EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data

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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.
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Funding Information
  • National Institutes of Health (1R41DA042464-01, 1U01HD079068, 1R01ES029212-01, R01HL125863)
  • National Natural Science Foundation of China (91643201, 21876134)
  • Ministry of Science and Technology of China (2016YFC0206507)
  • Transatlantic Networks of Excellence Award (12CVD02)
  • National Institute of Allergy and Infectious Diseases (U01AI090727, R56AI080627, R21AI088609)

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