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Fine mapping functional sites or regions from case-control data using haplotypes of multiple linked SNPs
Rong Cheng1, Jennie Z Ma, Robert C Elston
1Program in Genomics and Bioinformatics on Drug Addiction, Department of Psychiatry, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
Annals of Human Genetics
|January 11, 2005
Summary
This study introduces an enhanced algorithm for case-control association analysis, improving the detection of functional single nucleotide polymorphisms (SNPs) using linkage disequilibrium (LD) mapping. The method efficiently scans multiple SNPs for genetic association studies.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Previous work established an algorithm for family-based association designs using single nucleotide polymorphisms (SNPs).
- Extending linkage disequilibrium (LD) mapping methods to case-control designs presents unique analytical challenges.
Purpose of the Study:
- To adapt and validate a novel algorithm for analyzing tightly linked SNPs in a case-control association study design.
- To develop a computationally efficient tool for identifying functional genetic variants through LD mapping.
Main Methods:
- Utilized the expectation maximization (EM) algorithm to estimate haplotype frequencies for multiple linked SNPs.
- Constructed a contingency table statistic (S) for LD analysis, with empirical p-values derived from randomized permutations of the maximum statistic (S*).
- Implemented a flexible computer program allowing for variable haplotype window widths in association analysis.
Main Results:
- The algorithm demonstrated high sensitivity in detecting simulated and real functional SNPs.
- The developed program effectively identifies associated loci by exploring all possible haplotype widths within a defined maximum window.
- The method proved powerful for both regional and genome-wide association scanning.
Conclusions:
- The extended algorithm provides a robust and flexible approach for case-control association studies.
- This method enhances the power to detect functional SNPs, contributing to genetic mapping and disease association studies.
- The computational tool facilitates efficient LD-based association analysis across various genomic scales.