Evaluating the influence of quality control decisions and software algorithms on SNP calling for the affymetrix 6.0 SNP array platform.
Evaluating the influence of quality control decisions and software algorithms on SNP calling for the affymetrix 6.0 SNP array platform.
复制标题
评估质量控制决策和软件算法对 SNP 的影响，调用 affymetrix 6.0 SNP 阵列平台。
DOI：
10.1159/000328843
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发表时间：
2011
期刊：
影响因子：
1.8
通讯作者：
Kardia,SharonLR
中科院分区：
文献类型：
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作者：
deAndrade,Mariza;Atkinson,ElizabethJ;Bamlet,WilliamR;Matsumoto,MarthaE;Maharjan,Sooraj;Slager,SusanL;Vachon,CelineM;Cunningham,JulieM;Kardia,SharonLR
目的：我们的目标是使用专为 Affymetrix SNP Array 6.0 设计的两种基因型检出算法 CRLMM 和 Birdseed 来评估质量控制 (QC) 决策的影响。方法：使用这两种算法尝试各种 QC 选项，并使用两个数据集对受试者和检出率以及关联结果进行比较。结果：对于 Birdseed，我们建议使用对比 QC 而不是样品 QC 的 QC 检出率。对于 CRLMM，我们建议使用信噪比≥4 进行样品 QC，并使用 90% 的后验概率来保证基因型准确性。对于这两种算法，我们建议对每个板分别调用基因型，并在评估具有较低调用率的样本之前删除具有较低调用率 (<95%) 的 SNP。为了研究两种算法的基因型调用是否影响全基因组关联结果，我们使用来自 GENOA 队列的数据进行关联分析；我们观察到使用 CRLMM 或 Birdseed 时显着 SNP 的数量相似。结论：使用我们建议的工作流程，两种算法的表现相似；然而，与 Birdseed（8.4 小时）相比，移除的样本较少，并且 CRLMM 运行我们的 854 个研究样本所需的时间（4.2 小时）只有一半。
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).