Transforming estimated breeding values from observed to probability scale: how to make categorical data analyses more efficient.

Threshold models are often used in genetic analysis of categorical data, such as calving ease. Solutions in the liability scale are easily transformed into probabilities; therefore, estimated breeding values are published as the probability of expressing the category of main interest and are the industry's gold standard because they are easy to interpret and use for selection. However, because threshold models involve non-linear equations and probability functions, implementing such a method is complex. Challenges include long computing time and convergence issues, intensified by including genomic data. Linear models are an alternative to overcome those challenges. Estimated breeding values computed using linear models are highly correlated (≥ 0.96) with those from threshold models; however, the lack of a transformation from the observed to the probability scale limits the use of linear models. The objective of this study was to propose transformations from observed to probability scale analogous to the transformation from liability to probability scale. We assessed computing time, peak memory use, correlations between estimated breeding values, and estimated genetic trends from linear and threshold models. With 11M animals in the pedigree and almost 965k genotyped animals, linear models were 5x faster to converge than threshold models (32 vs. 145 h), and peak memory use was the same (189 GB). The transformations proposed provided highly correlated probabilities from linear and threshold models. Correlations between direct (maternal) estimated breeding values from linear and threshold models and transformed to probabilities were ≥ 0.99 (0.97) for all animals in the pedigree, sires with/without progeny records, or animals with phenotypic records; therefore, estimated genetic trends were analogous, suggesting no loss of genetic progress in breeding programs that would adopt linear instead of threshold models. Furthermore, linear models reduced computing time by five-fold compared to the threshold models; this enables weekly genetic evaluations and opens the possibility of using multi-trait models for categorical traits to improve selection effectiveness.

Marker effect p-values for single-step GWAS with the algorithm for proven and young in large genotyped populations.

BACKGROUND: Single-nucleotide polymorphism (SNP) effects can be backsolved from ssGBLUP genomic estimated breeding values (GEBV) and used for genome-wide association studies (ssGWAS). However, obtaining p-values for those SNP effects relies on the inversion of dense matrices, which poses computational limitations in large genotyped populations. In this study, we present a method to approximate SNP p-values for ssGWAS with many genotyped animals. This method relies on the combination of a sparse approximation of the inverse of the genomic relationship matrix ( G A P Y - 1 ) built with the algorithm for proven and young ( APY ) and an approximation of the prediction error variance of SNP effects which does not require the inversion of the left-hand side (LHS) of the mixed model equations. To test the proposed p-value computing method, we used a reduced genotyped population of 50K genotyped animals and compared the approximated SNP p-values with benchmark p-values obtained with the direct inverse of LHS built with an exact genomic relationship matrix ( G - 1 ) . Then, we applied the proposed approximation method to obtain SNP p-values for a larger genotyped population composed of 450K genotyped animals. RESULTS: The same genomic regions on chromosomes 7 and 20 were identified across all p-value computing methods when using 50K genotyped animals. In terms of computational requirements, obtaining p-values with the proposed approximation reduced the wall-clock time by 38 times and the memory requirement by ten times compared to using the exact inversion of the LHS. When the approximation was applied to a population of 450K genotyped animals, two new significant regions on chromosomes 6 and 14 were uncovered, indicating an increase in GWAS detection power when including more genotypes in the analyses. The process of obtaining p-values with the approximation and 450K genotyped individuals took 24.5 wall-clock hours and 87.66GB of memory, which is expected to increase linearly with the addition of noncore genotyped individuals. CONCLUSIONS: With the proposed method, obtaining p-values for SNP effects in ssGWAS is computationally feasible in large genotyped populations. The computational cost of obtaining p-values in ssGWAS may no longer be a limitation in extensive populations with many genotyped animals.

Genetic parameters for pelvic organ prolapse in purebred and crossbred sows.

This study aimed to investigate genetic parameters for sow pelvic organ prolapse in purebred and crossbred herds. Pelvic organ prolapse was recorded as normal or prolapsed on the individual sow level across 32 purebred and 8 crossbred farms. In total, 75,162 purebred Landrace sows from a single maternal line were recorded between 2018 and 2023, while 18,988 commercial two-way crossbred (Landrace x Large White) sows were available between 2020 and 2023. There were 5,122,005 animals included in the pedigree. The prolapse in purebreds and crossbreds was considered two different traits in the model. Pedigrees of the crossbred sows were determined based on genotypes through parentage assignment. The average incidence rates were 1.81% and 3.93% for purebreds and crossbreds, respectively. The bivariate model incorporated fixed effects of parity group and region with random effects of contemporary group (farm and mating year and month at the first parity), additive genetic, and residual. Genetic parameter estimates were obtained using BLUPF90+ with the AIREML option. The estimated additive variance was larger in crossbreds than in purebreds. Estimates of heritability in the observed scale were 0.09 (0.006) for purebreds and 0.11 (0.014) for crossbreds, with a genetic correlation of 0.83 using a linear model. Results suggested that including data from crossbreds with higher incidence rate is beneficial and selection to reduce the prolapse incidence in purebred sow herds would also benefit commercial crossbred sow herds.

Comparing algorithms to approximate accuracies for single-step genomic best linear unbiased predictor.

The exact accuracy of estimated breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for large datasets, especially if genomic information is available. Thus, different algorithms have been proposed to approximate accuracies. This study aimed to: 1) compare the approximated accuracies from 2 algorithms implemented in the BLUPF90 suite of programs, 2) compare the approximated accuracies from the 2 algorithms against the exact accuracy based on the inversion of the LHS of MME, and 3) evaluate the impact of adding genotyped animals with and without phenotypes on the exact and approximated accuracies. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data were provided by the American Angus Association and included 3 datasets of growth, carcass, and marbling traits. The genotype file contained 1,235,930 animals, and the pedigree file contained 12,492,581 animals. For the genomic evaluation, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. The correlations between exact and approximated accuracies from algorithms 1 and 2 of genotyped animals ranged from 0.87 to 0.90 and 0.98 to 0.99, respectively. The intercept and slope of the regression of exact on approximated accuracies from algorithm 2 ranged from 0.00 to 0.01 and 0.82 to 0.87, respectively. However, the intercept and the slope for algorithm 1 ranged from -0.10 to 0.05 and 0.98 to 1.10, respectively. In more than 80% of the traits, algorithm 2 exhibited a smaller mean square error than algorithm 1. The correlation between the approximated accuracies obtained from algorithms 1 and 2 ranged from 0.56 to 0.74, 0.38 to 0.71, and 0.71 to 0.97 in the groups of genotyped animals, genotyped animals without phenotype, and proven genotyped sires, respectively. The approximated accuracy from algorithm 2 showed a closer behavior to the exact accuracy when including genotyped animals in the analysis. According to the results, algorithm 2 is recommended for genetic evaluations since it proved more precise.

The genomic estimated breeding value (GEBV) represents an animal's genetic merit calculated using a combination of phenotypes, pedigree, and genomic information through a procedure known as single-step genomic best linear unbiased prediction (ssGBLUP). The accuracy of a GEBV reflects how closely it correlates with the true breeding value. However, calculating accuracies is not computationally feasible for large datasets with genomic information. In this context, methods for approximating accuracies have been proposed and implemented into genetic evaluations. This study aimed to compare 2 algorithms to approximate accuracies for ssGBLUP. In algorithm 1, genomic contributions are based on the diagonal of the genomic relationship matrix (G), combined with contributions from animal records and pedigrees. In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data for this study were provided by the American Angus Association and included datasets of growth, carcass, and marbling traits. Genotypes were available for 1,235,930 animals, and the pedigree had 12,492,581 animals. We showed that algorithm 2 is better suited for approximating accuracies, as its approximations closely matched the exact accuracy values obtained from the inverse of the mixed model equations.

Converting estimated breeding values from the observed to probability scale for health traits.

Dairy cattle health traits are paramount from a welfare and economic viewpoint; therefore, modern breeding programs prioritize the genetic improvement of these traits. Estimated breeding values for health traits are published as the probability of animals staying healthy. They are obtained using threshold models, which assume that the observed binary phenotype (i.e., healthy or sick) is dictated by an underlying normally distributed liability exceeding or not a threshold. This methodology requires significant computing time and faces convergence challenges as it implies a nonlinear system of equations. Linear models have more straightforward computations and provide a robust approximation to threshold models; thus, they could be used to overcome the mentioned challenges. However, linear models yield estimated breeding values on the observed scale, requiring an approximation to the liability scale analogous to that from threshold models to later obtain the estimated breeding values on the probability scale. In addition, the robustness of the approximation of linear to threshold models depends on the amount of information and the incidence of the trait, with extreme incidence (i.e., ≤ 5%) deviating from optimal approximation. Our objective was to test a transformation from the observed to the liability and then to the probability scale in the genetic evaluation of health traits with moderate and very low (extreme) incidence. Data comprised displaced abomasum (5.1M), ketosis (3.6M), lameness (5M), and mastitis (6.3M) records from a Holstein population with a pedigree of 6M animals, of which 1.7M were genotyped. Univariate threshold and linear models were performed to predict breeding values. The agreement between estimated breeding values on the probability scale derived from threshold and linear models was assessed using Spearman rank correlations and comparison of estimated breeding values distributions. Correlations were at least 0.95, and estimated breeding value distributions almost entirely overlapped for all the traits but displaced abomasum, the trait with the lowest incidence (2%). Computing time was ∼3x longer for threshold than for linear models. In this Holstein population, the approximation was suboptimal for a trait with extreme incidence (2%). However, when the incidence was ≥6%, the approximation was robust, and its use is recommended along with linear models for analyzing categorical traits in large populations to ease the computational burden.

Genetic correlations of direct and indirect genetic components of social dominance with fitness and morphology traits in cattle.

BACKGROUND: Within the same species, individuals show marked variation in their social dominance. Studies on a handful of populations have indicated heritable genetic variation for this trait, which is determined by both the genetic background of the individual (direct genetic effect) and of its opponent (indirect genetic effect). However, the evolutionary consequences of selection for this trait are largely speculative, as it is not a usual target of selection in livestock populations. Moreover, studying social dominance presents the challenge of working with a phenotype with a mean value that cannot change in the population, as for every winner of an agonistic interaction there will necessarily be a loser. Thus, to investigate what could be the evolutionary response to selection for social dominance, it is necessary to focus on traits that might be correlated with it. This study investigated the genetic correlations of social dominance, both direct and indirect, with several morphology and fitness traits. We used a dataset of agonistic contests involving cattle (Bos taurus): during these contests, pairs of cows compete in ritualized interactions to assess social dominance. The outcomes of 37,996 dominance interactions performed by 8789 cows over 20 years were combined with individual data for fertility, mammary health, milk yield and morphology and analysed using bivariate animal models including indirect genetic effects. RESULTS: We found that winning agonistic interactions has a positive genetic correlation with more developed frontal muscle mass, lower fertility, and poorer udder health. We also discovered that the trends of changes in the estimated breeding values of social dominance, udder health and more developed muscle mass were consistent with selection for social dominance in the population. CONCLUSIONS: We present evidence that social dominance is genetically correlated with fitness traits, as well as empirical evidence of the possible evolutionary trade-offs between these traits. We show that it is feasible to estimate genetic correlations involving dyadic social traits.

Derivation of indirect predictions using genomic recursions across generations in a broiler population.

Genomic estimated breeding values (GEBV) of animals without phenotypes can be indirectly predicted using recursions on GEBV of a subset. To maximize predictive ability of indirect predictions (IP), the subset must represent the independent chromosome segments segregating in the population. We aimed to 1) determine the number of animals needed in recursions to maximize predictive ability, 2) evaluate equivalency IP-GEBV, and 3) investigate trends in predictive ability of IP derived from recent vs. distant generations or accumulating phenotypes from recent to past generations. Data comprised pedigree of 825K birds hatched over 12 overlapping generations, phenotypes for body weight (BW; 820K), residual feed intake (RF; 200K) and weight gain during a trial period (WG; 200K), and breast meat percent (BP; 43K). A total of 154K birds (last six generations) had genotypes. The number of animals that maximize predictive ability was assessed based on the number of largest eigenvalues explaining 99% of variation in the genomic relationship matrix (1Me = 7,131), twice (2Me), or a fraction of this number (i.e., 0.75, 0.50, or 0.25Me). Equivalency between IP and GEBV was measured by correlating these two sets of predictions. GEBV were obtained as if generation 12 (validation animals) was part of the evaluation. IP were derived from GEBV of animals from generations 8 to 11 or generations 11, 10, 9, or 8. IP predictive ability was defined as the correlation between IP and adjusted phenotypes. The IP predictive ability increased from 0.25Me to 1Me (11%, on average); the change from 1Me to 2Me was negligible (0.6%). The correlation IP-GEBV was the same when IP were derived from a subset of 1Me animals chosen randomly across generations (8 to 11) or from generation 11 (0.98 for BW, 0.99 for RF, WG, and BP). A marginal decline in the correlation was observed when IP were based on GEBV of animals from generation 8 (0.95 for BW, 0.98 for RF, WG, and BP). Predictive ability had a similar trend; from generation 11 to 8, it changed from 0.32 to 0.31 for BW, from 0.39 to 0.38 for BP, and was constant at 0.33(0.22) for RF(WG). Predictive ability had a slight to moderate increase accumulating up to four generations of phenotypes. 1Me animals provide accurate IP, equivalent to GEBV. A minimum decay in predictive ability is observed when IP are derived from GEBV of animals from four generations back, possibly because of strong selection or the model not being completely additive.

Genomic estimated breeding values (GEBV) of genotyped animals without phenotypes can be obtained by indirect predictions (IP) using recursions on GEBV from a subset. Our objectives were to 1) evaluate the number of animals needed in recursions to maximize predictive ability, 2) assess equivalency between IP and GEBV, and 3) investigate trends in predictive ability of IP derived from recent vs. distant generations or accumulating phenotypes from recent to past generations. The number of animals (7,131) in the recursions that provided high-predictive ability was equal to the number of largest eigenvalues explaining 99% of variation in the genomic relationship matrix. IP and GEBV were equivalent (correlation ≥ 0.98). IP predictive ability was similar when recursions were based on animals from recent or distant generations; it marginally decayed with animals from four generations apart. The decline in predictive ability can be explained by strong selection or the model not being fully additive. A slight to moderate increase in IP predictive ability was observed accumulating up to four generations of phenotypes. If GEBV of animals in the subset chosen for recursions are estimated using sufficient data, animals can be from up to four generations back without significant loss in predictive ability.

Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study.

BACKGROUND: Identifying true positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of genomic information may give insights into the optimal number of individuals to be used in GWA. This study investigated different discovery set sizes based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). In addition, we investigated the impact on the prediction accuracy by adding variants, which were selected based on different set sizes, to the regular single nucleotide polymorphism (SNP) chips used for genomic prediction. METHODS: We simulated sequence data that included 500k SNPs with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one in every ten simulated SNPs. Effective population size (Ne) was set to 20 or 200. GWA were performed using a number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and large-effect size SNPs were added to the 50k panel and used for single-step genomic best linear unbiased prediction (ssGBLUP). RESULTS: Using a number of genotyped animals corresponding to at least EIG98 allowed the identification of QTN with the largest effect sizes when Ne was large. Populations with smaller Ne required more than EIG98. Furthermore, including genotyped animals with a higher reliability (i.e., a higher trait heritability) improved the identification of the most informative QTN. Prediction accuracy was highest when the significant or the large-effect SNPs representing twice the number of simulated QTN were added to the 50k panel. CONCLUSIONS: Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits.

Using social interaction models for genetic analysis of skin damage in gilts.

BACKGROUND: Skin damage is a trait of economic and welfare importance that results from social interactions between animals. These interactions may produce wound signs on the gilt's skin as a result of damage behavior (i.e., fighting), biting syndromes (i.e., tail, vulva, or ear biting), and swine inflammation and necrosis syndrome. Although current selection for traits that are affected by social interactions primarily focuses on improving direct genetic effects, combined selection on direct and social genetic effects could increase genetic gain and avoid a negative response to selection in cases of competitive behavior. The objectives of this study were to (1) estimate variance components for combined skin damage (CSD), with or without accounting for social genetic effects, (2) investigate the impact of including genomic information on the prediction accuracy, bias, and dispersion of CSD estimated breeding values, and (3) perform a single-step genome-wide association study (ssGWAS) of CSD under a classical and a social interaction model. RESULTS: Our results show that CSD is heritable and affected by social genetic effects. Modeling CSD with social interaction models increased the total heritable variance relative to the phenotypic variance by three-fold compared to the classical model. Including genomic information increased the prediction accuracy of direct, social, and total estimated breeding values for purebred sires by at least 21.2%. Bias and dispersion of estimated breeding values were reduced by including genomic information in classical and social interaction models but remained present. The ssGWAS did not identify any single nucleotide polymorphism that was significantly associated with social or direct genetic effects for CSD. CONCLUSIONS: Combined skin damage is heritable, and genetic selection against this trait will increase the welfare of animals in the long term. Combined skin damage is affected by social genetic effects, and modeling this trait with a social interaction model increases the potential for genetic improvement. Including genomic information increases the prediction accuracy of estimated breeding values and reduces their bias and dispersion, although some biases persist. The results of the genome-wide association study indicate that CSD has a polygenic architecture and no major quantitative trait locus was detected.

Efficient ways to combine data from broiler and layer chickens to account for sequential genomic selection.

In broiler breeding, superior individuals for growth become parents and are later evaluated for reproduction in an independent evaluation; however, ignoring broiler data can produce inaccurate and biased predictions. This research aimed to determine the most accurate, unbiased, and time-efficient approach for jointly evaluating reproductive and broiler traits. The data comprised a pedigree with 577K birds, 146K genotypes, phenotypes for three reproductive (egg production [EP], fertility [FE], hatch of fertile eggs [HF]; 9K each), and four broiler traits (body weight [BW], breast meat percent [BP], fat percent [FP], residual feed intake [RF]; up to 467K). Broiler data were added sequentially to assess the impact on the quality of predictions for reproductive traits. The baseline scenario (RE) included pedigrees, genotypes, and phenotypes for reproductive traits of selected animals; in RE2, we added their broiler phenotypes; in RE_BR, broiler phenotypes of nonselected animals, and in RE_BR_GE, their genotypes. We computed accuracy, bias, and dispersion of predictions for hens from the last two breeding cycles and their sires. We tested three core definitions for the algorithm of proven and young to find the most time-efficient approach: two random cores with 7K and 12K animals and one with 19K animals, containing parents and young animals. From RE to RE_BR_GE, changes in accuracy were null or minimal for EP (0.51 in hens, 0.59 in roosters) and HF (0.47 in hens, 0.49 in roosters); for FE in hens (roosters), it changed from 0.4 (0.49) to 0.47 (0.53). In hens (roosters), bias (additive SD units) decreased from 0.69 (0.7) to 0.04 (0.05) for EP, 1.48 (1.44) to 0.11 (0.03) for FE, and 1.06 (0.96) to 0.09 (0.02) for HF. Dispersion remained stable in hens (roosters) at ~0.93 (~1.03) for EP, and it improved from 0.57 (0.72) to 0.87 (1.0) for FE and from 0.8 (0.79) to 0.88 (0.87) for HF. Ignoring broiler data deteriorated the predictions' quality. The impact was significant for the low heritability trait (0.02; FE); bias (up to 1.5) and dispersion (as low as 0.57) were farther from the ideal value, and accuracy losses were up to 17.5%. Accuracy was maintained in traits with moderate heritability (~0.3; EP and HF), and bias and dispersion were less substantial. Adding information from the broiler phase maximized accuracy and unbiased predictions. The most time-efficient approach is a random core with 7K animals in the algorithm for proven and young.

In breeding programs with sequential selection, the estimation of breeding values becomes biased and inaccurate if the information from the past selection is ignored. We investigated the impact of incorporating broiler data (traits for past selection) into the evaluation of broiler reproductive traits. Including all the information increased the computing demands; therefore, we tested three core definitions for the algorithm for proven and young to determine the most accurate, unbiased, and time-efficient approach for jointly evaluating broiler and reproductive traits. When we ignored broiler data, the estimated breeding values for reproductive traits were biased (up to ~1.5 additive standard deviations). For low heritability traits, accuracy was reduced by up to 17.5%, and breeding values were overestimated (dispersion ~ 0.6). In contrast, incorporating broiler data eliminated bias and overestimation; and it maximized accuracy. A random core definition for the algorithm for proven and young with a number of animals equal to the number of the largest eigenvalues explaining 99% of the variation in the genomic relationship matrix is the most time-efficient, keeping accurate and unbiased predictions in the joint evaluation of broiler and reproductive traits.

The genetic basis of swine inflammation and necrosis syndrome and its genetic association with post-weaning skin damage and production traits.

The swine inflammation and necrosis syndrome (SINS) is a syndrome visually characterized by the presence of inflamed and necrotic skin at extreme body parts, such as the teats, tail, ears, and claw coronary bands. This syndrome is associated with several environmental causes, but knowledge of the role of genetics is still limited. Moreover, piglets affected by SINS are believed to be phenotypically more susceptible to chewing and biting behaviors from pen mates, which could cause a chronic reduction in their welfare throughout the production process. Our objectives were to 1) investigate the genetic basis of SINS expressed on piglets' different body parts and 2) estimate SINS genetic relationship with post-weaning skin damage and pre and post-weaning production traits. A total of 5,960 two to three-day-old piglets were scored for SINS on the teats, claws, tails, and ears as a binary phenotype. Later, those binary records were combined into a trait defined as TOTAL_SINS. For TOTAL_SINS, animals presenting no signs of SINS were scored as 1, whereas animals showing at least one affected part were scored as 2. Apart from SINS traits, piglets had their birth weight (BW) and weaning weight (WW) recorded, and up to 4,132 piglets were later evaluated for combined skin damage (CSD), carcass backfat (BF), and loin depth (LOD). In the first set of analyses, the heritability of SINS on different body parts was estimated with single-trait animal-maternal models, and pairwise genetic correlations between body parts were obtained from two-trait models. Later, we used four three-trait animal models with TOTAL_SINS, CSD, and an alternative production trait (i.e., BW, WW, LOD, BF) to access trait heritabilities and genetic correlations between SINS and production traits. The maternal effect was included in the BW, WW, and TOTAL_SINS models. The direct heritability of SINS on different body parts ranged from 0.08 to 0.34, indicating that reducing SINS incidence through genetic selection is feasible. The direct genetic correlation between TOTAL_SINS and pre-weaning growth traits (BW and WW) was favorable and negative (from -0.40 to -0.30), indicating that selection for animals genetically less prone to present signs of SINS will positively affect the piglet's genetics for heavier weight at birth and weaning. The genetic correlations between TOTAL_SINS and BF and between TOTAL_SINS and LOD were weak or not significant (-0.16 to 0.05). However, the selection against SINS was shown to be genetically correlated with CSD, with estimates ranging from 0.19 to 0.50. That means that piglets genetically less likely to present SINS signs are also more unlikely to suffer CSD after weaning, having a long-term increase in their welfare throughout the production system.

The swine inflammation and necrosis syndrome (SINS) is visually characterized by the presence of inflamed and necrotic skin at extreme body parts, such as the teats, tail, ears, and claw coronary bands. Piglets affected by this syndrome are considered phenotypically more susceptible to chewing and biting behaviors from pen mates. However, the genetic relationship between SINS and post-weaning skin damage is still unclear. In this study, we aimed to investigate the genetic basis of SINS expressed on piglets' different body parts and to estimate the SINS genetic relationship with skin damage and pre and post-weaning production traits. We showed that SINS on different body parts is heritable and that the direct selection against a combined score of SINS in different body parts (TOTAL_SINS) will favor the piglet's genetics for heavier weight at birth and weaning. However, TOTAL_SINS is not significantly correlated with carcass backfat thickness and loin depth at the piglet genetic level. The direct selection against SINS is genetically correlated with skin damage after weaning, meaning that piglets genetically more prone to present signs of SINS are more likely to receive skin damage later in life.

Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing.

BACKGROUND: In aquaculture, the proportion of edible meat (FY = fillet yield) is of major economic importance, and breeding animals of superior genetic merit for this trait can improve efficiency and profitability. Achieving genetic gains for fillet yield is possible using a pedigree-based best linear unbiased prediction (PBLUP) model with direct and indirect selection. To investigate the feasibility of using genomic selection (GS) to improve FY and body weight (BW) in rainbow trout, the prediction accuracy of GS models was compared to that of PBLUP. In addition, a genome-wide association study (GWAS) was conducted to identify quantitative trait loci (QTL) for the traits. All analyses were performed using a two-trait model with FY and BW, and variance components, heritability, and genetic correlations were estimated without genomic information. The data used included 14,165 fish in the pedigree, of which 2742 and 12,890 had FY and BW phenotypic records, respectively, and 2484 had genotypes from the 57K single nucleotide polymorphism (SNP) array. RESULTS: The heritabilities were moderate, at 0.41 and 0.33 for FY and BW, respectively. Both traits were lowly but positively correlated (genetic correlation; r = 0.24), which suggests potential favourable correlated genetic gains. GS models increased prediction accuracy compared to PBLUP by up to 50% for FY and 44% for BW. Evaluations were found to be biased when validation was performed on future performances but not when it was performed on future genomic estimated breeding values. CONCLUSIONS: The low but positive genetic correlation between fillet yield and body weight indicates that some improvement in fillet yield may be achieved through indirect selection for body weight. Genomic information increases the prediction accuracy of breeding values and is an important tool to accelerate genetic progress for fillet yield and growth in the current rainbow trout population. No significant QTL were found for either trait, indicating that both traits are polygenic, and that marker-assisted selection will not be helpful to improve these traits in this population.

Estimation of milkability breeding values and variance components for Italian Holstein.

The importance of milkability as a trait is growing because of the need to efficiently use labor and machinery; therefore, it is crucial to update the statistical model for the trait to improve the accuracy of the estimated breeding values, and thus provide a more accurate tool for decision-making at the farm level. In the Italian Holstein Friesian cattle population, milkability is recorded twice a year by the milk recording system as a binary trait (slow, coded as 2, or not slow, coded as 1). Data consisted of 7,862,371 records from 2,945,249 cows collected between 2004 and 2021. A single-trait threshold animal model with repeated measures was used, with parity, days in milk class, calving season, and regression of production (fat + protein grams) within days in milk class as fixed effects and herd-year-season of recording, permanent environment, and animal as random effects. The results for heritability and repeatability were 0.275 and 0.5, estimated with the Gibbs sampler THRGIBBS1F90. Genomic validation, carried out using genotyped proven bulls born before 2009 as the training set, gave a result of 0.386 for reliability. The genetic correlations of this trait confirmed that both extremes of the estimated breeding value must be treated cautiously, because correlations with important traits such as mastitis resistance, body condition score, and teat length are unfavorable.

Leveraging low-density crossbred genotypes to offset crossbred phenotypes and their impact on purebred predictions.

The objectives of this study were to 1) investigate the predictability and bias of genomic breeding values (GEBV) of purebred (PB) sires for CB performance when CB genotypes imputed from a low-density panel are available, 2) assess if the availability of those CB genotypes can be used to partially offset CB phenotypic recording, and 3) investigate the impact of including imputed CB genotypes in genomic analyses when using the algorithm for proven and young (APY). Two pig populations with up to 207,375 PB and 32,893 CB phenotypic records per trait and 138,026 PB and 32,893 CB genotypes were evaluated. PB sires were genotyped for a 50K panel, whereas CB animals were genotyped for a low-density panel of 600 SNP and imputed to 50K. The predictability and bias of GEBV of PB sires for backfat thickness (BFX) and average daily gain recorded (ADGX) recorded on CB animals were assessed when CB genotypes were available or not in the analyses. In the first set of analyses, direct inverses of the genomic relationship matrix (G) were used with phenotypic datasets truncated at different time points. In the next step, we evaluated the APY algorithm with core compositions differing in the CB genotype contributions. After that, the performance of core compositions was compared with an analysis using a random PB core from a purely PB genomic set. The number of rounds to convergence was recorded for all APY analyses. With the direct inverse of G in the first set of analyses, adding CB genotypes imputed from a low-density panel (600 SNP) did not improve predictability or reduce the bias of PB sires' GEBV for CB performance, even for sires with fewer CB progeny phenotypes in the analysis. That indicates that the inclusion of CB genotypes primarily used for inferring pedigree in commercial farms is of no benefit to offset CB phenotyping. When CB genotypes were incorporated into APY, a random core composition or a core with no CB genotypes reduced bias and the number of rounds to convergence but did not affect predictability. Still, a PB random core composition from a genomic set with only PB genotypes resulted in the highest predictability and the smallest number of rounds to convergence, although bias increased. Genotyping CB individuals for low-density panels is a valuable identification tool for linking CB phenotypes to pedigree; however, the inclusion of those CB genotypes imputed from a low-density panel (600 SNP) might not benefit genomic predictions for PB individuals or offset CB phenotyping for the evaluated CB performance traits. Further studies will help understand the usefulness of those imputed CB genotypes for traits with lower PB-CB genetic correlations and traits not recorded in the PB environment, such as mortality and disease traits.

Crossbred (CB) genotypes primarily used for inferring pedigree in commercial farms can be potentially used for genomic prediction and partially offset CB phenotyping. We investigated the predictability and bias of genomic breeding values (GEBV) of purebred (PB) sires for CB performance when CB genotypes are available, assessed if the availability of those CB genotypes can be used to partially offset CB phenotypic recording, and investigated the impact of including CB genotypes in genomic analyses when using the algorithm for proven and young (APY). The predictability and bias of GEBV of PB sires for two CB traits were assessed when CB genotypes were available or not in the analyses. Later, the performance of different APY core compositions accounting for CB genotypes was compared with a random core from a purely PB genomic set. Adding CB genotypes did not improve predictability or reduce the bias of PB sires' GEBV for CB performance, indicating that the inclusion of CB genotypes imputed from a low-density (600 SNP) panel is of no benefit to offset CB phenotyping. With APY, a random core composition from a genomic set with only PB genotypes resulted in the highest predictability and the smallest number of rounds to convergence, although bias increased.

Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP.

BACKGROUND: Although single-step GBLUP (ssGBLUP) is an animal model, SNP effects can be backsolved from genomic estimated breeding values (GEBV). Predicted SNP effects allow to compute indirect prediction (IP) per individual as the sum of the SNP effects multiplied by its gene content, which is helpful when the number of genotyped animals is large, for genotyped animals not in the official evaluations, and when interim evaluations are needed. Typically, IP are obtained for new batches of genotyped individuals, all of them young and without phenotypes. Individual (theoretical) accuracies for IP are rarely reported, but they are nevertheless of interest. Our first objective was to present equations to compute individual accuracy of IP, based on prediction error covariance (PEC) of SNP effects, and in turn, are obtained from PEC of GEBV in ssGBLUP. The second objective was to test the algorithm for proven and young (APY) in PEC computations. With large datasets, it is impossible to handle the full PEC matrix, thus the third objective was to examine the minimum number of genotyped animals needed in PEC computations to achieve IP accuracies that are equivalent to GEBV accuracies. RESULTS: Correlations between GEBV and IP for the validation animals using SNP effects from ssGBLUP evaluations were ≥ 0.99. When all available genotyped animals were used for PEC computations, correlations between GEBV and IP accuracy were ≥ 0.99. In addition, IP accuracies were compatible with GEBV accuracies either with direct inversion of the genomic relationship matrix (G) or using the algorithm for proven and young (APY) to obtain the inverse of G. As the number of genotyped animals included in the PEC computations decreased from around 55,000 to 15,000, correlations were still ≥ 0.96, but IP accuracies were biased downwards. CONCLUSIONS: Theoretical accuracy of indirect prediction can be successfully obtained by computing SNP PEC out of GEBV PEC from ssGBLUP equations using direct or APY G inverse. It is possible to reduce the number of genotyped animals in PEC computations, but accuracies may be underestimated. Further research is needed to approximate SNP PEC from ssGBLUP to limit the computational requirements with many genotyped animals.

Invited review: Unknown-parent groups and metafounders in single-step genomic BLUP.

Single-step genomic BLUP (ssGBLUP) is a method for genomic prediction that integrates matrices of pedigree (A) and genomic (G) relationships into a single unified additive relationship matrix whose inverse is incorporated into a set of mixed model equations (MME) to compute genomic predictions. Pedigree information in dairy cattle is often incomplete. Missing pedigree potentially causes biases and inflation in genomic estimated breeding values (GEBV) obtained with ssGBLUP. Three major issues are associated with missing pedigree in ssGBLUP, namely biased predictions by selection, missing inbreeding in pedigree relationships, and incompatibility between G and A in level and scale. These issues can be solved using a proper model for unknown-parent groups (UPG). The theory behind the use of UPG is well established for pedigree BLUP, but not for ssGBLUP. This study reviews the development of the UPG model in pedigree BLUP, the properties of UPG models in ssGBLUP, and the effect of UPG on genetic trends and genomic predictions. Similarities and differences between UPG and metafounder (MF) models, a generalized UPG model, are also reviewed. A UPG model (QP) derived using a transformation of the MME has a good convergence behavior. However, with insufficient data, the QP model may yield biased genetic trends and may underestimate UPG. The QP model can be altered by removing the genomic relationships linking GEBV and UPG effects from MME. This altered QP model exhibits less bias in genetic trends and less inflation in genomic predictions than the QP model, especially with large data sets. Recently, a new model, which encapsulates the UPG equations into the pedigree relationships for genotyped animals, was proposed in simulated purebred populations. The MF model is a comprehensive solution to the missing pedigree issue. This model can be a choice for multibreed or crossbred evaluations if the data set allows the estimation of a reasonable relationship matrix for MF. Missing pedigree influences genetic trends, but its effect on the predictability of genetic merit for genotyped animals should be negligible when many proven bulls are genotyped. The SNP effects can be back-solved using GEBV from older genotyped animals, and these predicted SNP effects can be used to calculate GEBV for young-genotyped animals with missing parents.

Metafounders May Reduce Bias in Composite Cattle Genomic Predictions.

Metafounders are pseudo-individuals that act as proxies for animals in base populations. When metafounders are used, individuals from different breeds can be related through pedigree, improving the compatibility between genomic and pedigree relationships. The aim of this study was to investigate the use of metafounders and unknown parent groups (UPGs) for the genomic evaluation of a composite beef cattle population. Phenotypes were available for scrotal circumference at 14 months of age (SC14), post weaning gain (PWG), weaning weight (WW), and birth weight (BW). The pedigree included 680,551 animals, of which 1,899 were genotyped for or imputed to around 30,000 single-nucleotide polymorphisms (SNPs). Evaluations were performed based on pedigree (BLUP), pedigree with UPGs (BLUP_UPG), pedigree with metafounders (BLUP_MF), single-step genomic BLUP (ssGBLUP), ssGBLUP with UPGs for genomic and pedigree relationship matrices (ssGBLUP_UPG) or only for the pedigree relationship matrix (ssGBLUP_UPGA), and ssGBLUP with metafounders (ssGBLUP_MF). Each evaluation considered either four or 10 groups that were assigned based on breed of founders and intermediate crosses. To evaluate model performance, we used a validation method based on linear regression statistics to obtain accuracy, stability, dispersion, and bias of (genomic) estimated breeding value [(G)EBV]. Overall, relationships within and among metafounders were stronger in the scenario with 10 metafounders. Accuracy was greater for models with genomic information than for BLUP. Also, the stability of (G)EBVs was greater when genomic information was taken into account. Overall, pedigree-based methods showed lower inflation/deflation (regression coefficients close to 1.0) for SC14, WWM, and BWD traits. The level of inflation/deflation for genomic models was small and trait-dependent. Compared with regular ssGBLUP, ssGBLUP_MF4 displayed regression coefficient closer to one SC14, PWG, WWM, and BWD. Genomic models with metafounders seemed to be slightly more stable than models with UPGs based on higher similarity of results with different numbers of groups. Further, metafounders can help to reduce bias in genomic evaluations of composite beef cattle populations without reducing the stability of GEBVs.

Investigating the persistence of accuracy of genomic predictions over time in broilers.

Accuracy of genomic predictions is an important component of the selection response. The objectives of this research were: 1) to investigate trends for prediction accuracies over time in a broiler population of accumulated phenotypes, genotypes, and pedigrees and 2) to test if data from distant generations are useful to maintain prediction accuracies in selection candidates. The data contained 820K phenotypes for a growth trait (GT), 200K for two feed efficiency traits (FE1 and FE2), and 42K for a carcass yield trait (CY). The pedigree included 1,252,619 birds hatched over 7 years, of which 154,318 from the last 4 years were genotyped. Training populations were constructed adding 1 year of data sequentially, persistency of accuracy over time was evaluated using predictions from birds hatched in the three generations following or in the years after the training populations. In the first generation, before genotypes became available for the training populations (first 3 years of data), accuracies remained almost stable with successive additions of phenotypes and pedigree to the accumulated dataset. The inclusion of 1 year of genotypes in addition to 4 years of phenotypes and pedigree in the training population led to increases in accuracy of 54% for GT, 76% for FE1, 110% for CY, and 38% for FE2; on average, 74% of the increase was due to genomics. Prediction accuracies declined faster without than with genomic information in the training populations. When genotypes were unavailable, the average decline in prediction accuracy across traits was 41% from the first to the second generation of validation, and 51% from the second to the third generation of validation. When genotypes were available, the average decline across traits was 14% from the first to the second generation of validation, and 3% from the second to the third generation of validation. Prediction accuracies in the last three generations were the same when the training population included 5 or 2 years of data, and a decrease of ~7% was observed when the training population included only 1 year of data. Training sets including genomic information provided an increase in accuracy and persistence of genomic predictions compared with training sets without genomic data. The two most recent years of pedigree, phenotypic, and genomic data were sufficient to maintain prediction accuracies in selection candidates. Similar conclusions were obtained using validation populations per year.

Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs.

It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV, respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. Similarly, the genetic correlation between HCW and RT was 0.26 ± 0.04, and between HCW and LIV was 0.09 ± 0.05, respectively. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Genetic correlations between HCW and other traits were lower due to the large quantity of missing records. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.