OBJECTIVE— Linkage to type 2 diabetes has been reported on chromosome 1q21-25 in Pima Indians. Fine mapping identified single nucleotide polymorphisms (SNPs) near the CACNA1E gene associated with this disease. CACNA1E encodes the voltage-dependent calcium channel Cav2.3 Ca2+, and mice lacking this channel exhibit impaired glucose tolerance and insulin secretion. Therefore, CACNA1E was investigated as a positional candidate gene.

RESEARCH DESIGN AND METHODS—CACNA1E was sequenced, and 28 SNPs were genotyped in the same group of Pima subjects who had been analyzed in the linkage study. Allele-specific expression was used to functionally evaluate a variant in the 3′ untranslated region (UTR).

RESULTS— A novel G/A variant in the 3′-UTR was associated with young-onset type 2 diabetes (odds ratio 2.09 per copy of the G-allele [95% CI 1.31–3.33], adjusted P = 0.001) and had an effect on the evidence for linkage at chromosome 1q21-25 (P = 0.004). Among 372 nondiabetic Pima subjects who had undergone metabolic testing, the risk allele was associated with reduced insulin action including increased fasting, 30, 60, and 120 min plasma glucose concentrations and increased fasting plasma insulin during an oral glucose tolerance test (all P < 0.01), as well as a decreased rate of insulin-stimulated glucose disposal at both physiologically and maximally stimulated insulin concentrations (both P < 0.002). Functional analysis of this variant showed that the nonrisk allele had a 2.3-fold higher expression compared with the risk allele.

CONCLUSIONS— A functional variant in CACNA1E contributes to type 2 diabetes susceptibility by affecting insulin action. This variant partially explains the linkage to type 2 diabetes on chromosome 1q21-25 in Pima Indians.

Voltage-gated Ca2+ channels mediate Ca2+ entry into cells in response to membrane depolarization (1). These channels are composed of a major pore-forming subunit (α1-subunit) and multiple auxiliary subunits. The human Cav2.3 subunit is expressed in neuronal cells (2) and pancreatic β-cells (3). In β-cells, this subunit has an important role in coordinating Ca2+-dependent processes such as excitation secretion of insulin. Recent studies in Cav2.3-deficient mice showed that disruption of Cav2.3 Ca2+ channels impaired glucose tolerance and insulin secretion (4,5), while insulin tolerance tests in Cav2.3, knockout mice indicated reduced insulin sensitivity (5). Cav2.3−/− mice were also significantly heavier than wild-type mice, and their basal levels of plasma glucose and insulin tended to be higher, suggesting that the cause of hyperglycemia in Cav2.3−/− mice may be insulin resistance (5).

Human Cav2.3 is encoded by the CACNA1E gene, which maps to a region on chromosome 1q21-25. Linkage to type 2 diabetes at chromosome 1q21-25 has been reported in diverse populations with European, East Asian, Native American, and African-American ancestries (612). In a genome-wide linkage scan in Pima Indians, this region was linked to young-onset type 2 diabetes (12), and follow-up studies in the Pima Indians included association mapping with single nucleotide polymorphisms (SNPs) at ∼10 kb density spanning the 25 Mb region of linkage. Results from association mapping identified several variants in/near CACNA1E that were modestly associated with type 2 diabetes. Therefore, CACNA1E was directly analyzed as a positional candidate gene for linkage to type 2 diabetes in Pima Indians.

A total of 24 non–first degree–related Pima Indians were selected for sequencing of the CACNA1E coding and promoter regions. Among these 24 subjects, 12 developed type 2 diabetes before the age of 25 years and 12 were nondiabetic after 45 years of age. Variants were genotyped for association analyses in 1,037 Pima Indians (from 332 nuclear families) who were participants in our prior genome-wide linkage study for determinants of type 2 diabetes (12). Among these 1,037 subjects, 680 contributed to the original linkage analysis on chromosome 1q and were analyzed in the present study for association with young-onset diabetes. These 680 subjects included 578 with type 2 diabetes with an age of onset of <45 years (mean ± SD age onset 31.0 ± 7.8 years; 62.1% female; maximum BMI 37.9 ± 8.0 kg/m2) and 102 who were nondiabetic and ≥45 years of age (mean age 53.7 ± 6.3 years; 60.8% female; maximum BMI 34.6 ± 6.3 kg/m2). The remaining genotyped subjects were analyzed for quantitative metabolic traits (see below). Diabetes was diagnosed by an oral glucose tolerance test (OGTT) according to World Health Organization criteria (13). Subjects provided written consent before participation. The study was approved by the Tribal Council of the Gila River Indian Community and the institutional review board of the National Institute of Diabetes and Digestive and Kidney Diseases.

Metabolic phenotyping.

Among the 1,037 subjects genotyped, 372 were nondiabetic full-heritage Pima Indians who had also undergone metabolic testing. Glucose tolerance was determined by a 75-g OGTT with measurements of fasting, 30, 60, 120, and 180 min plasma glucose and insulin concentrations (14). To measure the acute insulin response, blood samples were collected before a 25-g glucose bolus infusion and at 3, 4, 5, 6, 8, and 10 min after infusion. The acute insulin response was calculated as the mean increment in plasma insulin concentrations from 3 to 5 min (15). Insulin action in vivo was assessed using a two-step hyperinsulinemic-euglycemic clamp (14,15). Body composition was estimated by underwater weighing until January 1996 and by dual-energy X-ray absorptiometry (DPX-1; Lunar Radiation) thereafter.

Sequencing and genotyping.

Sequencing was done using Big Dye terminator (Applied Biosystems) on an automated DNA capillary sequencer (model 3730; Applied Biosystems). Variants were genotyped using the TaqMan Allelic Discrimination Assay (Applied Biosystems) on an ABI Prism 7700 (Applied Biosystems) or SNPlex genotyping System 48-plex (Applied Biosystems) on an automated DNA capillary sequencer.

Allelic-specific gene expression.

Subcutaneous abdominal adipose tissue biopsies were obtained from 11 subjects known to be heterozygous (G/A) for the 3′ untranslated region (UTR) SNP +8130G/A. Biopsies were performed in our clinical research center as previously described (16). Total RNA was extracted from the adipose tissue using an RNAeasy Mini Kit (Qiagen, Valencia, CA), and cDNA was synthesized by reverse transcription (Clonetech). Allelic-specific gene expression was assessed by real-time PCR using TaqMan allele-specific probes (TGCAGCTGGTGTGGG-VIC and TGCAGCTGATGTGGGP-FAM; Applied Biosystems) on an ABI Prism 7700 (Applied Biosystems) (17). Triplicate experiments were performed. Data were presented as a ratio of allelic expression (A/G). Background was calculated as allelic ratio of genomic DNA (assumed ratio = 1).

Statistical analysis.

Associations were assessed using SAS programs (SAS Institute, Cary, NC). The relationship between trait and marker was generally assessed via a numeric variable representing genotype (i.e., an additive model). For continuous variables, linear regression was used to adjust for covariates. The association with young-onset diabetes was assessed by logistic regression and was controlled for sex and heritage. Both linear and logistic models were fit with generalized estimating equations to account for the correlation among siblings (18). This method provides a general test of association that accounts for the nonindependence among siblings, but as it is not a test of within-family association, it is not robust to population stratification. Therefore, to control for potential population stratification, the association with diabetes was also analyzed with a within-family association test using the method of Abecasis et al. (19). To assess the impact of the observed association on the linkage, logarithm of odds (LOD) scores were recalculated with and without adjustment for the marker and the statistical significance of the difference assessed with a modified bivariate Haseman-Elston model (20). Haplotype frequencies for pairs of SNPs were calculated in all 1,037 individuals with the Estimating Haplotypes (EH) program. D′ was calculated as a measure of allelic association, and r2 as a measure of concordance. For haplotype analyses, SNP were placed in “blocks,” which were defined as consecutive SNPs with D′ > 0.9 . By this definition, blocks can overlap; for the sake of thoroughness, we analyzed all blocks, regardless of overlap, and made no attempt to place each SNP in a unique block. Association between traits and individual haplotypes were examined with a modification of the zero-recombinant haplotyping procedure, as described previously (21).

Sequencing of 49 exons and ∼2 kb of the upstream region of CACNA1E in 24 Pima Indians identified 14 variants. Two variants were novel, a −1039G/T (1,039 bp upstream of the ATG site) and a +8130G/A in the 3′-UTR (8,130 bp downstream from ATG in the cDNA), while the remaining 12 SNPs could be found in public databases. Sixteen additional SNPs, positioned within introns and more distal regions flanking the CACNA1E locus that were not sequenced, were selected from public databases to provide a denser coverage of genetic information across this region. All 28 SNPs (supplemental Fig. 1 [available in an online appendix at http://dx.doi.org/10.2337/db07-0587] and Table 1) were genotyped in 1,037 Pima Indians for association analyses.

The frequencies of the 28 variants in subjects with young-onset type 2 diabetes compared with the older (aged >45 years) nondiabetic subjects, are given in Table 1. All SNPs were in Hardy-Weinberg equilibrium. Five of the variants, namely, rs625226, rs3753737, rs798209, rs473200, and +8130G/A, were nominally associated with young-onset type 2 diabetes in a general association test. Among these SNPs, rs3753737 and +8130G/A were also nominally associated with type 2 diabetes using a within-family test. In simulation studies, we estimate that the power of the present sample is >70% to detect a significant (P < 0.05) association with a causal variant with minor allele frequency >0.05, accounting for 2% of the variance in age of onset of diabetes. Given the previous physiological implication of CACNA1E in diabetes in mice, we chose to preserve statistical power and did not correct for multiple comparisons. Therefore, it remains possible that these nominal associations may represent false-positive findings.

To help address whether these associations may be false-positives, we further investigated whether any of the 28 variants were associated with pre-diabetic traits among 372 nondiabetic, full-heritage Pima Indians who had undergone metabolic testing. Among these nondiabetic individuals, subjects with the G/G genotype at +8130G/A, which was the risk genotype for young-onset type 2 diabetes, had significantly higher mean fasting, 30, 60, and 120 min plasma glucose concentrations and higher mean fasting plasma insulin concentrations during an OGTT, as well as a lower mean rate of insulin-stimulated glucose disposal at both physiologically and maximally stimulated insulin concentrations during a hyperinsulinemic-euglycemic clamp (Table 2). These results in humans are consistent with the finding of increased postload plasma glucose levels and decreased insulin sensitivity in CACNA1E knockout mice (5). Prior in vitro studies using the insulinoma cell line INS-1 suggested a role of CACNA1E in insulin secretion (22), but our in vivo study did not detect an association between SNPs in this gene and acute insulin secretion using a 25-g glucose bolus infusion.

The degree of linkage disequilibrium (summarized by D′ and r2) between the 28 variants in 1,037 subjects is shown in Fig. 1. The common variants (minor allele frequency >0.05) fell into 13 haplotype “blocks.” Each block, ranging in size from 0.6 to 93.6 kb, contained on average two to three SNPs, except for one block located in 3′-UTR, which extended across five SNPs. Five haplotypes were associated with type 2 diabetes (P ≤ 0.05) (Table 3). The strongest association was with haplotype AA for +8130G/A and rs16858150, where its frequency was lower in case than in control subjects (frequency 0.09 in diabetic vs. 0.17 in nondiabetic subjects, odds ratio [OR] 0.44 [95% CI 0.28–0.70], P = 0.001). Conversely, the frequency of haplotype GA for +8130G/A and rs16858150 was higher in the case than in the control subjects (frequency 0.85 in diabetic vs. 0.74 in nondiabetic subjects; 1.82 [1.23–2.69], P = 0.005). This finding largely reflects the single-marker associations of +8130G/A. Additional analyses examined pairs of SNPs or haplotypes such that +8130G/A was analyzed in conjunction with each additional significant variant to determine their potential independent contributions to the association. The effect of +8130G/A was statistically significant (P < 0.05) conditional on each of these other variants, but none of the other variants was significantly conditional on +8130G/A; the association of the GAG haplotype in block 2 was of marginal significance controlled for +8130G/A (P = 0.07). Thus, +8130G/A represents the major associated variant in the present study.

Since SNP +8130G/A provided the strongest evidence for disease association using either individual SNP or haplotype analysis, this variant was functionally analyzed. The location of this variant in the 3′-UTR led to the hypothesis that it could affect mRNA expression/stability. Allelic-specific expression in adipose tissue mRNA isolated from 11 subjects heterozygous for the G/A showed that the ratio of A (nonrisk) allele versus G (risk) allele (A/G) was 2.37 ± 0.30 (mean ± SEM), which was significantly different from the background ratio of 0.97 ± 0.09 (t test P = 0.008).

To assess whether the +8130G/A contributed to the evidence for linkage to young-onset type 2 diabetes on chromosome 1 in Pima Indians, LOD scores were recalculated with and without adjustment for this marker. A reduction in linkage of 0.6 LOD (from 2.6 to 2.0, P = 0.004) was observed after adjustment for +8130G/A. No reduction, or a smaller reduction, was observed when the linkage was adjusted for the other SNPs genotyped in/near this gene.

Although the +8130G/A variant appears to underlie a portion of the linkage on chromosome 1q21-25 among families selected for having a high prevalence of young-onset type 2 diabetes, the effect of this variant on diabetes risk in the general Pima population appears to be minimal. The association of this variant with type 2 diabetes in a population-based sample of Pima Indian subjects (after excluding all participants of the linkage study, n = 2,427: 862 with diabetes and 1,565 without diabetes) was marginal (OR 1.17, one-sided P = 0.08). Thus, it appears that this variant has its greatest impact in families with a high prevalence of young-onset diabetes, such as those selected for the linkage study and the present follow-up study.

Linkage to type 2 diabetes on chromosome 1q21-25 has also been observed in seven other populations in the International Type 2 Diabetes Chromosome 1q Consortium (614). As part of this consortium, SNPs are being genotyped in subjects from all of these populations across a region that spans 148–170 Mb on chromosome 1 (23). CACNA1E is positioned slightly telomeric to this region at 179.5 Mb. However, SNP +8130G/A was specifically selected to attempt replication in the consortium samples (1,540 case subjects with type 2 diabetes and 1,512 control subjects without diabetes). The frequency of the G-allele was ∼0.70 among the Chinese and ∼0.95 for most of the European-derived populations and for African Americans; the one exception was the Amish, in whom the frequency was 0.83. This SNP was not associated with type 2 diabetes in the combined samples from the consortium (OR 1.06 per copy of the G-allele [95% CI 0.90–1.27], P = 0.46, adjusted for population), nor was this SNP significantly associated (P < 0.05) with diabetes among any of the individual populations. Two additional SNPs in CACNA1E (rs473200 and rs704326) were also genotyped in the consortium samples, but neither replicated. Further detailed mapping in the consortium samples or other populations with a high prevalence of young-onset type 2 diabetes is required to determine the role of other variants in or near CACNA1E in susceptibility to type 2 diabetes.

It is possible that the present findings represent a false-positive; however, this SNP has the second strongest evidence for association with young-onset type 2 diabetes in Pima Indians among the 5,150 SNPs typed to date across the region of linkage. The SNP with the strongest evidence for association maps to an intergenic region, and this SNP is similarly not associated with type 2 diabetes in the combined consortium samples. The third strongest association maps to ARHGEF11, where variants in this gene are also associated with type 2 diabetes in the Amish population but not in the combined consortium samples (24,25). Therefore, an alternative explanation for the lack of replication in other ethnic groups is that multiple variants underlie the various linkage observations on chromosome 1q21-25, and these variants have different effect sizes among the different ethnic groups. The observed difference between populations for the effect of this variant could also be explained by clinical heterogeneity. Pima Indians, in particular those selected for the linkage study and the current follow-up study, have a much earlier age of onset of diabetes than other populations. Pima Indians, on average, also have a much higher BMI. Studies of CACNA1E in other groups of subjects with young-onset diabetes and/or high BMI are required to examine this possibility.

Our allele-specific expression study suggests that +8130G/A may affect diabetes susceptibility via reduction of CACNA1E expression. Since the risk allele was the common allele (frequency 0.89), reduced CACNA1E expression, reduced in vivo insulin action, and a modest increase in diabetes risk is the common phenotype among Pima Indians.

FIG. 1.

Pairwise linkage disequilibrium among 28 SNPs genotyped across CACNA1E in 1,037 Pima Indians. Values in the upper right represent D′, while values in the bottom left represent r2. The bars at the right indicate 13 linkage disequilibrium “blocks” defined as consecutive SNPs among whom all pairs had D′ > 0.9.

FIG. 1.

Pairwise linkage disequilibrium among 28 SNPs genotyped across CACNA1E in 1,037 Pima Indians. Values in the upper right represent D′, while values in the bottom left represent r2. The bars at the right indicate 13 linkage disequilibrium “blocks” defined as consecutive SNPs among whom all pairs had D′ > 0.9.

TABLE 1

Association of SNPs in CACNA1E with young-onset type 2 diabetes in Pima Indians

SNPMajor/ minor alleleLocationRisk allele frequency (genotype distribution as %)
General test
Within-family test (n = 680)
Genotype call rate (%)
Diabetes (n = 578)Nondiabetic (n = 102)OR (95% CI)P additive/ recessive*OR (95% CI)P additive
rs10752856 T̅/C Upstream 0.78 (61, 34, 5) 0.73 (54, 39, 7) 1.19 (0.78–1.82) 0.41 0.97 (0.52–1.81) 0.91 86 
rs11582261 G̅/T Upstream 0.59 (34, 50, 16) 0.54 (26, 55, 19) 1.29 (0.94–1.79) 0.11 1.72 (1.12–2.64) 0.01 85 
rs10752858 A̅/G Upstream 0.78 (60, 37, 3) 0.74 (54, 40, 6) 1.30 (0.85–1.99) 0.21 1.38 (0.74–2.56) 0.3 89 
rs2877622 C̅/A Upstream 0.78 (60, 37, 3) 0.73 (53, 40, 7) 1.40 (0.91–2.16) 0.12 1.30 (0.69–2.46) 0.42 88 
rs2877651 G̅/A Upstream 0.79 (63, 32, 5) 0.75 (56, 38, 6) 1.15 (0.77–1.73) 0.47 0.97 (0.49–1.95) 0.93 82 
rs541886 T̅/C Upstream 0.76 (57, 37, 6) 0.70 (47, 46, 7) 1.31 (0.91–1.87) 0.13 1.17 (0.64–2.13) 0.61 89 
−1039G/T G̅/T Upstream 0.78 (61, 34, 5) 0.74 (53, 41, 6) 1.15 (0.79–1.66) 0.45 0.84 (0.49–1.45) 0.53 89 
rs679931 A/T Intron 0.47 (19, 55, 26) 0.45 (16, 58, 26) 1.01 (0.71–1.44) 0.93 1.46 (0.84–2.51) 0.17 84 
rs625226 A̅/G Intron 0.73 (62, 31, 7) 0.68 (45, 46, 8) 1.43 (1.02–2.03) 0.03 0.94 (0.48–1.84) 0.85 86 
rs10910955 G/A̅ Intron 0.27 (7, 41, 52) 0.24 (2, 43, 55) 1.09 (0.74–1.60) 0.63 0.54 (0.29–1.02) 0.05 86 
rs3856091 T̅/A Intron 0.72 (55, 34, 11) 0.66 (43, 45, 12) 1.37 (0.96–1.95) 0.07 2.44 (1.28–4.63) 0.006 85 
rs4651109 C/A̅ Intron 0.25 (7, 37, 56) 0.20 (1, 37, 61) 1.37 (0.93–2.04) 0.1 1.83 (0.95–3.51) 0.07 84 
rs199939 C/T̅ intron 0.31 (9, 43, 48) 0.27 (4, 47, 49) 1.23 (0.86–1.76) 0.23 1.69 (0.96–2.98) 0.06 87 
rs3753737 A̅/G Intron 0.55 (34, 42, 24) 0.46 (20, 51, 29) 1.41 (1.04–1.91) 0.02 2.05 (1.20–3.49) 0.008 90 
rs17743547 A/G̅ Intron 0.25 (7, 35, 58) 0.20 (4, 31, 65) 1.28 (0.88–1.87) 0.18 1.08 (0.58–2.01) 0.8 90 
rs3766999 C̅/G Intron 0.63 (41, 44, 15) 0.58 (33, 51, 16) 1.12 (0.80–1.57) 0.47 1.15 (0.66–2.03) 0.62 86 
rs4652678 T/C Hisl317His 0.30 (9, 42, 49) 0.29 (8, 42, 50) 1.00 (0.71–1.41) 0.95 0.77 (0.45–1.31) 0.33 90 
rs199930 C̅/T Asnl672Asn 0.75 (55, 39, 6) 0.74 (55, 38, 7) 1.05 (0.72–1.52) 0.79 1.04 (0.58–1.89) 0.88 89 
rs798209 T̅/G Intron 0.88 (77, 21, 2) 0.82 (65, 34, 1) 1.59 (1.06–2.38) 0.02/0.007 1.54 (0.90–2.61) 0.11 89 
rs546191 C/T̅ Intron 0.25 (56, 38, 6) 0.23 (60, 34, 6) 1.06 (0.70–1.59) 0.76 1.07 (0.54–2.11) 0.83 88 
rs473200 C̅/T intron 0.88 (77, 21, 2) 0.82 (66, 33, 1) 1.53 (1.03–2.27) 0.03/0.009 1.32 (0.83–2.09) 0.24 89 
rs704326 G/A̅ Ala1936Thr 0.33 (9, 48, 43) 0.28 (7, 41, 52) 1.35 (0.93–1.96) 0.1 1.86 (1.09–3.17) 0.02 89 
rs2280869 T̅/C 3′-UTR 0.64 (41, 44, 15) 0.56 (30, 51, 19) 1.31 (0.94–1.82) 0.1 1.57 (0.91–2.71) 0.1 89 
rs3831945 d̅e̅l̅/T 3′-UTR 0.96 (92, 8, 0) 0.95 (90, 10, 0) 1.16 (0.47–2.83) 0.73/0.72 0.33 (0.08–1.38) 0.12 89 
rs10632577 A̅G̅/del 3′-UTR 0.85 (63, 32, 5) 0.83 (70, 25, 5) 1.24 (0.76–2.02) 0.37/0.61 1.27 (0.50–3.21) 0.61 89 
rs638132 A̅/G 3′-UTR 0.80 (63, 34, 3) 0.79 (63, 32, 5) 1.17 (0.72–1.89) 0.51 0.91 (0.40–2.07) 0.81 90 
+8130G/A G̅/A 3′-UTR 0.90 (82, 17, 1) 0.83 (68, 31, 1) 2.09 (1.31–3.33) 0.001/0.0006 2.44 (1.27–4.67) 0.007 89 
rs16858150 A̅/C 3′-UTR 0.94 (88, 11, 1) 0.92 (85, 14, 1) 1.16 (0.54–2.47) 0.69/0.64 0.30 (0.09–0.96) 0.04 90 
SNPMajor/ minor alleleLocationRisk allele frequency (genotype distribution as %)
General test
Within-family test (n = 680)
Genotype call rate (%)
Diabetes (n = 578)Nondiabetic (n = 102)OR (95% CI)P additive/ recessive*OR (95% CI)P additive
rs10752856 T̅/C Upstream 0.78 (61, 34, 5) 0.73 (54, 39, 7) 1.19 (0.78–1.82) 0.41 0.97 (0.52–1.81) 0.91 86 
rs11582261 G̅/T Upstream 0.59 (34, 50, 16) 0.54 (26, 55, 19) 1.29 (0.94–1.79) 0.11 1.72 (1.12–2.64) 0.01 85 
rs10752858 A̅/G Upstream 0.78 (60, 37, 3) 0.74 (54, 40, 6) 1.30 (0.85–1.99) 0.21 1.38 (0.74–2.56) 0.3 89 
rs2877622 C̅/A Upstream 0.78 (60, 37, 3) 0.73 (53, 40, 7) 1.40 (0.91–2.16) 0.12 1.30 (0.69–2.46) 0.42 88 
rs2877651 G̅/A Upstream 0.79 (63, 32, 5) 0.75 (56, 38, 6) 1.15 (0.77–1.73) 0.47 0.97 (0.49–1.95) 0.93 82 
rs541886 T̅/C Upstream 0.76 (57, 37, 6) 0.70 (47, 46, 7) 1.31 (0.91–1.87) 0.13 1.17 (0.64–2.13) 0.61 89 
−1039G/T G̅/T Upstream 0.78 (61, 34, 5) 0.74 (53, 41, 6) 1.15 (0.79–1.66) 0.45 0.84 (0.49–1.45) 0.53 89 
rs679931 A/T Intron 0.47 (19, 55, 26) 0.45 (16, 58, 26) 1.01 (0.71–1.44) 0.93 1.46 (0.84–2.51) 0.17 84 
rs625226 A̅/G Intron 0.73 (62, 31, 7) 0.68 (45, 46, 8) 1.43 (1.02–2.03) 0.03 0.94 (0.48–1.84) 0.85 86 
rs10910955 G/A̅ Intron 0.27 (7, 41, 52) 0.24 (2, 43, 55) 1.09 (0.74–1.60) 0.63 0.54 (0.29–1.02) 0.05 86 
rs3856091 T̅/A Intron 0.72 (55, 34, 11) 0.66 (43, 45, 12) 1.37 (0.96–1.95) 0.07 2.44 (1.28–4.63) 0.006 85 
rs4651109 C/A̅ Intron 0.25 (7, 37, 56) 0.20 (1, 37, 61) 1.37 (0.93–2.04) 0.1 1.83 (0.95–3.51) 0.07 84 
rs199939 C/T̅ intron 0.31 (9, 43, 48) 0.27 (4, 47, 49) 1.23 (0.86–1.76) 0.23 1.69 (0.96–2.98) 0.06 87 
rs3753737 A̅/G Intron 0.55 (34, 42, 24) 0.46 (20, 51, 29) 1.41 (1.04–1.91) 0.02 2.05 (1.20–3.49) 0.008 90 
rs17743547 A/G̅ Intron 0.25 (7, 35, 58) 0.20 (4, 31, 65) 1.28 (0.88–1.87) 0.18 1.08 (0.58–2.01) 0.8 90 
rs3766999 C̅/G Intron 0.63 (41, 44, 15) 0.58 (33, 51, 16) 1.12 (0.80–1.57) 0.47 1.15 (0.66–2.03) 0.62 86 
rs4652678 T/C Hisl317His 0.30 (9, 42, 49) 0.29 (8, 42, 50) 1.00 (0.71–1.41) 0.95 0.77 (0.45–1.31) 0.33 90 
rs199930 C̅/T Asnl672Asn 0.75 (55, 39, 6) 0.74 (55, 38, 7) 1.05 (0.72–1.52) 0.79 1.04 (0.58–1.89) 0.88 89 
rs798209 T̅/G Intron 0.88 (77, 21, 2) 0.82 (65, 34, 1) 1.59 (1.06–2.38) 0.02/0.007 1.54 (0.90–2.61) 0.11 89 
rs546191 C/T̅ Intron 0.25 (56, 38, 6) 0.23 (60, 34, 6) 1.06 (0.70–1.59) 0.76 1.07 (0.54–2.11) 0.83 88 
rs473200 C̅/T intron 0.88 (77, 21, 2) 0.82 (66, 33, 1) 1.53 (1.03–2.27) 0.03/0.009 1.32 (0.83–2.09) 0.24 89 
rs704326 G/A̅ Ala1936Thr 0.33 (9, 48, 43) 0.28 (7, 41, 52) 1.35 (0.93–1.96) 0.1 1.86 (1.09–3.17) 0.02 89 
rs2280869 T̅/C 3′-UTR 0.64 (41, 44, 15) 0.56 (30, 51, 19) 1.31 (0.94–1.82) 0.1 1.57 (0.91–2.71) 0.1 89 
rs3831945 d̅e̅l̅/T 3′-UTR 0.96 (92, 8, 0) 0.95 (90, 10, 0) 1.16 (0.47–2.83) 0.73/0.72 0.33 (0.08–1.38) 0.12 89 
rs10632577 A̅G̅/del 3′-UTR 0.85 (63, 32, 5) 0.83 (70, 25, 5) 1.24 (0.76–2.02) 0.37/0.61 1.27 (0.50–3.21) 0.61 89 
rs638132 A̅/G 3′-UTR 0.80 (63, 34, 3) 0.79 (63, 32, 5) 1.17 (0.72–1.89) 0.51 0.91 (0.40–2.07) 0.81 90 
+8130G/A G̅/A 3′-UTR 0.90 (82, 17, 1) 0.83 (68, 31, 1) 2.09 (1.31–3.33) 0.001/0.0006 2.44 (1.27–4.67) 0.007 89 
rs16858150 A̅/C 3′-UTR 0.94 (88, 11, 1) 0.92 (85, 14, 1) 1.16 (0.54–2.47) 0.69/0.64 0.30 (0.09–0.96) 0.04 90 

The risk allele (major or minor) is underlined, and del = deleted nucleotide(s). Genotype distribution (in parentheses) is given as the percent of subjects homozygous for the risk allele and heterozygous and homozygous for the nonrisk allele, respectively. Subjects with diabetes had age of onset <45 years. Nondiabetic subjects were all >45 years of age. P values and ORs were adjusted for age, sex, birth year, heritage, and family membership.

*

Recessive P value (rare homozygotes combined with heterozygotes) is also given for SNPs with minor allele frequency <0.2. In the general test, OR is expressed as per copy of the underlined risk allele (and, thus, by definition is ≥1). In the within-family test, OR is expressed as the same allele as in the general test; therefore, those <1 indicate that the risk alleles are in the opposite directions in two tests.

Table 2

Metabolic characteristics of nondiabetic full-heritage subjects grouped by +8130G/A genotype

G/GG/A + A/AP
N (men/women) 291 (163/128) 81 (52/29)  
Age (year) 26 ± 0.3 27 ± 0.8  
% Fat 34 ± 0.5 31 ± 1.1 0.24 
Fasting plasma glucose (mg/dl) 91 ± 0.6 86 ± 1.1 0.002 
30 min plasma glucose (mg/dl) 147 ± 1.4 138 ± 2.8 0.003 
60 min plasma glucose (mg/dl) 152 ± 2.1 133 ± 3.4 0.00001 
2 h plasma glucose (mg/dl) 125 ± 1.7 114 ± 3.2 0.01 
Fasting insulin (μU/ml) 38 (36–40) 30 (27–33) 0.001 
2 h insulin (μU/ml) 166 (151–182) 126 (104–151) 0.06 
Glucose disposal low-dose insulin clamp (mg · kg EMBS−1 · min−13.4 (3.3–3.5) 3.9 (3.6–4.2) 0.002 
Glucose disposal high-dose insulin clamp (mg · kg EMBS−1 · min−18.2 ± 0.12 9.2 ± 0.23 0.003 
Acute insulin response (μU/ml) 224 (204–245) 229 (199–257) 0.73 
G/GG/A + A/AP
N (men/women) 291 (163/128) 81 (52/29)  
Age (year) 26 ± 0.3 27 ± 0.8  
% Fat 34 ± 0.5 31 ± 1.1 0.24 
Fasting plasma glucose (mg/dl) 91 ± 0.6 86 ± 1.1 0.002 
30 min plasma glucose (mg/dl) 147 ± 1.4 138 ± 2.8 0.003 
60 min plasma glucose (mg/dl) 152 ± 2.1 133 ± 3.4 0.00001 
2 h plasma glucose (mg/dl) 125 ± 1.7 114 ± 3.2 0.01 
Fasting insulin (μU/ml) 38 (36–40) 30 (27–33) 0.001 
2 h insulin (μU/ml) 166 (151–182) 126 (104–151) 0.06 
Glucose disposal low-dose insulin clamp (mg · kg EMBS−1 · min−13.4 (3.3–3.5) 3.9 (3.6–4.2) 0.002 
Glucose disposal high-dose insulin clamp (mg · kg EMBS−1 · min−18.2 ± 0.12 9.2 ± 0.23 0.003 
Acute insulin response (μU/ml) 224 (204–245) 229 (199–257) 0.73 

Only three subjects were homozygous for the A-allele of +8130 G/A; therefore, these individuals were combined with the G/A heterozygotes for statistical analyses. Plasma insulin concentrations (fasting and 2 h), rates of glucose disappearance during the low-dose insulin infusion clamp, and the acute insulin response were log transformed before analyses to approximate a normal distribution. Data for these variables are presented as the geometric mean (95% high and low CIs). The P value for % fat was adjusted for age, sex, and family membership. The P value for the acute insulin response was adjusted for age, sex, % fat, and rate of glucose disappearance during the low-dose insulin infusion clamp. All remaining P values (except for age) were adjusted for age, sex, % fat, and family membership. The acute insulin response was analyzed only in normal glucose-tolerant nondiabetic subjects (n = 203 with genotype G/G and n = 69 with genotype G/A or A/A). EMBS, estimated metabolic body size.

Table 3

CACNA1E haplotypes associated with early-onset type 2 diabetes in Pima Indians

Haplotype blockrs11582261rs10752858rs2877651rs679931rs625226rs10910955rs546191rs473200rs704326rs2280869rs3831945rs10632577rs638132+8130G/Ars16858150Haplotype frequency
OR (95% CI)P (corrected)Bonferroni correction
|_____||_____||_____||___________||___|
HaplotypeDiabeticNondiabetic
            0.38 0.25 1.94 (1.27–2.98) 0.006 
            0.22 0.31 0.63 (0.43–0.92) 0.04 
11             0.13 0.19 0.61 (0.40–0.92) 0.05 
12          Cdel(AG)    0.10 0.17 0.49 (0.31–0.78) 0.009 
13              0.85 0.74 1.82 (1.23–2.69) 0.005 
13              0.09 0.17 0.44 (0.28–0.70) 0.001 
Haplotype blockrs11582261rs10752858rs2877651rs679931rs625226rs10910955rs546191rs473200rs704326rs2280869rs3831945rs10632577rs638132+8130G/Ars16858150Haplotype frequency
OR (95% CI)P (corrected)Bonferroni correction
|_____||_____||_____||___________||___|
HaplotypeDiabeticNondiabetic
            0.38 0.25 1.94 (1.27–2.98) 0.006 
            0.22 0.31 0.63 (0.43–0.92) 0.04 
11             0.13 0.19 0.61 (0.40–0.92) 0.05 
12          Cdel(AG)    0.10 0.17 0.49 (0.31–0.78) 0.009 
13              0.85 0.74 1.82 (1.23–2.69) 0.005 
13              0.09 0.17 0.44 (0.28–0.70) 0.001 

Haplotype analysis was carried out in 13 “blocks” defined as consecutive SNPs among whom all pairs had D′ >0.9. All haplotypes with frequency >0.05 were analyzed within each block. ORs were expressed as per copy of the haplotype of interest (additive model). The corrected P values reflect a Bonferroni correction for the number of “independent” haplotypes in the same block (total number of haplotypes analyzed = 1). Only haplotypes with P value <0.05 are shown in the table, where brackets indicate the specific SNPs used in constructing each haplotype. Haplotye C-del-(AG)-A-A contains a deletion at the second position (rs3831945 = del/T) and an AG insertion at the third position (rs10632577 = AG/del).

Published ahead of print at http://diabetes.diabetesjournals.org on 24 August 2007. DOI: 10.2337/db07-0587.

Additional information for this article can be found in an online appendix at http://dx.doi.org/10.2337/db07-0587.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

This study was supported by the intramural research program of the National Institutes of Diabetes and Digestive and Kidney Diseases, National Institutes of Health (NIH). Funding was also provided by NIH Grant R01-DK073490. Y.L.M. is supported by a grant from the American Diabetes Association.

We thank the members of the Gila River Indian Community for participation and the staff of the Diabetes Epidemiology and Clinical Research Section and the Obesity and Diabetes Clinical Research Section for recruiting the participants and making the clinical and physiologic measurements.

1.
Catterall WA: Structure and regulation of voltage-gated Ca2+ channels.
Annu Rev Cell Dev Biol
16
:
521
–555,
2000
2.
Williams ME, Marubio LM, Deal, M Hans CR, Brust PF, Philipson LH, Miller RJ, Johnson EC, Harpold MM, Ellis SB: Structure and functional characterization of neuronal alpha 1E calcium channel subtypes.
J Biol Chem
269
:
22347
–22357,
1994
3.
Schneider T, Wei X, Olcese R, Costantin JL, Neely A, Palade P, Perez-Reyes E, Qin N, Zhou J, Crawford GD, Smith RG, Appel SH, Stefani E, Birnbaumer L: Molecular analysis and functional expression of the human type E neuronal Ca2+ channel alpha 1 subunit.
Receptors Channels
2
:
255
–270,
1994
4.
Pereverzev A, Mikhna M, Vajna R, Gissel C, Henry M, Weiergraber M, Hescheler J, Smyth N, Schneider T: Disturbances in glucose-tolerance, insulin-release, and stress-induced hyperglycemia upon disruption of the Cav2.3 (α1E) subunit of voltage-gated Ca2+ channels.
Mol Endocrinol
16
:
884
–895,
2005
5.
Matsuda Y, Saegusa H, Zong S, Hoda T, Tanabe T: Mice lacking Cav2.3(a1E) calcium channel exhibit hyperglycemia.
Biochem Biophys Res Commun
289
:
791
–795,
2001
6.
McCarthy M: Growing evidence for diabetes susceptibility genes from genome scan data.
Curr Diab Rep
3
:
159
–167,
2003
7.
Elbein SC, Hoffman MD, Teng K, Leppert MF, Hasstedt SJ: A genome-wide search for type 2 diabetes susceptibility genes in Utah Caucasians.
Diabetes
48
:
1175
–1182,
1999
8.
Hsueh WC, St Jean PL, Mitchell BD, Pollin TI, Knowler WC, Ehm MG, Bell CJ, Sakul H, Wagner MJ, Burns DK, Shuldiner AR: Genome-wide and fine-mapping linkage studies of type 2 diabetes and glucose traits in the Old Order Amish: evidence for a new diabetes locus on chromosome 14q11 and confirmation of a locus on chromosome 1q21–q24.
Diabetes
52
:
550
–557,
2003
9.
Wiltshire S, Hattersley AT, Hitman GA, Walker M, Levy JC, Sampson M, O'Rahilly S, Frayling TM, Bell JI, Lathrop GM, Bennett A, Dhillon R, Fletcher C, Groves CJ, Jones E, Prestwich P, Simecek N, Rao PV, Wishart M, Bottazzo GF, Foxon R, Howell S, Smedley D, Cardon LR, Menzel S, McCarthy MI: A genomewide scan for loci predisposing to type 2 diabetes in a U.K. population (the Diabetes UK Warren 2 Repository): analysis of 573 pedigrees provides independent replication of a susceptibility locus on chromosome 1q.
Am J Hum Genet
69
:
553
–569,
2001
10.
Vionnet N, Hani El-H, Dupont S, Gallina S, Francke S, Dotte S, De Matos F, Durand E, Lepretre F, Lecoeur C, Gallina P, Zekiri L, Dina C, Froguel P: Genomewide search for type 2 diabetes-susceptibility genes in French whites: evidence for a novel susceptibility locus for early-onset diabetes on chromosome 3q27-qter and independent replication of a type 2-diabetes locus on chromosome 1q21–q24.
Am J Hum Genet
67
:
1470
–1480,
2000
11.
Ng MC, So WY, Lam VK, Cockram CS, Bell GI, Cox NJ, Chan JC: Genome-wide scan for metabolic syndrome and related quantitative traits in Hong Kong Chinese and confirmation of a susceptibility locus on chromosome 1q21–q25.
Diabetes
53
:
2676
–2683,
2004
12.
Hanson RL, Ehm MG, Pettitt DJ, Prochazka M, Thompson DB, Timberlake D, Foroud T, Kobes S, Baier L, Burns DK, Almasy L, Blangero J, Garvey WT, Bennett PH, Knowler WC: An autosomal genomic scan for loci linked to type II diabetes mellitus and body mass index in Pima Indians.
Am J Hum Genet
63
:
1130
–1138,
1998
13.
World Health Organization:
Report of a WHO Study Group: Diabetes Mellitus.
Geneva, World Health Org.,
1985
(Tech. Rep. Ser., no. 727)
14.
Lillioja S, Mott DM, Spraul M, Ferraro R, Foley JE, Ravussin E, Knowler WC, Bennett PH, Bogardus C: Insulin resistance and insulin secretory dysfunction as precursors of non-insulin-dependent diabetes mellitus: prospective studies of Pima Indians.
N Engl J Med
329
:
1988
–1992,
1993
15.
Lillioja S, Bogardus C: Obesity and insulin resistance: lessons learned from the Pima Indians.
Diabetes Metab Rev
4
:
517
–540,
1988
16.
Lee YH, Nair S, Rousseau E, Allison DB, Page GP, Tataranni PA, Bogardus C, Permana PA: Microarray profiling of isolated abdominal subcutaneous adipocytes from obese vs non-obese Pima Indians: increased expression of inflammation-related genes.
Diabetologia
48
:
1776
–1783,
2005
17.
Buckland PR: Allele-specific gene expression differences in humans.
Hum Mol Genet
13
:
R255
–R260,
2004
18.
Zeger SL, Liang KY: Longitudinal data analysis for discrete and continuous outcomes.
Biometrics
42
:
121
–130,
1986
19.
Abecasis GR, Cardon LR, Cookson WOC: A general test of association for quantitative traits in nuclear families.
Am J Hum Genet
66
:
279
–292,
2000
20.
Hanson RL, Knowler WC: Design and analysis of genetic association studies to finely map a locus identified by linkage analysis: assessment of the extent to which an association can account for the linkage.
Ann Intern Med.
2007
Jul 12 [Epub ahead of print, DOI: 10.1111/j.1469-1809.2007.00382.x]
21.
Vozarova de Courten B, Hanson RL, Funahashi T, Lindsay RS, Matsuzawa Y, Tanaka S, Thameem F, Gruber JD, Froguel P, Wolford JK: Common polymorphisms in the adiponectin gene ACDC are not associated with diabetes in Pima Indians.
Diabetes
54
:
284
–289,
2005
22.
Vajna R, Klockner U, Pereverzev A, Weiergraber M, Chen X, Miljanich G, Klugbauer N, Hescheler J, Perez-Reyes E, Schneider T: Functional coupling between ‘R-type’ Ca2+ channels and insulin secretion in the insulinoma cell line INS-1.
Eur J Biochem
268
:
1066
–1075,
2001
23.
Zeggini E, Damcott CM, Hanson RL, Karim MA, Rayner NW, Groves CJ, Baier LJ, Hale TC, Hattersley AT, Hitman GA, Hunt SE, Knowler WC, Mitchell BD, Ng MCY, O'Connell JR, Pollin TI, Vaxillaire M, Walker M, Wang X, Whittaker P, Xiang K, Jia W, Chan CN, Froguell P, Deloukas P, Shuldiner AR, Elbein SC, McCarthy MI, for the International 1q Type 2 Diabetes Consortium: Variation within the gene encoding the Upstream Stimulatory Factor 1 (USF1) does not influence susceptibility to type 2 diabetes in samples from populations with replicated evidence of linkage to chromosome 1q.
Diabetes
55
:
2541
–2548,
2006
24.
Ma L, Hanson RL, Que LN, Cali AMG, Fu M, Mack JL, Infante AM, Kobes S, Bogardus C, Shuldiner AR, Baier LJ: Variants in ARHGEF11, a candidate gene for the linkage to type 2 diabetes mellitus on chromosome 1q, are nominally associated with insulin resistance and type 2 diabetes mellitus in Pima Indians.
Diabetes
56
:
1454
–1459,
2007
25.
Fu M, Sabra MM, Damcott C, Pollin TI, Ma L, Ott S, Shelton JC, Shi X, Reinhart L, O'Connell JO, Mitchell BD, Baier LJ, Shuldiner AR: Evidence that rho guanine nucleotide exchange factor 11 (ARHGEF11) on 1q21 is a type 2 diabetes susceptibility gene in the Old Order Amish.
Diabetes
56
:
1363
–1368,
2007