Assessing Consistency Between Versions of Genotype-Calling Algorithm Birdseed for the Genome-Wide Human SNP Array 6.0 Using HapMap Samples

Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 680)


Highly accurate and reproducible genotype calling is a key to success of genome-wide association studies (GWAS) since errors introduced by calling algorithms can lead to inflation of false associations between genotype and phenotype. The Affymetrix Genome-Wide Human SNP Array 6.0 is widely utilized and was used in the current GWAS. Birdseed, a genotype-calling algorithm for this chip, is available in two versions. It is important to know the reproducibility between the two versions. We assessed the inconsistency in genotypes called by the two versions of Birdseed and examined the propagation of the genotype inconsistency to the downstream association analysis by using the 270 HapMap samples. Our results revealed that genotypes called from version-1 and version-2 of Birdseed were slightly different and the inconsistency in genotypes propagated to the downstream association analysis.


Algorithm Association Genotype calling HapMap Reproducibility 


  1. 1.
    Shi J, Levinson DF, Duan J et al (2009) Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature 460:753–757PubMedGoogle Scholar
  2. 2.
    Ikram MA, Seshadri S, Bis JC et al (2009) Genomewide association studies of stroke. N Engl J Med 360:1718–1728PubMedCrossRefGoogle Scholar
  3. 3.
    Woodward OM, Köttgen A, Coresh J et al (2009) Identification of a urate transporter, ABCG2, with a common functional polymorphism causing gout. Proc Natl Acad Sci USA 106:10338–10342PubMedCrossRefGoogle Scholar
  4. 4.
    Erdmann J, Grosshennig A, Braund PS et al (2009) New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nat Genet 41:280–282PubMedCrossRefGoogle Scholar
  5. 5.
    Myocardial Infarction Genetics Consortium, Kathiresan S, Voight BF et al (2009) Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat Genet 41:334–341CrossRefGoogle Scholar
  6. 6.
    Zheng W, Long J, Gao YT et al (2009) Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet 41:334–341CrossRefGoogle Scholar
  7. 7.
    Köttgen A, Glazer NL, Dehghan A et al (2009) Multiple loci associated with indices of renal function and chronic kidney disease. Nat Genet 41:712–717PubMedCrossRefGoogle Scholar
  8. 8.
    Kanetsky PA, Mitra N, Vardhanabhuti S et al (2009) Common variation in KITLG and at 5q31.3 predisposes to testicular germ cell cancer. Nat Genet 41:811–815PubMedCrossRefGoogle Scholar
  9. 9.
  10. 10.
    Hong H, Su Z, Ge W et al (2008) Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500K Array Set using 270 HapMap samples. BMC Bioinform 9:S17CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Center for Toxicoinformatics, Division of Systems Toxicology, National Center for Toxicological ResearchUS Food and Drug AdministrationJeffersonUSA

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