Background: Copy number variations (CNVs) are widespread in the human or animal genome and are a significant source of genetic variation, which has been demonstrated to play an important role in phenotypic diversity. Advances in technology have allowed for identification of a large number of CNVs in cattle. Comprehensive explore novel CNVs in the bovine genome would provide valuable information for functional analyses of genome structural variation and facilitating follow-up association studies between complex traits and genetic variants. Results: In this study, we performed a genome-wide CNV detection based on high-density SNP genotyping data of 96 Chinese Holstein cattle. A total of 367 CNV regions (CNVRs) across the genome were identified, which cover 42.74Mb of the cattle genome and correspond to 1.61% of the genome sequence. The length of the CNVRs on autosomes range from 10.76 to 2,806.42 Kb with an average of 96.23 Kb. 218 out of these CNVRs contain 610 annotated genes, which possess a wide spectrum of molecular functions. To confirm these findings, quantitative PCR (qPCR) was performed for 17 CNVRs and 13(76.5%) of them were successfully validated. Conclusions: Our study demonstrates the high density SNP array can significantly improve the accuracy and sensitivity of CNV calling. Integration of different platforms can enhance the detection of genomic structure variants. Our results provide a significant replenishment for the high resolution map of copy number variation in the bovine genome and valuable information for investigation of genomic structural variation underlying traits of interest in cattle.

Genome wide association study (GWAS) has been proven to be a powerful tool for detecting genomic variants associated with complex traits. However, the specific genes and causal variants underlying these traits remain unclear. Here, we used target-enrichment strategy coupled with next generation sequencing technique to study target regions which were found to be associated with milk production traits in dairy cattle in our previous GWAS. Among the large amount of novel variants detected by targeted resequencing, we selected 200 SNPs for further association study in a population consisting of 2634 cows. Sixty six SNPs distributed in 53 genes were identified to be associated significantly with on milk production traits. Of the 53 genes, 26 were consistent with our previous GWAS results. We further chose 20 significant genes to analyze their mRNA expression in different tissues of lactating cows, of which 15 were specificly highly expressed in mammary gland. Our study illustrates the potential for identifying causal mutations for milk production traits using target-enrichment resequencing and extends the results of GWAS by discovering new and potentially functional mutations.

Identification of functional genes affecting milk production traits is very crucial for improving breeding efficiency in dairy cattle. Many potential candidate genes have been identified through our previous genome wide association study (GWAS). Of these, GPIHBP1 is an important novel candidate gene for milk production traits. However, the mRNA structure of the bovine GPIHBP1 gene is not fully determined up to now. In this study, we identified a novel alternatively splice transcript variant (X5) which leads to a 31 bp insertion in exon 3 and also confirmed the other four existed transcripts (X1, X2, X3 and X4) of the bovine GPIHBP1 gene. We showed that transcript X5 with a 31 bp insertion and transcript X1 with an 8 bp deletion might have tremendous effect on the protein function and structure of GPIHBP1, respectively. With semi-quantitative PCR and quantitative real-time RT-PCR, we found that the mRNA expression of GPIHBP1, GPIHBP1-X1 and GPIHBP1-X5 in mammary gland of lactating cows were much higher than that in other tissues. Our study reports a novel alternative splicing of GPIHBP1 in bovine for the first time and provide useful information for the further functional analyses of GPIHBP1 in dairy cattle.