CnvHap: An integrative population and haplotype-based multiplatform model of SNPs and CNVs

Coin, Lachlan J. M., Asher, Julian E., Walters, Robin G., El-Sayed Moustafa, Julia S., de Smith, Adam J., Sladek, Rob, Balding, David J., Froguel, Philippe and Blakemore, Alexandra I. F. (2010) CnvHap: An integrative population and haplotype-based multiplatform model of SNPs and CNVs. Nature Methods, 7 7: 541-546. doi:10.1038/nmeth.1466


Author Coin, Lachlan J. M.
Asher, Julian E.
Walters, Robin G.
El-Sayed Moustafa, Julia S.
de Smith, Adam J.
Sladek, Rob
Balding, David J.
Froguel, Philippe
Blakemore, Alexandra I. F.
Title CnvHap: An integrative population and haplotype-based multiplatform model of SNPs and CNVs
Journal name Nature Methods   Check publisher's open access policy
ISSN 1548-7091
1548-7105
Publication date 2010-01-01
Sub-type Article (original research)
DOI 10.1038/nmeth.1466
Volume 7
Issue 7
Start page 541
End page 546
Total pages 6
Place of publication London, United Kingdom
Publisher Nature Publishing
Language eng
Abstract Although genome-wide association studies have uncovered single-nucleotide polymorphisms (SNPs) associated with complex disease, these variants account for a small portion of heritability. Some contribution to this 'missing heritability' may come from copy-number variants (CNVs), in particular rare CNVs; but assessment of this contribution remains challenging because of the difficulty in accurately genotyping CNVs, particularly small variants. We report a population-based approach for the identification of CNVs that integrates data from multiple samples and platforms. Our algorithm, cnvHap, jointly learns a chromosome-wide haplotype model of CNVs and cluster-based models of allele intensity at each probe. Using data for 50 French individuals assayed on four separate platforms, we found that cnvHap correctly detected at least 14% more deleted and 50% more amplified genotypes than PennCNV or QuantiSNP, with an 82% and 115% improvement for aberrations containing 10 probes. Combining data from multiple platforms additionally improved sensitivity.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
 
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