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Related Experiment Videos

Comparative analysis of haplotype association mapping algorithms.

Phillip McClurg1, Mathew T Pletcher, Tim Wiltshire

  • 1Genomics Institute of the Novartis Research Foundation, San Diego, USA. pmcclurg@gnf.org

BMC Bioinformatics
|February 10, 2006
PubMed
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This study introduces a haplotype association mapping method for identifying genetic causes of quantitative traits in mice. The new approach offers advantages over traditional methods by utilizing diverse inbred strains and dense genotyping data.

Area of Science:

  • Statistical genetics
  • Genomics
  • Bioinformatics

Background:

  • Mapping quantitative trait loci (QTL) is challenging.
  • Traditional methods require extensive resources and are phenotype-specific.
  • Haplotype association mapping offers a phenotype-independent alternative using diverse mouse strains.

Purpose of the Study:

  • To compare variations of a marker association method for QTL mapping.
  • To evaluate the utility of inferred haplotypes and different statistical approaches.
  • To apply the method to existing mouse phenotype data.

Main Methods:

  • Utilized dense genotyping data from a diverse panel of inbred mouse strains.
  • Employed marker association algorithms and inferred haplotypes from adjacent SNPs.

Related Experiment Videos

  • Applied parametric and nonparametric statistics with control for multiple testing error.

    • Main Results:

    • Nonparametric methods showed slight advantages in test cases.
    • Inferred local haplotype structure using multi-SNP windows is crucial for QTL mapping.
    • Sensitive methods for controlling family-wise error are necessary due to small gene effects.

    Conclusions:

    • Inbred strains offer advantages for QTL mapping over traditional F2 crosses.
    • Algorithmic choices require careful consideration of theoretical and practical factors.
    • Further evaluation is needed as more genetic data for complex diseases becomes available.