Searching for Type 2 Diabetes Genes on Chromosome 20

Type 2 diabetic case subjects and control subjects were of Ashkenazi Jewish descent as described (2). As shown in Table 2, the control subjects were significantly older than the case subjects by design because control subjects were selected for old age and absence of diabetes.

The DNA samples were isolated from whole blood using the Puregene kit as described (Gentra Systems, Minneapolis, MN). DNA was quantified with the TKO 100 Mini-Fluorometer and Hoechst dye method as described (Hoefer Scientific Instruments, San Francisco, CA). For the purposes of creating DNA pools, efforts to accurately determine DNA concentrations for each sample are critical because errors will skew the proportion of each genotype in the pool. Spectrophotometric analysis was avoided because substances such as protein and salts may give spurious results (18). The DNA samples were gently mixed on a rocking platform to ensure homogeneity before pipetting. Equal volumes of each sample were delivered to a sterile 55-ml polypropylene solution basin (Labcor Products, Frederick, MD) using an accurately calibrated multichannel pipette. After mixing gently and thoroughly overnight, the pooled DNA was placed into 1.0-ml aliquots in sterile 1.5-ml polypropylene microtubes and stored at 4°C in the dedicated refrigerator. As a quality control, the uniformity of the mixing procedure was verified by genotyping replicate aliquots of the pools for several SNPs.

The reaction consisted of 2.5 μl GeneAmp 10× Buffer II (Applied Biosystems, Foster City, CA), 2.0–3.0 μl of 25 mmol/l MgCl₂ solution, 0.5 μl of each 20 mmol/l dNTP (Amersham Pharmacia, Piscataway, NJ), 1 μl of 10 pmol/μl 5′ biotin-triethylene glycol-labeled high-performance liquid chromatography (HPLC)-purified primer and 1 μl of 10 pmol/μl unlabeled primer (IDT, Coralville, IA), 0.25 μl (1.25 units) Amplitaq Gold (Applied Biosystems), 2.5 μl of 10 ng/μl DNA (pooled or individual), and sterile water to 25 μl total volume. Thermal cycling was done interchangeably on a GeneAmp 9700 (Applied Biosystems) or PTC-200 (MJ Research, Watertown, MA) using the following profile: heated lid, 95°C for 10 min × 1 cycle/95°C for 45 s/annealing temperature for 45 s/72°C for 1 min × 45 cycles to 4°C. The annealing temperature varied from 56 to 62°C. Forty-five cycles ensured that all PCR components were exhausted. PCR primers were designed with Primer3 Software (code available at http://www-genome.wi.mit.edu/genome_software/other/primer3.html) (19), and the predicted reaction conditions (annealing temperature and MgCl₂) were tested on several nonessential DNA samples. Fragment sizes between 100 and 500 bp were successfully analyzed.

There were 96-well plates (8 × 12) set up with eight replicates of the case and control pools. A variable number of replicates from three to ten were tested, and it was found that eight replicates most consistently resulted in an SD of ≤2%. Template preparation and pyrosequencing (Pyrosequencing AB) was conducted as described (17). Genotypes for a 96-well plate were generated in 10 min.

Allele frequencies in the samples were assessed by SNP Software AQ (Pyrosequencing AB) as described, and the data were exported to an Excel (Microsoft) spreadsheet for further analysis.

The PCR was as previously described. Heteroduplex DNA was formed and analyzed as described with the Wave (Transgenomic, Omaha, NE).

For each SNP assay, a reference individual and three DNA pools of 42 individuals from African-American, Asian (10 Chinese/32 Japanese), and Caucasian (the SNP Consortium DNA panels, Coriell Institute, Camden, NJ, http://arginine.umdnj.edu) populations were amplified by PCR. The Caucasian pool was used as a reference for Ashkenazi study. PCR products were cycle-sequenced using one of the original PCR primers and fluorescent dye-terminators and electrophoresed on an ABI 3700 (Applied Biosystems). For analysis, the allele frequencies in the pooled samples were estimated by comparison of the nucleotide peak heights in the electropherogram (20).

The P values quantifying the significance of the difference between pools of case and control subjects were calculated using the two-sample test for binomial proportions (normal theory test) (21), including twice the measurement variance in the calculation of the Z statistic.

The microsatellite markers bordering the 1-LOD interval around D20S107 include RPN2 and D20S911 at 35.7 and 43 Mb, respectively. This 7.3-Mb region contains a total of 25 known and 99 predicted genes (NCBI Human Genome Map, Build 28) (http://www.ncbi.nlm.nih.gov). All of the known genes in this region are shown in Table 3, along with the indication of those genes with expressed sequence tags (ESTs) expressed in pancreatic islets found in UniGene (http://www.ncbi.nlm.nih.gov/UniGene). As can be seen, seven of the known genes and two predicted genes were found with islet ESTs, and one gene (MAFB) showed relatively high expression in islets.

Because it would be difficult to directly sequence all 124 known and predicted genes within the putative at-risk region for a significant number of patients with diabetes, linkage disequilibrium mapping was used to evaluate the entire 7.3-Mb region. SNPs were identified in public databases and validated in the Ashkenazi population through a collaborative arrangement with a member of the SNP Consortium (P. Kwok, Washington University Medical School). Direct sequencing of pooled DNA from Caucasian, Asian, and African-American individuals was conducted. SNPs with minor allele frequencies greater than 10% in the Caucasian subjects were then tested in pooled samples of DNA from Ashkenazi subjects. Interestingly, only ∼50% of the SNPs submitted for validation were found to have a minor allele frequency of ≥10%. The validation data were entered into the public SNP database 1 week after testing (http://www.ncbi.nih.gov/SNP). The results for the validated SNPs for the four racial/ethnic groups are shown in Table A1 (in the appendix). The SNPs rs736823 and rs932440, which were monomorphic in the Caucasian subjects, had minor allele frequencies of 42.2 and 11.1, respectively.

The allele frequencies of SNPs between Ashkenazi case subjects and control subjects (n = 300 each) were tested. As shown in Table A2, a total of 91 SNPs were examined. Of these, 65 SNPs were located in known or predicted genes, and 26 were intragenic. Statistical analyses (Fig. 1) showed that the allele frequency for one SNP at TIX1 appeared to differ (13.2 vs. 20.8%T for control and case subjects, respectively; P = 0.035) but on replication was found to have no difference (17.8%T in case pool 2, P = 0.22 vs. control) (Table A2 and Fig. 1).

Two genes in the region, Mybl2 and HNF4a, were screened by denaturing HPLC for exonic mutations and new polymorphisms. Mybl2 was screened because of the marginally significant P value of 0.057 for rs419842. HNF4a has been described in maturity-onset diabetes of the young (MODY)-1 (22) and as a possible type 2 diabetes candidate gene and has not yet been examined in Ashkenazi subjects. No coding or obvious splicing mutations were identified in either gene. However, in Mybl2, an unpublished nonsynonymous SNP R687C in exon 14 was identified (nt2186 C to T, accession number NM 002466) (data not shown).

In addition, allele frequencies for reported type 2 diabetes candidate gene SNPs were determined for the islet ATP-sensitive K⁺ channel (KIR6.2 and SUR), peroxisome proliferator-activated receptor (PPAR)-γ, three SNPs for Calpain 10, and two SNPs in insulin receptor substrate 1, each previously shown in more than one study to be associated with type 2 diabetes (23,24). No differences were found between allele frequencies in case and control subjects for these candidate SNPs (Table 4).

This study involved SNP association in pooled DNA for a 7.3-Mb chromosomal region on 20q containing a total of 124 known and predicted genes. Allele frequencies were assessed in DNA pools rather than individual genotypes to expedite the study and decrease costs. By using pyrosequencing technology, allele frequencies in the pooled DNAs occurred within 2% of those frequencies defined by individual genotypes (17).

One SNP (TIX1) out of 91 tested showed marginal significance in case subjects versus control subjects, which was not replicated in a second pool of type 2 diabetic case subjects. Despite the lack of association to a specific SNP at this preliminary stage in the study, the occurrence of significant LOD scores along chromosome 20q from four racial/ethnic group studies supports the hypothesis that a genetic element contributing to type 2 diabetes is present. However, there are several limiting factors. First, each of the chromosome 20q peaks are fairly broad and encompass ∼10–20 Mb of DNA. This in turn makes it difficult to anticipate the risk of not identifying the disease genes in the region around D20S107 and to determine whether a shift in focus more centromeric toward D20S195 or more telomeric toward D20S197 should be indicated. Second, lack of reported SNPs in this region mandates denaturing HPLC and sequencing of the genome in an effort to find more. For example, MAFB, a transcription factor, had fourfold more expression in islet cDNAs than any other gene, and only one SNP has been tested. Testing of 91 SNPs does not constitute an adequate evaluation of a 7.3-Mb region, and the SNP association study is still in progress. Further, we used the NCBI Chromosome Mapviewer as a roadmap for our SNP work. There have been at least five different “builds” or updates to the map since we started about 1 year ago. The map has expanded from 25 to possibly 124 genes in the region. It is evolving and so are our studies.

A major question remains as to what SNP density is required for accurate analysis. Does this mean typing one SNP every 10 kb requiring ∼730 SNPs or one SNP every 1 kb requiring ∼7,300 SNPs? A public project initiative to identify linkage disequilibrium blocks in the human genome may facilitate an answer to this question; however, this project is at least 2 years from completion. Furthermore, the differences in SNP allele frequencies between Ashkenazi and Western Caucasian subjects (Table A1) suggest that a unique haplotype map for isolated Caucasian subjects may be necessary. On the positive side, five groups have identified essentially the same region as harboring a putative diabetes gene(s) on chromosome 20q. In fact, a gene associated with type 2 diabetes, PPAR-γ, on chromosome 3p has not given a positive linkage signal in any of the Caucasian genome scans, suggesting perhaps that the signal on chromosome 20q confers a greater risk than that for PPAR-γ. The results of the current study indicate that the process of linkage disequilibrium mapping to narrow regions of linkage for complex diseases such as type 2 diabetes will be a difficult one.

The results for the validated SNPs for the four racial/ethnic groups (Table A1) and all know genes on chromosome 20q (Table A2) are presented on the following pages.

This work was supported in part by National Institutes of Health Grants DK16746, DK07120, and DK49583 (to M.A.P.).