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An entropy-based measure for QTL mapping using extreme samples of population
Yu-Mei Li1, Yang Xiang, Zhen-Qiu Sun
1School of Public Health, Central South University, Changsha, PR China. liym74@yahoo.com
Human Heredity
|October 16, 2007
Summary
This study introduces a new method for quantitative trait locus (QTL) mapping using linkage disequilibrium (LD) and Shannon entropy. The novel measure effectively identifies QTLs by amplifying marker allele frequency differences in extreme population samples.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genomics
Background:
- Quantitative trait locus (QTL) mapping aids in identifying genes influencing complex traits.
- Selective genotyping enhances QTL mapping by analyzing extreme population samples.
- Shannon entropy can amplify marker allele frequency differences.
Purpose of the Study:
- To develop a novel measure for linkage disequilibrium (LD) between markers and single QTLs.
- To utilize Shannon entropy and conditional entropy for improved QTL mapping.
- To assess the performance of the new LD measure in identifying QTLs.
Main Methods:
- Developed a novel LD measure based on comparing entropy and conditional entropy in extreme population samples.
- Investigated mapping performance using analytic and simulation scenarios for a single QTL linked to a single marker.
- Conducted a simulation study using haplotype frequencies of 10 SNPs from angiotensin-I converting enzyme (ACE) genes.
Main Results:
- The novel LD measure demonstrated reasonable performance in mapping a single QTL linked to a single marker.
- The method effectively amplifies differences in marker allele frequencies in extreme samples.
- Simulation results validated the utility of the entropy-based LD measure.
Conclusions:
- The proposed entropy-based LD measure is a viable tool for quantitative trait locus (QTL) mapping.
- This approach enhances the probability of detecting QTLs by leveraging extreme sample differences.
- The method shows promise for genetic studies, including analysis of genes like ACE.
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