Cancer Informatics

Cumulative evidence has shown that structural variations, due to insertions, deletions, and inversions of DNA, may contribute considerably to the development of complex human diseases, such as breast cancer. High-throughput genotyping technologies, such as Affymetrix high density single-nucleotide polymorphism (SNP) arrays, have produced large amounts of genetic data for genome-wide SNP genotype calling and copy number estimation. Meanwhile, there is a great need for accurate and efficient statistical methods to detect copy number variants. In this article, we introduce a hidden-Markov-model (HMM)-based method, referred to as the PICR-CNV, for copy number inference. The proposed method first estimates copy number abundance for each single SNP on a single array based on the raw fluorescence values, and then standardizes the estimated copy number abundance to achieve equal footing among multiple arrays. This method requires no between-array normalization, and thus, maintains data integrity and independence of samples among individual subjects. In addition to our efforts to apply new statistical technology to raw fluorescence values, the HMM has been applied to the standardized copy number abundance in order to reduce experimental noise. Through simulations, we show our refined method is able to infer copy number variants accurately. Application of the proposed method to a breast cancer dataset helps to identify genomic regions significantly associated with the disease.