Single nucleotide polymorphism (SNP) and copy number variation (CNV) are both widespread characteristic of the human genome, but are often called separately on common genotyping platforms. To capture integrated SNP and CNV information, methods have been developed for calling allelic specific copy numbers or so called copy number polymorphism (CNP), using limited inter-marker correlation. In this paper, we proposed a haplotype-based maximum likelihood method to call CNP, which takes advantage of the valuable multi-locus linkage disequilibrium (LD) information in the population. We also developed a computationally efficient algorithm to estimate haplotype frequencies and optimize individual CNP calls iteratively, even at presence of missing data. Through simulations, we demonstrated our model is more sensitive and accurate in detecting various CNV regions, compared with commonly-used CNV calling methods including PennCNV, another hidden Markov model (HMM) using CNP, a scan statistic, segCNV, and cnvHap. Our method often performs better in the regions with higher LD, in longer CNV regions, and in common CNV than the opposite. We implemented our method on the genotypes of 90 HapMap CEU samples and 23 patients with acute lung injury (ALI). For each ALI patient the genotyping was performed twice. The CNPs from our method show good consistency and accuracy comparable to others.
Keywords: CNP; CNV; GWAS; haplotype; integrated SNP and CNV; joint SNP and CNV calling.