Comparison on genomic prediction using pedigree BLUP and single step GBLUP through the Hanwoo full-sib family

OBJECTIVE

When evaluating individuals with the same parent and no phenotype by pedigree best linear unbiased prediction (BLUP), it is difficult to explain carcass grade difference and select individuals because they have the same value in pedigree BLUP (PBLUP). However, single step GBLUP (ssGBLUP), which can estimate the breeding value suitable for the individual by adding genotype, is more accurate than the existing method.

METHODS

The breeding value and accuracy were estimated with pedigree BLUP and ssGBLUP using pedigree and genotype of 408 Hanwoo cattle from 16 families with the same parent among siblings produced by fertilized egg transplantation. A total of 14,225 Hanwoo cattle with pedigree, genotype and phenotype were used as the reference population. PBLUP obtained estimated breeding value (EBV) using the pedigree of the test and reference populations, and ssGBLUP obtained genomic EBV (GEBV) after constructing and H-matrix by integrating the pedigree and genotype of the test and reference populations.

RESULTS

For all traits, the accuracy of GEBV using ssGBLUP is 0.18 to 0.20 higher than the accuracy of EBV obtained with PBLUP. Comparison of EBV and GEBV of individuals without phenotype, since the value of EBV is estimated based on expected values of alleles passed down from common ancestors. It does not take Mendelian sampling into consideration, so the EBV of all individuals within the same family is estimated to be the same value. However, GEBV makes estimating true kinship coefficient based on different genotypes of individuals possible, so GEBV that corresponds to each individual is estimated rather than a uniform GEBV for each individual.

CONCLUSION

Since Hanwoo cows bred through embryo transfer have a high possibility of having the same parent, if ssGBLUP after adding genotype is used, estimating true kinship coefficient corresponding to each individual becomes possible, allowing for more accurate estimation of breeding value.

Understanding the phenotypic and genetic background of the lactose content in Sarda dairy sheep

Lactose, the principal carbohydrate found in milk, plays an important role in the physiological processes of milk production because it is related to milk volume, and it is responsible for the osmotic equilibrium between blood and milk in the mammary gland. In this study, factors affecting lactose content (LC) in sheep milk are investigated. For this purpose, 2,358 test-day records were sampled from 509 ewes (3-7 records per animal). The LC and other main milk traits were analyzed using a mixed linear model that included days in milk (DIM) class, parity, lambing month, and type of lambing as fixed effects and animal, permanent environment, and flock test day as random effects. The pedigree-based approach was used to estimate the heritability and repeatability of LC. Moreover, the genomic background of LC was investigated through a GWAS. The LC was affected by all tested factors (i.e., DIM class, parity, lambing month, and type of lambing). Low heritability (0.10 ± 0.05) and moderate repeatability (0.42 ± 0.02) were estimated for LC. High negative genetic correlations were estimated between LC and NaCl (-0.99 ± 0.01) and between LC and somatic cell count (-0.94 ± 0.05). Only 2 markers passed the chromosome-wide Bonferroni threshold. Results of the present study, although obtained on a relatively small sample, suggest the possibility to include LC in the breeding programs, particularly because of its strong relationship with NaCl and somatic cell count.

Dry Matter Intake Prediction from Milk Spectra in Sarda Dairy Sheep

Individual dry matter intake (DMI) is a relevant factor for evaluating feed efficiency in livestock. However, the measurement of this trait on a large scale is difficult and expensive. DMI, as well as other phenotypes, can be predicted from milk spectra. The aim of this work was to predict DMI from the milk spectra of 24 lactating Sarda dairy sheep ewes. Three models (Principal Component Regression, Partial Least Squares Regression, and Stepwise Regression) were iteratively applied to three validation schemes: records, ewes, and days. DMI was moderately correlated with the wavenumbers of the milk spectra: the largest correlations (around ±0.30) were observed at ~1100-1330 cm^-1 and ~2800-3000 cm^-1. The average correlations between real and predicted DMI were 0.33 (validation on records), 0.32 (validation on ewes), and 0.23 (validation on days). The results of this preliminary study, even if based on a small number of animals, demonstrate that DMI can be routinely estimated from the milk spectra.

Genomic prediction in Brazilian Guzerá cattle: application of a single-step approach to productive and reproductive traits

This study aimed to investigate the feasibility of genomic prediction for productive and reproductive traits in Guzerá cattle using single-step genomic best linear unbiased prediction (ssGBLUP). Evaluations included the 305-day cumulative yields (first lactation, in kg) of milk, lactose, protein, fat, and total solids; adjusted body weight (kg) at the ages of 450, 365, and 210 days; and age at first calving (in days), from a database containing 197,283 measurements from Guzerá males and females born between 1954 and 2018. The pedigree included 433,823 animals spanning up to 14 overlapping generations. A total of 1618 animals were genotyped. The analyses were performed using ssGBLUP and traditional BLUP methods. Predictive ability and bias were accessed using cross-validation: predictive ability was similar between the methods and ranged from 0.27 to 0.47 for the genomic-based model and from 0.30 to 0.45 for the pedigree-based model; the bias was also similar between the methods, ranging from 0.88 to 1.35 in the genomic-based model and from 0.96 to 1.41 in the pedigree-based model. The individual accuracies of breeding values were evidently increased in the genomic evaluation, with values ranging from 0.41 to 0.56 in the genomic-based model and from 0.26 to 0.54 in the pedigree-based model. Even based on a small number of genotyped animals and a small database for some traits, the results suggest that ssGBLUP is feasible and may be applied to national genetic evaluation of the breed to increase the accuracy of breeding values without greatly impacting predictive ability and bias.

Essential and Toxic Mineral Content and Fatty Acid Profile of Colostrum in Dairy Sheep

Simple Summary

Colostrum is of interest to the scientific community because of its nutritional and therapeutic capabilities. The aims of this study were to characterize the macro and micro composition of colostrum from Sarda dairy sheep and to compare it with the composition of the mature milk of the same breed. The results of this survey showed a large variation in the immunoglobulin concentration in colostrum, which could affect the acquisition of passive immunity by lambs. The strong correlation between immunoglobulin G and the total protein content suggests that this can be used to estimate the immunoglobulin content in sheep colostrum. The concentration of essential minerals is higher in colostrum than in milk as a result of mineral salt block supplementation at the end of gestation. Colostrum has a significantly different fatty acid profile than milk, and this is due to the specific needs of newborn lambs.

Abstract

Colostrum is a major source of immunity in ruminants. It allows the transfer of antibodies from the mother to the fetus, and it is the exclusive source of nutrients for the newborn. The objectives of this study were (i) to characterize the macro and the micro composition of colostrum; (ii) to analyze the antioxidant capacity, fatty acid profile, and essential and toxic mineral content of colostrum; and (iii) to compare FA profiles and the amount of trace elements between colostrum and mature milk. For these purposes, samples of colostrum and milk were collected from a representative sample of animals from eight sheep dairy farms in the north of Sardinia (Italy). Fat, proteins, and seven essential and toxic minerals were measured in all samples of colostrum and milk. Furthermore, the FA profile was also measured in both matrices, while total antioxidant capacity was measured only in colostrum samples. The average amounts of fat and protein (TP) concentration in colostrum were 7.8% and 16%, respectively. Additionally, an average amount of 40 ± 20 g dm⁻³ was found for immunoglobulin G (IgG). As regards the antioxidant capacity of colostrum, a large variation was observed between samples from different farms for test 2, 2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS), which was 30 ± 10% (mean ± standard deviation). High levels of selenium (Se), zinc (Zn), and copper (Cu) were found in colostrum (200 µg kg⁻¹, 25,000 µg kg⁻¹, and 1200 µg kg⁻¹, respectively). A strong positive correlation between TP and IgG was observed (r = 91%). In colostrum, the amount of IgG is positively correlated with Se and Zn, as they are essential minerals to the immune system. The FA profile demonstrated higher levels of medium and long chain fatty acids in colostrum than in mature milk, and this is mainly true for arachidonic acid (ARA), ecosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA). This study provided new information on the quality of colostrum in Sarda dairy sheep and showed the different composition of fatty acids between colostrum and mature milk.

Phenotypic and genetic characterization of the occurrence of noncoagulating milk in dairy sheep

Milk coagulation ability is of central importance for the sheep dairy industry because almost all sheep milk is destined for cheese processing. The occurrence of milk with impaired coagulation properties is an obstacle to cheese processing and, in turn, to the profitability of the dairy companies. In this work, we investigated the causes of noncoagulation of sheep milk; specifically, we studied the effect of milk physicochemical properties on milk coagulation status [coagulating and noncoagulating (NC) milk samples, which do or do not coagulate within 30 min, respectively], and whether mid-infrared spectroscopy (MIR) could be used to assess variability in coagulation status. We also investigated the genetic background of milk coagulation ability. Individual milk samples were collected from 996 Sarda ewes farmed in 47 flocks located in Sardinia (Italy). Considered traits were daily milk yield, milk composition traits, and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness), and MIR spectra were acquired. About 9% of samples did not coagulate within 30 min. A logistic regression approach was used to test the effect of milk-related traits on milk coagulation status. A principal component (PC) analysis was carried out on the milk MIR spectra, and PC scores were then used as covariates in a logistic regression model to assess their relationship with milk coagulation status. Results of the present work demonstrated that the probability of having NC samples increases as milk contents of proteins and chlorides and somatic cell score increase. The analysis of PC extracted from milk spectra that influenced coagulation status highlighted key regions associated with lactose and protein concentrations, and others not associated with routinely collected milk composition traits. These results suggest that the occurrence of NC is mostly related to damage of the epithelium secretory mammary cells, which occurs with the advancement of a lactation or due to unhealthy mammary gland status. Genetic analysis of milk coagulation status and of the extracted PC confirmed the genetic background of the milk coagulability of sheep milk.

Multivariate and Genome-Wide Analysis of Mid-Infrared Spectra of Non-Coagulating Milk of Sarda Sheep Breed

Milk coagulation ability is crucial for the dairy sheep industry since the whole amount of milk is processed into cheese. Non-coagulating milk (NCM) is defined as milk not forming a curd within the testing time. In sheep milk, it has been reported in literature that up to 20% of milk is NCM. Although the clotting properties of individual milk have been widely studied, little attention has been given to NCM and genomic dissection of this trait. Mid-infrared (MIR) spectra can be exploited both to predict cheese-making aptitude and to discriminate between coagulating milk and NCM. The main goals of this work were (i) to assess the predictivity of MIR spectra for NCM classification and (ii) to conduct a genome-wide association study on coagulation ability. Milk samples from 949 Sarda ewes genotyped and phenotyped for milk coagulation properties (MCPs) served as the training dataset. The validation dataset included 662 ewes. Three classical MCPs were measured: rennet coagulation time (RCT), curd firmness (a30), and curd firming time (k20). Moreover, MIR spectra were acquired and stored in the region between 925.92 and 5,011.54 cm−1. The probability of a sample to be NCM was modeled by step-wise logistic regression on milk spectral information (LR-W), logistic regression on principal component (LR-PC), and canonical discriminant analysis of spectral wave number (DA-W). About 9% of the samples did not coagulate at 30 min. The use of LR-W gave a poorer classification of NCM. The use of LR-PC improved the percentage of correct assignment (45 ± 9%). The DA-W method allows us to reach 75.1 ± 10.3 and 76.5 ± 18.4% of correct assignments of the inner and external validation datasets, respectively. As far as GWA of NCM, 458 SNP associations and 45 candidate genes were detected. The genes retrieved from public databases were mostly linked to mammary gland metabolism, udder health status, and a milk compound also known to affect the ability of milk to coagulate. In particular, the potential involvement of CAPNs deserves further investigation.

Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G (GAPY~-1) with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H-1) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h2 = 0.3) were available for all animals in generations 1-9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G-1 and GAPY~-1 using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates.

Evaluation of Genetic Parameters of Growth of Pelibuey and Blackbelly Sheep through Pedigree in Mexico

Simple Summary

The importance of sheep production in Mexico has been increasing in recent years. Animal performance can be improved through continuous selection and cumulative genetic management. Studies on non-genetic factors that influence the growth characteristics of sheep, due to their positive genetic correlation with other live weights, can present data of great interest to small producers with limited resources. In the present study, genealogical and functional information from the historical archive of the Mexican Association of Sheep Breeders (AMCO) was used. The objective was to estimate the heritability and genetic correlations of the growth of ewes born and weaned at different times of the same year from different herds with pedigree registration. The expected results could be incorporated into genetic selection programs with a favorable impact on the local economy of small sheep producers in Mexico.

Abstract

Birth weight (BW) and weaning weight (WW) data from Pelibuey and Blackbelly lambs belonging to the Asociación Mexicana de Criadores de Ovinos (AMCO) were used with the objective of estimating genetic parameters (heritability and genetic correlations) and analyzing the growth characteristics of ewes born and weaned at different times of the same year from different herds with pedigree registration. In the case of Pelibuey lambs, the animal model included the weaning weight at 75 days of age, considering the direct additive genetic effect, maternal additive genetic effect, covariance between direct and maternal effects, as well as the permanent environmental effect of the mother. The direct estimators of heritability for Pelibuey were BW = 0.01 ± 0.021 and WW = 0.31 ± 0.074 and for Blackbelly they were BW = 0.05 ± 0.042 and WW = 0.41 ± 0.146. In the case of the maternal heritability estimators in Pelibuey they were BW = 0.02 ± 0.040 and WW = 0.21 ± 0.121 and for Blackbelly they were BW = 0.12 ± 0.054 and WW = 0.28 ± 0.121. The magnitude of the estimates of genetic correlations between direct and maternal effects for adjusted weaning weight at 75 days of age indicate that genetic progress may be slow in a breeding program. However, these selection results could be included in the short term in the breeding programs for the Pelibuey and Blackbelly breeds in Mexico, for livestock development in low-income rural areas.

Estimation of direct and maternal genetic effects and annotation of potential candidate genes for weight and meat quality traits in a genotyped outdoor dual-purpose cattle breed

With regard to potential applications of genomic selection in small numbered breeds, we evaluated genomic models and focused on potential candidate gene annotations for weight and meat quality traits in the local Rotes Höhenvieh (RHV) breed. Traits included 6,003 birth weights (BWT), 5,719 200 d-weights (200dw), 4,594 365 d-weights (365dw), and 547 records for intramuscular fat content (IMF). A total of 581,304 SNP from 370 genotyped cattle with phenotypic records were included in genomic analyses. Model evaluations focused on single- and multiple-trait models with direct and with direct and maternal genetic effects. Genetic relationship matrices were based on pedigree (A-matrix), SNP markers (G-matrix), or both (H-matrix). Genome-wide association studies (GWASs) were carried out using linear mixed models to identify potential candidate genes for the traits of interest. De-regressed proofs (DRP) for direct and maternal genetic components were used as pseudo-phenotypes in the GWAS. Accuracies of direct breeding values were higher from models based on G or on H compared to A. Highest accuracies (> 0.89) were obtained for IMF with multiple-trait models using the G-matrix. Direct heritabilities with maternal genetic effects ranged from 0.62 to 0.66 for BWT, from 0.45 to 0.55 for 200dW, from 0.40 to 0.44 for 365dW, and from 0.48 to 0.75 for IMF. Maternal heritabilities for BWT, 200dW, and 365dW were in a narrow range from 0.21 to 0.24, 0.24 to 0.27, and 0.21 to 0.25, respectively, and from 0.25 to 0.65 for IMF. Direct genetic correlations among body weight traits were positive and favorable, and very similar from different models but showed a stronger variation with 0.31 (A), −0.13 (G), and 0.45 (H) between BWT and IMF. In gene annotations, we identified 6, 3, 1, and 6 potential candidate genes for direct genetic effect on BWT, 200dW, 365dW, and IMF traits, respectively. Regarding maternal genetic effects, four (SHROOM3, ZNF609, PECAM1, and TEX2) and two (TMEM182 and SEC11A) genes were detected as potential candidate genes for BWT and 365dW, respectively. Potential candidate genes for maternal effect on IMF were GRHL2, FGA, FGB, and CTNNA3. As the most important finding from a practical breeding perspective, a small number of genotyped RHV cattle enabled accurate breeding values for high heritability IMF.

Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.

Investigation of β-hydroxybutyrate in early lactation of Simmental cows: Genetic parameters and genomic predictions

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.

Estimation of Population-Specific Genetic Parameters Important for Long-Term Optimum Contribution Selection-Case Study on a Dairy Istrian Sheep Breed

The Istrian sheep breed has been subjected to selection for dairy traits for more than two decades. However, a detailed study of some important population-specific parameters such as effective population size (Ne) and connectedness between flocks has never been carried out. The aim of the study was to examine the above parameters in dairy Istrian sheep subjected to a national selection program. The Ne was estimated as the mean rate of increase in coancestry, and connectedness was determined using four different statistics. The Ne was estimated at 73 animals with pedigree constraints imposed on 4 equivalent generations and 3 full generations. Analysis of ΔNe ("sliding window approach") revealed a negative ΔNe indicating a progressive loss of genetic variability (ΔNeNEG≥4 = -6.6, p < 0.01; ΔNeNFG≥3 = -4.9, p > 0.05). The overall connectedness (r¯ ~ 0.0001) was below the acceptable level for unbiased ranking of the animals belonging to different flocks (ri,j  = 0.05). OCS appears to be the best option for the long-term survival (self-sufficiency) of the breed, but genetic links between flocks need to be strengthened to allow unbiased ranking of the animals based on the estimated breeding values.

Single-step genomic best linear unbiased predictor genetic parameter estimations and genome-wide associations for milk fatty acid profiles, interval from calving to first insemination, and ketosis in Holstein cattle

Milk fatty acids (FA) have been suggested as biomarkers for early-lactation metabolic diseases and for female fertility status. The aim of the present study was to infer associations between FA, the metabolic disorder ketosis (KET), and the interval from calving to first insemination (ICF) genetically and genomically. In this regard, we focused on a single-step genomic BLUP approach, allowing consideration of genotyped and ungenotyped cows simultaneously. The phenotypic data set considered 38,375 first-lactation Holstein cows, kept in 45 large-scale co-operator herds from 2 federal states in Germany. The calving years for these cows were from 2014 to 2017. Concentrations in milk from the first official milk recording test-day for saturated, unsaturated (UFA), monounsaturated (MUFA), polyunsaturated, palmitic, and stearic (C18:0) FA were determined via Fourier-transform infrared spectroscopy. Ketosis was defined as a binary trait according to a veterinarian diagnosis key, considering diagnoses within a 6-wk interval after calving. A subset of 9,786 cows was genotyped for 40,989 SNP markers. Variance components and heritabilities for all Gaussian distributed FA and for ICF, and for binary KET were estimated by applying single-step genomic BLUP single-trait linear and threshold models, respectively. Genetic correlations were estimated in series of bivariate runs. Genomic breeding values for the single-step genomic BLUP estimations were dependent traits in single-step GWAS. Heritabilities for FA were moderate in the range from 0.09 to 0.20 (standard error = 0.02-0.03), but quite small for ICF (0.08, standard error = 0.01) and for KET (0.05 on the underlying liability scale, posterior standard deviation = 0.02). Genetic correlations between KET and UFA, MUFA, and C18:0 were large (0.74 to 0.85, posterior standard deviation = 0.14-0.19), and low positive between KET and ICF (0.17, posterior standard deviation = 0.22). Genetic correlations between UFA, MUFA, and C18:0 with ICF ranged from 0.34 to 0.46 (standard error = 0.12). In single-step GWAS, we identified a large proportion of overlapping genomic regions for the different FA, especially for UFA and MUFA, and for saturated and palmitic FA. One identical significantly associated SNP was identified for C18:0 and KET on BTA 15. However, there was no genomic segment simultaneously significantly affecting all trait categories ICF, FA, and KET. Nevertheless, some of the annotated potential candidate genes DGKA, IGFBP4, and CXCL8 play a role in lipid metabolism and fertility mechanisms, and influence production diseases in early lactation. Genetic and genomic associations indicate that Fourier-transform infrared spectroscopy FA concentrations in milk from the first official test-day are valuable predictors for KET and for ICF.

Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed

Genomic selection has been implemented over the years in several livestock species, due to the achievable higher genetic progress. The use of genomic information in evaluations provides better prediction accuracy than do pedigree-based evaluations, and the makeup of the genotyped population is a decisive point. The aim of this work is to compare the effect of different genotyping strategies (number and type of animals) on the prediction accuracy for dairy sheep Latxa breeds. A simulation study was designed based on the real data structure of each population, and the phenotypic and genotypic data obtained were used in genetic (BLUP) and genomic (single-step genomic BLUP) evaluations of different genotyping strategies. The genotyping of males was beneficial when they were genetically connected individuals and if they had daughters with phenotypic records. Genotyping females with their own lactation records increased prediction accuracy, and the connection level has less relevance. The differences in genotyping females were independent of their estimated breeding value. The combined genotyping of males and females provided intermediate accuracy results regardless of the female selection strategy. Therefore, assuming that genotyping rams is interesting, the incorporation of genotyped females would be beneficial and worthwhile. The benefits of genotyping individuals from various generations were highlighted, although it was also possible to gain prediction accuracy when historic individuals were not considered. Greater genotyped population sizes resulted in more accuracy, even if the increase seems to reach a plateau.

Genomic information allows for more accurate breeding values for milkability in dual-purpose Italian Simmental cattle

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.

Functional Odd- and Branched-Chain Fatty Acid in Sheep and Goat Milk and Cheeses

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.

Principal component and multivariate factor analysis of detailed sheep milk fatty acid profile

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.

Accuracy of Imputation of Microsatellite Markers from a 50K SNP Chip in Spanish Assaf Sheep

Simple Summary

Parentage misassignments directly affect genetic gain in traditional breeding programs. The use of genetic markers facilitates parentage verification. In sheep, microsatellite markers and single nucleotide polymorphism (SNP) markers have been proposed by the International Society of Animal Sciences (ISAG) for parentage testing. Since the implementation of genomic selection, the microsatellite information used for parental testing in previous generations is gradually being replaced by SNPs. However, parentage verifications should all be performed using the same technology. A strategy for transitioning from microsatellites to SNP markers, while avoiding extra genotyping costs, is the imputation of microsatellite alleles from SNP haplotypes. This study aims to identify the optimum approach, using a minimum number of SNPs to accurately impute microsatellite markers and developing a low-density SNP chip for parentage verification in the Assaf sheep breed. The imputation approach described here reached high accuracies using a low number of SNP markers, which supports the development of a low-density SNP chip that could avoid the problems of genotyping with both technologies, being a cost-effective method for parentage testing. This study will help sheep breeders to perform parentage verification when different genotyping platforms have been used across generations.

Abstract

Transitioning from traditional to new genotyping technologies requires the development of bridging methodologies to avoid extra genotyping costs. This study aims to identify the optimum number of single nucleotide polymorphisms (SNPs) necessary to accurately impute microsatellite markers to develop a low-density SNP chip for parentage verification in the Assaf sheep breed. The accuracy of microsatellite marker imputation was assessed with three metrics: genotype concordance (C), genotype dosage (length r²), and allelic dosage (allelic r²), for all imputation scenarios tested (0.5–10 Mb microsatellite flanking SNP windows). The imputation accuracy for the three metrics analyzed for all haplotype lengths tested was higher than 0.90 (C), 0.80 (length r²), and 0.75 (allelic r²), indicating strong genotype concordance. The window with 2 Mb length provides the best accuracy for the imputation procedure and the design of an affordable low-density SNP chip for parentage testing. We additionally evaluated imputation performance under two null models, naive (imputing the most common allele) and random (imputing by randomly selecting the allele), which in comparison showed weak genotype concordances (0.41 and 0.15, respectively). Therefore, we describe a precise methodology in the present article to impute multiallelic microsatellite genotypes from a low-density SNP chip in sheep and solve the problem of parentage verification when different genotyping platforms have been used across generations.

Genomic prediction for latent variables related to milk fatty acid composition in Holstein, Simmental and Brown Swiss dairy cattle breeds

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.

Genetic Parameters of Bovine Milk Fatty Acid Profile, Yield, Composition, Total and Differential Somatic Cell Count

The growing interest of consumers for milk and dairy products of high nutritional value has pushed researchers to evaluate the feasibility of including fatty acids (FA) in selection programs to modify milk fat profile and improve its nutritional quality. Therefore, the aim of this study was to estimate genetic parameters of FA profile predicted by mid-infrared spectroscopy, milk yield, composition, and total and differential somatic cell count. Edited data included 35,331 test-day records of 25,407 Italian Holstein cows from 652 herds. Variance components and heritability were estimated using single-trait repeatability animal models, whereas bivariate repeatability animal models were used to estimate genetic and phenotypic correlations between traits, including the fixed effects of stage of lactation, parity, and herd-test-date, and the random effects of additive genetic animal, cow permanent environment and the residual. Heritabilities and genetic correlations obtained in the present study reflected both the origins of FA (extracted from the blood or synthesized de novo by the mammary gland) and their grouping according to saturation or chain length. In addition, correlations among FA groups were in line with correlation among individual FA. Moderate negative genetic correlations between FA and milk yield and moderate to strong positive correlations with fat, protein, and casein percentages suggest that actual selection programs are currently affecting all FA groups, not only the desired ones (e.g., polyunsaturated FA). The absence of association with differential somatic cell count and the weak association with somatic cell score indicate that selection on FA profile would not affect selection on resistance to mastitis and vice versa. In conclusion, our findings suggest that genetic selection on FA content is feasible, as FA are variable and moderately heritable. Nevertheless, in the light of correlations with other milk traits estimated in this study, a clear breeding goal should first be established.

The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep

Simple Summary

Genetic improvement of wool production and quality traits in fine-wool sheep is an appealing option for enhancing the market value of wool products. We estimated genetic parameters and the accuracies of estimated breeding values for various wool production and quality traits in fine-wool sheep using pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) strategies. ssGBLUP performed slightly better than PBLUP for the studied traits. Therefore, the single-step genetic evaluation method could be successfully implemented in genomic evaluations of fine-wool sheep and the prediction of future breeding values in young Merino sheep as part of an early preselection strategy in the near future.

Abstract

Genomic evaluations are a method for improving the accuracy of breeding value estimation. This study aimed to compare estimates of genetic parameters and the accuracy of breeding values for wool traits in Merino sheep between pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) using Bayesian inference. Data were collected from 28,391 yearlings of Chinese Merino sheep (classified in 1992–2018) at the Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm, China. Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The estimates of heritability for wool traits were low to moderate. The largest h² values were observed for FL (0.277) and MFD (0.290) with ssGBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. The accuracies of breeding values were low to moderate, ranging from 0.362 to 0.573 for the whole population and from 0.318 to 0.676 for the genotyped subpopulation. The correlation between the estimated breeding values (EBVs) and genomic EBVs (GEBVs) ranged from 0.717 to 0.862 for the whole population, and the relative increase in accuracy when comparing EBVs with GEBVs ranged from 0.372% to 7.486% for these traits. However, in the genotyped population, the rank correlation between the estimates obtained with PBLUP and ssGBLUP was reduced to 0.525 to 0.769, with increases in average accuracy of 3.016% to 11.736% for the GEBVs in relation to the EBVs. Thus, genomic information could allow us to more accurately estimate the relationships between animals and improve estimates of heritability and the accuracy of breeding values by ssGBLUP.

Multi-breed genome-wide association studies across countries for electronically recorded behavior traits in local dual-purpose cows

Basic bovine behavior is a crucial parameter influencing cattle domestication. In addition, behavior has an impact on cattle productivity, welfare and adaptation. The aim of the present study was to infer quantitative genetic and genomic mechanisms contributing to natural dual-purpose cow behavior in grazing systems. In this regard, we genotyped five dual-purpose breeds for a dense SNP marker panel from four different European countries. All cows from the across-country study were equipped with the same electronic recording devices. In this regard, we analyzed 97,049 longitudinal sensor behavior observations from 319 local dual-purpose cows for rumination, feeding, basic activity, high active, not active and ear temperature. According to the specific sensor behaviors and following a welfare protocol, we computed two different welfare indices. For genomic breed characterizations and multi-breed genome-wide association studies, sensor traits and test-day production records were merged with 35,826 SNP markers per cow. For the estimation of variance components, we used the pedigree relationship matrix and a combined similarity matrix that simultaneously included both pedigree and genotypes. Heritabilities for feeding, high active and not active were in a moderate range from 0.16 to 0.20. Estimates were very similar from both relationship matrix-modeling approaches and had quite small standard errors. Heritabilities for the remaining sensor traits (feeding, basic activity, ear temperature) and welfare indices were lower than 0.09. Five significant SNPs on chromosomes 11, 17, 27 and 29 were associated with rumination, and two different SNPs significantly influenced the sensor traits “not active” (chromosome 13) and “feeding” (chromosome 23). Gene annotation analyses inferred 22 potential candidate genes with a false discovery rate lower than 20%, mostly associated with rumination (13 genes) and feeding (8 genes). Mendelian randomization based on genomic variants (i.e., the instrumental variables) was used to infer causal inference between an exposure and an outcome. Significant regression coefficients among behavior traits indicate that all specific behavioral mechanisms contribute to similar physiological processes. The regression coefficients of rumination and feeding on milk yield were 0.10 kg/% and 0.12 kg/%, respectively, indicating their positive influence on dual-purpose cow productivity. Genomically, an improved welfare behavior of grazing cattle, i.e., a higher score for welfare indices, was significantly associated with increased fat and protein percentages.