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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
4.9K
Automated quality control for genome wide association studies
Sally R Ellingson1, David W Fardo2
1Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY, 40536, USA; Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, KY, 40536, USA.
F1000Research
|September 17, 2016
Summary
This study details essential steps for assessing and controlling genome-wide association study (GWAS) data quality. It provides scripts and directions to ensure high-quality genotype data for large-scale genetic research.
Area of Science:
- Genetics
- Bioinformatics
- Genomics
Background:
- Genome-Wide Association Studies (GWAS) are crucial for identifying genetic variants associated with diseases.
- Data quality is paramount in GWAS due to diverse study designs and potential genotyping errors.
- Ensuring high-quality genotype data is essential for accurate genetic association findings.
Purpose of the Study:
- To provide a comprehensive guide for assessing and controlling data quality in GWAS.
- To offer practical scripts and directions for researchers to implement data quality control measures.
- To facilitate the standardization of data processing across multiple GWAS projects.
Main Methods:
- Detailed description of data assessment steps for genotype information.
- Methods for controlling potential genotyping errors and inconsistencies.
- Utilization of scripts for efficient data quality checks and management.
Main Results:
- A standardized workflow for GWAS data quality assessment and control.
- Provided scripts that can be readily applied to large-scale genetic datasets.
- Demonstrated the importance of rigorous data quality measures for reliable GWAS results.
Conclusions:
- Implementing the outlined steps ensures high-quality genotype data for GWAS.
- The provided resources empower researchers to perform robust data quality control.
- Accurate GWAS findings depend on meticulous data assessment and control procedures.
