Artificial selection for economically important traits in cattle is expected to have left distinctive selection signatures on the genome. Access to high-density genotypes facilitates the accurate identification of genomic regions that have undergone positive selection. These findings help to better elucidate the mechanisms of selection and to identify candidate genes of interest to breeding programs. Information on 705 243 autosomal single nucleotide polymorphisms (SNPs) in 3122 dairy and beef male animals from seven cattle breeds (Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental) were used to detect selection signatures by applying two complementary methods, integrated haplotype score (iHS) and global fixation index (FST). To control for false positive results, we used false discovery rate (FDR) adjustment to calculate adjusted iHS within each breed and the genome-wide significance level was about 0.003. Using the iHS method, 83, 92, 91, 101, 85, 101 and 86 significant genomic regions were detected for Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental cattle, respectively. None of these regions was common to all seven breeds. Using the FST approach, 704 individual SNPs were detected across breeds. Annotation of the regions of the genome that showed selection signatures revealed several interesting candidate genes i.e. DGAT1, ABCG2, MSTN, CAPN3, FABP3, CHCHD7, PLAG1, JAZF1, PRKG2, ACTC1, TBC1D1, GHR, BMP2, TSG1, LYN, KIT and MC1R that play a role in milk production, reproduction, body size, muscle formation or coat color. Fifty-seven common candidate genes were found by both the iHS and global FST methods across the seven breeds. Moreover, many novel genomic regions and genes were detected within the regions that showed selection signatures; for some candidate genes, signatures of positive selection exist in the human genome. Multilevel bioinformatic analyses of the detected candidate genes suggested that the PPAR pathway may have been subjected to positive selection. This study provides a high-resolution bovine genomic map of positive selection signatures that are either specific to one breed or common to a subset of the seven breeds analyzed. Our results will contribute to the detection of functional candidate genes that have undergone positive selection in future studies.

Traditionally, Chinese indigenous sheep were classified geographically and morphologically into three groups: Mongolian, Kazakh and Tibetan. Herein, we aimed to evaluate the population structure and genome selection among 140 individuals from ten representative Chinese indigenous sheep breeds: Ujimqin, Hu, Tong, Large-Tailed Han and Lop breed (Mongolian group); Duolang and Kazakh (Kazakh group); and Diqing, Plateau-type Tibetan, and Valley-type Tibetan breed (Tibetan group). We analyzed the population using principal component analysis (PCA), STRUCTURE and a Neighbor-Joining (NJ)-tree. In PCA plot, the Tibetan and Mongolian groups were clustered as expected; however, Duolang and Kazakh (Kazakh group) were segregated. STRUCTURE analyses suggested two subpopulations: one from North China (Kazakh and Mongolian groups) and the other from the Southwest (Tibetan group). In the NJ-tree, the Tibetan group formed an independent branch and the Kazakh and Mongolian groups were mixed. We then used the d i statistic approach to reveal selection in Chinese indigenous sheep breeds. Among the 599 genome sequence windows analyzed, sixteen (2.7%) exhibited signatures of selection in four or more breeds. We detected three strong selection windows involving three functional genes: RXFP2, PPP1CC and PDGFD. PDGFD, one of the four subfamilies of PDGF, which promotes proliferation and inhibits differentiation of preadipocytes, was significantly selected in fat type breeds by the Rsb (across pairs of populations) approach. Two consecutive selection regions in Duolang sheep were obviously different to other breeds. One region was in OAR2 including three genes (NPR2, SPAG8 and HINT2) the influence growth traits. The other region was in OAR 6 including four genes (PKD2, SPP1, MEPE, and IBSP) associated with a milk production quantitative trait locus. We also identified known candidate genes such as BMPR1B, MSRB3, and three genes (KIT, MC1R, and FRY) that influence lambing percentage, ear size and coat phenotypes, respectively. Based on the results presented here, we propose that Chinese native sheep can be divided into two genetic groups: the thin type (Tibetan group), and the fat type (Mongolian and Kazakh group). We also identified important genes that drive valuable phenotypes in Chinese indigenous sheep, especially PDGFD, which may influence fat deposition in fat type sheep.

In this study, a single-trait genomic model (STGM) is compared with a multiple-trait genomic model (MTGM) for genomic prediction using conventional estimated breeding values (EBVs) calculated using a conventional single-trait and multiple-trait linear mixed models as the response variables. Three scenarios with and without missing data were simulated; no missing data, 90% missing data in a trait with high heritability, and 90% missing data in a trait with low heritability. The simulated genome had a length of 500 cM with 5000 equally spaced single nucleotide polymorphism markers and 300 randomly distributed quantitative trait loci (QTL). The true breeding values of each trait were determined using 200 of the QTLs, and the remaining 100 QTLs were assumed to affect both the high (trait I with heritability of 0.3) and the low (trait II with heritability of 0.05) heritability traits. The genetic correlation between traits I and II was 0.5, and the residual correlation was zero.

Background: In recent years, genome-wide association studies have successfully uncovered single-nucleotide polymorphisms (SNPs) associated with complex traits such as diseases and quantitative phenotypes. These variations account for a small proportion of heritability. With the development of high throughput techniques, abundant submicroscopic structural variations have been found in organisms, of which the main variations are copy number variations (CNVs). Therefore, CNVs are increasingly recognized as an important and abundant source of genetic variation and phenotypic diversity. Results: Analyses of CNVs in the genomes of three sheep breeds were performed using the Ovine SNP50 BeadChip array. A total of 238 CNV regions (CNVRs) were identified, including 219 losses, 13 gains, and six with both events (losses and gains), which cover 60.35 Mb of the sheep genomic sequence and correspond to 2.27% of the autosomal genome sequence. The length of the CNVRs on autosomes range from 13.66 kb to 1.30 Mb with a mean size of 253.57 kb, and 75 CNVRs events had a frequency > 3%. Among these CNVRs, 47 CNVRs identified by the PennCNV overlapped with the CNVpartition. Functional analysis indicated that most genes in the CNVRs were significantly enriched for involvement in the environmental response. Furthermore, 10 CNVRs were selected for validation and 6 CNVRs were further experimentally confirmed by qPCR. In addition, there were 57 CNVRs overlapped in our new dataset and other published ruminant CNV studies. Conclusions: In this study, we firstly constructed a sheep CNV map based on the Ovine SNP50 array. Our results demonstrated the differences of two detection tools and integration of multiple algorithms can enhance the detection of sheep genomic structure variations. Furthermore, our findings would be of help for understanding the sheep genome and provide preliminary foundation for carrying out the CNVs association studies with economically important phenotypes of sheep in the future.

Chinese indigenous sheep can be classified into three types based on tail morphology: fat-tailed, fat-rumped, and thin-tailed sheep, of which the typical breeds are large-tailed Han sheep, Altay sheep, and Tibetan sheep, respectively. To unravel the genetic mechanisms underlying the phenotypic differences among Chinese indigenous sheep with tails of three different types, we used ovine high-density 600K SNP arrays to detect genome-wide copy number variation (CNV). In large-tailed Han sheep, Altay sheep, and Tibetan sheep, 371, 301, and 66 CNV regions (CNVRs) with lengths of 71.35 Mb, 51.65 Mb, and 10.56 Mb, respectively, were identified on autosomal chromosomes. Ten CNVRs were randomly chosen for confirmation, of which eight were successfully validated. The detected CNVRs harboured 3130 genes, including genes associated with fat deposition, such as PPARA, RXRA, KLF11, ADD1, FASN, PPP1CA, PDGFA, and PEX6. Moreover, multilevel bioinformatics analyses of the detected candidate genes were significantly enriched for involvement in fat deposition, GTPase regulator, and peptide receptor activities. This is the first high-resolution sheep CNV map for Chinese indigenous sheep breeds with three types of tails. Our results provide valuable information that will support investigations of genomic structural variation underlying traits of interest in sheep.