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HIGH-DENSITY HAPLOTYPE STRUCTURE AND ASSOCIATION TESTING OF THE INSULIN-DEGRADING ENZYME (IDE) GENE WITH TYPE 2 DIABETES IN 4,206 PEOPLE
1 Departments of Molecular Biology and 2 Medicine (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts; 3 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Departments of 4 Medicine and 10 Genetics, Harvard Medical School, Boston, Massachusetts; 5 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Departments of 6 Endocrinology and 7 Clinical Science, University Hospital MAS, Lund University, Malmö, Sweden; 8 Department of Medicine, Helsinki University Central Hospital; Folkhalsan Genetic Institute, Folkhalsan Research Center; and Research Program for Molecular Medicine, University of Helsinki, Helsinki, Finland; 9 University of Montreal Community Genomic Center, Chicoutimi Hospital, Quebec, Canada; 11 Divisions of Genetics and Endocrinology, Children’s Hospital, Boston, Massachusetts; and 12 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
(*) These authors jointly supervised the project
Diabetes. 2006 Jan;55(1):128-35
ABSTRACT
The insulin degrading enzyme is responsible for the intracellular proteolysis of insulin. Its gene IDE is located on chromosome 10, in an area with suggestive linkage to type 2 diabetes and related phenotypes. Due to the impact of genetic variants of this gene in rodents and the function of its protein product, it has been proposed as a candidate gene for type 2 diabetes. Various groups have explored the role of common genetic variation of IDE on insulin resistance, and reported associations of various single nucleotide polymorphisms (SNPs) and haplotypes on both type 2 diabetes and glycemic traits.
We sought to characterize the haplotype structure of IDE in detail, and replicate the association of common variants with type 2 diabetes, fasting insulin, fasting glucose and insulin resistance. We assessed linkage disequilibrium, selected single-marker and multi-marker tags, and genotyped these markers in several case/control and family-based samples totalling 4,206 Caucasian individuals. We observed no statistically significant evidence of association between single-marker or multi-marker tests in IDE and type 2 diabetes. Nominally significant differences in quantitative traits are consistent with statistical noise. We conclude that common genetic variation at IDE is unlikely to confer clinically significant risk of type 2 diabetes in Caucasians.
Online supplementary information
Supplementary Table 1: Genotyping assays of SNPs in IDE.
Genotyping assays for 243 SNPs in IDE, showing the sequences of the PCR primers and the extension probe, its direction (forward or reverse) and reasons for assay failure. Position is according to the NCBI 35 release. HapMap, SNPs and genotypes obtained from the HapMap website (www.hapmap.org). Failed design, failed assay design on Sequenom platform; failed HW, failed Hardy-Weinberg equilibrium in the CEPH reference panel; failed geno, genotyping rate <75% or >1 Mendel errors; MONO, monomorphic SNP in the CEPH reference panelSupplementary Table 2: Genotype counts of individual SNPs in IDE.
Allele counts for each of the 21 single-marker (top) and 13 multi-marker (bottom) tests in our diabetic subsamples; within each SNP, the major allele is presented first. The components of the multi-marker tags (bottom panel) are numbered as in the top panel, and correspond to those in Table 2. Multi-marker haplotypes were assigned frequency probabilities according to an expectation-maximization algorithm, and are compared versus all other possibilities at those loci (see text for details). T, transmitted; U, untransmitted; C/C, case/control.Supplementary Table 3: Correlation of IDE SNPs to tagging tests selected by Tagger.
The Tagger algorithm (http://www.broad.mit.edu/mpg/tagger) was used to select single and multi-marker tests to capture all SNPs of minor allele frequency (MAF) ³5% with an r2 ³0.8 (see text for details). SNPs previously associated with type 2 diabetes or related phenotypes (8-10) were forced in as tags and are shown in bold. Five SNPs that are less well captured at r2 <0.8 have MAF <5%; a sixth SNP (rs6583826) which had been selected as a tag but failed genotyping in the disease samples was captured by rs10882083 with r2 = 0.74.Supplementary Fig. 2: (online Appendix): Reconstruction (bottom panel) of the haplotypes described by Gu et al. (10) (top panel). We used four of our tag SNPs (in bold) and three SNPs predicted by our set of tags to reconstruct the haplotypes of Gu et al.: rs1832197 was predicted by test 29 (r2 = 0.9), rs1999764 was predicted by test 28 (r2 = 1.0) and rs2421945 was predicted by test 27 (r2 = 1.0). The flanking SNP HHEX23 lies ~95 kb beyond the end of the last haplotype block, and was therefore not genotyped in our disease samples. Thus we could not resolve haplotypes H1 (frequency 28.6%) and H6 (frequency 4.4%), which were analyzed in combination in our samples. We note that haplotypes H3 and H8 cannot be distinguished with the use of the three tag SNPs by Gu et al. (shaded); because the G allele of rs2249960 tags haplotype H3 perfectly and its minor allele frequency is ~8% in Caucasians, we suspect that their assignment of labels H3 (frequency 11.7%) and H8 (frequency 2.7%) based on allele frequency may have been reversed. If this is the case, their allele frequencies would approximate ours closely
