We develop an algorithm to analyze data from Illumina genotyping arrays for the detection of copy number variations in a single individual or in a random sample of individuals. We use a Hidden Markov Model framework, appropriately extended to take into account linkage disequilibrium between nearby loci. We describe a multisample approach to estimate the frequency of copy number variants in the population. With appropriate dataset, our methodology simultaneously analyzes the data for copy-number variation and tests for association between this and a disease trait of interest.