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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
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A guide to genome-wide association analysis and post-analytic interrogation.
Eric Reed1, Sara Nunez1, David Kulp2
1Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA, U.S.A.
Statistics in Medicine
|September 8, 2015
Summary
This tutorial provides a guide for genome-wide association analysis using R and Bioconductor. It demonstrates methods for analyzing coronary artery disease data and visualizing genetic findings.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genomics
Background:
- Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with complex diseases.
- Implementing GWAS involves complex analytical steps and specialized software tools.
- Access to user-friendly resources and open-source software facilitates GWAS implementation.
Purpose of the Study:
- To provide a comprehensive tutorial for performing genome-wide association analysis.
- To present practical software tools for analysis, visualization, and interpretation of GWAS data.
- To illustrate the application of these methods using real-world coronary artery disease data.
Main Methods:
- Utilizing the R statistical computing environment and Bioconductor packages for bioinformatics analysis.
- Applying genome-wide association analysis to a dataset of 1401 individuals and 861,473 single nucleotide polymorphisms (SNPs).
- Employing the UCSC Genome Browser for post-analytic visualization and interrogation of genetic findings.
Main Results:
- Demonstration of a complete workflow for genome-wide association analysis.
- Successful identification and visualization of genetic associations within the coronary artery disease dataset.
- Public availability of all data, code, and supplementary resources for reproducibility and further learning.
Conclusions:
- The tutorial offers a practical and accessible approach to conducting genome-wide association studies.
- Open-source tools and publicly available data significantly enhance the accessibility and reproducibility of genetic research.
- This resource empowers researchers to explore genetic underpinnings of diseases like coronary artery disease.
Keywords:
BioconductorHardy-Weinberg equilibrium (HWE)IBDManhattan plotQ-Q plotR codeSNP filteringUCSC Genome Browserancestrycall rategenome-wide association (GWA) studyheatmapheterozygosityimputationlambda statisticminor allele frequency (MAF)parallel processingprincipal component analysis (PCA)regional association plotrelatednesssample filteringstatistical genomicssubstructuretutorial
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