Interpretation of custom designed Illumina genotype cluster plots for targeted association studies and next-generation sequence validation.
Interpretation of custom designed Illumina genotype cluster plots for targeted association studies and next-generation sequence validation.
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DOI：
10.1186/1756-0500-3-39
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发表时间：
2010-02-22
影响因子：
1.8
通讯作者：
Hayes VM
中科院分区：
文献类型：
--
作者：
Tindall EA;Petersen DC;Nikolaysen S;Miller W;Schuster SC;Hayes VM
高通量定制设计的基因分型阵列是生物学研究的宝贵资源，并且越来越多地用于验证下一代测序（NGS）技术预测的变异。我们分别使用定制设计的VeraCode和sentrix阵列矩阵（SAM）分析来研究Illumina GoldenGate化学。我们强调了Illumina生成的基因型聚类图的解释应用，以最大限度地提高数据的包容性并减少基因分型错误。我们说明了在基因型调用和数据解释中离群值的巨大影响，并提出了避免基因分型错误的简单方法。此外，我们提出这个平台作为一个成功的方法，两个集群罕见或非常染色体变异调用。高通量技术成功地准确识别罕见变异将成为未来关联研究的重要特征。最后，我们强调了Illumina GoldenGate化学在生成异常分离的聚类图方面的额外优势，这些聚类图可识别由最小覆盖度导致的潜在NGS生成的测序错误。我们证明了目视检查Illumina软件生成的基因型聚类图的重要性，并就普遍接受的质量控制参数发出警告。除了建议应用程序以最大限度地减少数据排除外，我们还建议Illumina聚类图可能有助于识别潜在的输入序列错误，对于验证NGS产生的变异的研究尤其重要。
High-throughput custom designed genotyping arrays are a valuable resource for biologically focused research studies and increasingly for validation of variation predicted by next-generation sequencing (NGS) technologies. We investigate the Illumina GoldenGate chemistry using custom designed VeraCode and sentrix array matrix (SAM) assays for each of these applications, respectively. We highlight applications for interpretation of Illumina generated genotype cluster plots to maximise data inclusion and reduce genotyping errors. We illustrate the dramatic effect of outliers in genotype calling and data interpretation, as well as suggest simple means to avoid genotyping errors. Furthermore we present this platform as a successful method for two-cluster rare or non-autosomal variant calling. The success of high-throughput technologies to accurately call rare variants will become an essential feature for future association studies. Finally, we highlight additional advantages of the Illumina GoldenGate chemistry in generating unusually segregated cluster plots that identify potential NGS generated sequencing error resulting from minimal coverage. We demonstrate the importance of visually inspecting genotype cluster plots generated by the Illumina software and issue warnings regarding commonly accepted quality control parameters. In addition to suggesting applications to minimise data exclusion, we propose that the Illumina cluster plots may be helpful in identifying potential in-put sequence errors, particularly important for studies to validate NGS generated variation.