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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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[gwasfilter: an R script to filter genome-wide association study].

S C Yang1, C Y Li2, Y Z Hu1

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.

Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi
|November 24, 2021
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Summary

A new R script, gwasfilter.R, efficiently filters genome-wide association studies (GWASs) from the GWAS Catalog. This tool allows flexible filtering based on replication, sample size, and ethnicity, making GWAS data more accessible.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWASs) are crucial for identifying genetic variants associated with traits.
  • The GWAS Catalog is a valuable resource but requires efficient filtering methods for large datasets.
  • Manual filtering of GWAS data can be time-consuming and prone to inconsistencies.

Purpose of the Study:

  • To develop an automated R script for filtering GWAS data from the GWAS Catalog.
  • To enhance the efficiency and accuracy of selecting relevant GWAS studies.
  • To provide a user-friendly tool for researchers working with large-scale genetic association data.

Main Methods:

  • Established selection principles for GWAS filtering based on prior research.
  • Abstracted manual filtering processes into standardized algorithms.
  • Developed and rigorously tested the R script 'gwasfilter.R' with multiple functions.

Main Results:

  • The gwasfilter.R script filters GWASs in six distinct steps.
  • Key filtering criteria include study replication, sample size, and population ethnicity.
  • The script processes single-trait GWAS data in under one second, demonstrating high efficiency.

Conclusions:

  • The gwasfilter.R script offers a user-friendly, efficient, and standardized method for flexible GWAS filtering.
  • This tool streamlines the process of accessing and analyzing GWAS data.
  • The source code is publicly available for community use and further development.