A Genome-Wide Analysis of Array-Based Comparative Genomic Hybridization (CGH) Data to Detect Intra-Species Variations and Evolutionary Relationships

Array-based comparative genomics hybridization (aCGH) has gained prevalence as an effective technique for measuring structural variations in the genome. Copy-number variations (CNVs) form a large source of genomic structural variation, but it is not known whether phenotypic differences between intra-species groups, such as divergent human populations, or breeds of a domestic animal, can be attributed to CNVs. Several computational methods have been proposed to improve the detection of CNVs from array CGH data, but few population studies have used CGH data for identification of intra-species differences. In this paper we propose a novel method of genome-wide comparison and classification using CGH data that condenses whole genome information, aimed at quantification of intra-species variations and discovery of shared ancestry. Our strategy included smoothing CGH data using an appropriate denoising algorithm, extracting features via wavelets, quantifying the information via wavelet power spectrum and hierarchical clustering of the resultant profile. To evaluate the classification efficiency of our method, we used simulated data sets. We applied it to aCGH data from human and bovine individuals and showed that it successfully detects existing intra-specific variations with additional evolutionary implications.