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Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies
K Miclaus1, M Chierici, C Lambert
1SAS Institute, Cary, NC 27513, USA. Kelci.Miclaus@sas.com
The Pharmacogenomics Journal
|August 3, 2010
Summary
Genotyping algorithm choice significantly impacts genome-wide association study (GWAS) results. Computational batch effects and array inconsistencies introduce variability, affecting single-nucleotide polymorphism association findings.
Area of Science:
- Genomics
- Bioinformatics
- Statistical Genetics
Background:
- The MicroArray Quality Consortium (MAQC) aims to enhance genomic experiment quality.
- The Genome-Wide Association Working Group (GWAWG) focuses on genome-wide association studies (GWASs).
- Assessing genotype calling algorithm variability is crucial for reliable GWAS.
Purpose of the Study:
- To evaluate genotype call variability across different algorithms.
- To determine the impact of these algorithms on coronary artery disease association analysis.
- To identify sources of systematic errors in GWAS data.
Main Methods:
- Utilized coronary artery disease data from WTCCC and University of Ottawa Heart Institute.
- Compared genotype calls from algorithms including BRLMM, CRLMM, and CHIAMO on Affymetrix 500K arrays.
- Analyzed data using HapMap samples to assess array and algorithm inconsistencies.
Main Results:
- Different genotyping algorithms introduce significant variability in GWAS results.
- Sample processing and computational batch effects influence genotype discordance.
- Inconsistencies between Affymetrix arrays and calling algorithms lead to genotyping errors.
Conclusions:
- The selection of genotyping algorithms critically affects GWAS outcomes.
- Computational batch effects and data processing methods propagate errors.
- Standardization of genotype calling is necessary for accurate GWAS interpretation.
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