Genomic inbreeding coefficients using imputation genotypes: assessing the effect of ancestral genotyping in Holstein-Friesian dairy cows.

The objective of this study was to assess the effect of using or not the genotypes of the parents of a cow for imputing single nucleotide polymorphisms (SNP), on the estimation of genomic inbreeding coefficients of cows. Imputation (i.e., genotyped plus imputed) genotypes from 68,127 Italian Holstein dairy cows registered in the Italian National Association of Holstein, Brown and Jersey Breeders (ANAFIBJ) were analyzed. Cows were genotyped with the HD Illumina Infinium BovineHD BeadChip and GeneSeek Genomic Profiler HD-150K, and the MD GeneSeek Genomic Profiler 3, GeneSeek Genomic Profiler 4, GeneSeek MD and the Labogena MD. To assess differences among estimators genomic inbreeding coefficients were estimated with 4 PLINK v1.9 estimators (F, Fhat1, 2, 3), 2 genomic relationship matrix (grm) based estimators (Fgrm and Fgrm2; with the latter including also pedigree information) and one estimator of runs of homozygosity (ROH; FROH). Assuming that the correct genomic inbreeding coefficients should be those estimated from genotyped SNP, a comparison of the genomic inbreeding coefficients estimated either with the genotyped SNP or the SNP after imputation was made. Information on the presence or absence of genotypic information from sire, dam and maternal grandsire during the imputation was investigated. Genomic inbreeding coefficients estimated with genotyped SNP or SNP after imputation were consistent for F, Fhat3, Fgrm2 and FROH, when at least one of the parents was genotyped. Biased (mainly higher) genomic inbreeding coefficients of imputation SNP were observed in cows that were genotyped with MD SNP panels whose SNP were poorly represented in the selected imputation SNP data set and also did not have their parents genotyped compared with what expected based on actual genotype data. For cows genotyped with MD the estimators Fhat1, Fhat2 and Fgrm provided higher genomic inbreeding coefficients of imputation SNP even with both parents and the maternal grandsire genotyped. Overall, FROH was the most robust estimator, followed by F and Fhat3. Our findings suggest that SNP selection, parental genotyping and estimator should be considered for designing imputation strategies in dairy cattle for estimating genomic inbreeding with imputation SNP. For computing genomic inbreeding coefficients, it is recommendable to have at least one parent genotyped and use an ROH based estimator.

The role of inbreeding depression on productive performance in the Italian Holstein breed.

Inbreeding depression has become an urgent issue in cosmopolitan breeds where the massive genetic progress achieved in the latest generations is counterbalanced by a dramatic loss of genetic diversity causing increased health issues. Thus, the aim of this study was to estimate inbreeding depression on productive traits in Holstein dairy cattle. More precisely, we aimed to i) determine the level of inbreeding in 27,735 Italian Holstein dairy cows using pedigree and genotype data, ii) quantify the effect of inbreeding on 305-d in milk yield (MY; kg), fat yield (FY; kg), and protein yield (PY; kg) based on different statistical approaches, iii) determine if recent inbreeding has a more harmful impact than ancestral ones, and iv) quantify chromosomal homozygosity effect on productive traits. Quality control was performed on the autosomal chromosomes resulting in a final dataset of 84,443 single nucleotide polymorphisms. Four statistical models were used to evaluate the presence of inbreeding depression, which included linear regression analysis and division of FPED and FROH into percentile classes. Moreover, FROH was partitioned into i) length classes to assess the role of recent and ancestral inbreeding and ii) chromosome-specific contributions (FROH-CHR). Results evidenced that inbreeding negatively impacted the productive performance of Italian Holstein Friesian cows. However, differences between the estimated FPED and FROH coefficients resulted in different estimates of inbreeding depression. For instance, a 1% increase in FPED and FROH was associated with a decrease in MY of about 44 and 61 kg (P < 0.01). Further, when considering the extreme inbreeding percentile classes moving from the 5th lowest to the 95th highest, there was a reduction of -263 kg and -561 kg per lactation for FPED and FROH. Increased inbreeding, estimated by FPED and FROH, had also a negative effect on PY and FY, either fit as a regressor or percentile classes. When evaluating the impact of inbreeding based on runs of homozygosity (ROH) length classes, longer ROH (over 8 Mb) had a negative effect in all traits, indicating that recent inbreeding might be more harmful than the ancestral one. Finally, results within chromosome homozygosity highlighted specific chromosomes with a more deleterious effect on productive traits.

Inbreeding depression is a reduction in performance or health due to the mating of closely related individuals. The overall aim of this study was to investigate the level of inbreeding in the Italian Holstein dairy cow breed and quantify its negative effect on productive performances. The level of inbreeding was estimated by pedigree (FPED) and genomic data by looking at stretches of homozygosity (FROH). Both methods revealed a reduction in milk yield, fat yield, and protein yield when inbreeding increased. Moreover, the study demonstrated that FROH was able to capture more inbreeding depression compared to FPED. In addition, the more recent inbreeding had a stronger negative impact on productive performances compared to ancestral ones. Then, since the amount of runs of homozygosity can vary across the chromosomes of an individual, the effect of each chromosomal homozygosity region on productive traits was also evaluated. The chromosome-level results might be included in breeding programs to limit the accumulation of homozygosity in particular regions that appear to have a more detrimental effect on productive traits. Overall, this study highlights the importance of avoiding inbreeding in animal breeding programs to keep productive animals in the long term.

Genomic inbreeding coefficients using imputed genotypes: assessing differences among SNP panels in Holstein-Friesian dairy cows.

The objective of this study was to evaluate the effect of imputation of single nucleotide polymorphisms (SNP) on the estimation of genomic inbreeding coefficients. Imputed genotypes of 68,127 Italian Holstein dairy cows were analyzed. Cows were initially genotyped with two high density (HD) SNP panels, namely the Illumina Infinium BovineHD BeadChip (678 cows; 777,962 SNP) and the Genomic Profiler HD-150K (641 cows; 139,914 SNP), and four medium density (MD): GeneSeek Genomic Profiler 3 (10,679 cows; 26,151 SNP), GeneSeek Genomic Profiler 4 (33,394 cows; 30,113 SNP), GeneSeek MD (12,030 cows; 47,850 SNP) and the Labogena MD (10,705 cows; 41,911 SNP). After imputation, all cows had genomic information on 84,445 SNP. Seven genomic inbreeding estimators were tested: (i) four PLINK v1.9 estimators (F, Fhat1,2,3), (ii) two genomic relationship matrix (grm) estimators [VanRaden's 1st method, but with observed allele frequencies (Fgrm) and VanRaden's 3rd method that is allelic free and pedigree dependent (Fgrm2)], and (iii) a runs of homozygosity (roh) - based estimator (Froh). Genomic inbreeding coefficients of each SNP panel were compared with genomic inbreeding coefficients derived from the 84,445 imputation SNP. Coefficients of the HD SNP panels were consistent between genotyped-imputed SNP (Pearson correlations ~99%), while variability across SNP panels and estimators was observed in the MD SNP panels, with Labogena MD providing, on average, more consistent estimates. The robustness of Labogena MD, can be partly explained by the fact that 97.85% of the SNP of this panel is included in the 84,445 SNP selected by ANAFIBJ for routine genomic imputations, while this percentage for the other MD SNP panels varied between 55 and 60%. Runs of homozygosity was the most robust estimator. Genomic inbreeding estimates using imputation SNP are influenced by the SNP number of the SNP panel that are included in the imputed SNP, and performance of genomic inbreeding estimators depends on the imputation.

Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk.

The objectives of this study were to explore the use of Fourier-transform infrared (FITR) spectroscopy on 458 goat milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. Calibration equations were developed using a Bayesian approach with three different scenarios: i) a random cross-validation (CV) [80% calibration (CAL); 20% validation (VAL) set], ii) a stratified CV [(SCV), 13 farms used as CAL, and the remaining one as VAL set], and iii) a SCV where 20% of the goats randomly selected from the VAL farm were included in the CAL set (SCV80). The best prediction performance was obtained for cheese yield solids, justifying for its practical application at population level. Overall results were similar to or outperformed those reported for bovine milk. Our results suggest considering specific procedures for calibration development to propose reliable tools applicable along the dairy goat chain.

Genomic inbreeding coefficients using imputed genotypes: Assessing different estimators in Holstein-Friesian dairy cows.

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, FHAT1, FHAT2, FHAT3), 3 genomic relationship matrix (GRM)-based methods [VanRaden's first method with observed allele frequencies (FGRM) or with fixed frequencies at 0.5 (FGRM05), VanRaden's third method, allelic frequency free and pedigree regressed (FGRM2)], runs of homozygosity (ROH)-based estimators in a complete (FROH) and simplified version (FROH2), and proportion of homozygous SNP (FPH). Pairwise comparisons among them were made, including the comparison with traditional pedigree-based inbreeding coefficients (FPED). Our results showed variability among the genomic inbreeding estimators. Coefficients of FGRM and FHAT3 were >1, meaning that more variability has been lost than the variability that existed in the base population. Regarding the remaining ones, FGRM05, FROH, FROH2, and FPH provided coefficients within the [0,1] space and are considered comparable to FPED. Not comparable to FPED, yet with an interpretable value, can be considered the coefficients of F, FHAT2, and FGRM2. 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.

Admixture and breed traceability in European indigenous pig breeds and wild boar using genome-wide SNP data.

Preserving diversity of indigenous pig (Sus scrofa) breeds is a key factor to (i) sustain the pork chain (both at local and global scales) including the production of high-quality branded products, (ii) enrich the animal biobanking and (iii) progress conservation policies. Single nucleotide polymorphism (SNP) chips offer the opportunity for whole-genome comparisons among individuals and breeds. Animals from twenty European local pigs breeds, reared in nine countries (Croatia: Black Slavonian, Turopolje; France: Basque, Gascon; Germany: Schwabisch-Hällisches Schwein; Italy: Apulo Calabrese, Casertana, Cinta Senese, Mora Romagnola, Nero Siciliano, Sarda; Lithuania: Indigenous Wattle, White Old Type; Portugal: Alentejana, Bísara; Serbia: Moravka, Swallow-Bellied Mangalitsa; Slovenia: Krskopolje pig; Spain: Iberian, Majorcan Black), and three commercial breeds (Duroc, Landrace and Large White) were sampled and genotyped with the GeneSeek Genomic Profiler (GGP) 70 K HD porcine genotyping chip. A dataset of 51 Wild Boars from nine countries was also added, summing up to 1186 pigs (~ 49 pigs/breed). The aim was to: (i) investigate individual admixture ancestries and (ii) assess breed traceability via discriminant analysis on principal components (DAPC). Albeit the mosaic of shared ancestries found for Nero Siciliano, Sarda and Moravka, admixture analysis indicated independent evolvement for the rest of the breeds. High prediction accuracy of DAPC mark SNP data as a reliable solution for the traceability of breed-specific pig products.

Predictive formulas for different measures of cheese yield using milk composition from individual goat samples.

The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CYCURD), total solids (%CYSOLIDS), and water retained (%CYWATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R2VAL) and the root mean square error of validation (RMSEVAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R2VAL = 0.65, 0.96, and 0.23 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively). The WAV procedure showed R2VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CYCURD and %CYWATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production.

Genetic diversity and population history of eight Italian beef cattle breeds using measures of autozygosity.

In the present study, GeneSeek GGP-LDv4 33k single nucleotide polymorphism chip was used to detect runs of homozygosity (ROH) in eight Italian beef cattle breeds, six breeds with distribution limited to Tuscany (Calvana, Mucca Pisana, Pontremolese) or Sardinia (Sarda, Sardo Bruna and Sardo Modicana) and two cosmopolitan breeds (Charolais and Limousine). ROH detection analyses were used to estimate autozygosity and inbreeding and to identify genomic regions with high frequency of ROH, which might reflect selection signatures. Comparative analysis among breeds revealed differences in length and distribution of ROH and inbreeding levels. The Charolais, Limousine, Sarda, and Sardo Bruna breeds were found to have a high frequency of short ROH (~ 15.000); Calvana and Mucca Pisana presented also runs longer than 16 Mbp. The highest level of average genomic inbreeding was observed in Tuscan breeds, around 0.3, while Sardinian and cosmopolitan breeds showed values around 0.2. The population structure and genetic distances were analyzed through principal component and multidimensional scaling analyses, and resulted in a clear separation among the breeds, with clusters related to productive purposes. The frequency of ROH occurrence revealed eight breed-specific genomic regions where genes of potential selective and conservative interest are located (e.g. MYOG, CHI3L1, CHIT1 (BTA16), TIMELESS, APOF, OR10P1, OR6C4, OR2AP1, OR6C2, OR6C68, CACNG2 (BTA5), COL5A2 and COL3A1 (BTA2)). In all breeds, we found the largest proportion of homozygous by descent segments to be those that represent inbreeding events that occurred around 32 generations ago, with Tuscan breeds also having a significant proportion of segments relating to more recent inbreeding.

A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens.

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.

Goat farm variability affects milk Fourier-transform infrared spectra used for predicting coagulation properties.

Driven by the large amount of goat milk destined for cheese production, and to pioneer the goat cheese industry, the objective of this study was to assess the effect of farm in predicting goat milk-coagulation and curd-firmness traits via Fourier-transform infrared spectroscopy. Spectra from 452 Sarda goats belonging to 14 farms in central and southeast Sardinia (Italy) were collected. A Bayesian linear regression model was used, estimating all spectral wavelengths' effects simultaneously. Three traditional milk-coagulation properties [rennet coagulation time (min), time to curd firmness of 20 mm (min), and curd firmness 30 min after rennet addition (mm)] and 3 curd-firmness measures modeled over time [rennet coagulation time estimated according to curd firmness change over time (RCTeq), instant curd-firming rate constant, and asymptotical curd firmness] were considered. A stratified cross validation (SCV) was assigned, evaluating each farm separately (validation set; VAL) and keeping the remaining farms to train (calibration set) the statistical model. Moreover, a SCV, where 20% of the goats randomly taken (10 replicates per farm) from the VAL farm entered the calibration set, was also considered (SCV80). To assess model performance, coefficient of determination (R2VAL) and the root mean squared error of validation were recorded. The R2VAL varied between 0.14 and 0.45 (instant curd-firming rate constant and RCTeq, respectively), albeit the standard deviation was approximating half of the mean for all the traits. Although average results of the 2 SCV procedures were similar, in SCV80, the maximum R2VAL increased at about 15% across traits, with the highest observed for time to curd firmness of 20 mm (20%) and the lowest for RCTeq (6%). Further investigation evidenced important variability among farms, with R2VAL for some of them being close to 0. Our work outlined the importance of considering the effect of farm when developing Fourier-transform infrared spectroscopy prediction equations for coagulation and curd-firmness traits in goats.

Breed of goat affects the prediction accuracy of milk coagulation properties using Fourier-transform infrared spectroscopy.

The prediction of traditional goat milk coagulation properties (MCP) and curd firmness over time (CFt) parameters via Fourier-transform infrared (FTIR) spectroscopy can be of significant economic interest to the dairy industry and can contribute to the breeding objectives for the genetic improvement of dairy goat breeds. Therefore, the aims of this study were to (1) explore the variability of milk FTIR spectra from 4 goat breeds (Camosciata delle Alpi, Murciano-Granadina, Maltese, and Sarda), and to assess the possible discriminant power of milk FTIR spectra among breeds, (2) assess the viability to predict coagulation traits by using milk FTIR spectra, and (3) quantify the effect of the breed on the prediction accuracy of MCP and CFt parameters. In total, 611 individual goat milk samples were used. Analysis of variance of measured MCP and CFt parameters was carried out using a mixed model including the farm and pendulum as random factors, and breed, parity, and days in milk as fixed factors. Milk spectra for each goat were collected over the spectral range from wavenumber 5,011 to 925 × cm-1. Discriminant analysis of principal components was used to assess the ability of FTIR spectra to identify breed of origin. A Bayesian model was used to calibrate equations for each coagulation trait. The accuracy of the model and the prediction equation was assessed by cross-validation (CRV; 80% training and 20% testing set) and stratified CRV (SCV; 3 breeds in the training set, one breed in the testing set) procedures. Prediction accuracy was assessed by using coefficient of determination of validation (R2VAL), the root mean square error of validation (RMSEVAL), and the ratio performance deviation. Moreover, measured and FTIR predicted traits were compared in the SCV procedure by assessing their least squares means for the breed effect, Pearson correlations, and variance heteroscedasticity. Results showed the feasibility of using FTIR spectra and multivariate analyses to correctly assign milk samples to their breeds of origin. The R2VAL values obtained with the CRV procedure were moderate to high for the majority of coagulation traits, with RMSEVAL and ratio performance deviation values increasing as the coagulation process progresses from rennet addition. Prediction accuracy obtained with the SCV were strongly influenced by the breed, presenting general low values restricting a practical application. In addition, the low Pearson correlation coefficients of Sarda breed for all the traits analyzed, and the heteroscedastic variances of Camosciata delle Alpi, Murciano-Granadina, and Maltese breeds, further indicated that it is fundamental to consider the differences existing among breeds for the prediction of milk coagulation traits.

Keep Garfagnina alive. An integrated study on patterns of homozygosity, genomic inbreeding, admixture and breed traceability of the Italian Garfagnina goat breed.

The objective of this study was to investigate the genetic diversity of the Garfagnina (GRF) goat, a breed that currently risks extinction. For this purpose, 48 goats were genotyped with the Illumina CaprineSNP50 BeadChip and analyzed together with 214 goats belonging to 9 other Italian breeds (~25 goats/breed), whose genotypes were available from the AdaptMap project [Argentata (ARG), Bionda dell'Adamello (BIO), Ciociara Grigia (CCG), Di Teramo (DIT), Garganica (GAR), Girgentana (GGT), Orobica (ORO), Valdostana (VAL) and Valpassiria (VSS)]. Comparative analyses were conducted on i) runs of homozygosity (ROH), ii) admixture ancestries and iii) the accuracy of breed traceability via discriminant analysis on principal components (DAPC) based on cross-validation. ROH analyses was used to assess the genetic diversity of GRF, while admixture and DAPC to evaluate its relationship to the other breeds. For GRF, common ROH (more than 45% in GRF samples) was detected on CHR 12 at, roughly 50.25-50.94Mbp (ARS1 assembly), which spans the CENPJ (centromere protein) and IL17D (interleukin 17D) genes. The same area of common ROH was also present in DIT, while a broader region (~49.25-51.94Mbp) was shared among the ARG, CCG, and GGT. Admixture analysis revealed a small region of common ancestry from GRF shared by BIO, VSS, ARG and CCG breeds. The DAPC model yielded 100% assignment success for GRF. Overall, our results support the identification of GRF as a distinct native Italian goat breed. This work can contribute to planning conservation programmes to save GRF from extinction and will improve the understanding of the socio-agro-economic factors related with the farming of GRF.

Genetic Diversity in the Italian Holstein Dairy Cattle Based on Pedigree and SNP Data Prior and After Genomic Selection.

Genetic diversity has become an urgent matter not only in small local breeds but also in more specialized ones. While the use of genomic data in livestock breeding programs increased genetic gain, there is increasing evidence that this benefit may be counterbalanced by the potential loss of genetic variability. Thus, in this study, we aimed to investigate the genetic diversity in the Italian Holstein dairy cattle using pedigree and genomic data from cows born between 2002 and 2020. We estimated variation in inbreeding, effective population size, and generation interval and compared those aspects prior to and after the introduction of genomic selection in the breed. The dataset contained 84,443 single-nucleotide polymorphisms (SNPs), and 74,485 cows were analyzed. Pedigree depth based on complete generation equivalent was equal to 10.67. A run of homozygosity (ROH) analysis was adopted to estimate SNP-based inbreeding (FROH). The average pedigree inbreeding was 0.07, while the average FROH was more than double, being equal to 0.17. The pattern of the effective population size based on pedigree and SNP data was similar although different in scale, with a constant decrease within the last five generations. The overall inbreeding rate (ΔF) per year was equal to +0.27% and +0.44% for Fped and FROH throughout the studied period, which corresponded to about +1.35% and +2.2% per generation, respectively. A significant increase in the ΔF was found since the introduction of genomic selection in the breed. This study in the Italian Holstein dairy cattle showed the importance of controlling the loss of genetic diversity to ensure the long-term sustainability of this breed, as well as to guarantee future market demands.

Estimation of Linkage Disequilibrium and Effective Population Size in Three Italian Autochthonous Beef Breeds.

The objective was to investigate the pattern of linkage disequilibrium (LD) in three local beef breeds, namely, Calvana (n = 174), Mucca Pisana (n = 270), and Pontremolese (n = 44). As a control group, samples of the Italian Limousin breed (n = 100) were used. All cattle were genotyped with the GeneSeek GGP-LDv4 33k SNP chip containing 30,111 SNPs. The genotype quality control for each breed was conducted separately, and SNPs with call rate < 0.95 and minor allele frequency (MAF) > 1% were used for the analysis. LD extent was estimated in PLINK v1.9 using the squared correlation between pairs of loci (r2) across autosomes. Moreover, r2 values were used to calculate historical and contemporary effective population size (Ne) in each breed. Average r2 was similar in Calvana and Mucca Pisana (~0.14) and higher in Pontremolese (0.17); Limousin presented the lowest LD extent (0.07). LD up to 0.11-0.15 was persistent in the local breeds up to 0.75 Mbp, while in Limousin, it showed a more rapid decay. Variation of different LD levels across autosomes was observed in all the breeds. The results demonstrated a rapid decrease in Ne across generations for local breeds, and the contemporary population size observed in the local breeds, ranging from 41.7 in Calvana to 17 in Pontremolese, underlined the demographic alarming situation.

Genetic Diversity and Signatures of Selection in a Native Italian Horse Breed Based on SNP Data.

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 (Ne) 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 ≤ 2Mbp 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.

Differential Somatic Cell Count as a Novel Indicator of Milk Quality in Dairy Cows.

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.

Population Structure and Genetic Diversity of Italian Beef Breeds as a Tool for Planning Conservation and Selection Strategies.

The aim was to investigate the population structure of eight beef breeds: three local Tuscan breeds under extinction, Calvana (CAL), Mucca Pisana (MUP), and Pontremolese (PON); three local unselected breeds reared in Sardinia, Sarda (SAR), Sardo Bruna (SAB), and Sardo Modicana (SAM); and two cosmopolitan breeds, Charolais (CHA) and Limousine (LIM), reared in the same regions. An effective population size ranges between 14.62 (PON) to 39.79 (SAM) in local breeds, 90.29 for CHA, and 135.65 for LIM. The average inbreeding coefficients were higher in Tuscan breeds (7.25%, 5.10%, and 3.64% for MUP, CAL, and PON, respectively) compared to the Sardinian breeds (1.23%, 1.66%, and 1.90% in SAB, SAM, and SAR, respectively), while for CHA and LIM they were <1%. The highest rates of mating between half-siblings were observed for CAL and MUP (~9% and 6.5%, respectively), while the highest rate of parent-offspring mating was ~8% for MUP. Our findings describe the urgent situation of the three Tuscan breeds and support the application of conservation measures and/or the development of breeding programs. Development of breeding strategies is suggested for the Sardinian breeds.

Genome scan for the possibility of identifying candidate resistance genes for goat lentiviral infections in the Italian Garfagnina goat breed.

Small ruminant lentiviruses (SRLVs) are a heterogeneous group of viruses of sheep, goat, and wild ruminants responsible of lifelong persistent infection leading to a multisystem chronic disease. Increased evidences indicate that host genetic factors could influence the individual SRLV resistance. The present study was conducted on the Garfagnina goat breed, an Italian goat population registered on the Tuscan regional repertory of genetic resources at risk of extinction. Forty-eight adult goats belonging to a single flock were studied. SRLV diagnosis was achieved by serological tests and 21 serologically positive animals were identified. All animals were genotyped with the Illumina GoatSNP60 BeadChip and a genome-wide scan was then performed on the individual marker genotypes, in an attempt to identify genomic regions associated with the infection. One SNP was found significant (P < 5 × 10-5) on CHR 18 at 62,360,918 bp. The SNP was an intron of the zinc finger protein 331 (ZNF331) protein. In the region 1 Mb upstream the significant SNP, the NLRP12 (NLR family pyrin domain containing 12), the PRKCG (protein kinase C gamma), and the CACNG7 (calcium voltage-gated channel auxiliary subunit gamma 7) were found.

SNP co-association and network analyses identify E2F3, KDM5A and BACH2 as key regulators of the bovine milk fatty acid profile.

The fatty acid (FA) profile has a considerable impact on the nutritional and technological quality of milk and dairy products. The molecular mechanism underlying the regulation of fat metabolism in bovine mammary gland have been not completely elucidated. We conducted genome-wide association studies (GWAS) across 65 milk FAs and fat percentage in 1,152 Brown Swiss cows. In total, we identified 175 significant single nucleotide polymorphism (SNPs) spanning all chromosomes. Pathway analyses revealed that 12:0 was associated with the greatest number of overrepresented categories/pathways (e.g. mitogen-activated protein kinase (MAPK) activity and protein phosphorylation), suggesting that it might play an important biological role in controlling milk fat composition. An Associated Weight Matrix approach based on SNP co-associations predicted a network of 791 genes related to the milk FA profile, which were involved in several connected molecular pathways (e.g., MAPK, lipid metabolism and hormone signalling) and undetectable through standard GWAS. Analysis of transcription factors and their putative target genes within the network identified BACH2, E2F3 and KDM5A as key regulators of milk FA metabolism. These findings contribute to increasing knowledge of FA metabolism and mammary gland functionality in dairy cows and may be useful in developing targeted breeding practices to improve milk quality.