Computational protocol: Detailed Investigation of the Role of Common and Low-Frequency WFS1 Variants in Type 2 Diabetes Risk

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Protocol publication

[…] PCR was performed on genomic DNA from Cambridgeshire case-control participants or whole-genome amplified DNA from ADDITION and Ely study participants. Fourteen primer pairs (sequences and cycling conditions available upon request), designed using Primer3 software (http://frodo.wi.mit.edu/primer3/), were required to amplify the eight WFS1 exons, including splice junctions, untranslated regions (UTRs), and selected conserved regions. Coverage is shown in supplemental Fig. 1 in the online appendix. PCR and bi-directional sequencing were performed using standard conditions and following manufacturers' protocols (supplemental Methods). Sequencing reactions were run on ABI3730 capillary machines (Applied Biosystems) and analyzed using an automatic SNP caller, ExoTrace (S. Leonard, Wellcome Trust Sanger Institute, unpublished data). The results of SNP calling were displayed and low-frequency variants were manually reviewed in a specific implementation of GAP4 (Staden Sequence Analysis Package software). All regions produced usable sequences for >90% of samples. [...] Details are provided in the supplemental Results and supplemental Fig. 2. Linkage disequilibrium (LD) was calculated using Haploview version 4.0 (http://www.broad.mit.edu/mpg/haploview), and pairwise tagging SNPs were selected by Tagger using r2 ≥ 0.8, force including nonsynonymous variants. [...] Statistical analyses were conducted in StataSE 9. Logistic regression was used to assess the contribution of individual SNPs under a log-additive model (1 df) to risk of type 2 diabetes using study as a categorical covariate. Log-likelihood ratio tests were used to assess whether associated SNPs independently contributed to risk of type 2 diabetes by comparing the log likelihood of a nested model (2 df) containing an associated SNP and study with that of the full model (3 df) also containing the test SNP. The difference in prevalence of type 2 diabetes in carriers versus noncarriers of rare variants was analyzed using Fisher exact test. Power was calculated using the Power and Sample Size Program () and Quanto version 1.1.1 (http://hydra.usc.edu/gxe). Fixed-effects meta-analyses were performed using the metan command, combining summary estimates (log odds ratios and lower and upper CIs for each study), weighted using the inverse-variance method. An expectation-maximization algorithm was used to estimate haplotype frequencies, and GENEBPM software was used to cluster haplotypes by allelic make-up and risk of type 2 diabetes to obtain a Bayes' factor (BF) in favor of association (). […]

Pipeline specifications

Software tools Haploview, Tagger, GENEBPM
Application GWAS
Diseases Diabetes Mellitus, Diabetes Mellitus, Type 2, Wolfram Syndrome
Chemicals Nucleotides