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Related Experiment Videos

Tag SNP selection for association studies.

Daniel O Stram1

  • 1Division of Biostatistics and Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA. stram@usc.edu

Genetic Epidemiology
|September 17, 2004
PubMed
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This study presents methods for selecting informative single nucleotide polymorphisms (SNPs) for genetic analysis. It evaluates how SNP density impacts tag SNP selection and proposes a novel approach for assessing study power in disease risk detection.

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Selecting informative single nucleotide polymorphisms (SNPs) is crucial for genetic association studies.
  • Existing methods for tag SNP selection depend on SNP density and haplotype predictability.
  • Evaluating the power of case-control studies for disease risk detection requires robust analytical approaches.

Purpose of the Study:

  • To describe current methods for selecting informative single nucleotide polymorphisms (SNPs).
  • To investigate the impact of SNP marker density on tag SNP selection performance.
  • To propose a quasi-empirical approach for evaluating the power of case-control studies in haplotype-based analyses.

Main Methods:

  • Discussed optimal SNP selection criteria, maximizing predictability of unmeasured SNPs or SNP haplotypes.

Related Experiment Videos

  • Analyzed the dependence of tag SNP selection on SNP marker density for haplotype discovery.
  • Proposed a quasi-empirical method for power evaluation using SNP genotype and haplotype frequencies from a discovery panel.

    • Main Results:

    • Demonstrated the influence of SNP density on the performance of tag SNP selection methods.
    • Illustrated a quasi-empirical approach for power evaluation using HapMap ENCODE data.
    • Showcased the application of correlation analysis for common SNPs as potential causal variants.

    Conclusions:

    • The proposed quasi-empirical approach provides a framework for evaluating the power of large genetic studies.
    • SNP density is a critical factor influencing the effectiveness of tag SNP selection strategies.
    • This work contributes to optimizing SNP selection for genetic association studies and disease risk assessment.