Abstract
Simulated Genetic Analysis Workshop14 data were analyzed by jointly testing linkage and association and by accounting for epistasis using a candidate gene approach. Our group was unblinded to the "answers." The 48 single-nucleotide polymorphisms (SNPs) within the six disease loci were analyzed in addition to five SNPs from each of two non-disease-related loci. Affected sib-parent data was extracted from the first 10 replicates for populations Aipotu, Kaarangar, and Danacaa, and analyzed separately for each replicate. We developed a likelihood for testing association and/or linkage using data from affected sib pairs and their parents. Identical-by-descent (IBD) allele sharing between sibs was explicitly modeled using a conditional logistic regression approach and incorporating a covariate that represents expected IBD allele sharing given the genotypes of the sibs and their parents. Interactions were accounted for by performing likelihood ratio tests in stages determined by the highest order interaction term in the model. In the first stage, main effects were tested independently, and in subsequent stages, multilocus effects were tested conditional on significant marginal effects. A reduction in the number of tests performed was achieved by prescreening gene combinations with a goodness-of-fit chi square statistic that depended on mating-type frequencies. SNP-specific joint effects of linkage and association were identified for loci D1, D2, D3, and D4 in multiple replicates. The strongest effect was for SNP B03T3056, which had a median p-value of 1.98 × 10-34. No two- or three-locus effects were found in more than one replicate.
Highlights
The need to account for gene × gene interactions in the search for susceptibility genes for complex diseases such as cancer, diabetes, hypertension, and obesity has been widely suggested
Significant tests for stage 1 marginal single-nucleotide polymorphisms (SNPs) effects demonstrate that joint effects of linkage and association were detected in the four disease regions, D1–D4, despite heterogeneity in the definition of Kofendrerd Personality Disorder (KPD) between populations (Table 1)
The lack of linkage disequilibrium (LD) in region D1 explains the absence of an association signal but not the lack of a linkage effect (Figure 2), which may be attributed to the low frequency of the disease allele (0.015 from Genetic Analysis Workshop 14 (GAW14) Answers)
Summary
The need to account for gene × gene interactions in the search for susceptibility genes for complex diseases such as cancer, diabetes, hypertension, and obesity has been widely suggested. In the context of modeling association in unrelated individuals, Devlin et al [1] proposed a testing strategy that conserves power by jointly testing main effects together with interactions and by adjusting for multiple tests by controlling false discovery rates (FDR). This method suffers from interpretability difficulties because a positive test of a set of (page number not for citation purposes). This strategy incorporates a screening statistic that reduces the number of marker combinations that need to be tested for multilocus effects
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