Copy-number variation in goat genome sequence: A comparative analysis of the different litter size trait groups
Introduction
Goats are an economically important livestock breed, and have an important position in the livestock industry, providing meat, dairy products, wool and other products to humans (Luo, 2009). In order to meet the dairy needs of consumers, it is necessary to improve the fecundity and production characteristics of dairy goats. At present, genetic marker assisted breeding has been widely carried out in many domestic animal breeding programs. However, genetic marker assisted breeding of dairy goats has lagged behind other species due to a lack of research focusing on the goat genome. Progress in this regard will involve the identification of genomic biomarkers associated with reproductive and production traits in dairy goats.
Copy-number variation (CNV) is the result of rearrangements of the genome. Abnormal fragments ranging from 50 bp to several Mb are characterized as CNV, including deletions insertions, recombinations and complex variations of multiple sites (Mills et al., 2011). Certain CNVs provide the basis to study the pathogenesis of some diseases, important economic traits, and genetic assisted breeding of domestic animals (Pirooznia et al., 2015). The number of nucleotides involved in CNVs is much larger than that of single nucleotide polymorphisms (SNPs), leading to the potential to cause a large change in the gene sequence within the genome. Depending on the CNV this can lead to abnormalities in the gene structure and alterations in gene expression (Carter, 2007). For example, altering the structure of a single or multiple genes with repeated or missing DNA fragments resulted in an increase or decrease in gene expression, or even loss of gene function (Zhang et al., 2009; Hou et al., 2012; Pirooznia et al., 2015). In 2004, Sebat et al. discovered a large quantity of copy number polymorphisms (CNPs) in the human genome, confirming that CNV genes are involved in the regulation of neurological function, cell growth, metabolism and disease occurrence (Sebat et al., 2004).
Previous studies have completed genome-wide CNV analysis in many domestic species including sheep (Zhang et al., 2014), pigs (Wang et al., 2014; Zhou et al., 2016), goats (Fontanesi et al., 2010), horse and others. Many genes involved in CNV are related to complex traits and environmental adaptability, suggesting that CNV is vital to revealing important animal economic traits and a useful tool for analyzing the genetic structures of populations. Gao et al. used whole genome re-sequence technology to study the CNVs of 4 high-fat, milk producing and 4 low-fat, milk producing Holstein cattle (Gao et al., 2017). They identified that the differential copy-number variation regions (CNVRs) mainly involved genes responsible for lipid and protein metabolism, and identified 10 candidate genes as molecular markers. Revay et al. used Illumina chip technology to identify CNVs in high and LF breeding boars, and identified 14 of the 35 found CNVRs specifically in the HF group (Revay et al., 2015). To date, studies on CNVs in goats have reported a close relationship between CNVs identified and quantitative traits, with CNVs being used as a molecular marker for studying complex traits using genome-wide association analysis (GWAS). By comparing the genomes of wild goats (Capra aegagrus hircus) and domestic goats, Dong et al. detect 13 genes with CNVs that were related to fur color in domestic goats (Dong et al., 2015). In order to explore the variation in coat colors in Boer goats, Fontanesi et al. found a locus that caused the white spot phenotype on chromosome 17 through GWAS and detected the length using whole genome sequencing (Fontanesi et al., 2010). Copy number variation as the main source of genomic structural variation is suitable for population evolution studies. Yang et al. observed the CNV detection rates different sheep populations were different, and found several important CNV overlapping genes such as muscle development, prostaglandin synthesis and bone color. Liu et al. constructing CNV variation maps by studying multiple goat breeds, and identification of multiple genes associated with coat color, muscle development and bone development such as EDNRA, ASIP, DGAT1, CLCN7, and EXOSC4(Liu et al., 2018; Yang et al., 2018).
We investigated how CNVs differed in dairy goat populations with extreme differences in productivity. We separately sequenced HF group and LF group of Laoshan dairy goat. We screened for reliable CNVs in the two extreme populations. We identified specific CNVs in the high-fertility populations and identified genes present in those regions that may affect lambing traits in the dairy goats.
Section snippets
Sample collection
Sequencing data from our previous study was used. However, the results of the CNV analysis involved in this study were not published previously (Lai et al., 2016; Zhang et al., 2018). A brief description of the sample collection methods: Laoshan dairy goats were categorized into two extreme groups based on productivity and a goat ear tissue samples were collected. A total of 34 goat samples were collected, 20 individuals with one lamb were assigned to the low-yield group; 14 individuals are the
CNVs analysis
The previous whole genome re-sequencing data from the two groups differing on litter sizes (HF and LF groups) were subsequently analyzed (Lai et al., 2016; Zhang et al., 2018). As the database is constantly updated, the new goat reference genome was used in this study. After filtering out the adapter and low quality reads of the raw sequence data, we mapped the clean reads to the San Clemente goat reference genome (Dong et al., 2013). Next, following GATK best practices pipeline, we predicted
Discussion
In this study, we utilized genomic sequencing to identify differing CNVs in high and LF goats. In order to compare diverse populations we categorized goats birthing more than three lambs as the HF group. Goats birthing one lamb were categorized as the LF group. Studying HF phenotypes with a small number of extreme populations not only reduces sequence costs, but our previous studies also demonstrate the feasibility of this approach.
High-quality CNV detection is required to ensure the
Availability of data and materials
The sequence data generated in this study has been uploaded to the NCBI SRA database, with the accession number: PRJNA322364 (http://www.ncbi.nlm.nih.gov/bioproject/PRJNA 322364/).
Conflict of interest statement
The authors declare that they have no competing interests.
Author contributions
R.Q.Z., J.J.W., T. Z. and H.L.Z. conducted the experiments; R.Q.Z. analyzed the data; R.Q.Z. and W.S. wrote the manuscript; W.S. designed the experiments. All authors reviewed the manuscript.
Acknowledgements
This work was supported by Science & Technology Fund Planning Projects of Qingdao City (17-3-3-48-nsh) and Yantai City (2016JH027) of China. The authors would also like to thank Prof. Paul W. Dyce for his careful editing of this manuscript.
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