EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.
EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.
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EnsembleCNV：一种集成机器学习算法，用于使用 SNP 阵列数据识别拷贝数变异并对其进行基因分型。
DOI：
10.1093/nar/gkz068
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发表时间：
2019
影响因子：
14.9
通讯作者：
Hao,Ke
中科院分区：
文献类型：
--
作者：
Zhang,Zhongyang;Cheng,Haoxiang;Hong,Xiumei;DiNarzo,AntonioF;Franzen,Oscar;Peng,Shouneng;Ruusalepp,Arno;Kovacic,JasonC;Bjorkegren,JohanLM;Wang,Xiaobin;Hao,Ke
疾病/性状和拷贝数变异（CNVs）之间的关联尚未在全基因组关联研究（GWAS）中进行系统研究，这主要是由于缺乏可靠和准确的CNV基因分型工具。在这里，我们提出了一种新的集成学习框架，ensembleCNV，检测和基因型CNVs使用单核苷酸多态性（SNP）阵列数据。EnsembleCNV（a）在原始数据水平上鉴定并消除批次效应;（B）通过启发式算法将来自多个具有互补强度的现有识别者的单个CNV识别组装成CNV区域（CNVR）;（c）用局部似然模型对每个CNVR进行重新基因型化，所述局部似然模型通过跨多个CNVR的全局信息进行调整;（d）通过拷贝数强度中的局部相关性结构来细化CNVR边界;（e）提供直接的CNV基因分型，伴随着置信度评分，可直接用于下游质量控制和关联分析。在两个大型数据集上进行基准测试，ensembleCNV优于竞争方法，并实现了高调用率（93.3%）和再现性（98.6%），同时通过捕获1000个基因组计划中记录的85%的常见CNV来实现高灵敏度。鉴于这种CNV调用率和准确性，这是可比的SNP基因分型，我们建议ensembleCNV具有显着的承诺进行全基因组CNV关联研究和调查CNV如何易感于人类疾病。
The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV (a) identifies and eliminates batch effects at raw data level; (b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; (c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; (d) refines CNVR boundaries by local correlation structure in copy number intensities; (e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis. Benchmarked on two large datasets, ensembleCNV outperformed competing methods and achieved a high call rate (93.3%) and reproducibility (98.6%), while concurrently achieving high sensitivity by capturing 85% of common CNVs documented in the 1000 Genomes Project. Given this CNV call rate and accuracy, which are comparable to SNP genotyping, we suggest ensembleCNV holds significant promise for performing genome-wide CNV association studies and investigating how CNVs predispose to human diseases.