Evaluating the influence of quality control decisions and software algorithms on SNP calling for the affymetrix 6.0 SNP array platform.

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).