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.