Horses are nowadays mainly used for sport and leisure activities, and several local breeds, traditionally used in agriculture, have been exposed to a dramatic loss in population size and genetic diversity. The loss of genetic diversity negatively impacts individual fitness and reduces the potential long-term survivability of a breed. Recent advances in molecular biology and bioinformatics have allowed researchers to explore biodiversity one step further. This study aimed to evaluate the loss of genetic variability and identify genomic regions under selection pressure in the Bardigiano breed based on GGP Equine70k SNP data. The effective population size based on Linkage Disequilibrium (N_e) was equal to 39 horses, and it showed a decline over time. The average inbreeding based on runs of homozygosity (ROH) was equal to 0.17 (SD = 0.03). The majority of the ROH were relatively short (91% were ≤2 Mbp long), highlighting the occurrence of older inbreeding, rather than a more recent occurrence. A total of eight ROH islands, shared among more than 70% of the Bardigiano horses, were found. Four of them mapped to known quantitative trait loci related to morphological traits (e.g., body size and coat color) and disease susceptibility. This study provided the first genome-wide scan of genetic diversity and selection signatures in an Italian native horse breed.

The recent release of the Bovine HapMap dataset represents the most detailed survey of bovine genetic diversity to date, providing an important resource for the design and development of livestock production. We studied this dataset, comprising more than 30,000 Single Nucleotide Polymorphisms (SNPs) for 19 breeds (13 taurine, three zebu, and three hybrid breeds), seeking to identify small panels of genetic markers that can be used to trace the breed of unknown cattle samples. Taking advantage of the power of Principal Components Analysis and algorithms that we have recently described for the selection of Ancestry Informative Markers from genomewide datasets, we present a decision-tree which can be used to accurately infer the origin of individual cattle. In doing so, we present a thorough examination of population genetic structure in modern bovine breeds. Performing extensive cross-validation experiments, we demonstrate that 250-500 carefully selected SNPs suffice in order to achieve close to 100% prediction accuracy of individual ancestry, when this particular set of 19 breeds is considered. Our methods, coupled with the dense genotypic data that is becoming increasingly available, have the potential to become a valuable tool and have considerable impact in worldwide livestock production. They can be used to inform the design of studies of the genetic basis of economically important traits in cattle, as well as breeding programs and efforts to conserve biodiversity. Furthermore, the SNPs that we have identified can provide a reliable solution for the traceability of breed-specific branded products.

Recent available instruments allow to record the number of differential somatic cell count (DSCC), representing the combined proportion of polymorphonuclear leukocytes and lymphocytes, on a large number of milk samples. Milk DSCC provides indirect information on the udder health status of dairy cows. However, literature is limited regarding the effect of DSCC on milk composition at the individual cow level, as well as its relation to the somatic cell score (SCS). Hence, the aims of this study were to (i) investigate the effect of different levels of DSCC on milk composition (fat, protein, casein, casein index, and lactose) and (ii) explore the combined effect of DSCC and SCS on these traits. Statistical models included the fixed effects of days in milk, parity, SCS, DSCC and the interaction between SCS × DSCC, and the random effects of herd, animal within parity, and repeated measurements within cow. Results evidenced a decrease of milk fat and an increase in milk fatty acids at increasing DSCC levels, while protein, casein and their proportion showed their lowest values at the highest DSCC. A positive association was found between DSCC and lactose. The interaction between SCS and DSCC was important for lactose and casein index, as they varied differently upon high and low SCS and according to DSCC levels.

It is becoming common to complement genome-wide association studies (GWAS) with gene-set enrichment analysis to deepen the understanding of the biological pathways affecting quantitative traits. Our objective was to conduct a gene ontology and pathway-based analysis to identify possible biological mechanisms involved in the regulation of bovine milk technological traits: coagulation properties, curd firmness modeling, individual cheese yield (CY), and milk nutrient recovery into the curd (REC) or whey loss traits. Results from 2 previous GWAS studies using 1,011 cows genotyped for 50k single nucleotide polymorphisms were used. Overall, the phenotypes analyzed consisted of 3 traditional milk coagulation property measures [RCT: rennet coagulation time defined as the time (min) from addition of enzyme to the beginning of coagulation; k₂₀: the interval (min) from RCT to the time at which a curd firmness of 20 mm is attained; a₃₀: a measure of the extent of curd firmness (mm) 30 min after coagulant addition], 6 curd firmness modeling traits [RCT_eq: RCT estimated through the CF equation (min); CF_P: potential asymptotic curd firmness (mm); k_CF: curd-firming rate constant (% × min⁻¹); k_SR: syneresis rate constant (% × min⁻¹); CF_max: maximum curd firmness (mm); and t_max: time to CF_max (min)], 3 individual CY-related traits expressing the weight of fresh curd (%CY_CURD), curd solids (%CY_SOLIDS), and curd moisture (%CY_WATER) as a percentage of weight of milk processed and 4 milk nutrient and energy recoveries in the curd (REC_FAT, REC_PROTEIN, REC_SOLIDS, and REC_ENERGY calculated as the % ratio between the nutrient in curd and the corresponding nutrient in processed milk), milk pH, and protein percentage. Each trait was analyzed separately. In total, 13,269 annotated genes were used in the analysis. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were queried for enrichment analyses. Overall, 21 Gene Ontology and 17 Kyoto Encyclopedia of Genes and Genomes categories were significantly associated (false discovery rate at 5%) with 7 traits (RCT, RCT_eq, k_CF, %CY_SOLIDS, REC_FAT, REC_SOLIDS, and REC_ENERGY), with some being in common between traits. The significantly enriched categories included calcium signaling pathway, salivary secretion, metabolic pathways, carbohydrate digestion and absorption, the tight junction and the phosphatidylinositol pathways, as well as pathways related to the bovine mammary gland health status, and contained a total of 150 genes spanning all chromosomes but 9, 20, and 27. This study provided new insights into the regulation of bovine milk coagulation and cheese ability that were not captured by the GWAS.

Background: Body weight (BW) is an economically important trait in the broiler (meat-type chickens) industry. Under the assumption of polygenicity, a “large” number of genes with “small” effects is expected to control BW. To detect such effects, a large sample size is required in genome-wide association studies (GWAS). Our objective was to conduct a GWAS for BW measured at 35 days of age with a large sample size. Methods: The GWAS included 137,343 broilers spanning 15 pedigree generations and 392,295 imputed single nucleotide polymorphisms (SNPs). A false discovery rate of 1% was adopted to account for multiple testing when declaring significant SNPs. A Bayesian ridge regression model was implemented, using AlphaBayes, to estimate the contribution to the total genetic variance of each region harbouring significant SNPs (1 Mb up/downstream) and the combined regions harbouring non-significant SNPs. Results: GWAS revealed 25 genomic regions harbouring 96 significant SNPs on 13 Gallus gallus autosomes (GGA1 to 4, 8, 10 to 15, 19 and 27), with the strongest associations on GGA4 at 65.67–66.31 Mb (Galgal4 assembly). The association of these regions points to several strong candidate genes including: (i) growth factors (GGA1, 4, 8, 13 and 14); (ii) leptin receptor overlapping transcript ( LEPROT )/leptin receptor ( LEPR ) locus (GGA8), and the STAT3 / STAT5B locus (GGA27), in connection with the JAK/STAT signalling pathway; (iii) T-box gene ( TBX3/TBX5 ) on GGA15 and CHST11 (GGA1), which are both related to heart/skeleton development); and (iv) PLAG1 (GGA2). Combined together, these 25 genomic regions explained ~ 30% of the total genetic variance. The region harbouring significant SNPs that explained the largest portion of the total genetic variance (4.37%) was on GGA4 (~ 65.67–66.31 Mb). Conclusions: To the best of our knowledge, this is the largest GWAS that has been conducted for BW in chicken to date. In spite of the identified regions, which showed a strong association with BW, the high proportion of genetic variance attributed to regions harbouring non-significant SNPs supports the hypothesis that the genetic architecture of BW35 is polygenic and complex. Our results also suggest that a large sample size will be required for future GWAS of BW35.

The objective of this study was to estimate inbreeding coefficients in Holstein dairy cattle using imputed SNPs data. A data set of 95,540 Italian Holstein dairy cows from the routine genomic evaluations of the Italian National Association of Holstein, Brown, and Jersey Breeders were analyzed, with 84,445 imputed SNP. Ten widely used genomic inbreeding estimators were tested, including 4 PLINK v1.9 estimators (F, F_HAT1, F_HAT2, F_HAT3), 3 genomic relationship matrix (GRM)-based methods [VanRaden's first method with observed allele frequencies (F_GRM) or with fixed frequencies at 0.5 (F_GRM05), VanRaden's third method, allelic frequency free and pedigree regressed (F_GRM2)], runs of homozygosity (ROH)-based estimators in a complete (F_ROH) and simplified version (F_ROH2), and proportion of homozygous SNP (F_PH). Pairwise comparisons among them were made, including the comparison with traditional pedigree-based inbreeding coefficients (F_PED). Our results showed variability among the genomic inbreeding estimators. Coefficients of F_GRM and F_HAT3 were >1, meaning that more variability has been lost than the variability that existed in the base population. Regarding the remaining ones, F_GRM05, F_ROH, F_ROH2, and F_PH provided coefficients within the [0,1] space and are considered comparable to F_PED. Not comparable to F_PED, yet with an interpretable value, can be considered the coefficients of F, F_HAT2, and F_GRM2. Estimators based on ROH had the highest correlation with pedigree-based coefficients (0.59-0.66), among all estimators tested. In this study, Spearman correlations were shown to possibly provide a clearer estimation of the strength of the relationship between estimators. We hypothesize that imputation might cause extreme genomic inbreeding values that deserves further investigation.