Integrating GWAS,CNV And ROH Analysis Reveals Candidate Genes Of Important Traits In Ningxia Holstein Cow

The study based on pedigree information,DHI records and GGP Bovine 150k SNP genotype data by using random regression test-day model,GWAS,CNV and ROH analysis strategies to identify and mine SNP markers,genomic structure variation regions and candidate genes that effect on important production traits in Holstein dairy in Ningxia area,and to explore the selective signal that remained in the breeding and evolution process of the experimental population,the results show that:(1)Genetic analysis of five milk production traits and SCS in the first lactation of Ningxia Holstein cows was performed by using single trait random regression model,the results show that the genetic variance and permanent environmental variance of all traits are tended to be larger at the beginning and end of lactation.The heritability of milk yield traits(milk yield,fat yield and protein yield)ranged from 0.19?0.24,the heritability of milk component traits(fat percentage and protein percentage)ranged from 0.39?0.42,the heritability of SCS was 0.11.(2)In total of 13 genomic regions(1 Mb),10 SNPs and some candidate genes were significantly associated with traits analyzed in studied population,including of DGAT1,ABCG2,PTK2,TRAPPC9,SCARB1 and SLCO1A2.In addition,some polygenic genes were identified that not only related to multiple milk traits,but also to mastitis resistance,growth traits and carcass traits,which including of TIGAR?SPP1?LCORL?NCAPG and LAP3.(3)There are 1,790?1,667and 619 CNV regions(CNVRs)were detected by using PennCNV(ARS-UCD1.2)?PennCNV(UMD3.1)and CNVPartition(UMD3.1)on autosomal genome in studied population,the total length of CNVRs accounted for 14.2%?13.7%and 11.0%of total length of autosomal genomes,respectively.41 high frequency CNVRs(population frequency>5%)were found in the ARS-UCD1.2.In the results of UMD3.1 reference genome,the overlapping CNVRs detected by different methods showed high similarity at the population level,and the total length of overlapping CNVRs is about 168.71Mb,accounting for 48.9%of PennCNV results and 58.43%of CNVPartition results,respectively.(4)In total of 23 CNV regions were identified that associated with milk production traits and SCS in studied population,six CNVRs overlapped with QTLs of milk traits that previous reported,five CNV regions include important and potential candidate genes that related to milk production traits in dairy cattle.In addition,the study found 55 samples have copy number variation in the DGAT1 gene,since this gene has been proved to be a key gene affecting dairy milk traits,it is also of great significance to explore the influence of copy number variation on phenotypes of this gene,which could be used as a follow-up validation population list.(5)There are 23 high frequency ROH regions were detected in studied population,five important regions include of candidate genes that affecting dairy phenotypes through literature review and gene functional annotation.Candidate genes related to growth traits,body size and food intake were located in the selection regions on BTA14,include of pleioreopic genes PLAG1 and CHCHD7.In addition,most of candidate genes in the selection region of BTA20,and most of candidate genes related to cattle reproduction traits in the selection regions on BTA7,10 and 29.In the breeding process of Holstein cattle,traits such as body size,milk production performance and bull fertility are artificially selected intensively.These high frequency loss of heterozygosity regions are the selection signals left by this breed during the breeding process.In summary,the study using GWAS,CNV,and ROH genomic analysis strategies to mine genetic variations and candidate genes that effect on important production traits in Ningxia Holstein cow,the results can provide important basic data for Chinese Holstein cattle breeding,and hopeful to provide clues to understanding genetic architecture and molecular mechanism of important traits in Holstein population,and provide important basis and ideas for further experimental design,and lay the foundation for more accuracy genomic selection scheme of dairy cattle in the future.