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Haplotype-based quantitative trait mapping using a clustering algorithm.

Jing Li1, Yingyao Zhou, Robert C Elston

  • 1Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@case.edu

BMC Bioinformatics
|May 20, 2006
PubMed
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This study introduces a haplotype-based method for quantitative trait mapping in unrelated individuals, outperforming single-marker tests. Large sample sizes are crucial for fine mapping quantitative trait loci (QTLs).

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Association mapping using single-nucleotide polymorphism (SNP) markers is common for disease gene identification.
  • Quantitative traits are clinically significant, but mapping methods for them using unrelated individuals are less developed.
  • Previous work developed a haplotype-based method for complex disease association mapping.

Purpose of the Study:

  • To extend a haplotype-based association mapping method to quantitative traits in unrelated individuals.
  • To compare the performance of the new method against single-marker based approaches.

Main Methods:

  • Developed a non-parametric, haplotype-based association mapping method for quantitative traits.
  • Utilized permutation tests for statistical significance.

Related Experiment Videos

  • Integrated the method into the ANCOVA framework for covariate incorporation.
  • Validated the approach using simulated and real data.

    • Main Results:

    • The haplotype-based method demonstrated greater robustness compared to single SNP association (SSA) and Mann-Whitney U-test (MWU).
    • The algorithm, named HapMiner, was implemented in available software.
    • Effectiveness is influenced by recombination rates and SNP density.

    Conclusions:

    • Large sample sizes (>=500) are necessary for fine mapping quantitative trait loci (QTLs) and identifying quantitative trait nucleotides (QTNs).
    • HapMiner showed superior performance over SSA and MWU.
    • Haplotype-based methods may offer higher power than single-SNP methods, especially with tag SNPs.