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Genomic information allows for a more accurate calculation of relationships among animals than the pedigree information, leading to an increase in accuracy of breeding values. Here, we used pedigree‐based and single‐step genomic approaches to estimate variance components and breeding values for β‐hydroxybutyrate milk content (BHB). Additionally, we performed a genome‐wide association study (GWAS) to depict its genetic architecture. BHB concentrations within the first 90 days of lactation, estimated from milk medium infrared spectra, were available for 30,461 cows (70,984 records). Genotypes at 42,152 loci were available for 9,123 animals. Low heritabilities were found for BHB using pedigree‐based (0.09 ± 0.01) and genomic (0.10 ± 0.01) approaches. Genetic correlation between BHB and milk traits ranged from −0.27 ± 0.06 (BHB and protein percentage) to 0.13 ± 0.07 (BHB and fat‐to‐protein ratio) using pedigree and from −0.26 ± 0.05 (BHB and protein percentage) to 0.13 ± 0.06 (BHB and fat‐to‐protein ratio) using genomics. Breeding values were validated for 344 genotyped cows using linear regression method. The genomic EBV (GEBV) had greater accuracy (0.51 vs. 0.45) and regression coefficient (0.98 vs. 0.95) compared to EBV. The correlation between two subsequent evaluations, without and with phenotypes for validation cows, was 0.85 for GEBV and 0.82 for EBV. Predictive ability (correlation between (G)EBV and adjusted phenotypes) was greater when genomic information was used (0.38) than in the pedigree‐based approach (0.31). Validation statistics in the pairwise two‐trait models (milk yield, fat and protein percentage, urea, fat/protein ratio, lactose and logarithmic transformation of somatic cells count) were very similar to the ones highlighted for the single‐trait model. The GWAS allowed discovering four significant markers located on BTA20 (57.5–58.2 Mb), where the ANKH gene is mapped. This gene has been associated with lactose, alpha‐lactalbumin and BHB. Results of this study confirmed the usefulness of genomic information to provide more accurate variance components and breeding values, and important insights about the genomic determination of BHB milk content.

Milkability is a trait related to the milking efficiency of an animal, and it is a component of the herd profitability. Due to its economic importance, milkability is currently included in the selection index of the Italian Simmental cattle breed with a weight of 7.5%. This lowly heritable trait is measured on a subjective scale from 1 to 3 (1 = slow, 3 = fast), and genetic evaluations are performed by pedigree-based BLUP. Genomic information is now available for some animals in the Italian Simmental population, and its inclusion in the genetic evaluation system could increase accuracy of breeding values and genetic progress for milkability. The aim of this study was to test the feasibility and advantages of having a genomic evaluation for this trait in the Italian Simmental population. Phenotypes were available for 131,308 cows. A total of 9,526 animals had genotypes for 42,152 loci; among the genotyped animals, 2,455 were cows with phenotypes, and the other were their relatives. The youngest cows with both phenotypes and genotypes (n = 900) were identified as selection candidates. Variance components and heritability were estimated using pedigree information, whereas genetic and genomic evaluations were carried out using BLUP and single-step genomic BLUP (ssGBLUP), respectively. In addition, a weighted ssGBLUP was assessed using genomic regions from a genome-wide association study. Evaluation models were validated using theoretical and realized accuracies. The estimated heritability for milkability was 0.12 ± 0.01. The mean theoretical accuracies for selection candidates were 0.43 ± 0.08 (BLUP) and 0.53 ± 0.06 (ssGBLUP). The mean realized accuracies based on linear regression statistics were 0.29 (BLUP) and 0.40 (ssGBLUP). No genomic regions were significantly associated with milkability, thus no improvements in accuracy were observed when using weighted ssGBLUP. Results indicated that genomic information could improve the accuracy of breeding values and increase genetic progress for milkability in Italian Simmental.

Fatty acid (FA) profile is one of the most important aspects of the nutritional properties of milk. The FA content in milk is affected by several factors such as diet, physiology, environment, and genetics. Recently, principal component analysis (PCA) and multivariate factor analysis (MFA) have been used to summarize the complex correlation pattern of the milk FA profile by extracting a reduced number of new variables. In this work, the milk FA profile of a sample of 993 Sarda breed ewes was analyzed with PCA and MFA to compare the ability of these 2 multivariate statistical techniques in investigating the possible existence of latent substructures, and in studying the influence of physiological and environmental effects on the new extracted variables. Individual scores of PCA and MFA were analyzed with a mixed model that included the fixed effects of parity, days in milking, lambing month, number of lambs born, altitude of flock location, and the random effect of flock nested within altitude. Both techniques detected the same number of latent variables (9) explaining 80% of the total variance. In general, PCA structures were difficult to interpret, with only 4 principal components being associated with a clear meaning. Principal component 1 in particular was the easiest to interpret and agreed with the interpretation of the first factor, with both being associated with the FA of mammary origin. On the other hand, MFA was able to identify a clear structure for all the extracted latent variables, confirming the ability of this technique to group FA according to their function or metabolic origin. Key pathways of the milk FA metabolism were identified as mammary gland de novo synthesis, ruminal biohydrogenation, desaturation performed by stearoyl-coenzyme A desaturase enzyme, and rumen microbial activity, confirming previous findings in sheep and in other species. In general, the new extracted variables were mainly affected by physiological factors as days in milk, parity, and lambing month; the number of lambs born had no effect on the new variables, and altitude influenced only one principal component and factor. Both techniques were able to summarize a larger amount of the original variance into a reduced number of variables. Moreover, factor analysis confirmed its ability to identify latent common factors clearly related to FA metabolic pathways.

Background Water buffalo is one of the most important livestock species in the world. Two types of water buffalo exist: river buffalo (Bubalus bubalis bubalis) and swamp buffalo (Bubalus bubalis carabanensis). The buffalo genome has been recently sequenced, and thus a new 90 K single nucleotide polymorphism (SNP) bead chip has been developed. In this study, we investigated the genomic population structure and the level of inbreeding of 185 river and 153 swamp buffaloes using runs of homozygosity (ROH). Analyses were carried out jointly and separately for the two buffalo types. Results The SNP bead chip detected in swamp about one-third of the SNPs identified in the river type. In total, 18,116 ROH were detected in the combined data set (17,784 SNPs), and 16,251 of these were unique. ROH were present in both buffalo types mostly detected (~ 59%) in swamp buffalo. The number of ROH per animal was larger and genomic inbreeding was higher in swamp than river buffalo. In the separated datasets (46,891 and 17,690 SNPs for river and swamp type, respectively), 19,760 and 10,581 ROH were found in river and swamp, respectively. The genes that map to the ROH islands are associated with the adaptation to the environment, fitness traits and reproduction. Conclusions Analysis of ROH features in the genome of the two water buffalo types allowed their genomic characterization and highlighted differences between buffalo types and between breeds. A large ROH island on chromosome 2 was shared between river and swamp buffaloes and contained genes that are involved in environmental adaptation and reproduction.

The aim of this study was to test the feasibility of genomic selection in the Italian Mediterranean water buffalo, which is farmed mainly in the south Italy for milk, and mozzarella, production. A total of 498 animals were genotyped at 49,164 loci. Test day records (80,417) of milk (MY), fat (FY) and protein (PY) yields from 4127 cows, born between 1975 and 2009, were analysed in a three-trait model. Cows born in 2008 and 2009 with phenotypes and genotypes were selected as validation animals (n = 50). Variance components (VC) were estimated with BLUP and ssGBLUP. Heritabilities for BLUP were 0.25 ? 0.02 (MY), 0.16 ? 0.01 (FY) and 0.25 ? 0.01 (PY). Breeding values were computed using BLUP and ssGBLUP, using VC estimated from BLUP. ssGBLUP was applied in five scenarios, each with a different number of genotypes available: (A) bulls (35); (B) validation cows (50); (C) bulls and validation cows (85); (D) all genotyped cows (463); (E) all genotypes (498). Model validation was performed using the LR method: correlation, accuracy, dispersion, and bias statistics were calculated. Average correlations were 0.71 ? 0.02 and 0.82 ? 0.01 for BLUP and ssGBLUP-E, respectively. Accuracies were also higher in ssGBLUP-E (0.75 ? 0.03) compared to BLUP (0.57 ? 0.03). The best dispersions (i.e. closer to 1) were found for ssGBLUP-C. The use of genotypes only for the 35 bulls did not change the validation values compared to BLUP. Results of the present study, even if based on small number of animals, showed that the inclusion of genotypes of females can improve breeding values accuracy in the Italian Buffalo.HighlightsThe genotypes of males did not improve the predictions.Genotypes of females improve breeding values accuracy.Slight increase in prediction accuracy with weighted ssGBLUP.

The study of Runs of Homozygosity (ROH) is a useful approach for the characterization of the genome of livestock populations. Due to their high relationship with autozygosity, ROH allow to make inference about population genetic history, to estimate the level of inbreeding, to assess within breed heterogeneity and to detect the footprints of selection on livestock genomes. Aim of this study was to investigate the distribution of runs of homozygosity in bulls belonging to five European Simmental populations and to assess the relationship between three production traits (milk yield, fat and protein contents) and autozygosity. ROH count, distribution and ROH-based coefficient of inbreeding (FROH ) were calculated for 3,845 Simmental bulls of five different European countries: Austria (AT), Switzerland (CH), Czech Republic (CZ), Germany (DE) and Italy (IT). Average values of ROH number per animal, and total genome length covered by ROH were 77.8 ± 20.7 and 205 ± 74.4 Mb, respectively. Bulls from AT, DE and IT exhibited similar ROH characteristics. Swiss animals showed the highest (12.6%), while CZ the lowest (4.6%) FROH coefficient. The relationship between ROH occurrence and milk production traits was investigated through a genome-wide ROH-traits association analysis (GWRA). A total of 34 regions previously associated with milk traits (yield and/or composition) were identified by GWRA. Results of the present research highlight a mixed genetic background in the 5 European Simmental populations, with the possible presence of three subgroups. Moreover, a strong relationship between autozygosity and production traits has been detected.

Genetic diversity of two closely related sheep breeds, the Sarda and the Sardinian Ancestral Black, was investigated using different statistical approaches. A total of 210 sheep (94 Sarda and 116 Sardinian Ancestral Black) were genotyped using Infinium Ovine SNP50 v1 BeadChip. After filtering, 43,431 markers mapped on the 26 autosomal chromosomes were used in the analysis Basic population statistics (heterozygosity, minor allele frequency), linkage disequilibrium level, indicators of selection sweeps as the Wright fixation index (FST), and of autozygosity (Runs of Homozygosity) were calculated. Sarda breed showed higher linkage disequilibrium levels and a smaller effective population size. The FST and ROH approaches highlighted some regions on chromosomes 7, 10, 14, 19, and 20 that differ between the two breeds. Several known genes involved in morphological traits such as coat color (MC1R, MITF), horned/polled (RXFP2) and body size (NPY, VIP) and new putative ones (MYO5A, INSIG2 and ELOVL1) were detected.

Annual and interannual dynamics of shellfish toxins and associated harmful algal species (HAS) were analyzed from 2015 to 2020 in Tortolì Lagoon (Sardinia, west Mediterranean Sea). Analysis of seasonal occurrence of different harmful algae, such as Dinophysis spp., Prorocentrum spp., Pseudo-nitzschia spp. and Alexandrium minutum, was performed. The species Dinophysis acuminata and Dinophysis sacculus were responsible for the accumulation of lipophilic toxins belonging to the okadaic acid group (OAs) and pectenotoxins2 (PTX2) in bivalve mollusks. The highest HAS detection was recorded in the winter months, in particular, Dinophysis spp. was mostly present in January–February. Out of 1090 analyzed mollusk samples, 39 were non-compliant, exceeding the legal limits (160 μg OA eq/kg e.p.) reported in Regulation 853/2004 of the European Commission. A statistical analysis related to the presence of OA and PTX2 in mollusks with various environmental parameters (pH, water temperature, dissolved oxygen, algal density) was implemented, proving a clear winter seasonality. The present study highlights the necessity to better understand the different factors able to influence the production and accumulation of toxins in bivalve mollusks bred in an important Sardinian production area. The contribution of this research is important not only from an environmental and productive point of view but also from the view of implementing management in order to mitigate any harm to human health.

The inverse association between the groups of odd-chain (OCFA) and branched-chain (BCFA) and the development of diseases in humans have generated interest in the scientific community. In experiment 1, the extent of the passage of odd- and branched-chain fatty acids (OBCFA) from milk fat to fresh cheese fat was studied in sheep and goats. Milk collected in two milk processing plants in west Sardinia (Italy) was sampled every 2 weeks during spring (March, April and May). In addition, a survey was carried out to evaluate the seasonal variation of the OBCFA concentrations in sheep and goats’ cheeses during all lactation period from January to June. Furthermore, to assess the main differences among the sheep and goat cheese, principal component analysis (PCA) was applied to cheese fatty acids (FA) profile. Concentrations of OBCFA in fresh cheese fat of both species were strongly related to the FA content in the unprocessed raw milk. The average contents of OBCFA were 4.12 and 4.13 mg/100 mg of FA in sheep milk and cheese, respectively, and 3.12 and 3.17 mg/100 mg of FA in goat milk and cheese, respectively. The OBCFA concentration did no differed between milk and cheese in any species. The content of OBCFA was significantly higher in sheep than goats’ dairy products. The OBCFA composition of the cheese was markedly affected by the period of sampling in both species: odd and branched FA concentrations increased from March to June. The seasonal changes of OBCFA in dairy products were likely connected to variations in the quality of the diet. The PCA confirmed the higher nutritional quality of sheep cheese for beneficial FA, including OBCFA compared to the goat one, and the importance of the period of sampling in the definition of the fatty acids profile.

Residual feed intake (RFI) is the most used measure of feed efficiency. However, considering the importance of concentrates in the ration, a new index, the residual concentrate intake (RCI), was here defined. RCI aims to measure the individual efficiency in converting the concentrate into animal products. Brown Swiss young bulls (N = 736) were genotyped at 41,183 loci. Animals were housed in pens equipped with an automatic feeding system able to recognise the animal and record the concentrate intake. The diet consisted of concentrate and hay (ad libitum). The new RCI index was calculated as the residuals of the linear regression of concentrate intake on metabolic live weight and average daily gain. Animals were ranked according to their corrected RCI and divided into low (LRCI) and high phenotypes (HRCI). A low heritability (0.06 ± 0.03) was estimated using only genomics for this new index. Results from multivariate (M-GWAS) and Bayesian (B-GWAS) approaches were combined to identify SNP associated with RCI. The M-GWAS selected 698 SNPs potentially associated, whereas no significant markers were obtained in B-GWAS. Markers in the last approach were ranked according to their posterior inclusion probability and the first 698 were retained. Only SNPs in common between sorted B-GWAS and M-GWAS (N = 11) were considered associated with RCI. A total of 48 candidate genes were retrieved near these SNPs. Most of them were previously reported to be associated with feed efficiency and RFI. The combined use of multivariate and Bayesian techniques allow to identify SNPs associated with the investigated trait.Highlights RCI could be promising to select animals 48 candidate genes were found associated with RCI Multivariate technique allowed to identify significant SNPs

The objective of this study was to assess the reliability and bias of estimated breeding values (EBV) from traditional BLUP with unknown parent groups (UPG), genomic EBV (GEBV) from single-step genomic BLUP (ssGBLUP) with UPG for the pedigree relationship matrix (A) only (SS_UPG), and GEBV from ssGBLUP with UPG for both A and the relationship matrix among genotyped animals (A_; SS_UPG2) using 6 large phenotype-pedigree truncated Holstein data sets. The complete data included 80 million records for milk, fat, and protein yields from 31 million cows recorded since 1980. Phenotype-pedigree truncation scenarios included truncation of phenotypes for cows recorded before 1990 and 2000 combined with truncation of pedigree information after 2 or 3 ancestral generations. A total of 861,525 genotyped bulls with progeny and cows with phenotypic records were used in the analyses. Reliability and bias (inflation/deflation) of GEBV were obtained for 2,710 bulls based on deregressed proofs, and on 381,779 cows born after 2014 based on predictivity (adjusted cow phenotypes). The BLUP reliabilities for young bulls varied from 0.29 to 0.30 across traits and were unaffected by data truncation and number of generations in the pedigree. Reliabilities ranged from 0.54 to 0.69 for SS_UPG and were slightly affected by phenotype-pedigree truncation. Reliabilities ranged from 0.69 to 0.73 for SS_UPG2 and were unaffected by phenotype-pedigree truncation. The regression coefficient of bull deregressed proofs on (G)EBV (i.e., GEBV and EBV) ranged from 0.86 to 0.90 for BLUP, from 0.77 to 0.94 for SS_UPG, and was 1.00 ± 0.03 for SS_UPG2. Cow predictivity ranged from 0.22 to 0.28 for BLUP, 0.48 to 0.51 for SS_UPG, and 0.51 to 0.54 for SS_UPG2. The highest cow predictivities for BLUP were obtained with the most extreme truncation, whereas for SS_UPG2, cow predictivities were also unaffected by phenotype-pedigree truncations. The regression coefficient of cow predictivities on (G)EBV was 1.02 ± 0.02 for SS_UPG2 with the most extreme truncation, which indicated the least biased predictions. Computations with the complete data set took 17 h with BLUP, 58 h with SS_UPG, and 23 h with SS_UPG2. The same computations with the most extreme phenotype-pedigree truncation took 7, 36, and 15 h, respectively. The SS_UPG2 converged in fewer rounds than BLUP, whereas SS_UPG took up to twice as many rounds. Thus, the ssGBLUP with UPG assigned to both A and A_ provided accurate and unbiased evaluations, regardless of phenotype-pedigree truncation scenario. Old phenotypes (before 2000 in this data set) did not affect the reliability of predictions for young selection candidates, especially in SS_UPG2.

Simple Summary The study was carried out in order to investigate the genetic background of arthrogryposis and macroglossia in the Piemontese cattle breed, for which limited information is available so far. The genotyping of affected and healthy animals with a high-density chip and the subsequent genome-wide association study did not evidence a single strong association with the two pathologies. Therefore, for arthrogryposis, the results do not support the existence of a single-gene model, as reported for other breeds. Rather, 23 significant markers on different chromosomes were found, associated to arthrogryposis, to macroglossia, or to both pathologies, suggesting a more complex genetic mechanism underlying both diseases in the Piemontese breed. The significant single nucleotide polymorphisms (SNPs) allowed the identification of some genes (NTN3, KCNH1, KCNH2, and KANK3) for which a possible role in the pathologies can be hypothesized. The real involvement of these genes needs to be further investigated and validated. Abstract Arthrogryposis and macroglossia are congenital pathologies known in several cattle breeds, including Piemontese. As variations in single genes were identified as responsible for arthrogryposis in some breeds, we decided: (i) to test the hypothesis of a similar genetic determinism for arthrogryposis in the Piemontese breed by genotyping affected and healthy animals with a high-density chip and applying genome-wide association study (GWAS), FST and canonical discriminant analysis (CDA) procedures, and (ii) to investigate with the same approach the genetic background of macroglossia, for which no genetic studies exist so far. The study included 125 animals (63 healthy, 30 with arthrogryposis, and 32 with macroglossia). Differently from what reported for other breeds, the analysis did not evidence a single strong association with the two pathologies. Rather, 23 significant markers on different chromosomes were found (7 associated to arthrogryposis, 11 to macroglossia, and 5 to both pathologies), suggesting a multifactorial genetic mechanism underlying both diseases in the Piemontese breed. In the 100-kb interval surrounding the significant SNPs, 20 and 26 genes were identified for arthrogryposis and macroglossia, respectively, with 12 genes in common to both diseases. For some genes (NTN3, KCNH1, KCNH2, and KANK3), a possible role in the pathologies can be hypothesized, being involved in processes related to muscular or nervous tissue development. The real involvement of these genes needs to be further investigated and validated.

This study aimed to evaluate the changes in variance components over time to identify a subset of data from the Italian Simmental (IS) population that would yield the most appropriate estimates of genetic parameters and breeding values for beef traits to select young bulls. Data from bulls raised between 1986 and 2017 were used to estimate genetic parameters and breeding values for four beef traits (average daily gain [ADG], body size [BS], muscularity [MUS], and feet and legs [FL]). The phenotypic mean increased during the years of the study for ADG, but it decreased for BS, MUS, and FL. The complete dataset (ALL) was divided into four generational subsets (Gen1, Gen2, Gen3, and Gen4). Additionally, ALL was divided into two larger subsets: the first one (OLD) combined data from Gen1 and Gen2 to represent the starting population, and the second one (CUR) combined data from Gen3 and Gen4 to represent a subpopulation with stronger ties to the current population. Genetic parameters were estimated with a four-trait genomic animal model using a single-step genomic average information restricted maximum likelihood algorithm. Heritability estimates from ALL were 0.26 ± 0.03 for ADG, 0.33 ± 0.04 for BS, 0.55 ± 0.03 for MUS, and 0.23 ± 0.03 for FL. Higher heritability estimates were obtained with OLD and ALL than with CUR. Considerable changes in heritability existed between Gen1 and Gen4 due to fluctuations in both additive genetic and residual variances. Genetic correlations also changed over time, with some values moving from positive to negative or even to zero. Genetic correlations from OLD were stronger than those from CUR. Changes in genetic parameters over time indicated that they should be updated regularly to avoid biases in genomic estimated breeding values (GEBV) and low selection accuracies. GEBV estimated using CUR variance components were less biased and more consistent than those estimated with OLD and ALL variance components. Validation results indicated that data from recent generations produced genetic parameters that more appropriately represent the structure of the current population, yielding accurate GEBV to select young animals and increasing the likelihood of higher genetic gains.

Genomic selection (GS) reports on milk fatty acid (FA) profiles have been published quite recently and are still few despite this trait represents the most important aspect of milk nutritional and sensory quality. Reasons for this can be found in the high costs of phenotype recording but also in issues related to its nature of complex trait constituted by multiple genetically correlated variables with low heritabilities. One possible strategy to deal with such constraint is represented by the use of dimension reduction methods. We analysed 40 individual FAs from Italian Brown Swiss, Holstein and Simmental milk through multivariate factor analysis (MFA) to study the genetics of milk FA-related latent variables (factors) and assess their potential use in breeding. A total of nine factors were obtained, and their genetic parameters were inferred under a Bayesian framework using two statistical approaches: the classical pedigree best linear unbiased prediction (ABLUP) and the single-step genomic BLUP (ssGBLUP). The resulting factorial solutions were able to represent groups of FAs with common origin and function and can be considered concise pathway-level phenotypes. The heritability (h2 ) values showed relevant variations across different factors in each breed (0.03 ≤ h2 ≤ 0.38). The accuracies of breeding values predicted were low to high, ranging from 0.13 to 0.72 and from 0.18 to 0.74 considering the pedigree and the genomic model, respectively. The gain in accuracy in genetic prediction due to the addition of genomic information was ~30% and ~5% in validation and training groups respectively, confirming the contribution of genomic information in yielding more accurate predictions compared to the traditional ABLUP methodology. Our results suggest that MFA in combination with GS can be a valuable tool in dairy cattle breeding and deserves to be further investigated for use in future breeding programs to improve cow's milk FA-related traits.

Functional traits, such as fertility and lactation persistency, are becoming relevant breeding goals for dairy cattle. Fertility is a key element for herd profitability and animal welfare; in particular, calving interval (CIN) is an indicator of female fertility that can be easily recorded. Lactation persistency (LPE; i.e., the ability of a cow to maintain a high milk yield after the lactation peak) is economically important and is related to several other traits, such as feed efficiency, health, and reproduction. The selection of these functional traits is constrained by their low heritability. In this study, variance components for CIN and LPE in the Italian Simmental cattle breed were estimated using genomic and pedigree information under the single-step genomic framework. A data set of 594,257 CIN records (from 275,399 cows) and 285,213 LPE records (from 1563,389 cows) was considered. Phenotypes were limited up to the third parity. The pedigree contained about 2 million animals, and 7,246 genotypes were available. Lactation persistency was estimated using principal component analysis on test day records, with higher values of the second extracted principal component (PC2) values associated with lower LPE, and lower PC2 values associated with higher LPE. Heritability of CIN and LPE were estimated using single-trait repeatability models. A multiple-trait analysis using CIN and production traits (milk, fat, and protein yields) was performed to estimate genetic correlations among these traits. Heritability for CIN in the single-trait model was low (0.06 ± 0.002). Unfavorable genetic correlations were found between CIN and production traits. A measure of LPE was derived using principal component analysis on test day records. The heritability and repeatability of LPE were 0.11 ± 0.004 and 0.20 ± 0.02, respectively. Genetic correlation between CIN and LPE was weak but had a favorable direction. Despite the low heritability estimates, results of the present work suggest the possibility of including these traits in the Italian Simmental breeding program. The use of a single-step approach may provide better results for young genotyped animals without their own phenotypes.

The influence of different altitudes of flock location, grazing seasons and ripening time on the fatty acid (FA) profile of Pecorino Sardo cheese was investigated. Milk and thirty-six cheeses, representative of 12 zones (4 provinces × 3 altitudes of flock) and 3 seasons of milk production, were analysed by gas-chromatography for FA profile, at 60 and 120 days of ripening. Data were analysed with a linear model, where altitude, season, ripening time, and their interactions were the fixed factors. Milk FA profile was considered at 0 day of ripening. Altitude did not affect the FA profile, but this was influenced by the seasonality of the botanical composition of grazed pasture, with higher concentration of some interesting FA (i.e., vaccenic, rumenic and α-linolenic acids) in late winter, and lower concentrations occurred in summer-time, possibly caused by under-nutrition during this period. The FA profile was also influenced by the ripening time.

The ultimate goal of genetic selection is to improve genetic progress by increasing favorable alleles in the population. However, with selection, homozygosity, and potentially harmful recessive alleles can accumulate, deteriorating genetic variability and hampering continued genetic progress. Such potential adverse side effects of selection are of particular interest in populations with a small effective population size like the Romosinuano beef cattle in Mexico. The objective of this study was to evaluate the genetic background and inbreeding depression in Mexican Romosinuano cattle using pedigree and genomic information. Inbreeding was estimated using pedigree (FPED) and genomic information based on the genomic relationship matrix (FGRM) and runs of homozygosity (FROH) of different length classes. Linkage disequilibrium (LD) was evaluated using the correlation between pairs of loci, and the effective population size (Ne) was calculated based on LD and pedigree information. The pedigree file consisted of 4875 animals born between 1950 and 2019, of which 71 had genotypes. LD decreased with the increase in distance between markers, and Ne estimated using genomic information decreased from 610 to 72 animals (from 109 to 1 generation ago), the Ne estimated using pedigree information was 86.44. The reduction in effective population size implies the existence of genetic bottlenecks and the decline of genetic diversity due to the intensive use of few individuals as parents of the next generations. The number of runs of homozygosity per animal ranged between 18 and 102 segments with an average of 55. The shortest and longest segments were 1.0 and 36.0 Mb long, respectively, reflecting ancient and recent inbreeding. The average inbreeding was 2.98 ± 2.81, 2.98 ± 4.01, and 7.28 ± 3.68% for FPED, FGRM, and FROH, respectively. The correlation between FPED and FGRM was −0.25, and the correlations among FPED and FROH of different length classes were low (from 0.16 to 0.31). The correlations between FGRM and FROH of different length classes were moderate (from 0.44 to 0.58), indicating better agreement. A 1% increase in population inbreeding decreased birth weight by 0.103 kg and weaning weight by 0.685 kg. A strategy such as optimum genetic contributions to maximize selection response and manage the long-term genetic variability and inbreeding could lead to more sustainable breeding programs for the Mexican Romosinuano beef cattle breed.

Fatty acid (FA) composition is one of the most important aspects of milk nutritional quality. However, the inclusion of this trait as a breeding goal for dairy species is hampered by the logistics and high costs of phenotype recording. Fourier-transform infrared spectroscopy (FTIR) is a valid and cheap alternative to laboratory gas chromatography (GC) for predicting milk FA composition. Moreover, as for other novel phenotypes, the efficiency of selection for these traits can be enhanced by using genomic data. The objective of this research was to compare traditional versus genomic selection approaches for estimating genetic parameters and breeding values of milk fatty acid composition in dairy sheep using either GC-measured or FTIR-predicted FA as phenotypes. Milk FA profiles were available for a total of 923 Sarda breed ewes. The youngest 100 had their own phenotype masked to mimic selection candidates. Pedigree relationship information and genotypes were available for 923 and 769 ewes, respectively. Three statistical approaches were used: the classical-pedigree-based BLUP, the genomic BLUP that considers the genomic relationship matrix G, and the single-step genomic BLUP (ssGBLUP) where pedigree and genomic relationship matrices are blended into a single H matrix. Heritability estimates using pedigree were lower than ssGBLUP, and very similar between GC and FTIR regarding the statistical approach used. For some FA, mostly associated with animal diet (i.e., C18:2n-6, C18:3n-3), random effect of combination of flock and test date explained a relevant quota of total variance, reducing the heritability estimates accordingly. Genomic approaches (genomic BLUP and ssGBLUP) outperformed the traditional pedigree method both for GC and FTIR FA. Prediction accuracies in the older cohort were larger than the young cohort. Genomic prediction accuracies (obtained using either G or H relationship matrix) in the young cohort of animals, where their own phenotypes were masked, were similar for GC and FTIR. Multiple-trait analysis slightly affected genomic breeding value accuracies. These results suggest that FTIR-predicted milk FA composition could represent a valid option for inclusion in breeding programs.

We investigated the effects of different strategies for genotyping populations on variance components and heritabilities estimated with an animal model under restricted maximum likelihood (REML), genomic REML (GREML), and single-step GREML (ssGREML). A population with 10 generations was simulated. Animals from the last one, two or three generations were genotyped with 45,116 SNP evenly distributed on 27 chromosomes. Animals to be genotyped were chosen randomly or based on EBV. Each scenario was replicated five times. A single trait was simulated with three heritability levels (low, moderate, high). Phenotypes were simulated for only females to mimic dairy sheep and also for both sexes to mimic meat sheep. Variance component estimates from genomic data and phenotypes for one or two generations were more biased than from three generations. Estimates in the scenario without selection were the most accurate across heritability levels and methods. When selection was present in the simulations, the best option was to use genotypes of randomly selected animals. For selective genotyping, heritabilities from GREML were more biased compared to those estimated by ssGREML, because ssGREML was less affected by selective or limited genotyping.

In the present study, a sample of 88 animals belonging to four local (Modicana, Sarda, Sardo-Bruna and Sardo-Modicana) and one cosmopolitan (Italian Brown Swiss) cattle breeds were genotyped with a medium density SNP beadchip and compared to investigate their genetic diversity and the existence of selection signatures. A total of 43 012 SNPs distributed across all 29 autosomal chromosomes were retained after data quality control. Basic population statistics, Wright fixation index and runs of homozygosity (ROH) analyses confirmed that the Italian Brown Swiss genome was shaped mainly by selection, as underlined by the low values of heterozygosity and minor allele frequency. As expected, local cattle exhibited a large within-breed genetic heterogeneity. The FST comparison revealing the largest number of significant SNPs was Sardo-Bruna vs. Sardo-Modicana, whereas the smallest was observed for Italian Brown Swiss vs. Sardo-Modicana. Modicana exhibited the largest number of detected ROHs, whereas the smallest was observed for Sardo-Modicana. Signatures of selection were detected in genomic regions that harbor genes involved in milk production traits for Italian Brown Swiss and fitness traits for local breeds. According to the results of multi-dimensional scaling and the admixture analysis the Sardo-Bruna is more similar to the Sarda than to the Italian Brown Swiss breed. Moreover, the Sardo-Modicana is genetically closer to the Modicana than to the Sarda breed. Results of the present work confirm the usefulness of single nucleotide polymorphisms in deciphering the genetic architecture of livestock breeds.