, additional large-scale case-control studies are clearly warranted to confirm the involvement of this variant in the genetic basis of type 2 diabetes susceptibility. This is especially true given that the rs1071592 association result fell just under the significance threshold after accounting for multiple testing.

SNP screening was only carried out in and around the exons and promoter region; therefore, we cannot rule out the possibility that other unassayed type 2 diabetes susceptibility SNPs exist within as yet unidentified regulatory regions within the AHSG gene. With the aim of identifying additional regulatory regions, we examined the human-mouse comparative genomic sequence of the AHSG gene together with 10 kb of flanking sequence on the VISTA website (available from http://pipeline.lbl.gov/cgi-bin/gateway2). Apart from two regions showing ≥70 conservation identities (in the promoter and proximal intron six regions) that were actually included within the sequences screened for SNPs, no other conserved regions were identified.

Although AHSG has been shown to inhibit the activity of the insulin receptor (1,8,9) we failed to find an association between AHSG SNPs and HOMA-IR. However, the number of normoglycemic subjects with fasting insulin levels (n = 372) provided only modest statistical power to detect an association of moderate effect. We also found no evidence for association of rs2077119 with type 2 diabetes, a promoter SNP that had previously been associated with insulin-mediated lipid metabolism in adipocytes (12).

Finally, we found no evidence that SNP rs1071592 is associated with the evidence for linkage of type 2 diabetes to 3q27, indicating that an as yet unidentified gene or genes at 3q27 is responsible for the linkage signal. Our efforts aimed at identifying the “causative” type 2 diabetes gene at 3q27 are ongoing.