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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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HapMap filter 1.0: a tool to preprocess the HapMap genotypic data for association studies.

Wei Zhang1, Shiwei Duan, M Eileen Dolan

  • 1Section of Hematology/Oncology, Department of Medicine, Cancer Research Center, The University of Chicago, IL 60637, USA.

Bioinformation
|August 8, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a user-friendly bioinformatics program for preprocessing International HapMap Project genotypic data. The software filters single nucleotide polymorphisms (SNPs) based on minor allele frequencies and genotyping rates, optimizing data for association studies.

Keywords:
HapMapgenotypegenotyping rateminor allele frequencysingle nucleotide polymorphism

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Area of Science:

  • Genetics
  • Bioinformatics

Background:

  • The International HapMap Project provides genotypic data on single nucleotide polymorphisms (SNPs) crucial for association studies.
  • Preprocessing HapMap data is essential to reduce computation time and manage the multiple testing problem in genetic association studies.
  • Existing tools for HapMap data optimization are limited, necessitating a more efficient and user-friendly solution.

Purpose of the Study:

  • To develop a fast and user-friendly bioinformatics program for preprocessing International HapMap Project genotypic data.
  • To enable filtering of SNPs based on minor allele frequencies and genotyping rates.
  • To facilitate the preparation of HapMap data for association studies by allowing specification of cell lines and filtering criteria.

Main Methods:

  • Developed a flexible and straightforward bioinformatics program.
  • The software is written in C++ for Microsoft Windows.
  • Implemented filtering based on minor allele frequencies and genotyping rates, with options to specify cell lines.

Main Results:

  • The program efficiently filters HapMap genotypic data.
  • It allows users to select specific cell lines for analysis.
  • Provides optimized datasets for downstream association studies by removing less informative SNPs.

Conclusions:

  • The developed software offers a beneficial tool for association studies by simplifying HapMap data preparation.
  • It addresses the need for a fast and user-friendly method to filter genotypic data.
  • The program enhances the usability of HapMap data for identifying genetic determinants of phenotypic variations.