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SNP subset selection for genetic association studies.

M C Byng1, J C Whittaker, A P Cuthbert

  • 1Division of Medical and Molecular Genetics, Guy's, King's and St. Thomas' School of Medicine, London, UK.

Annals of Human Genetics
|December 4, 2003
PubMed
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Selecting a subset of single nucleotide polymorphisms (SNPs) significantly reduces genotyping needs in disease association studies. This study presents methods for optimal SNP subset selection, balancing genotyping costs and statistical power for identifying disease susceptibility genes.

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genetics

Background:

  • Association studies for disease susceptibility genes utilize high-density single nucleotide polymorphisms (SNPs) within candidate genes.
  • Linkage disequilibrium (LD) between SNPs indicates that not all identified SNPs require genotyping, suggesting potential for SNP subset selection.

Purpose of the Study:

  • To develop and evaluate methods for selecting optimal SNP subsets to reduce genotyping costs in association studies.
  • To establish criteria for determining the overall SNP subset size based on cost-benefit analysis.

Main Methods:

  • Application of clustering algorithms to pairwise LD measures for SNP subset identification.
  • Determination of SNP subsets based on the proportion of haplotypes identified.

Related Experiment Videos

  • Utilizing power calculations for haplotype-based and logistic regression analyses to rank SNP subsets.
  • Incorporating power calculations into a decision analysis framework for selecting optimal subset size.

    • Main Results:

    • Developed several approaches for SNP subset selection, substantially reducing the number of SNPs to be genotyped.
    • Demonstrated that clustering algorithms and haplotype proportion methods can identify effective SNP subsets.
    • Showcased power calculations as a means to select SNP subsets and determine optimal size based on genotyping costs and association benefits.
    • Illustrated the methods using eleven SNPs in the MMP2 gene.

    Conclusions:

    • SNP subset selection is a viable strategy to enhance the efficiency of genetic association studies.
    • The developed methods provide a framework for optimizing SNP selection, balancing cost and statistical power.
    • Decision analysis incorporating power calculations offers criteria for choosing the most cost-effective SNP subset size.