Genome scans in families with type 2 diabetes identified a putative locus on chromosome 20q. For this study, linkage disequilibrium mapping was used in an effort to narrow a 7.3-Mb region in an Ashkenazi type 2 diabetic population. The region encompassed a 1-logarithm of odds (LOD) interval around the microsatellite marker D20S107, which gave a LOD score of >3 in linkage analysis of a combined Caucasian population. This 7.3-Mb region contained 25 known and 99 predicted genes. Predicted single nucleotide polymorphisms (SNPs) were chosen from public databases and validated. Two SNPs were unique to the Ashkenazi. Here, 91 SNPs with a minor allele frequency of ≥10% were genotyped in pooled DNA from 150 case subjects and 150 control subjects of Ashkenazi Jewish descent. The SNP association study showed that SNP rs2664537 in the TIX1 gene had a significant P value of 0.035, but the finding did not replicate in an additional case pool. In addition, HNF4a and Mybl2 were screened for mutations and new polymorphisms. No mutations were identified, and a new nonsynonymous SNP (R687C in exon 14 of Mybl2) was found. The limits to this type of association study are discussed.

Type 2 diabetes is a complex metabolic disorder characterized by abnormal hepatic glucose output, insulin resistance, and impaired insulin production (1). Multiple environmental and genetic factors contribute, and genome scans in families with multiple affected individuals from several racial/ethnic groups have been undertaken (Table 1). A number of potential loci have been identified, but in general, the evidence for linkage has not been strong, and the regions identified have been quite broad. A major question remaining is how to proceed with the search for complex disease genes, knowing that a single gene is neither necessary nor sufficient and that recombinant mapping in families will not suffice. The next phase of the search for diabetes susceptibility genes will likely require new strategies.

A genome scan with microsatellite markers at an average distance of 9.5 cM was completed in type 2 diabetic sibling pairs (n = 472) of Ashkenazi Jewish descent (2). The Ashkenazi population is relatively young and homogeneous, having undergone several constrictions and expansions resulting in reduced genetic heterogeneity in comparison to that of most Western Caucasian subjects. Studies of DNA polymorphisms have suggested that present-day Ashkenazi Jews descended from a small founder population, numbering perhaps as few as 10,000 individuals who existed in Eastern Europe at about 1500 AD (3). Today, there are about 10 million, representing a 1,000-fold expansion in roughly 20 generations. In the genome scan, five regions on four chromosomes exhibited nominal evidence for linkage (P < 0.05) (2). A maximal signal of Z = 2.05 was observed on chromosome 20 near D20S195. Because four other groups had previously reported evidence for linkage in the same region of chromosome 20q in Caucasians (see summary in Table 1), this region was further considered.

Subsequent to the Ashkenazi type 2 diabetes genome scan, several investigators contributed linkage data for chromosome 20 to the International Type 2 Diabetes Linkage Analysis Consortium (http://www.sfbr.org/external/diabetes), a collaborative effort organized principally by the National Institute of Diabetes and Digestive and Kidney Diseases to map genes for the disease. Common markers on chromosome 20 were genotyped, and results were combined for a total of 1,852 families. The initial results of this analysis suggested a peak of linkage at D20S107 with a logarithm of odds (LOD) of >3. We therefore decided to target this region in our search in Ashkenazi patients with type 2 diabetes.

Gene mapping by linkage disequilibrium analysis in related families identifies broad conserved regions of chromosomal DNA. In contrast, “unrelated” affected individuals share smaller conserved chromosomal regions because there are many more meioses, resulting in greater recombination around the region harboring the disease gene. Theoretically, polymorphic markers in linkage disequilibrium with the disease locus can be used to find associations of regions containing the gene mutation.

Linkage disequilibrium mapping has been shown to be an important tool for fine-mapping of monogenic diseases (1012), and recently there has been success in identifying a gene involved in a complex disease—inflammatory bowel disease (13). However, there is little doubt that linkage disequilibrium mapping for most other complex diseases will be more difficult (14). The distance over which disequilibrium extends between markers and disease loci is not well understood nor is the degree of genetic risk contributed by any particular locus, suggesting that genotyping closely spaced markers in many case and control subjects would be required. Single nucleotide polymorphisms (SNPs), whereas biallelic, have been preferred over simple sequence repeat polymorphisms for this type of analysis because SNPs are more abundant in the genome (15). As a general rule, the extent of linkage disequilibrium or association between a marker and a disease locus depends on the genetic distance between the two and the number of generations that have occurred since the mutation originated (16). In isolated populations such as the Ashkenazi Jews, linkage disequilibrium has been shown to extend over a broad region (3). We therefore undertook a search for diabetes susceptibility gene(s) on chromosome 20q in Ashkenazi Jewish unrelated patients with diabetes. Here we report the initial results of our association studies with 91 validated SNPs spanning a 1-LOD interval (7.3 Mb) around the microsatellite marker D20S107 candidate region on chromosome 20. DNA from case and control subjects was genotyped in pools by a recently described method involving pyrosequencing technology (17). No significant associations have been found to date.

Patient population.

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.

DNA isolation, quantification, and construction of pools.

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.

PCR.

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.

PCR plate setup, template preparation, and pyrosequencing.

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 quantification software.

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.

Denaturing HPLC.

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

SNP validation.

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).

Statistical analysis.

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.

FIG. 1.

P values of differences (93 P values in a 1-LOD interval around D20S107) in minor allele frequencies between case and control pools (n = 300 alleles for each pool), plotted on the y-axis, versus the SNP position in megabases, plotted on the x-axis.

FIG. 1.

P values of differences (93 P values in a 1-LOD interval around D20S107) in minor allele frequencies between case and control pools (n = 300 alleles for each pool), plotted on the y-axis, versus the SNP position in megabases, plotted on the x-axis.

TABLE 1

Results of genome scans for chromosome 20

ReferencePopulationSampleScoresP<MarkerLocation
4  Caucasian 21 families, 53 ASP 1.48 LOD 0.005 D20S197 46 Mb 
5  Caucasian 29 families, 498 individuals, 159 affected 2.36 LOD 0.009 D20S197 46 Mb 
6  Caucasian-French 148 families, 301 ASP 2.27 LOD 0.001 RPNII 35.6 Mb 
7  Finnish 477 families, 716 ASP 2.06 MLS 0.009 D20S909 34.3 Mb 
   2.00 MLS 0.01 D20S107 59 cM 
   1.92 MLS 0.013 D20S886 38.7 Mb 
     D20S905 69.5 cM 
      18.5 cM 
2  Ashkenazi Jew 267 families, 896 individuals, 472 ASP 2.05 NPL Z 0.05 D20S195 31.7 Mb 
8  Chinese 102 families, 478 individuals, 282 affected 1.641 NPL 0.03 D20S117 2.9 cM 
   1.959 NPL 0.013 D20S889 11 cM 
   1.517 NPL 0.04 D20S196 73.5 cM 
9  Japanese 159 families, 224 ASP 2.32 MLS 0.00102 D20S119 43.5 Mb 
      66.15 cM 
ReferencePopulationSampleScoresP<MarkerLocation
4  Caucasian 21 families, 53 ASP 1.48 LOD 0.005 D20S197 46 Mb 
5  Caucasian 29 families, 498 individuals, 159 affected 2.36 LOD 0.009 D20S197 46 Mb 
6  Caucasian-French 148 families, 301 ASP 2.27 LOD 0.001 RPNII 35.6 Mb 
7  Finnish 477 families, 716 ASP 2.06 MLS 0.009 D20S909 34.3 Mb 
   2.00 MLS 0.01 D20S107 59 cM 
   1.92 MLS 0.013 D20S886 38.7 Mb 
     D20S905 69.5 cM 
      18.5 cM 
2  Ashkenazi Jew 267 families, 896 individuals, 472 ASP 2.05 NPL Z 0.05 D20S195 31.7 Mb 
8  Chinese 102 families, 478 individuals, 282 affected 1.641 NPL 0.03 D20S117 2.9 cM 
   1.959 NPL 0.013 D20S889 11 cM 
   1.517 NPL 0.04 D20S196 73.5 cM 
9  Japanese 159 families, 224 ASP 2.32 MLS 0.00102 D20S119 43.5 Mb 
      66.15 cM 

ASP, affected sib-pair; MLS, maximum linkage score; NPL, nonparametric linkage score.

TABLE 2

Phenotypic characteristics in case subjects versus control subjects

AveragesCase subjectsElderly control subjects
M/F 72/78 69/74 
Age at diagnosis (years) 48 76 
BMI 31 ± 4.49 26 ± 3.99 
Triglycerides 2.09 ± 1.24  
Cholesterol 5.41 ± 0.99  
HDL 0.99 ± 0.39  
LDL 4.02 ± 0.87  
HbA1c 8.3 ± 2.4  
Fasting blood glucose 164 ± 34.0  
Fastin insulin 19.86 ± 9.18  
Leptin 13.02 ± 6.82  
AveragesCase subjectsElderly control subjects
M/F 72/78 69/74 
Age at diagnosis (years) 48 76 
BMI 31 ± 4.49 26 ± 3.99 
Triglycerides 2.09 ± 1.24  
Cholesterol 5.41 ± 0.99  
HDL 0.99 ± 0.39  
LDL 4.02 ± 0.87  
HbA1c 8.3 ± 2.4  
Fasting blood glucose 164 ± 34.0  
Fastin insulin 19.86 ± 9.18  
Leptin 13.02 ± 6.82  

Data are means ± SD unless otherwise indicated.

TABLE 3

Known and predicted genes in a 1-LOD interval around D20S107 with islet expression

GeneDescriptionNumber of islet cDNAs in UniGene
RBL-1 Retinoblastoma-like 1 — 
RPN2 Ribophorin II — 
GHRH Growth hormone-releasing hormone — 
SRC v-src sarcoma 
BLCAP Bladder cancer-associated protein — 
NNAT Neuronatin — 
KIAA1219 Predicted protein 
TGM2 Transglutaminase 2 
KIAA0406 Predicted protein 
BPI Bactericidal/permeability-increasing protein — 
LBP Lipopolysaccharide-binding protein — 
VIAAT Vesicular inhibitory amino acid — 
PPP1R16B Protein phosphatase 1, regulatory (inhibitor) subunit 
DDX35 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 35 — 
MAFB v-maf musculoaponeurotic fibro-sarcoma oncogene B 
TOP1 Topoisomerase (DNA) I 
PRO0628 PRO0628 protein — 
PLCG1 Phospholipase C, gamma 1 — 
TIX1 Triple homeobox 1 — 
RPL12L2 Ribosomal protein L12-like — 
PTPRT Protein tyrosine phosphatase, receptor type, T — 
SFRS6 Splicing factor, arginine/serine rich 6 
H-L(3)MBT Lethal (3) malignant brain tumor — 
SGK2 Serum glucocorticoid-regulated kinase 2 — 
MYBL2 v-myb myeloblastosis viral oncogene (avian)-like 2 — 
HNF4α Hepatoctye nuclear factor 4, alpha 
TDE1 tumor differentially expressed 1 — 
GeneDescriptionNumber of islet cDNAs in UniGene
RBL-1 Retinoblastoma-like 1 — 
RPN2 Ribophorin II — 
GHRH Growth hormone-releasing hormone — 
SRC v-src sarcoma 
BLCAP Bladder cancer-associated protein — 
NNAT Neuronatin — 
KIAA1219 Predicted protein 
TGM2 Transglutaminase 2 
KIAA0406 Predicted protein 
BPI Bactericidal/permeability-increasing protein — 
LBP Lipopolysaccharide-binding protein — 
VIAAT Vesicular inhibitory amino acid — 
PPP1R16B Protein phosphatase 1, regulatory (inhibitor) subunit 
DDX35 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 35 — 
MAFB v-maf musculoaponeurotic fibro-sarcoma oncogene B 
TOP1 Topoisomerase (DNA) I 
PRO0628 PRO0628 protein — 
PLCG1 Phospholipase C, gamma 1 — 
TIX1 Triple homeobox 1 — 
RPL12L2 Ribosomal protein L12-like — 
PTPRT Protein tyrosine phosphatase, receptor type, T — 
SFRS6 Splicing factor, arginine/serine rich 6 
H-L(3)MBT Lethal (3) malignant brain tumor — 
SGK2 Serum glucocorticoid-regulated kinase 2 — 
MYBL2 v-myb myeloblastosis viral oncogene (avian)-like 2 — 
HNF4α Hepatoctye nuclear factor 4, alpha 
TDE1 tumor differentially expressed 1 — 
TABLE 4

Estimates of allele frequencies for SNPs in known candidate genes for type 2 diabetes

GeneVariantProband pool*SDNormal pool*SDP
KIR6.2 Glu23Lys 67.1 G/32.9 A 1.8 66.9 G/33.1 A 2.1 1.026 
CALPAIN 63 nt16378 89.6 C/10.4 T 1.8 87.6 C/12.4 T 1.5 0.583 
CALPAIN 43 nt4852 67.6 C/32.4 T 0.9 63.8 C/36.2 T 1.2 0.407 
CALPAIN 62 nt16112 40 G/60 A 2.5 39.8 G/60.2 A 1.7 1.025 
SUR 18 C/T exon 22 97.2 C/2.8 T 0.7 98.0 C/2.0 T 0.4 0.816 
PPARγ 21 Pro12Ala 6.6 G/93.4 C 7.7 G/92.3 C 2.3 0.770 
IRS512 Ala512Pro 4.4 G/95.6 C 0.8 4.6 G/95.3 C 0.6 1.046 
IRS972 Gly972Arg 80.9 C/19.1 T 2.9 82.6 C/17.4 T 2.2 0.698 
GeneVariantProband pool*SDNormal pool*SDP
KIR6.2 Glu23Lys 67.1 G/32.9 A 1.8 66.9 G/33.1 A 2.1 1.026 
CALPAIN 63 nt16378 89.6 C/10.4 T 1.8 87.6 C/12.4 T 1.5 0.583 
CALPAIN 43 nt4852 67.6 C/32.4 T 0.9 63.8 C/36.2 T 1.2 0.407 
CALPAIN 62 nt16112 40 G/60 A 2.5 39.8 G/60.2 A 1.7 1.025 
SUR 18 C/T exon 22 97.2 C/2.8 T 0.7 98.0 C/2.0 T 0.4 0.816 
PPARγ 21 Pro12Ala 6.6 G/93.4 C 7.7 G/92.3 C 2.3 0.770 
IRS512 Ala512Pro 4.4 G/95.6 C 0.8 4.6 G/95.3 C 0.6 1.046 
IRS972 Gly972Arg 80.9 C/19.1 T 2.9 82.6 C/17.4 T 2.2 0.698 
*

Allele frequency (%); n = 300.

TABLE A1

Minor SNP allele frequencies in Ashkenazi versus other populations

SNP rs numberAshkenazi control subjects, pool 1, 300 allelesAshkenazi cases, pool 1, 300 allelesAshkenazi cases, pool 2, 252 allelesCaucasian pool, 84 allelesAsian pool, 84 allelesAfrican-American pool, 84 alleles
1124692 24.1 25.4 — 20 42 
1936999 27.2 26.9 — 13 36 
1892205 44.1 44 — nr 39 nr 
1744765 14.6 15.2 — 12 40 
765630 21.7 20 — 11 36 12 
2012439 18.1 14.5 — 34 48 41 
1001351 30.3 28 — 30 25 25 
754625 37.8 34.2 37.2 40 
1962746 22.1 21.5 — 11 27 46 
990792 38.9 37.1 — 21 24 30 
1341024 44.2 43.1 — 36 40 42 
2066252 46.8 47 — 41 13 
1341021 18.4 18.6 18.3 40 35 
2022498 41.1 42.9 — 50 46 nr 
1609800 35.8 35.5 35.1 40 10 15 
737091 46.9 49 — 35 20 
737090 49.6 49.9 48 39 nr nr 
2016946 25 21.4 — 21 31 18 
742421 31.6 28.1 — 10 30 
2064181 38.1 36.3 — 47 23 
742277 39.4 36.4 — 36 47 48 
926393 46.1 43.6 — 29 41 50 
1534928 42.3 41.2 — 25 40 50 
932440 8.7 13.5 — 
926345 43.9 48.2 46.5 40 14 23 
753381 34.5 32.7 — 42 11 nr 
909775 45.3 39.9 — 30 40 40 
1014757 45.2 44.4 — 23 38 
929071 24.3 27.9 — 40 20 20 
1033641 49.5 45.5 — 35 20 15 
742420 29 34.3 — 30 40 45 
2038668 40.4 40.4 — 35 40 20 
916410 36.9 42 — 15 25 35 
763227 26.3 31.1 — 17 17 35 
442143 33.5 40.7 30.5 20 15 
956609 16 16.8 — 10 46 
764383 18.2 22.8 — 15 15 40 
717247 32.7 27.9 — 30 30 45 
736823 40.6 44.3 41.7 nr 
1028583 26.8 32.9 33.1 40 30 45 
SNP rs numberAshkenazi control subjects, pool 1, 300 allelesAshkenazi cases, pool 1, 300 allelesAshkenazi cases, pool 2, 252 allelesCaucasian pool, 84 allelesAsian pool, 84 allelesAfrican-American pool, 84 alleles
1124692 24.1 25.4 — 20 42 
1936999 27.2 26.9 — 13 36 
1892205 44.1 44 — nr 39 nr 
1744765 14.6 15.2 — 12 40 
765630 21.7 20 — 11 36 12 
2012439 18.1 14.5 — 34 48 41 
1001351 30.3 28 — 30 25 25 
754625 37.8 34.2 37.2 40 
1962746 22.1 21.5 — 11 27 46 
990792 38.9 37.1 — 21 24 30 
1341024 44.2 43.1 — 36 40 42 
2066252 46.8 47 — 41 13 
1341021 18.4 18.6 18.3 40 35 
2022498 41.1 42.9 — 50 46 nr 
1609800 35.8 35.5 35.1 40 10 15 
737091 46.9 49 — 35 20 
737090 49.6 49.9 48 39 nr nr 
2016946 25 21.4 — 21 31 18 
742421 31.6 28.1 — 10 30 
2064181 38.1 36.3 — 47 23 
742277 39.4 36.4 — 36 47 48 
926393 46.1 43.6 — 29 41 50 
1534928 42.3 41.2 — 25 40 50 
932440 8.7 13.5 — 
926345 43.9 48.2 46.5 40 14 23 
753381 34.5 32.7 — 42 11 nr 
909775 45.3 39.9 — 30 40 40 
1014757 45.2 44.4 — 23 38 
929071 24.3 27.9 — 40 20 20 
1033641 49.5 45.5 — 35 20 15 
742420 29 34.3 — 30 40 45 
2038668 40.4 40.4 — 35 40 20 
916410 36.9 42 — 15 25 35 
763227 26.3 31.1 — 17 17 35 
442143 33.5 40.7 30.5 20 15 
956609 16 16.8 — 10 46 
764383 18.2 22.8 — 15 15 40 
717247 32.7 27.9 — 30 30 45 
736823 40.6 44.3 41.7 nr 
1028583 26.8 32.9 33.1 40 30 45 

Data are %. nr, no result.

TABLE A2

All known genes on chromosome 20q (35.7–43.0 Mb) with validated SNPs listed for allele frequency in probands and normal subjects

Geners numberSNP positionAllele frequency, normal pool 1, n = 300 allelesSDAllele frequency, proband pool 1, n = 300 allelesSDΔ*PAllele frequency, proband pool 2, n = 252 alleles
RBL1 1124692 35442334 24.1 C 25.4 C 0.9 0.802  
 1936999 35501910 27.2 G 3.4 26.9 G 3.6 0.3  
 1892205 35513009 44.1 G 0.7 44 G 1.3 0.1 1.04  
 1744765 35525386 14.6 G 0.7 15.2 G 0.6 0.936  
RPN2 1076686 35613623 47 A 48.2 A 0.9 1.2 0.843  
 765630 35653552 21.7 G 1.3 (6) 20 G 1.8 1.7 0.709  
 2012439 35668823 18.1 T 1.9 14.5 T 1.2 3.6 0.337  
G 1001351 35674649 30.3 T 0.6 28 T 1.1 2.3 0.625  
GHRH  No Gene SNPs        
1043415 35747791 31.9 G 2.4 27.6 G 1.5 4.3 0.327  
SRC 754625 35820080 37.8 G 1.3 34.2 G 1.8 3.6 0.439 37.2 G 
 1570209 35831387 13.6 C 2.3 11 C 2.1 2.6 0.466  
 1885257 35833495 20.4 G 1.9 20.8 G 3.4 0.4 0.985  
1033623 35938915 8.3 A 2.7 11.1 A 2.6 2.8 0.392 8.0 A 
 1007373 35944844 24.6 A 0.4 22.1 A 0.8 2.5 0.568 21.9 A 
BLCAP/NNAT  No Gene SNPs        
2024794 35972553 48.3 C 1.5 49.2 C 0.6 0.9 0.897 48.1 C 
C20orf77 1962746 36466943 22.1 C 1.6 21.5 C 1.8 0.6 0.943  
 990792 36474026 38.9 A 1.5 37.1 A 2.8 1.8 0.731  
 910664 36484857 47.3 C 1.2 48.3 C 4.4 0.352  
TGM2 1555074 36563758 15.9 T 19.8 T 1.2 3.9 0.308 16.1 T 
1883484 36601263 19.1 T 1.2 16.1 T 0.5 3.1 0.444  
BPI 1341024 36735232 44.2 C 0.9 43.1 C 1.4 1.1 0.86  
 2066252 36739802 46.8 T 0.6 47 T 0.6 0.2 1.02  
 1341021 36740791 18.4 T 1.7 18.6 T 2.5 0.2 1.03 18.3 T 
 154374 36765781 45.8 C 1.8 48.8 C 0.7 5.4 0.542  
2022498 36768649 41.1 T 1.2 42.9 T 0.9 1.8 0.734  
 1609800 36781066 35.8 A 1.2 35.5 A 1.5 0.3 35.1 A 
 737091 36812479 46.9 A 1.2 49 A 1.1 2.1 0.686  
 1780617 36776713 24.1 G 1.3 22.2 G 1.9 0.679  
LBP 1739654 36780526 19.4 T 1.3 19.6 T 1.1 0.2 1.03  
 1780623 36791825 49.7 G 1.3 47.6 G 0.7 2.1 0.686  
 1780624 36791858 46.8 G 1.9 46.7 G 1.5 0.1 1.04  
 1780627 36800188 46.7 T 1.3 46.7 T 1.9 1.06  
 1739640 36804490 41.8 T 0.9 42.8 T 1.1 0.878  
737090 36812180 49.6 G 2.9 49.9 G 1.7 3.1 48 G 
 2016946 37258761 25 A 0.7 21.4 A 3.6 0.389  
VIAAT 2667326 37154262 18.3 T 1.2 (7) 17.6 T 1.7 0.7 0.917  
 1321099 37161201 20.6 G 1.4 (7) 18.4 G 1.5 2.2 0.603  
PPP1R16B 208817 37294872 49.5 C/50.5 T 1.1 49 C 0.7 0.5 0.969  
 742421 37321877 31.6 T 1.4 28.1 T 0.7 3.5 0.434  
 10392 37353491 26.8 T 0.8 22.8 T 0.9 0.342  
DDX35 974560 37393754 42 A 1.2 42.5 A 2.1 0.5 0.969 40.9 A 
 2064181 37415795 38.1 T 1.8 36.3 T 1.9 1.8 0.73  
 742277 37431305 39.4 G 0.5 36.4 G 0.8 0.531  
 926393 37448584 46.1 T 1.6 43.6 T 2.5 0.619  
 101120 37459975 47.8 A 1.9 46.5 A 1.9 1.3 0.825  
 1534928 37467120 42.3 A 1.3 41.2 A 1.1 1.1 0.859  
KRML 3577 39117226 8.7 T 0.4 13.5 T 0.5 4.8 0.137  
742739 39148496 28.2 C 1.7 28.1 C 1.2 0.1 1.05  
 932440 39129071 8.7 T 0.4 13.5 T 0.5 4.8 0.137  
 742740 39148437 23.6 T 26.8 T 1.1 3.1 0.46  
TOP 1 2076575 39523386 16.7 G 1.1 18.7 G 1.4 0.633  
PRO0628  No Gene SNPs        
PLCG1 926345 39574503 43.9 C 1.9 48.2 C 0.5 4.3 0.363 46.5 C 
 753381 39600021 34.5 A 0.3 32.7 A 0.5 1.8 0.725  
TIX1 2235363 39610325        
 2235364 39610606        
 2076147 39615562        
 2664537 39616285 13.2 T 0.9 20.8 T 1.3 7.6 0.035 17.8 T 
 2235367 39632678        
RPL12L2 903374 39983421        
PTPRT 746413 40512545 44.4 A 0.9 40.3 A 0.7 4.1 0.384 41.2 A 
 2016647 40517034 23.9 T 2.4 21.3 T 2.2 2.6 0.546  
 909775 40530430 45.3 C 0.7 39.9 C 1.1 5.4 0.242  
 1014757 40664178 45.2 G 0.7 44.4 G 0.8 0.915  
 929071 40781985 24.3 T 1.2 27.9 T 1.8 2.6 0.403  
 1033641 41002123 49.5 C 0.9 45.5 T 0.6 0.401  
 742420 41137137 29 T 0.7 34.3 T 0.8 5.3 0.227  
 2038668 41180421 40.4 A 1.4 40.4 A 1.3 1.06  
761039 41885512 10.2 C 1.2 15.3 C 0.6 5.1 0.129 12.5 C 
SFRS6 2235611 41892066 15.2 T 1.3 (6) 17.8 T 1.6 2.6 0.506  
12264 41893912 76.3 A 1.1 74.5 A 0.9 1.2 0.818  
H-L(3)MBT 2071968 41966323 32.5 C 0.9 30.6 C 1.5 1.9 0.703  
 3205 41982000 18.2 C 1.8 17 C 1.2 0.803  
2009280 41991469 18.8 T 1.3 19.6 T 1.8 0.8 0.764  
SGK2 916410 42005792 36.9 G 1.7 42 G 0.9 5.1 0.266  
 763227 42007616 26.3 C 1.4 31.1 C 3.3 4.8 0.266  
MYBL2 826950 42104926 7.2 G 10.5 G 3.3 0.289  
 442143 42113064 33.5 T 0.9 40.7 T 1.6 7.2 0.106 30.5 T 
 419842 42113366 20.2 A 0.7 27.8 A 0.6 7.6 0.057 18.2 A 
 285186 42117646 7.4 G 0.7 12.9 G 1.5 5.5 0.77 7.5 G 
C20orf111 9875 42628042 21.1 G 0.8 26.5 G 2.3 5.4 0.184  
 8268 42628484 22.1 A 1.6 (7) 20.7 A 0.9 (5) 1.4 0.773  
 956609 42639996 16 G 1.5 16.8 G 1.3 (7) 0.8 0.891  
 2143606 42641105 38.3 C 1.2 39.2 C 1.9 0.9 0.876  
MGC3129 761331 42696185 44.6 C 0.8 49.3 C 0.9 4.7 0.317  
764383 42719649 18.2 C 1.3 22.8 C 1.2 (7) 4.6 0.242  
 1884614 42783074 23.4 T 1.1 27.2 T 1.3 3.8 0.372  
 717247 42828339 32.7 G 1.9 27.9 G 1.2 4.8 0.272  
HNF4a 736821 42836634 18.5 T 0.8 22.8 T 0.6 4.3 0.278  
 736823 42837338 40.6 A 2.9 44.3 A 1.2 3.7 0.437 41.7 A 
 1028583 42853316 26.8 T 32.9 T 0.9 6.1 0.155 33.1 T 
TDE1 2015437 42944500 24.7 T 2.2 24.8 T 0.9 0.1 1.05 24.3 T 
Geners numberSNP positionAllele frequency, normal pool 1, n = 300 allelesSDAllele frequency, proband pool 1, n = 300 allelesSDΔ*PAllele frequency, proband pool 2, n = 252 alleles
RBL1 1124692 35442334 24.1 C 25.4 C 0.9 0.802  
 1936999 35501910 27.2 G 3.4 26.9 G 3.6 0.3  
 1892205 35513009 44.1 G 0.7 44 G 1.3 0.1 1.04  
 1744765 35525386 14.6 G 0.7 15.2 G 0.6 0.936  
RPN2 1076686 35613623 47 A 48.2 A 0.9 1.2 0.843  
 765630 35653552 21.7 G 1.3 (6) 20 G 1.8 1.7 0.709  
 2012439 35668823 18.1 T 1.9 14.5 T 1.2 3.6 0.337  
G 1001351 35674649 30.3 T 0.6 28 T 1.1 2.3 0.625  
GHRH  No Gene SNPs        
1043415 35747791 31.9 G 2.4 27.6 G 1.5 4.3 0.327  
SRC 754625 35820080 37.8 G 1.3 34.2 G 1.8 3.6 0.439 37.2 G 
 1570209 35831387 13.6 C 2.3 11 C 2.1 2.6 0.466  
 1885257 35833495 20.4 G 1.9 20.8 G 3.4 0.4 0.985  
1033623 35938915 8.3 A 2.7 11.1 A 2.6 2.8 0.392 8.0 A 
 1007373 35944844 24.6 A 0.4 22.1 A 0.8 2.5 0.568 21.9 A 
BLCAP/NNAT  No Gene SNPs        
2024794 35972553 48.3 C 1.5 49.2 C 0.6 0.9 0.897 48.1 C 
C20orf77 1962746 36466943 22.1 C 1.6 21.5 C 1.8 0.6 0.943  
 990792 36474026 38.9 A 1.5 37.1 A 2.8 1.8 0.731  
 910664 36484857 47.3 C 1.2 48.3 C 4.4 0.352  
TGM2 1555074 36563758 15.9 T 19.8 T 1.2 3.9 0.308 16.1 T 
1883484 36601263 19.1 T 1.2 16.1 T 0.5 3.1 0.444  
BPI 1341024 36735232 44.2 C 0.9 43.1 C 1.4 1.1 0.86  
 2066252 36739802 46.8 T 0.6 47 T 0.6 0.2 1.02  
 1341021 36740791 18.4 T 1.7 18.6 T 2.5 0.2 1.03 18.3 T 
 154374 36765781 45.8 C 1.8 48.8 C 0.7 5.4 0.542  
2022498 36768649 41.1 T 1.2 42.9 T 0.9 1.8 0.734  
 1609800 36781066 35.8 A 1.2 35.5 A 1.5 0.3 35.1 A 
 737091 36812479 46.9 A 1.2 49 A 1.1 2.1 0.686  
 1780617 36776713 24.1 G 1.3 22.2 G 1.9 0.679  
LBP 1739654 36780526 19.4 T 1.3 19.6 T 1.1 0.2 1.03  
 1780623 36791825 49.7 G 1.3 47.6 G 0.7 2.1 0.686  
 1780624 36791858 46.8 G 1.9 46.7 G 1.5 0.1 1.04  
 1780627 36800188 46.7 T 1.3 46.7 T 1.9 1.06  
 1739640 36804490 41.8 T 0.9 42.8 T 1.1 0.878  
737090 36812180 49.6 G 2.9 49.9 G 1.7 3.1 48 G 
 2016946 37258761 25 A 0.7 21.4 A 3.6 0.389  
VIAAT 2667326 37154262 18.3 T 1.2 (7) 17.6 T 1.7 0.7 0.917  
 1321099 37161201 20.6 G 1.4 (7) 18.4 G 1.5 2.2 0.603  
PPP1R16B 208817 37294872 49.5 C/50.5 T 1.1 49 C 0.7 0.5 0.969  
 742421 37321877 31.6 T 1.4 28.1 T 0.7 3.5 0.434  
 10392 37353491 26.8 T 0.8 22.8 T 0.9 0.342  
DDX35 974560 37393754 42 A 1.2 42.5 A 2.1 0.5 0.969 40.9 A 
 2064181 37415795 38.1 T 1.8 36.3 T 1.9 1.8 0.73  
 742277 37431305 39.4 G 0.5 36.4 G 0.8 0.531  
 926393 37448584 46.1 T 1.6 43.6 T 2.5 0.619  
 101120 37459975 47.8 A 1.9 46.5 A 1.9 1.3 0.825  
 1534928 37467120 42.3 A 1.3 41.2 A 1.1 1.1 0.859  
KRML 3577 39117226 8.7 T 0.4 13.5 T 0.5 4.8 0.137  
742739 39148496 28.2 C 1.7 28.1 C 1.2 0.1 1.05  
 932440 39129071 8.7 T 0.4 13.5 T 0.5 4.8 0.137  
 742740 39148437 23.6 T 26.8 T 1.1 3.1 0.46  
TOP 1 2076575 39523386 16.7 G 1.1 18.7 G 1.4 0.633  
PRO0628  No Gene SNPs        
PLCG1 926345 39574503 43.9 C 1.9 48.2 C 0.5 4.3 0.363 46.5 C 
 753381 39600021 34.5 A 0.3 32.7 A 0.5 1.8 0.725  
TIX1 2235363 39610325        
 2235364 39610606        
 2076147 39615562        
 2664537 39616285 13.2 T 0.9 20.8 T 1.3 7.6 0.035 17.8 T 
 2235367 39632678        
RPL12L2 903374 39983421        
PTPRT 746413 40512545 44.4 A 0.9 40.3 A 0.7 4.1 0.384 41.2 A 
 2016647 40517034 23.9 T 2.4 21.3 T 2.2 2.6 0.546  
 909775 40530430 45.3 C 0.7 39.9 C 1.1 5.4 0.242  
 1014757 40664178 45.2 G 0.7 44.4 G 0.8 0.915  
 929071 40781985 24.3 T 1.2 27.9 T 1.8 2.6 0.403  
 1033641 41002123 49.5 C 0.9 45.5 T 0.6 0.401  
 742420 41137137 29 T 0.7 34.3 T 0.8 5.3 0.227  
 2038668 41180421 40.4 A 1.4 40.4 A 1.3 1.06  
761039 41885512 10.2 C 1.2 15.3 C 0.6 5.1 0.129 12.5 C 
SFRS6 2235611 41892066 15.2 T 1.3 (6) 17.8 T 1.6 2.6 0.506  
12264 41893912 76.3 A 1.1 74.5 A 0.9 1.2 0.818  
H-L(3)MBT 2071968 41966323 32.5 C 0.9 30.6 C 1.5 1.9 0.703  
 3205 41982000 18.2 C 1.8 17 C 1.2 0.803  
2009280 41991469 18.8 T 1.3 19.6 T 1.8 0.8 0.764  
SGK2 916410 42005792 36.9 G 1.7 42 G 0.9 5.1 0.266  
 763227 42007616 26.3 C 1.4 31.1 C 3.3 4.8 0.266  
MYBL2 826950 42104926 7.2 G 10.5 G 3.3 0.289  
 442143 42113064 33.5 T 0.9 40.7 T 1.6 7.2 0.106 30.5 T 
 419842 42113366 20.2 A 0.7 27.8 A 0.6 7.6 0.057 18.2 A 
 285186 42117646 7.4 G 0.7 12.9 G 1.5 5.5 0.77 7.5 G 
C20orf111 9875 42628042 21.1 G 0.8 26.5 G 2.3 5.4 0.184  
 8268 42628484 22.1 A 1.6 (7) 20.7 A 0.9 (5) 1.4 0.773  
 956609 42639996 16 G 1.5 16.8 G 1.3 (7) 0.8 0.891  
 2143606 42641105 38.3 C 1.2 39.2 C 1.9 0.9 0.876  
MGC3129 761331 42696185 44.6 C 0.8 49.3 C 0.9 4.7 0.317  
764383 42719649 18.2 C 1.3 22.8 C 1.2 (7) 4.6 0.242  
 1884614 42783074 23.4 T 1.1 27.2 T 1.3 3.8 0.372  
 717247 42828339 32.7 G 1.9 27.9 G 1.2 4.8 0.272  
HNF4a 736821 42836634 18.5 T 0.8 22.8 T 0.6 4.3 0.278  
 736823 42837338 40.6 A 2.9 44.3 A 1.2 3.7 0.437 41.7 A 
 1028583 42853316 26.8 T 32.9 T 0.9 6.1 0.155 33.1 T 
TDE1 2015437 42944500 24.7 T 2.2 24.8 T 0.9 0.1 1.05 24.3 T 
*

Average difference between the normal and proband pool frequencies for the SNP;

intragenic SNPs. Numbers in parentheses indicate the number of replications if <8.

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

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Address correspondence and reprint requests to M.A. Permutt, 660 S. Euclid Ave., Campus Box 8127, Saint Louis, MO 63110. E-mail: apermutt@im.wustl.edu.

Received for publication 20 March 2002 and accepted in revised form 15 May 2002.

LOD, logarithm of odds; PPAR, peroxisome proliferator-activated receptor; SNP, single nucleotide polymorphism.

The symposium and the publication of this article have been made possible by an unrestricted educational grant from Servier, Paris.