EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data. Issue 7 (5th February 2019)

• Record Type: Journal Article
• Authors: Zhang, Zhongyang; Cheng, Haoxiang; Hong, Xiumei; Di Narzo, Antonio F; Franzen, Oscar; Peng, Shouneng; Ruusalepp, Arno; Kovacic, Jason C; Bjorkegren, Johan L M; Wang, Xiaobin; Hao, Ke
• Is Part Of: Nucleic acids research. Volume 47:Issue 7(2019)
• Page Start: e39
• Page End: e39
• Publication Date: 2019-02-05
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkz068
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: 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.