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Look who is calling: a comparison of genotype calling algorithms
Maren Vens1, Arne Schillert, Inke R König
1Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, 23538 Lübeck, Germany. maren.vens@imbs.uni-luebeck.de.
BMC Proceedings
|December 19, 2009
Summary
This study compared genotype calling algorithms (GCAs) for genome-wide association studies. JAPL and Chiamo++ are recommended for retaining subjects and single-nucleotide polymorphisms (SNPs), respectively.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genomics
Background:
- Genome-wide association studies (GWAS) require accurate genotype calling for robust statistical analysis.
- Various genotype calling algorithms (GCAs) exist, each with potential impacts on data quality and sample/marker retention.
Purpose of the Study:
- To compare the performance of three GCAs: Bayesian robust linear modeling using Mahalanobis distance (BRLMM), Chiamo++, and JAPL.
- To evaluate their effectiveness in standard quality control (sQC) and identify potential errors in genotype data.
Main Methods:
- Utilized autosomal single-nucleotide polymorphisms (SNPs) from the Framingham Heart Study 500k Affymetrix Array data.
- Applied standard quality control (sQC) procedures to genotypes called by BRLMM, Chiamo++, and JAPL.
- Assessed genotype concordance between algorithms to detect errors.
Main Results:
- JAPL retained the most individuals for analysis.
- Chiamo++ achieved the highest number of SNPs passing sQC, while BRLMM had the lowest.
- All three GCAs met sQC criteria for 79% of SNPs, but at least one GCA failed sQC for 18% of SNPs.
- Comparison revealed previously undetected strand coding errors.
Conclusions:
- JAPL is recommended when maximizing subject retention is the priority.
- Chiamo++ is preferred for maximizing the number of single-nucleotide polymorphisms (SNPs) passing quality control.
- Algorithm choice impacts data validity and error detection in GWAS.
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