Molecular Analysis of KCNJ10 on 1q as a Candidate Gene for Type 2 Diabetes in Pima Indians

The present data are derived from participants in a longitudinal study of the development of type 2 diabetes (2) diagnosed according to World Health Organization criteria (2). Association analyses were conducted using a case-control study design including 149 affected individuals with the youngest age at onset of diabetes <25 years and 150 unaffected individuals ≥45 years of age who remained nondiabetic based on the oral glucose tolerance test. None of the individuals were first-degree relatives. This sample size has 75–95% power to detect a difference of at least 15% in the prevalence of diabetes between groups with and without the high-risk genotype at P < 0.05 (15). A number of the individuals in the case-control study were not included in the original linkage study. To account for the possibility of heterogeneity in the genetic causes of diabetes between those included in the linkage study and other individuals in the population, statistical analyses were done both in the full case-control sample as well as in a subset (50 affected and 50 unaffected) who had participated in the genomic scan. Any SNP associated with diabetes was subsequently analyzed in the entire population sample of 1,338 Pima Indians used in the original linkage study (2).

BACs 201L5 and 536C5 were purchased from Research Genetics (Huntsville, AL) and used to determine the genomic structure of KCNJ10 by long-distance PCR with the Expand Long Template PCR system (Roche Molecular Biochemicals, Indianapolis, IN) combined with BLAST searches of human genomic sequences available in the nonredundant and high throughput genomic sequences databases (http:/www.ncbi.nlm.nih.gov/BLAST). Sequences were determined using the BigDye Terminator cycle sequencing kit and analyzed on an ABI 377 Sequence Detection System (Applied Biosystems, Foster City, CA).

The tissue distribution of the KCNJ10 transcript was determined by PCR using Quick-Clone cDNAs from 19 human tissues and Advantage Klentaq polymerase (Clontech, Palo Alto, CA). The β-2-Microglobulin transcript was utilized as an internal standard to roughly estimate quantitative differences in KCNJ10 expression between tissues. A duplex PCR was performed using a KCNJ10 sense primer (5′-GGACAAGCCCTTATCTGATC-3′) designed from exon 1 combined with an antisense primer (5′-TCTTTTGTCAGGACTCTCCG-3′) from exon 2 of KCNJ10, and the sense primer 5′-TGTCTTTCAGCAAGGACTGGTC-3′ plus antisense primer 5′-TGATGCTGCTTACATGTCTCGAT-3′ for the β-2-Microglobulin control.

The primers used for KCNJ10 structure determination, variant (SNP) detection, and sequencing are available in Online Appendix 1. For SNP screening, we used two separate DNA pools, each pool comprised of five diabetic and five nondiabetic samples. All samples were from different subjects, thus representing a total of 20 individuals. Potential sequence variants were detected by DHPLC using the WAVE DNA fragment analysis system (Transgenomic, Omaha, NE) and confirmed by sequencing as previously described (16).

All SNPs were initially genotyped by either PCR-restriction fragment length polymorphism (RFLP) using the appropriate primers and restriction enzymes, pyrosequencing, or by capillary sequencing (Online Appendix 2). The KCNJ10-SNP2 did not alter a restriction site and was therefore typed by mismatch PCR-RFLP (17). The mismatch consisted of a C→G substitution at the second base preceding the 3′ end of this primer (5′-CAGGAGGCTCCATTTGGGAgT-3′; the mismatched nucleotide shown in lower, bold case). In combination with this primer, the presence of a ‘T’ at the variant position results in a unique artificial RsaI site (GTAc; in this orientation the mismatched ‘g’ introduced by the reverse primer corresponds to a ‘c’), whereas the recognition site is absent in the context of the alternative ‘C’ allele (GCAc). The digested products were resolved on 3–4% agarose gels. All restriction enzymes were purchased from New England Biolabs (Boston, MA) or Gibco BRL (Gaithersburg, MD) and used according to the manufacturer’s instructions.

KCNJ10-SNP1 was genotyped in 1,338 Pima Indians using the pyrosequencing technology (Pyrosequencing, Uppsala, Sweden). Briefly, templates were amplified from genomic DNA using the biotinylated forward primer 5′-TGACCACAAATCAGCACAACT-3′ and unmodified reverse primer 5′-CCAGTGTTACCATATTGAATTG-3′. The pyrosequencing was performed with primer 5′-GAATGTAGCTTAGTTTAAC-3′. KCNJ10-SNP5 and KCNJ10-SNP8 were also genotyped by pyrosequencing using the following forward primers and biotinylated reverse primers: 5′-GTGTGTGTCTATACCTTCCTAT-3′ plus 5′-GAATCCTAAGACATATGAGACA-3′ for SNP5 and 5′-AGAAGATGCCAGATCGCATTA-3′ plus 5′-GTTCCTGTCCCTGTGAAATGT-3′ for SNP8. SNPs were scored with sequencing primers 5′-GTTAACCTCCATCTGGTT-3′ (SNP5) and 5′-AAGCAATAGCAGGTACCA-3′ (SNP8), respectively. Pyrosequencing was carried out as instructed by the manufacturer with slight modifications. The PCR was carried out in a final volume of 25 μl with an initial denaturing at 96°C for 3 min followed by 40 cycles of 96°C for 20 s, 57°C for 20 s, 72°C for 30s, and a final extension at 72°C for 5 min.

KCNJ10-SNP5 was genotyped in 1,338 Pima Indians by sequencing with the BigDye Terminator chemistry and the ABI 3700 Sequence Detection System (Applied Biosystems, Foster City, CA).

A χ² test and Fisher’s exact test were (SAS Institute, Cary, NC) used for analysis of the case and control subjects to assess statistical evidence for association. P values <0.05 were considered statistically significant, and the odds ratio was used to determine the strength of the association between prevalence of the at-risk genotype and affection status. To maximize power, associations were calculated under three different models, one assuming the common allele is dominant to the less common allele, one assuming it is recessive, and one assuming an additive relation between number of common alleles and prevalence of diabetes. In addition, an overall P value was calculated for the null hypothesis of no difference in diabetes prevalence among the three different genotypes (2 df). None of the genotype frequencies differed significantly from that predicted by Hardy-Weinberg equilibrium. LD between pairs of SNPs within KCNJ10 was estimated among the 299 unrelated individuals in the case-control group with the Estimating Haplotypes program (18). The degree of LD was quantified by D′, which represents the degree to which observed haplotype frequencies differ from that expected under linkage equilibrium, expressed as a proportion of the maximum possible disequilibrium given the observed allele frequencies (19). Haplotype frequencies for pairs of SNP1, SNP5, and SNP6 within KCNJ10 and KCNJ9-SNP14 were calculated using the ILINK program, which accounts for the relations among individuals (20). Associations with combination KCNJ10-SNP1 and KCNJ9-SNP14 polymorphisms were assessed using the same procedures used for single SNPs.

For assessment of the association of the KCNJ10-SNP1 genotypes with diabetes among the participants in linkage study, logistic regression was used to control for age, sex, and year of birth. To account for nonindependence introduced by inclusion of multiple siblings in a sibship, the logistic models were fit using binomial generalized estimating equations (21).

Linkage analyses were performed as in the previously published genomic scan using the Haseman-Elston method (2). The extent to which KCNJ10-SNP1 and KCNJ10-SNP5 contributed to the observed linkage results was investigated by determining the association of genotypes with type 2 diabetes and “adjusting” the diabetes score for the SNP effect by subtracting the prevalence of diabetes in the individual’s genotypic category from an indicator variable for affection status. The Haseman-Elston regression with this “adjusted” diabetes score in the 1q21-q23 region was compared with the original unadjusted linkage result.

Sequences reported in this article were deposited with the GenBank database under accession nos. AF482709, AF482710, and AH011548.

We thank the members of the Gila River Indian Community for their continuing participation and cooperation in the ongoing longitudinal studies.