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Title:   Author  item
                                            XU, LINGYANG - University Of Marylanditem
                                            HOU, YALI - Chinese Academy Of Sciencesitem
                                            Bickhart, Derekitem
                                            JIUZHOU, SONG - University Of Marylanditem
                                            Liu, Ge

                                                         - *George*
  Submitted to: Microarrays
                                    Publication Type: Review Article
                                    Publication Acceptance Date: 5/12/2013
                                    Publication  Date: 6/5/2013
                                    Citation: Xu, L., Hou, Y., Bickhart, D.M., Jiuzhou, S., Liu, G. 2013. Comparative analysis of CNV calling algorithms: literature survey and a case study using bovine high-density SNP data. Microarrays. 2(3):171-185.Interpretive Summary: Genomic copy number variation (CNV) is abundant in livestock, differing from single nucleotide polymorphisms (SNP) in extent, origin and functional impact.  This review compares 10 published array-based cattle CNV studies, advocates for CNV documentation standards and provides insights into future research directions.  Users of genome-enabled animal selection tools to improve animal health and production will benefit from this book chapter.
                                Technical Abstract: Copy number variations (CNVs) are gains and losses of genomic sequence between two individuals of a species.  The data from single nucleotide polymorphism (SNP) microarrays are now routinely used for genotyping, but they also can be utilized for copy number detection.  Substantial progress has been made in array design and CNV calling algorithms and at least ten comparison studies in humans have been published to assess them.  In this review, we first survey the literature on existing microarray platforms and CNV calling algorithms.  We then examine a number of CNV calling tools to evaluate their impacts using bovine high-density SNP data.  Large incongruities in the results from different CNV calling tools highlight the needs for standardizing array data collection, quality assessment and experimental validation.  Only after careful experimental design and rigorous data filtering can the impacts of CNVs on both normal phenotypic variability and disease susceptibity be fully revealed.


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