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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #336616

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

Location: Animal Genomics and Improvement Laboratory

Title: CNV discovery for milk composition traits in dairy cattle using whole genome resequencing

Author
item SUN, DONGXIAO - China Agricultural University
item GAO, YAHUI - China Agricultural University
item JIANG, SHAOHUA - China Agricultural University
item YANG, SHAOHUA - China Agricultural University
item HOU, YALI - China Agricultural University
item Liu, Ge - George
item ZHANG, SHENGLI - China Agricultural University
item ZHANG, QIN - China Agricultural University

Submitted to: Biomed Central (BMC) Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/17/2017
Publication Date: 3/29/2017
Citation: Sun, D., Gao, Y., Jiang, S., Yang, S., Hou, Y., Liu, G., Zhang, S., Zhang, Q. 2017. CNV discovery for milk composition traits in dairy cattle using whole genome resequencing. Biomed Central (BMC) Genomics. 18(1):265.

Interpretive Summary: Copy number variation's functional impacts are not well studied in livestock. This study applied whole-genome sequencing to assess CNV's impact on milk protein percentage and fat percentage in Chinese Holstein cattle. These results fill gaps in our knowledge by providing a list of potential milk production-related pathways and genes. Farmers, scientists, and policy planners who need improve animal health and production based on genome-enable animal selection will benefit from this study.

Technical Abstract: Copy number variations (CNVs) detection open a new avenue for exploring genes associated with complex traits in humans, animals, and plants. In this study, CNVs were detected based on whole-genome re-sequencing of eight Holstein data from bulls from four half- or full-sib families, with extremely high and low estimated breeding values (EBVs) of milk protein percentage and fat percentage. The depth of coverage per individual was 8.2-11.9x. Using CNVnator, we identified a total of 14,821 CNVs, including 5,025 duplications and 9,796 deletions. Among them, 487 differential CNV regions (CNVRs) comprising of ~8.23 Mb of the cattle genome were observed between the high and low groups. Annotation of these differential CNVRs were performed based on the cattle genome reference assembly (UMD3.1) and totally 235 functional genes were found within the CNVRs. By Gene Ontology and KEGG pathway analyses, we found that genes were significantly enriched for specific biological functions related to protein and lipid metabolism, insulin/IGF pathway-protein kinase B signaling cascade, prolactin signaling pathway, and AMPK signaling pathway. These genes included INS, IGF2, FOXO3, TH, SCD5, GALNT18, GALNT16, ART3, SNCA, and WNT7A, implying potential association with milk protein and fat traits. In addition, 71 of the 487 CNRVs were overlapped with 43 known QTLs that are associated with milk protein and fat traits of dairy cattle (Cattle QTLdb). In summary, by revealing candidate CNVRs and genes which may be involved in milk composition traits in dairy cattle, this study provided a basis for future CNV studies.