BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variants.
BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variants.
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BMI-CNV：用于检测拷贝数变异的多个基因分型平台的贝叶斯框架。
DOI：
10.1093/genetics/iyac147
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发表时间：
2022
期刊：
影响因子：
3.3
通讯作者：
Xiao,Feifei
中科院分区：
文献类型：
--
作者：
Luo,Xizhi;Cai,Guoshuai;Mclain,AlexanderC;Amos,ChristopherI;Cai,Bo;Xiao,Feifei
全外显子组测序技术(WES)能够以高分辨率检测蛋白质编码区的拷贝数变异(CNV)。然而，基因间区或基因内区的变异被排除在研究之外。幸运的是，这些样本中的许多以前已经被其他基因分型平台测序过，这些平台虽然稀疏，但覆盖了广泛的基因组区域，如SNP阵列。此外，由于突出的数据噪声，传统的基于单样本的方法存在较高的错误发现率。因此，对整合多个基因分型平台和多个样本的方法提出了更高的要求，以改进拷贝数变异检测。我们开发了BMI-CNV，一种贝叶斯多样本整合CNV(BMI-CNV)方法，数据通过全外显子组测序和微阵列进行测序。对于多样本集成，我们使用贝叶斯概率折断过程模型和高斯混合模型估计来识别样本之间的共享拷贝数变异区域。通过大量的模拟，BMI拷贝数变量在精度上优于现有方法。在1000基因组计划和HapMap计划数据的匹配数据中，BMI-CNV还准确地检测到常见的变异，显著扩大了整个外显子组测序的检测范围。进一步应用国际肺癌研究联盟(TRICL)的数据，确定了17q11.2、1p36.12、8q23.1和5q22.2区域的肺癌风险变异候选区域。
Whole-exome sequencing (WES) enables the detection of copy number variants (CNVs) with high resolution in protein-coding regions. However, variants in the intergenic or intragenic regions are excluded from studies. Fortunately, many of these samples have been previously sequenced by other genotyping platforms which are sparse but cover a wide range of genomic regions, such as SNP array. Moreover, conventional single sample-based methods suffer from a high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and multiple samples are highly demanded for improved copy number variant detection. We developed BMI-CNV, a Bayesian Multisample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both whole-exome sequencing and microarray. For the multisample integration, we identify the shared copy number variants regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-copy number variant outperformed existing methods with improved accuracy. In the matched data from the 1000 Genomes Project and HapMap project data, BMI-CNV also accurately detected common variants and significantly enlarged the detection spectrum of whole-exome sequencing. Further application to the data from The Research of International Cancer of Lung consortium (TRICL) identified lung cancer risk variant candidates in 17q11.2, 1p36.12, 8q23.1, and 5q22.2 regions.