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Related Experiment Videos

Generating samples for association studies based on HapMap data.

Jing Li1, Yixuan Chen

  • 1Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@case.edu

BMC Bioinformatics
|January 26, 2008
PubMed
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A new program, gs, generates large-scale genetic and phenotypic data using real data patterns. This tool aids in evaluating methods for haplotype inference, tag SNP selection, and association studies, incorporating complex disease models.

Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • The HapMap project spurred development of computational tools for genetic analyses like haplotype inference and genome-wide association studies (GWAS).
  • Simulated data are crucial for evaluating these tools, with empirical data perturbations offering realistic properties.
  • A gap existed for publicly available tools generating large-scale simulated variation data informed by HapMap project knowledge.

Purpose of the Study:

  • To develop a computational tool for generating large-scale simulated genetic and phenotypic variation data.
  • To provide a resource for evaluating methods in haplotype inference, tag SNP selection, and association studies.
  • To incorporate realistic linkage disequilibrium (LD) patterns and complex genetic models.

Main Methods:

Related Experiment Videos

  • Developed the 'gs' computer program to generate large sample datasets based on real data.
  • Implemented two approaches for generating SNP haplotype/genotype data: using sample haplotype pairs or haplotype block structures.
  • Incorporated quantitative and qualitative traits, with user-specified disease models (single-locus and two-locus with epistasis) and penetrance tables.

Main Results:

  • The 'gs' program efficiently generates large-scale genetic and phenotypic data with realistic local LD patterns.
  • The tool supports various genetic models, including complex two-locus disease models with epistasis.
  • Phenotypes can be simulated based on user-defined disease models or quantitative trait nucleotide effects.

Conclusions:

  • The 'gs' program effectively generates large-scale genetic and phenotypic variation data for evaluating new computational approaches.
  • The tool is freely available, facilitating research in genetic analysis and association studies.
  • It addresses the need for realistic simulated data informed by human population genetics.