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Statistical multilocus methods for disequilibrium analysis in complex traits.

J Ott1, J Hoh

  • 1Rockefeller University, New York, New York, USA. ott@rockefeller.edu

Human Mutation
|April 11, 2001
PubMed
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This study introduces a two-stage method for analyzing complex traits using single nucleotide polymorphism (SNP) markers. It efficiently selects key genetic markers for disease association studies when data is limited.

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Complex traits are influenced by multiple genes, necessitating genetic association studies.
  • Genome-wide association studies (GWAS) generate vast amounts of single nucleotide polymorphism (SNP) data.
  • High dimensionality of SNP data (more markers than samples) hinders joint analysis and interaction modeling.

Purpose of the Study:

  • To develop and present a computationally feasible two-stage approach for analyzing SNP data in complex trait association studies.
  • To address the challenge of analyzing a large number of genetic markers relative to the number of observations.
  • To enable the modeling of main and interaction effects of selected genetic markers on disease susceptibility.

Main Methods:

  • A two-stage strategy is proposed: first, select a subset of relevant SNP markers.

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  • Second, model the effects of these selected markers on disease status.
  • Two distinct marker selection procedures are demonstrated, followed by statistical modeling of genetic effects.

    • Main Results:

    • The proposed two-stage approach facilitates the analysis of complex traits with a large number of SNP markers.
    • Demonstrated application on a dataset with 89 SNPs, showcasing its utility compared to genome-wide screens.
    • The method allows for the effective modeling of both individual marker effects and their interactions.

    Conclusions:

    • The presented two-stage method offers a practical solution for genetic association studies with high-dimensional SNP data.
    • This approach enhances the feasibility of identifying susceptibility genes for complex traits.
    • The methodology is valuable for researchers investigating the genetic architecture of complex diseases.