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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #292645
Title: Comparative analysis of CNV calling algorithms: literature survey and a case study using bovine high-density SNP data

Author
item XU, LINGYANG - University Of Maryland
item HOU, YALI - Chinese Academy Of Sciences
item Bickhart, Derek
item JIUZHOU, SONG - University Of Maryland
item 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.