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Discovering joint associations between disease and gene pairs with a novel similarity test.

Lin WY, Lee WC - BMC Genet. (2010)

Bottom Line: The simulation results show that our similarity test with the matching measure is more powerful than the Pearson's χ² test when the disease mutants were introduced at common haplotypes, but is less powerful when the disease mutants were introduced at rare haplotypes.Our similarity tests with the counting measures are more sensitive to marker informativity and linkage disequilibrium patterns, and thus are often inferior to the similarity test with the matching measure and the Pearson's χ² test.In detecting joint associations between disease and gene pairs, our similarity test is a complementary method to the Pearson's χ² test.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd., Taipei 100, Taiwan. wlin@uab.edu

ABSTRACT

Background: Genes in a functional pathway can have complex interactions. A gene might activate or suppress another gene, so it is of interest to test joint associations of gene pairs. To simultaneously detect the joint association between disease and two genes (or two chromosomal regions), we propose a new test with the use of genomic similarities. Our test is designed to detect epistasis in the absence of main effects, main effects in the absence of epistasis, or the presence of both main effects and epistasis.

Results: The simulation results show that our similarity test with the matching measure is more powerful than the Pearson's χ² test when the disease mutants were introduced at common haplotypes, but is less powerful when the disease mutants were introduced at rare haplotypes. Our similarity tests with the counting measures are more sensitive to marker informativity and linkage disequilibrium patterns, and thus are often inferior to the similarity test with the matching measure and the Pearson's χ² test.

Conclusions: In detecting joint associations between disease and gene pairs, our similarity test is a complementary method to the Pearson's χ² test.

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Related in: MedlinePlus

Type-I error rates under different nominal significance levels. The x-axis is nominal significance level, and the y-axis is type-I error rate.
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Figure 1: Type-I error rates under different nominal significance levels. The x-axis is nominal significance level, and the y-axis is type-I error rate.

Mentions: In Additional file 1, Model 0 (disease status independent of the composite genotypes) was used to evaluate the type-I error rates. This model demonstrates our hypothesis: no main effects and no interactions. In this model, the penetrance of each composite genotype was set to be 0.05. The sample size was set at 200 subjects, of which half were cases and half were controls. Figure 1 presents the type-I error rates under different nominal significance levels (α). For α smaller than 0.2, the type-I error rates of all the tests corresponded to the nominal significance levels (α), suggesting the validity of these tests. (For α larger than 0.2, the type-I error rates of HapForest failed to match with the nominal significance levels. HapForest reported P values as 1.0 when the association signal was not strong. However, this makes no influence on our following discussions because α is usually set at a small value.)


Discovering joint associations between disease and gene pairs with a novel similarity test.

Lin WY, Lee WC - BMC Genet. (2010)

Type-I error rates under different nominal significance levels. The x-axis is nominal significance level, and the y-axis is type-I error rate.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2959050&req=5

Figure 1: Type-I error rates under different nominal significance levels. The x-axis is nominal significance level, and the y-axis is type-I error rate.
Mentions: In Additional file 1, Model 0 (disease status independent of the composite genotypes) was used to evaluate the type-I error rates. This model demonstrates our hypothesis: no main effects and no interactions. In this model, the penetrance of each composite genotype was set to be 0.05. The sample size was set at 200 subjects, of which half were cases and half were controls. Figure 1 presents the type-I error rates under different nominal significance levels (α). For α smaller than 0.2, the type-I error rates of all the tests corresponded to the nominal significance levels (α), suggesting the validity of these tests. (For α larger than 0.2, the type-I error rates of HapForest failed to match with the nominal significance levels. HapForest reported P values as 1.0 when the association signal was not strong. However, this makes no influence on our following discussions because α is usually set at a small value.)

Bottom Line: The simulation results show that our similarity test with the matching measure is more powerful than the Pearson's χ² test when the disease mutants were introduced at common haplotypes, but is less powerful when the disease mutants were introduced at rare haplotypes.Our similarity tests with the counting measures are more sensitive to marker informativity and linkage disequilibrium patterns, and thus are often inferior to the similarity test with the matching measure and the Pearson's χ² test.In detecting joint associations between disease and gene pairs, our similarity test is a complementary method to the Pearson's χ² test.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd., Taipei 100, Taiwan. wlin@uab.edu

ABSTRACT

Background: Genes in a functional pathway can have complex interactions. A gene might activate or suppress another gene, so it is of interest to test joint associations of gene pairs. To simultaneously detect the joint association between disease and two genes (or two chromosomal regions), we propose a new test with the use of genomic similarities. Our test is designed to detect epistasis in the absence of main effects, main effects in the absence of epistasis, or the presence of both main effects and epistasis.

Results: The simulation results show that our similarity test with the matching measure is more powerful than the Pearson's χ² test when the disease mutants were introduced at common haplotypes, but is less powerful when the disease mutants were introduced at rare haplotypes. Our similarity tests with the counting measures are more sensitive to marker informativity and linkage disequilibrium patterns, and thus are often inferior to the similarity test with the matching measure and the Pearson's χ² test.

Conclusions: In detecting joint associations between disease and gene pairs, our similarity test is a complementary method to the Pearson's χ² test.

Show MeSH
Related in: MedlinePlus