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Related Experiment Videos

Positional cloning by linkage disequilibrium.

Nikolas Maniatis1, Andrew Collins, Jane Gibson

  • 1Human Genetics Division, University of Southampton, Southampton SO16 6YD, United Kingdom. N.Maniatis@soton.ac.uk

American Journal of Human Genetics
|March 30, 2004
PubMed
Summary

This study introduces a new method for positional cloning using linkage disequilibrium (LD) maps. The LD unit (LDU) map offers greater power for localizing disease loci compared to physical maps (kb), improving SNP-based positional cloning efficiency.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Metric linkage disequilibrium (LD) maps using LD units (LDU) have been developed.
  • Positional cloning aims to identify disease-associated genes using genetic markers.

Purpose of the Study:

  • To present a multiple pairwise method for positional cloning by LD within a composite likelihood framework.
  • To investigate the operating characteristics of physical maps (kb) and LDU maps for positional cloning.
  • To evaluate the efficiency and error rates of different mapping units.

Main Methods:

  • Developed a multiple pairwise method for positional cloning using LD and a composite likelihood framework.
  • Examined false-negative (Type II) and false-positive (Type I) errors by simulating causal single-nucleotide polymorphisms (SNPs).

Related Experiment Videos

  • Adapted the Malecot model to associate SNP allelic counts with pseudophenotypes, incorporating gene location parameters.

    • Main Results:

    • The LDU map demonstrated greater power for localizing disease loci compared to the kb map.
    • The efficiency of kb maps relative to LDU maps varied significantly (0.36-0.87, mean 0.62).
    • Type I errors were consistent with Wald's likelihood theorem for both metrics; smaller sample sizes with higher SNP density yielded less error.

    Conclusions:

    • The LDU map is more effective than physical maps for positional cloning by LD.
    • The developed methods are robust to a large number of markers in candidate regions.
    • Block definition may be less critical for mapping causal SNPs than anticipated, with precision similar in blocks and steps.