A comparison of methods to estimate genomic relationships using pedigree and markers in livestock populations

Accurate prediction of breeding values depends on capturing the variability in genome sharing of relatives with the same pedigree relationship. Here, we compare two approaches to set up genomic relationship matrices for precision of genomic relationships (GR) and accuracy of estimated breeding values (GEBV). Real and simulated data (pigs, 60k SNP) were analysed, and GR were estimated using two approaches: (i) identity by state, corrected with either the observed ( G _{VR ‐O} ) or the base population ( G _{VR ‐B} ) allele frequencies and (ii) identity by descent using linkage analysis ( G _{IBD ‐L} ). Estimators were evaluated for precision and empirical bias with respect to true pedigree IBD GR. All three estimators had very low bias. G _{IBD ‐L} displayed the lowest sampling error and the highest correlation with true genome‐shared values. G _{VR ‐B} approximated G _{IBD ‐L} 's correlation and had lower error than G _{VR ‐O} . Accuracy of GEBV for selection candidates was significantly higher when G _{IBD ‐L} was used and identical between G _{VR ‐O} and G _{VR ‐B} . In real data, G _{IBD ‐L} 's sampling standard deviation was the closest to the theoretical value for each pedigree relationship. Use of pedigree to calculate GR improved the precision of estimates and the accuracy of GEBV.     

500 years of breeding in the Carthusian Strain of Pura Raza Español horse: An evolutional analysis using genealogical and genomic data

The Carthusian horse is a Pura Raza Español (PRE) strain (CS), bred as a closed population since its creation more than 500 years ago. The aim of this study was to analyse for the first time its population structure and situation of variability combining both genealogical (GEL) and genomic (GEN) data. The GEL data comprised 348,429 pedigree records (56,105 CS horses), while the GEN analysis included the high-density genotypes (670,804 SNPs) of 287 horses. Pedigree completeness demonstrated its accuracy, showing a good correlation of GEL (F) and GEN (F_ROH) inbreeding coefficient in the case of PRE subpopulations partially related and non-related to Carthusian strain (0.68) but a lower value in the 100% Carthusian horses (0.42), due to the high weight of founders not detected by GEL analysis. GEN (PCA, AMOVA, and Admixture) and GEL analysis showed a good differentiation of subpopulations, but also a high level of introgression of the CS in the breed during past decades. A recent change in this trend was noteworthy, with a considerable reduction in CS variability and a genetic bottleneck (effective population sizes of 31.57 and 30.20 in GEL and GEN analysis, respectively, in last generation). The PRE has maintained its variability, and a considerable difference in estimated N_e by GEL (60.77) and GEN (188.0) data was observed. Using two sources of complementary information, it was found the existence of an ancient PRE strain with a unique genetic landmark, practically free from the influence of other equine populations.     

The importance of haplotype length and heritability using genomic selection in dairy cattle

Reliabilities for genomic estimated breeding values (GEBV) were investigated by simulation for a typical dairy cattle breeding setting. Scenarios were simulated with different heritabilites ( h ²) and for different haplotype sizes, and seven generations with only genotypes were generated to investigate reliability of GEBV over time. A genome with 5000 single nucleotide polymorphisms (SNP) at distances of 0.1 cM and 50 quantitative trait loci (QTL) was simulated, and a Bayesian variable selection model was implemented to predict GEBV. Highest reliabilities were obtained for 10 SNP haplotypes. At optimal haplotype size, reliabilities in generation 1 without phenotypes ranged from 0.80 for h ² = 0.02 to 0.93 for h ² = 0.30, and in the seventh generation without phenotypes ranged from 0.69 for h ² = 0.02 to 0.86 for h ² = 0.30. Reliabilities of GEBV were found sufficiently high to implement dairy selection schemes without progeny testing in which case a data time-lag of two to three generations may be present. Reliabilities were also relatively high for low heritable traits, implying that genomic selection could be especially beneficial to improve the selection on, e.g. health and fertility.     

Comparison of heritabilities of dairy traits in Australian Holstein‐Friesian cattle from genomic and pedigree data and implications for genomic evaluations

The reliability of genomic evaluations depends on the proportion of genetic variation explained by the DNA markers. In this study, we have estimated the proportion of variance in daughter trait deviations (DTDs) of dairy bulls explained by 45 993 genome wide single‐nucleotide poly‐ morphism (SNP) markers for 29 traits in Australian Holstein‐Friesian dairy cattle. We compare these proportions to the proportion of variance in DTDs explained by the additive relationship matrix derived from the pedigree, as well as the sum of variance explained by both pedigree and marker information when these were fitted simultaneously. The propor‐ tion of genetic variance in DTDs relative to the total genetic variance (the total genetic variance explained by the genomic relationships and pedigree relationships when both were fitted simultaneously) varied from 32% for fertility to approximately 80% for milk yield traits. When fitting genomic and pedigree relationships simultaneously, the variance unexplained (i.e. the residual variance) in DTDs of the total variance for most traits was reduced compared to fitting either individually, suggesting that there is not complete overlap between the effects. The proportion of genetic variance accounted by the genomic relationships can be used to modify the blending equations used to calculate genomic estimated breeding value (GEBV) from direct genomic breeding value (DGV) and parent average. Our results, from a validation population of young dairy bulls with DTD, suggest that this modification can improve the reliability of GEBV by up to 5%.     

Use of different statistical models to predict direct genomic values for productive and functional traits in Italian Holsteins

One of the main issues in genomic selection was the huge unbalance between number of markers and phenotypes available. In this work, principal component analysis is used to reduce the number of predictors for calculating direct genomic breeding values (DGV) for production and functional traits. 2093 Italian Holstein bulls were genotyped with the 54 K Illumina beadchip, and 39 555 SNP markers were retained after data editing. Principal Components (PC) were extracted from SNP matrix, and 15 207 PC explaining 99% of the original variance were retained and used as predictors. Bulls born before 2001 were included in the reference population, younger animals in the test population. A BLUP model was used to estimate the effect of principal component on deregressed proof (DRPF) for 35 traits and results were compared to those obtained by using SNP genotypes as predictors either with BLUP or with Bayes_A models. Correlations between DGV and DRPF did not substantially differ among the three methods except for milk fat content. The lowest prediction bias was obtained for the method based on the use of principal component. Regression coefficients of DRPF on DGV were lower than one for the approach based on the use of PC and higher than one for the other two methods. The use of PC as predictors resulted in a large reduction of number of predictors (approximately 38%) and of computational time that was approximately 2% of the time needed to estimate SNP effects with the other two methods. Accuracies of genomic predictions were in most of cases only slightly higher than those of the traditional pedigree index, probably due to the limited size of the considered population.     

Genetic diversity and genomic inbreeding in Japanese Black cows in the islands of Okinawa Prefecture evaluated using single-nucleotide polymorphism array

Maintaining genetic diversity and inbreeding control are important in Japanese Black cattle production, especially in remote areas such as the islands of Okinawa Prefecture. Using a single-nucleotide polymorphism (SNP) array, we evaluated the genetic diversity and genomic inbreeding in Japanese Black cows from the islands of Okinawa Prefecture and compared them to those from other locations across Japan. Linkage disequilibrium decay was slower in cows in the islands of Okinawa Prefecture. The estimated effective population size declined over time in both populations. The genomic inbreeding coefficient (F_ROH) was estimated using long stretches of consecutive homozygous SNPs (runs of homozygosity; ROH). F_ROH was higher in the cows on the islands of Okinawa Prefecture than on other locations. In total, 818 ROH fragments, including those containing NCAPG and PLAG1, which are major quantitative trait loci for carcass weight in Japanese Black cattle, were present at significantly higher frequencies in cows in the islands of Okinawa Prefecture. This suggests that the ROH fragments are under strong selection and that cows in the islands of Okinawa Prefecture have low genetic diversity and high genomic inbreeding relative to those at other locations. SNP arrays are useful tools for evaluating genetic diversity and genomic inbreeding in cattle.     

Prediction of response to marker-assisted and genomic selection using selection index theory

Selection index methods can be used for deterministic assessment of the potential benefit of including marker information in genetic improvement programmes using marker-assisted selection (MAS). By specifying estimates of breeding values derived from marker information (M-EBV) as a correlated trait with heritability equal to 1, it was demonstrated that marker information can be incorporated in standard software for selection index predictions of response and rates of inbreeding, which requires specifying phenotypic traits and their genetic parameters. Path coefficient methods were used to derive genetic and phenotypic correlations between M-EBV and the phenotypic data. Methods were extended to multi-trait selection and to the case when M-EBV are based on high-density marker genotype data, as in genomic selection. Methods were applied to several example scenarios, which confirmed previous results that MAS substantially increases response to selection but also demonstrated that MAS can result in substantial reductions in the rates of inbreeding. Although further validation by stochastic simulation is required, the developed methodology provides an easy means of deterministically evaluating the potential benefits of MAS and to optimize selection strategies with availability of marker data.     

Disentangling data dependency using cross-validation strategies to evaluate prediction quality of cattle grazing activities using machine learning algorithms and wearable sensor data

Wearable sensors have been explored as an alternative for real-time monitoring of cattle feeding behavior in grazing systems. To evaluate the performance of predictive models such as machine learning (ML) techniques, data cross-validation (CV) approaches are often employed. However, due to data dependencies and confounding effects, poorly performed validation strategies may significantly inflate the prediction quality. In this context, our objective was to evaluate the effect of different CV strategies on the prediction of grazing activities in cattle using wearable sensor (accelerometer) data and ML algorithms. Six Nellore bulls (average live weight of 345 ± 21 kg) had their behavior visually classified as grazing or not-grazing for a period of 15 d. Elastic Net Generalized Linear Model (GLM), Random Forest (RF), and Artificial Neural Network (ANN) were employed to predict grazing activity (grazing or not-grazing) using 3-axis accelerometer data. For each analytical method, three CV strategies were evaluated: holdout, leave-one-animal-out (LOAO), and leave-one-day-out (LODO). Algorithms were trained using similar dataset sizes (holdout: n = 57,862; LOAO: n = 56,786; LODO: n = 56,672). Overall, GLM delivered the worst prediction accuracy (53%) compared with the ML techniques (65% for both RF and ANN), and ANN performed slightly better than RF for LOAO (73%) and LODO (64%) across CV strategies. The holdout yielded the highest nominal accuracy values for all three ML approaches (GLM: 59%, RF: 76%, and ANN: 74%), followed by LODO (GLM: 49%, RF: 61%, and ANN: 63%) and LOAO (GLM: 52%, RF: 57%, and ANN: 57%). With a larger dataset (i.e., more animals and grazing management scenarios), it is expected that accuracy could be increased. Most importantly, the greater prediction accuracy observed for holdout CV may simply indicate a lack of data independence and the presence of carry-over effects from animals and grazing management. Our results suggest that generalizing predictive models to unknown (not used for training) animals or grazing management may incur poor prediction quality. The results highlight the need for using management knowledge to define the validation strategy that is closer to the real-life situation, i.e., the intended application of the predictive model.     

A comparison of accuracy validation methods for genomic and pedigree‐based predictions of swine litter size traits using Large White and simulated data

The objective of this study was to compare and determine the optimal validation method when comparing accuracy from single‐step GBLUP (ssGBLUP) to traditional pedigree‐based BLUP. Field data included six litter size traits. Simulated data included ten replicates designed to mimic the field data in order to determine the method that was closest to the true accuracy. Data were split into training and validation sets. The methods used were as follows: (i) theoretical accuracy derived from the prediction error variance (PEV) of the direct inverse (iLHS), (ii) approximated accuracies from the accf90(GS) program in the BLUPF90 family of programs (Approx), (iii) correlation between predictions and the single‐step GEBVs from the full data set (GEBV_Full), (iv) correlation between predictions and the corrected phenotypes of females from the full data set (Y_c), (v) correlation from method iv divided by the square root of the heritability (Y_ch) and (vi) correlation between sire predictions and the average of their daughters' corrected phenotypes (Y_cs). Accuracies from iLHS increased from 0.27 to 0.37 (37%) in the Large White. Approximation accuracies were very consistent and close in absolute value (0.41 to 0.43). Both iLHS and Approx were much less variable than the corrected phenotype methods (ranging from 0.04 to 0.27). On average, simulated data showed an increase in accuracy from 0.34 to 0.44 (29%) using ssGBLUP. Both iLHS and Y_ch approximated the increase well, 0.30 to 0.46 and 0.36 to 0.45, respectively. GEBV_Full performed poorly in both data sets and is not recommended. Results suggest that for within‐breed selection, theoretical accuracy using PEV was consistent and accurate. When direct inversion is infeasible to get the PEV, correlating predictions to the corrected phenotypes divided by the square root of heritability is adequate given a large enough validation data set.     

Identification of genomic regions related to age at first calving and first calving interval in water buffalo using single-step GBLUP

In Brazil, water buffaloes have been used to produce milk for mozzarella cheese production. Consequently, the main selection criterion applied for the buffalo genetic improvement is the estimated mozzarella yield as a function of milk, fat and protein production. However, given the importance of reproductive traits in production systems, this study aimed to use techniques for identifying genomic regions that affect the age at first calving (AFC) and first calving interval (FCI) in buffalo cows and to select candidate genes for the identification of QTL and gene expression studies. The single-step GBLUP method was used for the identification of genomic regions. Windows of 1 Mb containing single-nucleotide polymorphisms were constructed and the 10 windows that explained the greatest proportion of genetic variance were considered candidate regions for each trait. Genes present into the selected windows were identified using the UOA_WB_1 assembly as the reference, and their ontology was defined with the Panther tool. Candidate regions for both traits were identified on BBU 3, 12, 21 and 22; for AFC, candidates were detected on BBU 6, 7, 8, 9 and 15 and for first calving interval on BBU 4, 14 and 19. This study identified regions with great contribution to the additive genetic variance of age at first calving and first calving interval in the population of buffalo cows studied. The ROCK2, PMVK, ADCY2, MAP2K6, BMP10 and GFPT1 genes are main candidates for reproductive traits in water dairy buffaloes, and these results may have future applications in animal breeding programs or in gene expression studies of the species.     

Comparative susceptibility of indigenous and improved pig breeds to Classical swine fever virus infection: Practical and epidemiological implications in a subsistence-based, developing country setting

This study investigated the comparative susceptibility of indigenous Moo Laat and improved Large White/Landrace pig breeds to infection with classical swine fever virus (CSFV) under controlled conditions in the Lao People’s Democratic Republic (Lao PDR). The Moo Laat (ML) and Large White/Landrace crossbreed (LWC) pigs were inoculated with a standard challenge strain designated Lao/Kham225 (infectivity titre of 10^2.75 TCID₅₀/ml). The results demonstrated that both the native breed and an improved pig breed are fully susceptible to CSFV infection and the mortality rate is high. LWC pigs demonstrated lower (or shorter) survival times (50% survival time: 11 days), earlier and higher pyrexia and earlier onset of viraemia compared to ML pigs (50% survival time: 18 days). In the context of village-based pig production, the longer time from infection to death in native ML pigs means that incubating or early sick pigs are likely to be sold once an outbreak of CSF is recognized in a village. This increased longevity probably contributes to the maintenance and spread of disease in a population where generally the contact rate is low.     

Cost-effectiveness of diagnostic strategies using quantitative real-time PCR and bacterial culture to identify contagious mastitis cases in large dairy herds

Diagnostic strategies to detect contagious mastitis caused by Mycoplasma bovis, Staphylococcus aureus, and Streptococcus agalactiae in dairy herds during an outbreak have been minimally studied with regard to cost and diagnostic sensitivity. The objective of this cross-sectional study was to compare the cost-effectiveness of diagnostic strategies for identification of infected cows in two California dairy herds during contagious mastitis outbreaks.     

A novel strategy for resynchronization of ovulation in Nelore cows using injectable progesterone (P4) and P4 releasing devices to perform two timed inseminations within 22 days

We aimed to evaluate the reproductive performance of Nelore lactating cows submitted to a resynchronization 12 days after timed artificial insemination (TAI) with or without a long‐acting progesterone (P4‐LA) treatment. Nelore cows were submitted to a P4/oestradiol‐based TAI protocol (D0 = insemination). On D12, cows in the control group (n = 184) received a new P4 intravaginal device (0.96 g), whereas cows in the P4‐LA group (n = 192) received the P4 device and 75 mg P4‐LA. Cows identified as non‐pregnant (n = 120) by regression of corpus luteum using colour Doppler ultrasonography on D20 had the P4 device removed and received 500ug of sodium cloprostenol, 1 mg of oestradiol cypionate and 300 IU of eCG and were re‐inseminated on D22. There was no difference (p > 0.10) in the pregnancy rate at D20, D30 and D60 after first TAI between the control (69%, 59.7% and 57%, respectively) and P4‐LA (67%, 55.7%, and 55.2%, respectively) groups. Pregnancy losses were similar between both groups (p > 0.1). For cows submitted to the second TAI, the pre‐ovulatory follicle size did not differ (p > 0.1), but the oestrous detection and pregnancy rates were greater (p < 0.05) in the P4‐LA group (92.2% [59/64] and 60.9% [39/64], respectively) than in controls (75% [42/56] and 44.6% [25/56]). The cumulative pregnancy rate after two TAIs did not differ (p > 0.1) between control (73.3% [135/184]) and P4‐LA (76% [146/192]) groups. The use of P4‐LA at 12 days after TAI potentially increases the pregnancy rates for a new early resynchronization strategy associated with the Doppler imaging for pregnancy diagnosis and results in an alternative to perform two TAIs in 22 days in beef cows.