Page Banner

United States Department of Agriculture

Agricultural Research Service

Research Project: ENHANCING GENETIC MERIT OF RUMINANTS THROUGH GENOME SELECTION AND ANALYSIS

Location: Animal Genomics and Improvement Laboratory

Title: Comparative analysis of CNV calling algorithms: literature survey and a case study using bovine high-density SNP data

Authors
item Xu, Lingyang -
item Hou, Yali -
item Bickhart, Derek
item Jiuzhou, Song -
item Liu, Ge

Submitted to: Microarrays
Publication Type: Review Article
Publication Acceptance Date: May 12, 2013
Publication Date: June 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.

Last Modified: 9/10/2014
Footer Content Back to Top of Page