1 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
2 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan
3 Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland
4 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
5 Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion, National Public Health Institute, Helsinki, Finland
6 Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
7 Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California
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ABSTRACT |
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INTRODUCTION |
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We genotyped SNPs 43, 56, and 63 in 1,603 Finnish subjects, including two samples of 526 FUSION 1 (F1) and 255 FUSION 2 (F2) index case subjects with type 2 diabetes, 185 nondiabetic spouses and 414 nondiabetic offspring of F1 index case subjects or their affected siblings, and 223 normal glucose-tolerant elderly (70 years of age) control subjects. SNP 56 was more easily typed than SNP 19 using our genotyping platform (see RESEARCH DESIGN AND METHODS) and was reportedly in complete linkage disequilibrium with SNP 19 (verified; see RESEARCH DESIGN AND METHODS). Table 1 shows selected phenotypic traits for each FUSION study group. A similar table for all phenotypic traits discussed here can be found on-line on our website (http://www.sph.umich.edu/csg/CAPN10).
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Although there were many phenotypic comparisons that reached the nominal significance levels of P 0.05 and P
0.01, the number of dependent comparisons made within each group for each trait was large. Across all comparisons, these findings were consistent with random chance. Specifically, 4% of the comparisons were significant at the 5% level and 1% at the 1% level. As such, many of these differences are not likely to be physiologically meaningful. Nonetheless, we note a few items of potential interest. First, weight-related traits were consistently nominally significant across many of the different comparisons. Specifically, for three of the four haplotypes and four of the nine haplogenotypes, the comparisons for one or more of these traits had P values
0.05 in at least two FUSION study groups. Although a definite phenotypic pattern associated with a specific variant or combination of variants was not clear and many of the P-values were quite modest, it is possible that variation at this locus influences adiposity in some way that we currently cannot discern.
Second, among elderly control subjects, variation at SNP 56 was associated with fasting and 2-h serum insulin levels. The mean fasting insulin level for the AG genotypic group was higher than that for each of the AA and GG groups (76.1 ± 44.4, 56.9 ± 27.9, and 58.0 ± 19.8 for AG, AA, and GG groups, respectively; genotypic P = 0.011). Those with either the GG or AG genotype had higher 2-h insulin levels (402.1 ± 235.7) than those with the AA genotype (311.0 ± 173.4; G-allele P = 0.037). After adjustment for BMI, the genotypic differences in fasting insulin levels remained significant (P = 0.012). In addition, the presence of at least one copy of haplotype 121 (which includes the G-allele at SNP 56) was associated with higher 2-h insulin levels (430.1 ± 244.9 vs. 330.5 ± 197.0; P = 0.001; BMI-adjusted P = 0.007). There were no consistent patterns of association (both in terms of direction of the association and combination of variants) between these two insulin traits and other haplotypes or haplogenotypes, but we mention the above because of the obvious importance of insulin levels in the pathogenesis of type 2 diabetes.
The fact that we were not able to replicate the association results of Horikawa et al. (3) for Mexican-Americans in our Finnish sample is not particularly surprising given the previously reported lack of linkage evidence for this region in our sample (5) and the likely genetic heterogeneity between Mexican-American and Finnish subjects. Indeed, based on their samples, Horikawa et al. estimated that the population-attributable risk associated with variation in calpain-10 is only 4% in Europeans compared with 14% in Mexican-American populations (1). Perhaps the more intriguing question is why our Finnish sample is so dissimilar to the Botnian-Finnish sample in terms of the risk of type 2 diabetes associated with variation in the three polymorphisms. Whereas in the Botnian sample the G-allele at SNP 43 and the T-allele at SNP 63 were significantly associated with diabetes status and a trend in increased risk was demonstrated for those with the implicated haplotype combination (112/121) first identified in the Mexican-Americans, we found no evidence for this in our sample. One plausible explanation is that Swedish admixture in the Botnian-Finnish sample is sufficient such that the disease-predisposing variants differ between the two populations (6). However, the allele frequencies for all three polymorphisms in the Botnian patient sample are very similar to those in each of our case and control (elderly and spouse) samples. Consequently, differences in allele frequencies between the Botnian and FUSION control samples are responsible for the observed differences in risk associated with these polymorphisms in each population. Hence, Swedish admixture that led to the presence of different disease susceptibility loci in the two populations does not appear to be a satisfactory explanation for differences in risk between the two samples.
Clearly, inter-population heterogeneity is a possible explanation for our findings. It is also possible that there exists heterogeneity within our population with respect to risk associated with variation in the three SNPs. We carried out two types of analyses that investigate the most obvious subgroups of our index case samples for whom variation at these loci may be associated with type 2 diabetes status. First, despite the overall evidence against linkage to this region in our F1 sample (5), there are individual families that demonstrate evidence for linkage (defined here as a nonparametric linkage [NPL] score >0). We identified index case subjects from families with NPL scores >0 at 258.5 cM on the FUSION map, approximately corresponding to the linkage peak in the Mexican-American sample (3). We compared allele, genotype, haplotype, and haplogenotype frequencies between these case subjects (F1 n = 178, F2 n = 95, and F1 + F2 n = 273) and control subjects (spouses and elderly control subjects combined). Across these 57 comparisons, all P values were >0.062.
Second, for each of the index case groups (F1, F2, and F1 + F2), we also carried out logistic regression analyses to test for interactions between variation at SNP 43 and age, sex, and BMI in models that included each of these factors as main effects. There were no statistically significant (P < 0.05) interactions between any of these three factors and either genotypic or allelic variation at SNP 43. Similarly, we tested for interactions between the indicator of having the putative at-risk haplogenotype 112/121 and age, sex, and BMI. The only interaction that was statistically significant (P < 0.05) was that with sex in the F2 index case analysis (P = 0.012). However, the parameter estimates indicate that having haplogenotype 112/121 is protective for men and is not associated with disease for women (OR and 95% CI for men and women, respectively: 0.16 [0.030.73] and 1.61 [0.634.11]).
We conclude that the lack of association between both SNP 43 and haplogenotype 112/121 and type 2 diabetes remains after considering interaction effects with age, sex, and BMI. We limited our analyses to those described because, in the absence of obvious a priori hypotheses, the number of competing analyses to be computed is very large. As such, the results would likely be very difficult, if not impossible, to interpret. In particular, we chose not to test for interactions in each of the continuous trait analyses because this would triple the very large number of comparisons already done for those traits.
Recently, a study in the U.K. also found no evidence for linkage between type 2 diabetes and the NIDDM-1 region on chromosome 2, nor any evidence for any association between SNP 43, -19, or -63 and type 2 diabetes using family-based and case-control studies (7). However, the rare C-allele at UCSNP-44 was associated with type 2 diabetes in one of two case-control samples (P = 0.005) and was found to be in perfect linkage disequilibrium with a coding polymorphism, T504A, suggesting that other polymorphisms in the calpain-10 gene may be relevant in different study populations. Therefore, we cannot exclude the possibility that one or more other polymorphisms in the calpain-10 gene may be associated with type 2 diabetes in our sample.
Further study by these investigators in the U.K. found that the GG genotype at SNP 43 was associated with higher 2-h plasma glucose levels (P = 0.05) and that the 112/121 haplogenotype was associated with measures of insulin secretion and action in nondiabetic British subjects (8). Although we did not find any association between either variation at SNP 43 or the 112/121 haplogenotype and these traits, we did observe at least nominal associations with insulin levels at SNP 56 and with haplotype 121. More studies are needed to fully understand how variation at these loci may impact insulin secretion and action in different populations.
We conclude that variation in SNP 43, -56, and -63 in the calpain-10 gene does not appear to either confer susceptibility to type 2 diabetes or strongly influence diabetes-related traits in this Finnish cohort.
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RESEARCH DESIGN AND METHODS |
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Genotyping.
We genotyped SNP 19 on 93 subjects representing all three genotypes at SNP 56 to verify that the two SNPs were in complete linkage disequilibrium and to determine phase. SNP 19 is a 32-base tandem repeat of either two or three units. PCR amplification of the region surrounding the repeats was performed using the forward oligonucleotide primer 5'-AGGCCCAGTTTGGTTCTCTT and the reverse primer 5'- AGCTACGGCCACAGACAGAG under standard thermocycling conditions. The reaction products were separated by electrophoresis in a 2% agarose gel and stained with ethidium bromide. The presence of a band 135 bp in size corresponded to the 2-unit repeat, and a band of 167 bp corresponded to the 3-unit repeat. In all 93 subjects, there was complete correlation between SNPs 19 and 56, indicating complete linkage disequilibrium.
The SNPs 43, 56, and 63 were amplified from genomic DNA by PCR followed by a primer extension reaction, and the resulting products were analyzed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry as previously described (11). PCR amplification of DNA fragments containing the variant site were performed using the following primer sets: SNP 43, 5'-CTGTGTGTGGGCAGAGGAC, 5'-AGCGGATAACAATTTCACACAGGCCTCATCCTCACCAAGTCAAG; SNP-56, 5'-CAAGGGTGGTGTCCTCAGTT, 5'-AGCGGATAACAATTTCACACAGGCCTCGCACTAGTGGAAAGGA; and SNP-63, 5'-AGCGGATAACAATTTCACACAGGCCCTGGTCACTGGATGTTGC, 5'-AGCGGATAACCCTGAAGGTTCCACTCTCCA. A univer-sal sequence biotinylated primer, 5'-biotin-AGCGGATAACAATTTCACACAGG, corresponding to the 23 nucleotides at the 5' end of the longer primers in each reaction, was included to enable purification of single strand template for the primer extension assay. The genomic DNA was amplified in a thermocycling protocol of 95°C for 15 min followed by 55 cycles of 95°C for 5 s, 53°C for 20 s, and 72°C for 30 s. The primer extension reaction was performed using the SNP-specific primers abutting the polymorphic nucleotide. The extension products were applied to a SpectroChip (Sequenom, San Diego, CA) prespotted with a matrix of 3-hydroxypicolinic acid. The genotype of each sample was determined by analysis of the mass of the primer extension products using a modified Bruker Biflex III MALDI-TOF mass spectrometer (DNA MassArray; Sequenom). The sequence of the extension reaction oligoprimers and the expected masses of the products identifying each allele are available on our website (http://www.sph.umich.edu/csg/CAPN10).
Statistical analyses.
Differences in allele, genotype, haplotype, and haplogenotype frequencies between index case subjects and control subjects were assessed by 2 tests of independence. The genotypic comparisons for SNP 63 were computed via Fishers exact test because of the small number of homozygotes for the T-allele. The spouse and elderly control groups were combined for all haplotype and haplogenotype comparisons. Trait differences associated with variation at both the single-SNP (allele and genotype) and three-SNP (haplotype and haplogenotype) level within index case, spouse control, and elderly control subgroups were assessed using ANOVA. Because many of the offspring were related, we used a generalized estimating equation approach for this group (12). Each analysis was adjusted for age and sex and, for nonanthropometric measures, BMI. Traits were transformed to approximate normality when necessary. No adjustments for multiple comparisons were made. Excluded from all analyses were those with a first-degree relative with type 1 diabetes and those with uncertain affection status. In addition, those who on the day of examination took medication that might be expected to strongly affect the trait of interest were excluded from the analyses for that trait.
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ACKNOWLEDGMENTS |
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We thank the many Finnish subjects who volunteered to participate in the FUSION study. We gratefully acknowledge Peggy White and Terry Gliedt for help in preparing the tables.
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FOOTNOTES |
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Received for publication 28 November 2001 and accepted in revised form 7 February 2002.
T.E.F. and M.R.E. contributed equally to this work.
AIR, acute insulin response; FUSION, Finland-U.S. Investigation of NIDDM Genetics; MALDI-TOF, matrix-assisted laser desorption/ionization time-of-flight; NPL, nonparametric linkage; OR, odds ratio; SI, insulin sensitivity; SNP, single nucleotide polymorphism.
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REFERENCES |
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