%0 Generic %A de Andrade M. %A E.J., Atkinson %A W.R., Bamlet %A M.E., Matsumoto %A S., Maharjan %A S.L., Slager %A C.M., Vachon %A J.M., Cunningham %A S.L.R., Kardia %D 2011 %T Supplementary Material for: Evaluating the Influence of Quality Control Decisions and Software Algorithms on SNP Calling for the Affymetrix 6.0 SNP Array Platform %U https://karger.figshare.com/articles/dataset/Supplementary_Material_for_Evaluating_the_Influence_of_Quality_Control_Decisions_and_Software_Algorithms_on_SNP_Calling_for_the_Affymetrix_6_0_SNP_Array_Platform/5122510 %R 10.6084/m9.figshare.5122510.v1 %2 https://ndownloader.figshare.com/files/8707513 %K Genotype call %K Birdseed %K CRLMM %K Quality control decisions %K Association %X Objective: Our goal was to evaluate the influence of quality control (QC) decisions using two genotype calling algorithms, CRLMM and Birdseed, designed for the Affymetrix SNP Array 6.0. Methods: Various QC options were tried using the two algorithms and comparisons were made on subject and call rate and on association results using two data sets. Results: For Birdseed, we recommend using the contrast QC instead of QC call rate for sample QC. For CRLMM, we recommend using the signal-to-noise rate ≧4 for sample QC and a posterior probability of 90% for genotype accuracy. For both algorithms, we recommend calling the genotype separately for each plate, and dropping SNPs with a lower call rate (<95%) before evaluating samples with lower call rates. To investigate whether the genotype calls from the two algorithms impacted the genome-wide association results, we performed association analysis using data from the GENOA cohort; we observed that the number of significant SNPs were similar using either CRLMM or Birdseed. Conclusions: Using our suggested workflow both algorithms performed similarly; however, fewer samples were removed and CRLMM took half the time to run our 854 study samples (4.2 h) compared to Birdseed (8.4 h). %I Karger Publishers