Haplotype-sharing analysis for alcohol dependence based on quantitative traits and the Mantel statistic

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Haplotype-based methods are valuable for localizing disease-related genes.
• Shared haplotype lengths can serve as indicators for genetic linkage analysis.
• Quantitative traits require robust statistical approaches for genetic mapping.

Purpose of the Study:

• To develop a novel linkage-based haplotype-sharing method for quantitative traits.
• To apply the Mantel statistic and permutation testing for robust significance evaluation.
• To identify chromosomal regions associated with quantitative traits using genome-wide data.

Main Methods:

• Utilized Mantel statistics, a class closely related to weighted pair-wise correlation.
• Implemented a permutation test to assess the statistical significance of findings.
• Applied the method to genome-wide autosomal data from the Collaborative Study on the Genetics of Alcoholism (COGA).

Main Results:

• Identified four chromosomal regions (4, 8, 16, and 20) with p-values < 0.005.
• Observed a highly significant locus on chromosome 16 (tsc0520638 at 72.8 cM) with a minimum p-value < 0.0001.
• Validated three of the four identified regions (chromosomes 4, 16, and 20) against previous Genetic Analysis Workshop 11 findings.

Conclusions:

• The proposed haplotype-sharing approach effectively identifies quantitative trait loci.
• The Mantel statistic combined with permutation testing provides a reliable method for genetic mapping.
• The findings highlight specific chromosomal regions potentially harboring genes influencing quantitative traits.