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Testing for candidate gene linkage disequilibrium using a dense array of single nucleotide polymorphisms in
1Graduate Institute of Epidemiology, College of Public Health, National Taiwan University, Taipei, Taiwan. wenchung@ha.mc.ntu.edu.tw
Epidemiology (Cambridge, Mass.)
|August 23, 2002
Summary
A new method, the Adaptive Principal Component Test (APRICOT), offers a powerful approach for genetic studies of complex diseases. It efficiently analyzes single nucleotide polymorphisms, outperforming traditional methods like Bonferroni correction.
Area of Science:
- Genetics
- Epidemiology
- Bioinformatics
Background:
- Genetic studies of complex human diseases increasingly use the epidemiologic association paradigm, specifically the transmission/disequilibrium test (TDT).
- Genomic advancements yield numerous single nucleotide polymorphisms (SNPs) at close physical distances, posing challenges for TDT analysis.
- Bonferroni correction for multiple TDTs in candidate gene studies leads to conservative testing and reduced statistical power.
Purpose of the Study:
- To introduce a novel statistical method, the Adaptive Principal Component Test (APRICOT), for analyzing linkage disequilibrium in case-parent studies.
- To address the power loss associated with Bonferroni correction when analyzing multiple SNPs in genetic association studies.
- To provide a robust and efficient tool for candidate gene testing using SNP data.
Main Methods:
- The proposed Adaptive Principal Component Test (APRICOT) is a nonparametric method that does not require haplotype information.
- APRICOT is designed to be robust to population history and structure.
- The method simplifies the calculation of test statistics and significance levels.
Main Results:
- Monte Carlo simulations demonstrate that APRICOT maintains nominal significance levels under the null hypothesis of no linkage disequilibrium.
- APRICOT performs well even in complex scenarios involving multiple ancestral haplotypes and structured populations.
- The Adaptive Principal Component Test shows a substantial power advantage over the conventional Bonferroni correction approach.
Conclusions:
- APRICOT is a promising and powerful method for genetic association studies, particularly for candidate gene testing using SNPs.
- The method overcomes limitations of traditional approaches by offering increased power and robustness.
- APRICOT provides a straightforward and effective alternative for analyzing complex genetic disease data.