Inferring phenotypic causal structure among farrowing and weaning traits in pigs

Direct selection for litter size or weight at weaning in pigs is often hindered by external interventions such as cross‐fostering. The objective of this study was to infer the causal structure among phenotypes of reproductive traits in pigs to enable subsequent direct selection for these traits. Examined traits included: number born alive (NBA), litter size on day 21 (LS21), and litter weight on day 21 (LW21). The study included 6,240 litters from 1,673 Landrace dams and 5,393 litters from 1,484 Large White dams. The inductive causation (IC) algorithm was used to infer the causal structure, which was then fitted to a structural equation model (SEM) to estimate causal coefficients and genetic parameters. Based on the IC algorithm and temporal and biological information, the causal structure among traits was identified as: NBA → LS21 → LW21 and NBA → LW21. Owing to the causal effect of NBA on LS21 and LW21, the genetic, permanent environmental, and residual variances of LS21 and LW21were much lower in the SEM than in the multiple‐trait model for both breeds. Given the strong effect of NBA on LS21 and LW21, the SEM and causal information might assist with selective breeding for LS21 and LW21 when cross‐fostering occurs.     

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%.     

Multivariate analyses of carcass traits for Angus cattle fitting reduced rank and factor analytic models

Summary Multivariate analyses of carcass traits for Angus cattle, consisting of six traits recorded on the carcass and eight auxiliary traits measured by ultrasound scanning of live animals, are reported. Analyses were carried out by restricted maximum likelihood, fitting a number of reduced rank and factor analytic models for the genetic covariance matrix. Estimates of eigenvalues and eigenvectors for different orders of fit are contrasted and implications for the estimates of genetic variances and correlations are examined. Results indicate that at most eight principal components (PCs) are required to model the genetic covariance structure among the 14 traits. Selection index calculations suggest that the first seven of these PCs are sufficient to obtain estimates of breeding values for the carcass traits without loss in the expected accuracy of evaluation. This implied that the number of effects fitted in genetic evaluation for carcass traits can be halved by estimating breeding values for the leading PCs directly.     

Relationship between calving difficulty and fertility traits in first‐parity Iranian Holsteins under standard and recursive models

The main objective of this study was to estimate the genetic and phenotypic relationships between calving difficulty (CD) and fertility traits, including success at first service (SF), number of inseminations to conception (INS), interval from calving to first service (CTFS), interval between first and last service (IFL) and days open (DO), in first‐parity Iranian Holsteins under standard (SMMs) and recursive (RMMs) mixed models. The data analysed in this paper included 29 950 records on CD and fertility traits, collected in the time period from 1995 to 2014 by the Animal Breeding and Improvement Center of Iran. Under all observed SMMs and RMMs, five bivariate sire‐maternal grandsire models (ten bivariate analyses in total) were used for the analyses. Recursive models were applied with a view to consider that CD influences the fertility traits in the subsequent reproductive cycle and the genetic determination of CD and fertility traits by fitting CD as covariate for any of the fertility traits studied. The existence of such cause‐and‐effect is considered in RMMs but not in SMMs. Our results implied a statistically non‐zero magnitude of the causal relationships between CD and all the fertility traits studied, with the former influencing the latter. The causal effects of CD on SF (on the observed scale, %), INS, CTFS, IFL and DO were ?2.23%, 0.10 services, 1.93 days, 3.76 days and 5.61 days, respectively. Direct genetic correlations between CD and the fertility traits under both models were not statistically different from zero (95% HPD interval included zero), except for the correlation between CD and CTFS, which were 0.197 and 0.134 under SMM and RMM, respectively, indicating that genes associated with difficult births also increase intervals between calving and the first insemination afterwards. Comparison of both models by the deviance information criterion (DIC) demonstrated the plausibility of RMMs over SMMs.     

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single‐nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic‐based [genomic best linear unbiased prediction (GBLUP)‐REML and BayesC] and pedigree‐based (PBLUP‐REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP‐REML across traits, from 0 to 0.03 with GBLUP‐REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic‐based methods were small (0.01–0.05), with GBLUP‐REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP‐REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.     

Bayesian inference for genetic parameter estimation on growth traits for Nelore cattle in Brazil,using the Gibbs sampler

This data set consisted of over 29 245 field records from 24 herds of registered Nelore cattle born between 1980 and 1993, with calves sires by 657 sires and 12 151 dams. The records were collected in south‐eastern and midwestern Brazil and animals were raised on pasture in a tropical climate. Three growth traits were included in these analyses: 205‐ (W205), 365‐ (W365) and 550‐day (W550) weight. The linear model included fixed effects for contemporary groups (herd‐year‐season‐sex) and age of dam at calving. The model also included random effects for direct genetic, maternal genetic and maternal permanent environmental (MPE) contributions to observations. The analyses were conducted using single‐trait and multiple‐trait animal models. Variance and covariance components were estimated by restricted maximum likelihood (REML) using a derivative‐free algorithm (DFREML) for multiple traits (MTDFREML). Bayesian inference was obtained by a multiple trait Gibbs sampling algorithm (GS) for (co)variance component inference in animal models (MTGSAM). Three different sets of prior distributions for the (co)variance components were used: flat, symmetric, and sharp. The shape parameters (ν) were 0, 5 and 9, respectively. The results suggested that the shape of the prior distributions did not affect the estimates of (co)variance components. From the REML analyses, for all traits, direct heritabilities obtained from single trait analyses were smaller than those obtained from bivariate analyses and by the GS method. Estimates of genetic correlations between direct and maternal effects obtained using REML were positive but very low, indicating that genetic selection programs should consider both components jointly. GS produced similar but slightly higher estimates of genetic parameters than REML, however, the greater robustness of GS makes it the method of choice for many applications.     

Estimation of direct and correlated responses to selection using univariate animal models.

Analytic results obtained using simple models show that estimates of selection response of univariate experiments using animal models are completely dependent on the heritability used as prior when fixed effects are nested within generations, and both on the prior and on the true heritability parameter when fixed effects overlap across generations. Univariate animal model estimators of correlated changes of a trait not selected directly are usually biased. The absolute value of the estimate of the correlated response is smaller than the true value when the traits are only genetically correlated and larger than the expected value of zero when they are only environmentally correlated. The validity of the results derived from the analysis of simple models is confirmed using computer simulations, which illustrate the magnitude of the bias. It is emphasized that use of univariate animal models to estimate response in breeding programs whose breeding objectives include several correlated traits may lead to erroneous conclusions.     

Disease incidence and immunological traits for the selection of healthy pigs. A review

Disease is a major issue in animal production systems and society demands that the use of medicines and vaccines be reduced. This review describes the breeding approaches that could be used to improve disease resistance and focuses especially on their application to pigs. Disease reduction by genetic means has certain advantages through cumulative and permanent effects, and direct and indirect selection methods are available. Direct selection for disease incidence requires, besides a unique pig identification and disease registration system, challenge routines that are inconvenient in intensive pig production. Indirect selection for the expression of immune capacity may be an alternative but requires detailed knowledge of the different components of the immune system. There is ample opportunity for genetic improvement of the immune capacity because immune traits show substantial genetic variation between pigs. We therefore conclude that indirect selection via immune traits is very interesting, also for practical implementation, and that there is an urgent need for knowledge, within lines, about the genetic relationships between immune capacity traits and resistance to specific diseases or to disease incidence in general. Furthermore, knowledge about the relationship between immune system traits and production traits is needed as well as knowledge about the effect of selection on the epidemiology of disease at a farm/population level and on the host-pathogen interaction and coevolution.     

Multi-breed genetic evaluation in a Gelbvieh population

A multi-breed model was presented for the genetic evaluation of growth traits in beef cattle. In addition to the fixed effects, random direct and maternal genetic effects, and random maternal permanent environmental effects are considered; the model also fits direct and maternal heterosis and direct and maternal breed-of-founder (BOF) x generation group effects using a Bayesian approach that weights prior literature estimates relative to information supplied by the dataset to which the model will be applied. The multi-breed evaluation procedures also allow the inclusion of external evaluations for animals of other breeds. The multi-breed model was applied to a dataset provided by the American Gelbvieh Association. Different analyses were conducted by varying the weights given to the prior literature relative to the information provided by the dataset. Large differences were observed for the heterosis estimates, the BOF x generation group effect estimates, and the predicted breeding values across breeds due to the weights posed on prior literature estimates versus estimates derived directly from data. However, the rankings within breed were observed to be relatively robust to the different weights on prior information.     

Genetic correlations between competition traits and traits scored at breeding field-tests in Icelandic horses

Relationships between breeding field-test traits and competition traits were studied to investigate whether the latter could be usefully included in the genetic evaluation of Icelandic horses. The current method of genetic evaluation is based on records from breeding field-tests only. The breeding field-test data included 16 401 individual records of Icelandic horses evaluated in 11 countries during 1990–2005. Competition results included 18 982 records of 3790 horses competing in sport and gæðinga competitions in Iceland and Sweden during 1998–2004. In the breeding field-tests, eight conformation traits and eight riding ability traits were scored; height of withers was also recorded. These traits were analysed together with the competition traits tölt(comp), 4-gait, 5-gait and pace test, in bivariate analyses. Animal models were used; the fixed effects for breeding field-test traits included sex by age interaction and country by year interaction. For the competition traits the model included fixed effects of sex, age and event, and a random permanent environmental effect. Estimated heritabilities and genetic correlations for breeding field-test traits were consistent with earlier results; heritabilities ranged from 0.20 to 0.67, and moderate to high genetic correlations were estimated between many of the riding ability traits, and between riding ability traits and some conformation traits. The estimated heritabilities for competition traits were about 0.20, and genetic correlations between competition traits varied from − 0.12 to 1.00. In general, high genetic correlations were estimated between breeding field-test riding ability traits and competition traits. Moderately positive genetic correlations were found between some breeding field-test conformation traits and competition traits. Competition traits add information relating to the breeding goal of the Icelandic horse; they should therefore be added to genetic evaluation in future.     

Genetic parameters and relationships between hip height and weight in Brahman cattle

Genetic parameters for weaning hip height (WHH), weaning weight (WWT), postweaning hip height growth (PHG), and hip height at 18 mo of age (HH18) and their relationships were estimated for Brahman cattle born from 1984 to 1994 at the Subtropical Agricultural Research Station, Brooksville, FL. Records per trait were 889 WHH, 892 WWT, and 684 HH18. (Co)variances were estimated using REML with a derivative-free algorithm and fitting three two-trait animal models (i.e., WHH-WWT, WHH-PHG, and WWT-HH18). Heritability estimates of WHH direct effects were 0.73 and 0.65 for models WHH-WWT and WHH-PHG and were 0.29 and 0.33 for WWT direct for models WHH-WWT and WWT-HH18, respectively. Estimates of heritability for PHG and HH18 direct were 0.13 and 0.87, respectively. Heritability estimates for maternal effects were 0.10 and 0.09 for WHH for models WHH-WWT and WHH-PHG and 0.18 and 0.18 for WWT for models WHH-WWT and WWT-HH18, respectively. Heritability estimates for PHG and HH18 maternal were 0.00 and 0.03. Estimates of the genetic correlation between direct effects for the different traits were moderate and positive; they were also positive between WHH and WWT maternal and WWT and HH18 maternal but negative (-0.19) between WHH and PHG maternal, which may indicate the existence of compensatory growth. Negative genetic correlations existed between direct and maternal effects for WHH, WWT, PHG, and HH18. The correlation between direct and WWT maternal effects was low and negative, moderate and negative between WHH direct and PHG maternal, and high and negative (-0.80) between WWT direct and HH18 maternal. There is a strong genetic relationship between hip height and weight at weaning that also affects hip height at 18 mo of age. Both product-moment and rank correlations between estimated breeding values (EBV) for direct values indicate that almost all of the same animals would be selected for PHG EBV if the selection criterion used was WHH EBV, and that it is possible to accomplish a preliminary selection for HH18 EBV using WHH EBV. Correlations between breeding values for WHH, WWT, and HH18 indicate that it will be possible to identify animals that will reduce, maintain, or increase hip height while weaning weight is increased. Thus, if the breeding objective is to manipulate growth to 18 mo of age, implementation of multiple-trait breeding programs considering hip height and weight at weaning will help to predict hip height at 18 mo of age.     

A robust method for simultaneous estimation of single gene and polygenic effects in dairy cows using externally estimated breeding values as prior information

The aim of this study was to develop a robust method to estimate single gene and random polygenic animal effects simultaneously in a small field dataset with limited pedigree information. The new method was based on a Bayesian approach using additional prior information on the distribution of externally estimated breeding values. The field dataset consisted of 40 269 test‐day records for milk performance traits for 1455 genotyped dairy cows for the 11 bp‐deletion in the coding sequence of the myostatin gene. For all traits, estimated additive effects of the favoured wild‐type allele (‘+’ allele) were smaller when applying the new method in comparison with the application of a conventional mixed inheritance test‐day model. Dominance effects of the myostatin gene showed the same behaviour but were generally lower than additive effects. Robustness of methods was tested using a data‐splitting technique, based on the correlation of estimated breeding values from two samples, with one‐half of the data eliminated randomly from the first sample and the remaining data eliminated from the second sample. Results for 100 replicates showed that the correlation between split datasets when prior information included was higher than the conventional method. The new method led to more robust estimations for genetic effects and therefore has potential for use when only a small number of genotyped animals with field data and limited pedigree information are available.     

Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations

The objectives of this study were to estimate the additive and dominance variance component of several weight and ultrasound scanned body composition traits in purebred and combined cross‐bred sheep populations based on single nucleotide polymorphism (SNP) marker genotypes and then to investigate the effect of fitting additive and dominance effects on accuracy of genomic evaluation. Additive and dominance variance components were estimated in a mixed model equation based on “average information restricted maximum likelihood” using additive and dominance (co)variances between animals calculated from 48,599 SNP marker genotypes. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of prediction was assessed based on a random 10‐fold cross‐validation. Across different weight and scanned body composition traits, dominance variance ranged from 0.0% to 7.3% of the phenotypic variance in the purebred population and from 7.1% to 19.2% in the combined cross‐bred population. In the combined cross‐bred population, the range of dominance variance decreased to 3.1% and 9.9% after accounting for heterosis effects. Accounting for dominance effects significantly improved the likelihood of the fitting model in the combined cross‐bred population. This study showed a substantial dominance genetic variance for weight and ultrasound scanned body composition traits particularly in cross‐bred population; however, improvement in the accuracy of genomic breeding values was small and statistically not significant. Dominance variance estimates in combined cross‐bred population could be overestimated if heterosis is not fitted in the model.     

Genetic relationships between pelt quality, maternal ability, and lamb production in the Gotland sheep breed

The aim of the present investigation was to study the genetic relationships between pelt quality traits (shade of fleece, size of curl, score for fleece colour, score for curl, score for quality of hair, score for thickness of fleece, sum of pelt scores, and overall score) on one hand and maternal ability, live weight, and carcass traits on the other hand for the Gotland sheep breed. Data were received from the Swedish Sheep Recording Scheme and included observations on 4-month weight (4MW) and pelt quality for 51,402 lambs and on weight (CW), fatness (FAT), and fleshiness (FLESH) of the carcass for 12,440 lambs. The lambs were born during the period 1991–2003. When maternal genetic and permanent environmental effects were included in the model direct heritabilities for the pelt quality traits varied between 0.16 and 0.25. Maternal heritabilities (0.01 to 0.05) and common environmental variances as a fraction of the total phenotypic variances (0.07 to 0.10) were low. Maternal heritabilities were higher for 4MW (0.11) and CW (0.12) than for the pelt quality traits. Direct-maternal genetic correlations were both for the pelt quality traits and for 4MW and CW generally negative and low to medium high. Direct genetic correlations between pelt quality traits on one hand and 4MW, CW, FAT or FLESH on the other hand were low (− 0.16 to 0.12). Maternal genetic correlations between pelt quality traits and 4MW or CW were positive and high (0.38 to 0.96). It was concluded that breeding for increased growth and improved carcass quality would not influence pelt quality negatively or vice versa. If maternal genetic effects are considered for 4MW and CW in the breeding program for the Gotland sheep breed, selection for maternal effects on 4MW and CW will have positive effects both on lamb weight and pelt quality.     

Phenotypic and genetic parameter estimates for reproductive traits in Zandi sheep

This study reports on the phenotypic and genetic (co)variance components for reproductive traits in Zandi sheep, using between 1,859 and 2,588 records obtained from 577 ewes. The data were collected from the Khojir Breeding Station of Zandi sheep in Tehran, Iran from 1994 to 2008. The basic traits were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), and litter mean weight per lamb weaned (LMWLW), and the composite traits were total litter weight at birth (TLWB) and total litter weight at weaning (TLWW). Genetic analyses were carried out using the restricted maximum likelihood method that was explored by fitting the additive direct genetic effects and permanent environmental effects of the ewes as random effects and the ewe age at lambing and lambing year as fixed effects for all of the investigated traits. Akaike’s information criterion was used to choose the most appropriate model. LSB, LSW, LMWLB, LMWLW, TLWB, and TLWW direct heritability estimates were 0.07, 0.05, 0.12, 0.10, 0.08, and 0.14, respectively. The estimated fractions of variance due to the permanent environmental effects of the ewe ranged from 0.03 for LMWLB to 0.08 for LMWLW and TLWW. Corresponding repeatability estimates ranged from 0.10 for LSW to 0.22 for TLWW. Direct genetic correlations varied from ?0.61 for LSB–LMWLB to 0.88 for LSB–LSW and LSB–TLWB. Results indicate that genetic change depends not only on the heritability of traits, but also on the observed phenotypic variation; therefore, improvement of non-genetic factors should be included in the breeding programs.     

Influence of model specifications on the reliabilities of genomic prediction in a Swedish-Finnish red breed cattle population

Using a combined multi‐breed reference population, this study explored the influence of model specification and the effect of including a polygenic effect on the reliability of genomic breeding values (DGV and GEBV). The combined reference population consisted of 2986 Swedish Red Breed (SRB) and Finnish Ayrshire (FAY) dairy cattle. Bayesian methodology (common prior and mixture models with different prior distribution settings for the marker effects) as well as a best linear unbiased prediction with a genomic relationship matrix [genomic best linear unbiased predictor (GBLUP)] was used in the prediction of DGV. Mixture models including a polygenic effect were used to predict GEBV. In total, five traits with low, high and medium heritability were analysed. For the models using a mixture prior distribution, reliabilities of DGV tended to decrease with an increasing proportion of markers with small effects. The influence of the inclusion of a polygenic effect on the reliability of DGV varied across traits and model specifications. Average correlation between DGV with the Mendelian sampling term, across traits, was highest (R² = 0.25) for the GBLUP model and decreased with increasing proportion of markers with large effects. Reliabilities increased when DGV and parent average information were combined in an index. The GBLUP model with the largest gain across traits in the reliability of the index achieved the highest DGV mean reliability. However, the polygenic models showed to be less biased and more consistent in the estimation of DGV regardless of the model specifications compared with the mixture models without the polygenic effect.     

Genetic parameters for early lamb survival and growth in Scottish Blackface sheep

The objectives of this study were 1) to estimate the heritability of lamb survival and growth in the Scottish Blackface breed; 2) to examine the relationship between lamb survival and live BW; and 3) to investigate the possibility of using lamb survival in a breeding program for this breed. The data used for the analyses contained information about survival and live BW at different ages on 4,459 animals. The records were collected from 1988 to 2003 in a Scottish Blackface flock. Live BW was recorded every 4 wk from birth to 24 wk. Survival was defined either by perinatal or postnatal mortality (up to weaning at 12 wk), or as cumulative survival to 1, 4, 8, and 12 wk. The pedigree file comprised 1,416 dams and 178 sires. A sire model was used to estimate genetic parameters for binary survival traits. Heritabilities of BW traits, and phenotypic and genetic correlations between BW and between survival and BW were estimated by fitting an animal model. Further, correlations of survival with live BW were estimated by using a Markov chain Monte Carlo threshold model, implemented by Gibbs sampling. The heritability estimates for cumulative lamb survival declined from birth onward (from 0.33 to 0.08), and postnatal survival had a heritability of 0.01. The direct and maternal heritabilities for BW traits ranged from 0.08 to 0.26 and from 0.06 to 0.21, respectively, whereas the maternal environmental component was between 0.04 and 0.16. The genetic correlations between BW traits at different ages were high. The genetic and phenotypic correlations between survival and BW were always positive (ranging from 0.04 to 0.54), so there was no antagonism between these traits. Therefore, it is possible to simultaneously improve both survival and live BW in a breeding program for this breed.     

Estimates of genetic parameters for worm resistance,wool and growth traits in Merino sheep of Uruguay

The genotype of an individual and the environment as the maternal ability of its dam have substantial effects on the phenotype expression of many production traits. The aim of the present study was to estimate the (co)variance components for worm resistance, wool and growth traits in Merino sheep, testing the importance of maternal effects and to determine the most appropriate model for each trait. The traits analyzed were Greasy Fleece Weight (GFW), Clean Fleece Weight (CFW), average Fibre Diameter (FD), Coefficient of Variation of FD (CVFD), Staple Length (SL), Comfort Factor (CF30), Weaning Weight (WWT), Yearling Body Weight (YWT) and Faecal worm Egg Count (FEC). The data were recorded during a 15-year period from 1995 to 2010, from Uruguayan Merino stud flocks. A Bayesian analysis was performed to estimate (co)variance components and genetic parameters. By ignoring or including maternal genetic or environmental effects, five different univariate models were fitted in order to determine the most effective for each trait. For CVFD and YWT, the model fitting the data best included direct additive effects as the only significant random source of variation. For GFW, CFW, FD, SL and CF30 the most appropriate model included direct-maternal covariance; while for FEC included maternal genetics effects with a zero direct-maternal covariance. The most suitable model for WWT included correlated maternal genetic plus maternal permanent environmental effects. The estimates of direct heritability were moderate to high and ranged from 0.15 for log transformed FEC to 0.74 for FD. Most of the direct additive genetic correlation (rg) estimations were in the expected range for Merino breed. However, the estimate of rg between FEC and FD was unfavourable (−0.18±0.03). In conclusion, there is considerable genetic variation in the traits analyzed, indicating the potential to make genetic progress on these traits. This study showed that maternal effects are influencing most of traits analyzed, thus these effects should be considered in Uruguayan Merino breeding programs; since the implementation of an appropriate model of analysis is critical to obtain accurate estimates.     

Estimation of additive and non-additive genetic effects for fertility and reproduction traits in North American Holstein cattle using genomic information

Non-additive genetic effects are usually ignored in animal breeding programs due to data structure (e.g., incomplete pedigree), computational limitations and over-parameterization of the models. However, non-additive genetic effects may play an important role in the expression of complex traits in livestock species, such as fertility and reproduction traits. In this study, components of genetic variance for additive and non-additive genetic effects were estimated for a variety of fertility and reproduction traits in Holstein cattle using pedigree and genomic relationship matrices. Four linear models were used: (a) an additive genetic model; (b) a model including both additive and epistatic (additive by additive) genetic effects; (c) a model including both additive and dominance effects; and (d) a full model including additive, epistatic and dominance genetic effects. Nine fertility and reproduction traits were analysed, and models were run separately for heifers (N = 5,825) and cows (N = 6,090). For some traits, a larger proportion of phenotypic variance was explained by non-additive genetic effects compared with additive effects, indicating that epistasis, dominance or a combination thereof is of great importance. Epistatic genetic effects contributed more to the total phenotypic variance than dominance genetic effects. Although these models varied considerably in the partitioning of the components of genetic variance, the models including a non-additive genetic effect did not show a clear advantage over the additive model based on the Akaike information criterion. The partitioning of variance components resulted in a re-ranking of cows based solely on the cows’ additive genetic effects between models, indicating that adjusting for non-additive genetic effects could affect selection decisions made in dairy cattle breeding programs. These results suggest that non-additive genetic effects play an important role in some fertility and reproduction traits in Holstein cattle.     

Evaluation of body weight and visual scores for genetic improvement of Nelore cattle

The aims of our study were to estimate genetic parameters for body weight and visual scores and to evaluate their inclusion as selection criteria in the Nelore breeding program in Brazil. The traits studied were the body weight adjusted to 210 (W210) and to 450 (W450) days of age and visual scores for body structure, finishing precocity, and muscling evaluated at weaning (BS_W, FP_W, and MS_W) and yearling (BS_Y, FP_Y, and MS_Y) ages. A total of 33,242, 26,259, 23,075, and 26,057 observations were considered to analyze W210, W450, and visual scores at weaning and yearling. The significant (P?<?0.05) fixed effects for all traits were farm, birth season, birth year, sex, and management group. Single-trait analyses were performed to define the most fitting model to our data using the average information restricted maximum likelihood algorithm, for weaning traits. Subsequently, these models were used in single- and two-trait analyses considering the Bayesian inference algorithm. Two-trait Bayesian analyses resulted in average direct heritability estimates for BS_W, FP_W, MS_W, W210, BS_Y, FP_Y, MS_Y, and W450 of 0.28, 0.30, 0.27, 0.28, 0.40, 0.44, 0.39, and 0.50, respectively. Genetic correlations varied from 0.40 to 0.96. Benefits to animal performance can best be achieved by considering body structure, finishing precocity, and muscling as selection criteria in the Nelore breeding programs. The decision to use visual scores measured at weaning should be considered in order to decrease generation interval and assist pre-selecting individuals, expecting carcass improvements in the future progeny.