Comparative Analysis of CNV Calling Algorithms: Literature Survey and a Case Study Using Bovine High-Density SNP Data

Microarrays

Lingyang Xu²  Yali Hou⁴  Derek M. Bickhart¹  Jiuzhou Song³ 

[1] Animal Improvement Programs Laboratory, BARC, BA, USDA-ARS, Beltsville, MD 20705, USA; E-Mail:;Bovine Functional Genomics Laboratory, BARC, BA, USDA-ARS, Beltsville, MD 20705, USA; E-Mail:;Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA; E-Mail:;Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China; E-Mail:

关键词: copy number variation (CNV);    algorithm;    segmental duplication;    single nucleotide polymorphism (SNP);    cattle genome;   

DOI  :  10.3390/microarrays2030171

来源: mdpi

【 摘 要 】

Copy number variations (CNVs) are gains and losses of genomic sequence between two individuals of a species when compared to a reference genome. 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 10 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 need 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 susceptibility be fully revealed.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.