Evaluating the Influence of Quality Control Decisions and Software Algorithms on SNP Calling for the Affymetrix 6.0 SNP Array Platform

Abstract

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).