Genome-wide association studies using copy number variants in Brown Swiss Dairy cattle.

Detecting Copy Number Variation (CNV) in cattle provides the opportunity to study their association with quantitative traits (Winchester et al., 2009; Zhang et al., 2009; Hou et al., 2011; Clop et al., 2012; de Almeida et al., 2016;). The aim of this study was to map CNVs in 1,410 Brown Swiss males and females using Illumina BovineHD Genotyping BeadChip data and to perform a genome-wide association analysis for production functional and health traits. After quality control, CNVs were called with the GoldenHelix SVS 8.3.1 and PennCNV software and were summarized to CNV regions (CNVRs) at a population level, using BEDTools. Additionally, common CNVRs between the two software were set as consensus. CNV-association studies were executed with the CNVRuler software using a linear regression model. Genes within significant associated CNVRs for each trait were annotated with a GO analysis using the DAVID Bioinformatics Resources 6.7.The quality control filtered out 294 samples. The GoldenHelix SVS 8.3.1 software identified 25,030 CNVs summarized to 398 CNVRs while PennCNV identified 62,341 CNVs summarized to 5,578 CNVRs. A total of 127 CNVRs were identified to be significantly associated with one or more of the evaluated traits. The result of this study is a comprehensive genomic analysis of the Brown Swiss breed, which enriches the bovine CNV map in its genome. Finally, the results of the association studies deliver new information for quantitative traits considered in selection programs of the Brown Swiss breed

A high-resolution CNV map across Brown Swiss cattle populations.

Genomic studies and their use in selection programs are having a strong impact in dairy cattle selection (E. Liu et al., 2010). The first aim was to create a high resolution map of CNV regions (CNVRs) in Brown Swiss cattle and the characterization of identified CNVs as markers for quantitative and population genetic studies. CNVs were called in a set of 164 sires with PennCNV and genoCN. PennCNV identified 2,377 CNVRs comprising 1,162 and 1,131 gain and loss events, respectively, and 84 regions of complex nature. GenoCN detected 41,519 CNVRs comprising 3,475 and 34,485 gain and loss events, respectively, and 3,559 regions of complex ones. Consensus calls between algorithms were summarized to CNVRs at the population level. GenoCN was also used to identify total allelic content in consensus CNVRs. Moreover, population haplotype frequencies were calculated. Linkage disequilibrium (LD) was established between CNVs and SNPs in and around CNVRs. In this study the potential contribution of CNVs as genetic markers for genome wide association studies (GWAS) has been assessed thanks to PIC and LD values. The next aim is to investigate genomic structural variation in cattle using dense SNP information in more than 1000 samples of the Italian and Swiss Brown Swiss breed genotyped on HD Bovine BeadChips. Today there is still no CNV map available across Brown Swiss populations belonging to different countries. This study therefore expands the catalogue of CNVRs in the bovine genome, delivers an international based high-resolution map of CNVRs specific to Brown Swiss dairy cattle and will lastly provide information for GEBV estimation with CNVs