PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data

TLDR

Comprehensive cataloging of copy number variations (CNVs) is essential for understanding human genetic diversity, yet previous detection methods were limited to tens or hundreds of kilobases. PennCNV was developed as a hidden Markov model–based approach to detect CNVs at kilobase resolution from Illumina high‑density SNP genotyping data. The algorithm integrates total signal intensity, allelic intensity ratios, inter‑SNP distances, SNP allele frequencies, and available pedigree information within an HMM framework. When applied to 112 HapMap individuals, PennCNV identified an average of ~27 CNVs per person (median size ~12 kb) and revealed that 3.3% of offspring CNVs were not present in parents, demonstrating the feasibility of whole‑genome fine‑mapping of CNVs using high‑density SNP data.

Abstract

Comprehensive identification and cataloging of copy number variations (CNVs) is required to provide a complete view of human genetic variation. The resolution of CNV detection in previous experimental designs has been limited to tens or hundreds of kilobases. Here we present PennCNV, a hidden Markov model (HMM) based approach, for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data. This algorithm incorporates multiple sources of information, including total signal intensity and allelic intensity ratio at each SNP marker, the distance between neighboring SNPs, the allele frequency of SNPs, and the pedigree information where available. We applied PennCNV to genotyping data generated for 112 HapMap individuals; on average, we detected ∼27 CNVs for each individual with a median size of ∼12 kb. Excluding common rearrangements in lymphoblastoid cell lines, the fraction of CNVs in offspring not detected in parents (CNV-NDPs) was 3.3%. Our results demonstrate the feasibility of whole-genome fine-mapping of CNVs via high-density SNP genotyping.