cnvHap: an integrative population and haplotype-based multiplatform model of SNPs and CNVs.
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.
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St. Mary's Hospital, London, UK. l.coin@imperial.ac.uk
Lachlan J M
LJ
Coin
Julian E
JE
Asher
Robin G
RG
Walters
Julia S El-Sayed
JS
Moustafa
Adam J
AJ
de Smith
Rob
R
Sladek
David J
DJ
Balding
Philippe
P
Froguel
Alexandra I F
AI
Blakemore
D002874:Chromosome Mapping; D002878:Chromosomes, Human, Pair 1; D016208:Databases, Factual; D006239:Haplotypes; D006801:Humans; D021141:Nucleic Acid Amplification Techniques / Q000379:methods*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D015203:Reproducibility of Results