Fast detection of de novo copy number variants from SNP arrays for case-parent trios.
Fast detection of de novo copy number variants from SNP arrays for case-parent trios.
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DOI：
10.1186/1471-2105-13-330
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发表时间：
2012-12-12
影响因子：
3
通讯作者：
Ruczinski I
中科院分区：
文献类型：
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作者：
Scharpf RB;Beaty TH;Schwender H;Younkin SG;Scott AF;Ruczinski I
在病例-父母三重奏的研究中，我们将后代中与父母拷贝数不同的拷贝数变异（CNV）定义为从头发生的，并且因其在疾病中的潜在功能作用而受到关注。在用于在病例-亲本三组中发现从头CNV的基于阵列的主要方法中，PennCNV软件中实现的联合隐马尔可夫模型（HMM）是其中之一。然而，联合HMM的计算需求是巨大的，在何种程度上发生的情况下，父母三人组的假阳性识别还没有得到很好的描述。我们评估这些问题在一项研究中的口裂的情况下，父母三人组。我们对口裂三人组的分析表明，尽管在PennCNV中实施了波校正，但基因组波是联合HMM中假阳性识别的重要来源。此外，相对拷贝数（log R比）的低水平总结的噪声与批次密切相关，并与从头CNV调用的频率相关。利用三人设计，我们提出了一个单变量的相对拷贝数的统计称为最小距离，可以减少从探针效应和基因组波的技术变化。我们使用循环二进制分割分割的最小距离和最大后验估计推断从头CNVs从分割的基因组。与模拟数据上的PennCNV相比，MinimumDistance平均识别更少的假阳性，并且在假阴性方面与PennCNV相当。基因组波有助于PennCNV和MinimumDistance的不一致性，用于高覆盖度从头调用，而22号染色体上的高度一致调用通过定量PCR进行验证。在计算上，MinimumDistance相对于联合HMM在口裂三重奏的研究中提供了近8倍的速度增加。我们的研究结果表明，批次效应和基因组波是从头CNV的病例-亲本研究的重要考虑因素，并且最小距离是减少导致错误从头发现的技术变化的有效统计。再加上分割和最大后验估计，我们的算法相比，联合HMM与MinimumDistance是快得多。
In studies of case-parent trios, we define copy number variants (CNVs) in the offspring that differ from the parental copy numbers as de novo and of interest for their potential functional role in disease. Among the leading array-based methods for discovery of de novo CNVs in case-parent trios is the joint hidden Markov model (HMM) implemented in the PennCNV software. However, the computational demands of the joint HMM are substantial and the extent to which false positive identifications occur in case-parent trios has not been well described. We evaluate these issues in a study of oral cleft case-parent trios. Our analysis of the oral cleft trios reveals that genomic waves represent a substantial source of false positive identifications in the joint HMM, despite a wave-correction implementation in PennCNV. In addition, the noise of low-level summaries of relative copy number (log R ratios) is strongly associated with batch and correlated with the frequency of de novo CNV calls. Exploiting the trio design, we propose a univariate statistic for relative copy number referred to as the minimum distance that can reduce technical variation from probe effects and genomic waves. We use circular binary segmentation to segment the minimum distance and maximum a posteriori estimation to infer de novo CNVs from the segmented genome. Compared to PennCNV on simulated data, MinimumDistance identifies fewer false positives on average and is comparable to PennCNV with respect to false negatives. Genomic waves contribute to discordance of PennCNV and MinimumDistance for high coverage de novo calls, while highly concordant calls on chromosome 22 were validated by quantitative PCR. Computationally, MinimumDistance provides a nearly 8-fold increase in speed relative to the joint HMM in a study of oral cleft trios. Our results indicate that batch effects and genomic waves are important considerations for case-parent studies of de novo CNV, and that the minimum distance is an effective statistic for reducing technical variation contributing to false de novo discoveries. Coupled with segmentation and maximum a posteriori estimation, our algorithm compares favorably to the joint HMM with MinimumDistance being much faster.
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