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...

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Bibliographic Details
Published in:Human heredity Vol. 71; no. 4; pp. 221 - 233
Main Authors: de Andrade, Mariza, Atkinson, Elizabeth J., Bamlet, William R., Matsumoto, Martha E., Maharjan, Sooraj, Slager, Susan L., Vachon, Celine M., Cunningham, Julie M., Kardia, Sharon L.R.
Format: Journal Article
Language:English
Published: Basel, Switzerland S. Karger AG 01.01.2011
Subjects:
Adult
Algorithms
Genotype
Genotype & phenotype
Humans
Oligonucleotide Array Sequence Analysis - methods
Original Paper
Polymorphism
Polymorphism, Single Nucleotide
Quality Control
Software
Birdseed
Genotype call
Association
CRLMM
Quality control decisions
ISSN:0001-5652, 1423-0062, 1423-0062
Online Access:Get full text
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Summary: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).
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ISSN:0001-5652
1423-0062
1423-0062
DOI:10.1159/000328843