A genomewide association study was carried out on a sample of Marchigiana breed cattle to detect markers significantly associated with carcass and meat traits. Four hundred and nine young bulls from 117 commercial herds were genotyped by Illumina 50K BeadChip assay. Eight growth and carcass traits (average daily gain, carcass weight, dressing percentage, body weight, skin weight, shank circumference, head weight and carcass conformation) and two meat quality traits (pH at slaughter and pH 24 h after slaughter) were measured. Data were analysed with a linear mixed model that included fixed effects of herd, slaughter date, fixed covariables of age at slaughter and SNP genotype, and random effects of herd and animal. A permutation test was performed to correct SNP genotype significance level for multiple testing. A total of 96 SNPs were significantly associated at genomewide level with one or more of the considered traits. Gene search was performed on genomic regions identified on the basis of significant SNP position and level of linkage disequilibrium. Interesting loci affecting lipid metabolism (SOAT1), bone (BMP4) and muscle (MYOF) biology were highlighted. These results may be useful to better understand the genetic architecture of growth and body composition in cattle.

Analysis of genomic data is increasingly becoming part of the livestock industry. Therefore, the routine collection of genomic information would be an invaluable resource for effective management of breeding programs in small, endangered populations. The objective of the paper was to demonstrate how genomic data could be used to analyse (1) linkage disequlibrium (LD), LD decay and the effective population size (NeLD); (2) Inbreeding level and effective population size (NeROH) based on runs of homozygosity (ROH); (3) Prediction of genomic breeding values (GEBV) using small within-breed and genomic information from other breeds. The Tyrol Grey population was used as an example, with the goal to highlight the potential of genomic analyses for small breeds. In addition to our own results we discuss additional use of genomics to assess relatedness, admixture proportions, and inheritance of harmful variants. The example data set consisted of 218 Tyrol Grey bull genotypes, which were all available AI bulls in the population. After standard quality control restrictions 34,581 SNPs remained for the analysis. A separate quality control was applied to determine ROH levels based on Illumina GenCall and Illumina GenTrain scores, resulting into 211 bulls and 33,604 SNPs. LD was computed as the squared correlation coefficient between SNPs within a 10 mega base pair (Mb) region. ROHs were derived based on regions covering at least 4, 8, and 16 Mb, suggesting that animals had common ancestors approximately 12, 6, and 3 generations ago, respectively. The corresponding mean inbreeding coefficients (F ROH) were 4.0% for 4 Mb, 2.9% for 8 Mb and 1.6% for 16 Mb runs. With an average generation interval of 5.66 years, estimated NeROH was 125 (NeROH>16 Mb), 186 (NeROH>8 Mb) and 370 (NeROH>4 Mb) indicating strict avoidance of close inbreeding in the population. The LD was used as an alternative method to infer the population history and the Ne. The results show a continuous decrease in NeLD, to 780, 120, and 80 for 100, 10, and 5 generations ago, respectively. Genomic selection was developed for and is working well in large breeds. The same methodology was applied in Tyrol Grey cattle, using different reference populations. Contrary to the expectations, the accuracy of GEBVs with very small within breed reference populations were very high, between 0.13-0.91 and 0.12-0.63, when estimated breeding values and deregressed breeding values were used as pseudo-phenotypes, respectively. Subsequent analyses confirmed the high accuracies being a consequence of low reliabilities of pseudo-phenotypes in the validation set, thus being heavily influenced by parent averages. Multi-breed and across breed reference sets gave inconsistent and lower accuracies. Genomic information may have a crucial role in management of small breeds, even if its primary usage differs from that of large breeds. It allows to assess relatedness between individuals, trends in inbreeding and to take decisions accordingly. These decisions would be based on the real genome architecture, rather than conventional pedigree information, which can be missing or incomplete. We strongly suggest the routine genotyping of all individuals that belong to a small breed in order to facilitate the effective management of endangered livestock populations.

The aim of the study was to screen the whole bull genome to identify markers and candidate genes underlying poor sperm motility. The analyzed data set originates from the Polish Holstein-Friesian bull population and consists of 41 Case and 279 Control bulls (selected from 1581 bulls). The most distinguishing trait of case group was very poor sperm motility (average 25.61%) when compared to control samples (average 72.95%). Each bull was genotyped using the Illumina BovineSNP50 BeadChip. Genome-wide association analysis was performed with the use of GoldenHelix SVS7 software. An additive model with a Cohran-Armitage test, Correlation/Trend adjusted by Bonferroni test were used to estimate the effect of Single Nucleotide Polymorphism (SNP) marker for poor sperm motility. Markers (n=34) reached genome-wide significance. The most significant SNP were located on chromosome 24 (rs110876480), 5 (rs110827324 and rs29011704), and 1 (rs110596818), in the close vicinity of melanocortin 4 receptor (MC4R), PDZ domain containing ring finger 4 (PDZRN4) and ethanolamine kinase 1 (ETNK1), olfactory receptor 5K3-like (LOC785875) genes, respectively. For five other candidate genes located close to significant markers (in distance of ca. 1 Mb), namely alkaline phosphatase, liver/bone/kidney (ALPL), tripartite motif containing 36 (TRIM36), 3-hydroxyisobutyrate dehygrogenase (HIBADH), kelch-like 1 (KLHL1), protein kinase C, beta (PRKCB), their potential role in sperm motility was confirmed in the earlier studies. Five additional candidate genes, cystic fibrosis transmembrane conductance regulator (CFTR), insulin-like growth factor 1 receptor (IGF1R), steroid-5-alpha-reductase, alpha polypeptide 2 (SRD5A2), cation channel, sperm associated 1 (CATSPER1) calpain 1 (mu/I) large subunit (CAPN1) were suggested to be significantly associated with sperm motility or semen biochemistry. Results of the present study indicate there is a genetic complexity of poor sperm motility but also indicate there might be a causal polymorphism useful in marker-assisted selection. Identifying genomic regions associated with poor sperm motility may be very important for early recognition of a young sire as unsuitable for effective semen production in artificial insemination centers.