Title Markov models for inferring Copy Number Variations from genotype data Permalink

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. 1 Copy number variation and genotyping arrays Two important surveys appeared in 2004 [5, 14] documented the presence of copy number variation in the genome in unsuspected high frequencies, both in the form of deletion of small genomic segments as well as duplication. Since then, considerable attention has been paid to the study of copy number polymorphisms and their relevance in disease traits. It has become apparent that high resolution genotype data offers an important source of information for detection of copy number polymorphisms: on the one hand, stretches of homozygous markers can indicate deletions (see, for example, [17]) as well as duplications, on the other hand, patterns of Mendelian inconsistencies can also provide indication of the presence of copy number variations [3]. Moreover, the technology used to obtain high density genotyping provides quantitative information on DNA amounts that can, and should, be used to detect copy number variations. Linkage disequilibrium by itself explain the presence of stretches of homozygous markers in the genome. Modeling linkage disequilibrium helps to narrow the number of genomic region that could harbor copy number variation on the basis of genotype information alone [18], however, it is not sufficient. Only by looking at variation on the intensity levels associated to DNA amounts at each marker, one can reliably distinguish deletions, duplications or homozygosity corresponding to a regular diploid genome segment. A number of 1 recent studies have indeed explored the effectiveness of high density genotyping arrays to detect copy number variations in relation to other experimental methodologies: a careful comparison of different technologies can be found, for example in [4], while [12] represents one of the most recent and comprehensive screens. This interest in the detection of copy number variation has spurred the development of statistical methods to analyze the experimental data: while a number of algorithms and software are available, both from commercial and academic sources (see, for example, the list provided by affymetrix http://www.affymetrix.com/products/application/cna dataanalysis.affx), this area of research is still in its infancy. With new contributions appearing in almost any issue of relevant journals, it is hard to give a complete account of the current literature. Instead, we focus on the specific problem we would like to tackle, which is in large part still open. 1. Our main goal is to 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. 2. In the present effort, we use a Hidden Markov Model framework, appropriately extended to take into account linkage disequilibrium between nearby loci; 3. We adopt a Bayesian approach with which information on copy number variation detected in previous studies informs the analysis; 4. 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. 2 Illumina genotype data We have become interested in studying the data obtained with the Illumina genotyping arrays for a number of reasons. On the one hand, this is the platform of choice for a number of studies we 2 0 2 4 6 8 10 14 0 2 4 6 8 10 log B lo g A ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 0.5 1.0 1.5 7 8 9 10 12 14