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Efficient strategies for genome scanning using maximum-likelihood affected-sib-pair analysis

P Holmans1, N Craddock

  • 1Department of Psychological Medicine, University of Wales College of Medicine, Cardiff, United Kingdom.

American Journal of Human Genetics
|March 1, 1997
PubMed
Summary
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Optimizing genetic studies for complex disorders involves combining grid tightening and sample splitting. Typing half the affected sibling pair sample with a coarse marker grid in the initial stage is an efficient strategy.

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Detecting genetic linkage in families with affected sibling pairs is crucial for identifying susceptibility genes in complex disorders.
  • Optimizing genome scan efficiency involves maximizing statistical power while minimizing genotyping costs and Type I error rates.

Purpose of the Study:

  • To investigate the relative efficiency of two-stage strategies combining grid tightening and sample splitting for affected-sib-pair genome scans.
  • To identify the optimal strategy for maximizing power and minimizing genotyping in linkage studies for complex genetic disorders.

Main Methods:

  • Computer simulations were used to evaluate various two-stage strategies.
  • Strategies combined grid tightening (using progressively finer marker grids) and sample splitting (typing a subset of samples initially).

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Main Results:

  • The optimal strategy incorporates both grid tightening and sample splitting.
  • Typing half of the affected sibling pair sample with a coarse marker grid in the screening stage proved efficient across various conditions.
  • If Hardy-Weinberg equilibrium holds, omitting parents in the screening stage is most efficient; otherwise, including them in the final analysis mitigates increased genotyping needs.

Conclusions:

  • A combined approach of grid tightening and sample splitting offers the most efficient strategy for affected-sib-pair genome scans.
  • The proposed two-stage strategy balances statistical power with genotyping resource allocation for complex genetic disorder research.