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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

Powers of the eight tests, stratified by the property of disease mutants introduced at rare/common haplotypes. The x-axis is significance level, and the y-axis is power. The top row is for disease mutants introduced at rare haplotypes; the bottom row, at common haplotypes. The numbers shown in the parentheses are the numbers of repetitions summed from all the datasets with disease mutants introduced at rare/common haplotypes.
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Figure 2: Powers of the eight tests, stratified by the property of disease mutants introduced at rare/common haplotypes. The x-axis is significance level, and the y-axis is power. The top row is for disease mutants introduced at rare haplotypes; the bottom row, at common haplotypes. The numbers shown in the parentheses are the numbers of repetitions summed from all the datasets with disease mutants introduced at rare/common haplotypes.

Mentions: Figure 2 presents the powers of the eight tests when α is set to be smaller than 0.1, stratified by the property of disease mutants introduced at rare/common haplotypes. For most models, the two most powerful tests are our similarity method with the matching measure (MATCH) and the Pearson's χ2 test. MATCH is more powerful than the Pearson's χ2 test when the disease mutants were introduced at common haplotypes. Conversely, MATCH is less powerful than the Pearson's χ2 test when the disease mutants were introduced at rare haplotypes. For Model 1, haplotype-perspective methods provide no power, while diplotype-perspective methods (IBS and W-IBS) and the test for SNP × SNP epistasis by using case-only data (CS) have better performances.


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

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

Powers of the eight tests, stratified by the property of disease mutants introduced at rare/common haplotypes. The x-axis is significance level, and the y-axis is power. The top row is for disease mutants introduced at rare haplotypes; the bottom row, at common haplotypes. The numbers shown in the parentheses are the numbers of repetitions summed from all the datasets with disease mutants introduced at rare/common haplotypes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Powers of the eight tests, stratified by the property of disease mutants introduced at rare/common haplotypes. The x-axis is significance level, and the y-axis is power. The top row is for disease mutants introduced at rare haplotypes; the bottom row, at common haplotypes. The numbers shown in the parentheses are the numbers of repetitions summed from all the datasets with disease mutants introduced at rare/common haplotypes.
Mentions: Figure 2 presents the powers of the eight tests when α is set to be smaller than 0.1, stratified by the property of disease mutants introduced at rare/common haplotypes. For most models, the two most powerful tests are our similarity method with the matching measure (MATCH) and the Pearson's χ2 test. MATCH is more powerful than the Pearson's χ2 test when the disease mutants were introduced at common haplotypes. Conversely, MATCH is less powerful than the Pearson's χ2 test when the disease mutants were introduced at rare haplotypes. For Model 1, haplotype-perspective methods provide no power, while diplotype-perspective methods (IBS and W-IBS) and the test for SNP × SNP epistasis by using case-only data (CS) have better performances.

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