Two-trait Analysis Research Articles

228 The effect of temperature-humidity index in different pregnancy stages on litter traits in Taiwan Landrace sows

Abstract Because of climate change, annual average temperatures are gradually rising. Hence, increasing heat stress can impair the litter traits of Taiwanese sows located in tropical and subtropical zones. However, some pregnancy stages may be impacted more by heat stress than other periods. Thus, the purpose of this study was to demonstrate the effect of temperature-humidity index (THI) in different pregnancy stages on total number born (TNB), number born alive (NBA), and stillborn rate (STB). Data were collected in two Taiwanese farms from 2008 to 2021 for TNB, NBA, and STB, while weather data were collected from the closest respective weather station. There were 4,247 and 6,812 pure line Landrace record from farm 1 and farm 2, respectively. Pregnancy stages included 28 d before mating (BEFORE), mating to 30 d of pregnancy (EARLY), 31 to 70 d of pregnancy (MIDDLE), and 71 d of pregnancy to farrowing (LATE). Average THI was calculated in each pregnancy stage and regression coefficients were estimated by linear mixed models within ASReml. A two-trait analysis was used to estimate regression coefficients and genetic correlations among TNB, NBA, and STB. Results showed THI impacted (P < 0.05) TNB and NBA BEFORE and EARLY, while THI influenced (P < 0.05) STB EARLY and MIDDLE. Regression coefficients for TNB were -0.0069 and -0.0228 for BEFORE and EARLY, respectively. For NBA, regression coefficients were 0.0106 and -0.0391 for BEFORE and EARLY, respectively. Regression coefficients for STB were -0.0651 and 0.1332 for EARLY and MIDDLE, respectively. Heritability estimates for TNB, NBA, and STB were 0.168, 0.113, and 0.069, respectively. Genetic correlations between TNB with NBA and STB were 0.913 and 0.539, respectively. While the genetic correlation between NBA and STB was 0.158. In conclusion, THI impacted BEFORE and EARLY stages for TNB and NBA, while the stages of EARLY and MIDDLE were impacted by THI for STB. Hence, control of the environments in these specific stages for sows could improve the performance of litter traits.

Genetic association between fat-to-protein ratio and traits of economic interest in early lactation Holstein cows in Brazil.

The aims of this study were to estimate the genetic parameters for fat-to-protein ratio (F:P) within the first 90days of lactation and to examine their genetic associations with daily milk yield (MY), somatic cell score (SCS), and calving interval between the first and second calving (IFSC) and between the second and third calving (ISTC) during the first three lactations of Holstein cows. We utilized 200,626 production-related data officially recorded from 77,436 cows milked two or three times a day from 2012 to 2022, sourced from the Holstein Cattle Breeders Association of Paraná State, Brazil. The (co)variance components were estimated using animal models, adopting the restricted maximum likelihood (REML) method with single-trait analysis (for heritability and repeatability) and two-trait analysis (for genetic and phenotypic correlations), per lactation. Regardless of lactation number, heritability estimates were relatively low, ranging from 0.08 ± 0.005 to 0.10 ± 0.003 for F:P; 0.08 ± 0.01 to 0.18 ± 0.005 for MY; 0.04 ± 0.01 to 0.07 ± 0.004 for SCS; and 0.03 ± 0.01 for both IFSC and ISTC. Repeatability estimates within the same lactation were low for F:P (ranging from 0.17 ± 0.002 to 0.19 ± 0.03), high for MY (between 0.50 ± 0.003 and 0.53 ± 0.002), and moderate to high for SCS (between 0.39 ± 0.003 and 0.44 ± 0.004). Genetic correlations between F:P and MY ranged from -0.26 ± 0.03 to -0.15 ± 0.02; F:P and SCS, from -0.06 ± 0.03 to -0.03 ± 0.08; F:P and IFSC, 0.31 ± 0.01; F:P and ISTC, 0.20 ± 0.01; MY and IFSC, 0.24 ± 0.05; and MY and ISTC, 0.13 ± 0.08. The fat-to-protein ratio during early lactation showed low genetic variability, regardless of lactation number. Furthermore, it was genetically correlated with MY, IFSC, and ISTC, although there is an antagonistic and unfavorable correlation between traits that can limit genetic progress.

Efeito da idade a primeira concepção como critério de seleção em características de crescimento e carcaça em bovinos Nelore

Abstract The aim was to identify predictive traits of relatively easy measurement and which can be recorded early in life (age at first conception - AFCo), besides estimating its genetic correlations with growth and carcass traits in Nellore cattle. Age at first conception was considered the age at which the female had the first positive diagnosis for pregnancy. The estimation of (co)variance components and genetic parameters was performed using a linear animal model in two-trait analysis. The estimates of heritability were moderate, enabling genetic selection for growth-, carcass-, and sexual precocityrelated traits. The genetic correlation obtained between AFCo and age at first calving (AFCa) was high (0.88), indicating the feasibility of using AFCo as a selection criterion for early calving heifers. Genetic correlation estimates between AFCo and AFCa with weight at 120, 210, 365, and 450 days of age and carcass traits were moderate and negative (-0.33 to -0.62). Thus, genetic selection for animals with early AFCo and AFCa would enhance carcass yield, fat deposition, and growth performance, despite not affecting birth weight or daily weight gain. The results of this study encourage the use of AFCo in Nellore cattle since this trait displayed enough genetic variability in Nellore cattle, and can be used as selection criteria to improve sexual precocity. When the objective of genetic selection is to increase heifer sexual precocity, we could use the first trait as a criterion, as the measurement of this trait occurs at a lower AFCa.

142 Genotype-By-Environment Interactions in Dairy Cattle Raised in California and New England

Abstract Genotype-by-environmental interactions (G x E) have been reported to cause sire reranking in dairy populations. However, the effects of such interactions are not included in the current US national dairy genetic evaluation. Phenotypes collected from animals across the country are considered the same trait, disregarding interactions between genetic effects and region or other environmental factors. Variations in production systems may interact with the genotypes of the animals, leading to distinct phenotypes. Separating data into regions could change decision-making on bull selection, which is the most important step of a breeding program. The goal of this study is to evaluate the extent of genotype-by-environment interaction between the state of California and the region of New England in the US Holstein Population. Data from Holstein cows in the aforementioned regions collected from 2010 to 2020 were provided by the Council on Dairy Cattle Breeding. The genetic evaluation models used for the national evaluation were modified to account for New England and California phenotypes as separated yet correlated traits. The mixed model accounted for additive genetic, sire-by-herd interaction, and permanent environmental effects as random, and for the management group, age of parity, birth year, and parity as fixed effects. In some scenarios, sire-by-herd interaction and permanent environment were removed due to convergence problems. Variance components were estimated using AIREML under the Method R procedure. Such a method allows computing variance components for large databases by splitting the data into several random groups. Ten random samples of data containing 100,000 records were used for each analysis. The scenarios included single trait for each state, single trait with both states, and two-trait with separated states. Breeding values from the top 100 bulls in each region were compared with evaluate potential sire reranking. Heritabilities varied across regions for all three traits (Table 1). California presented lower heritability than New England across methods. The genetic correlations on the two-trait analysis averaged at 0.96, 0.92, and 0.95 for yield, fat, and protein, respectively. The Spearman correlation coefficient between breeding values of the bulls with more than 200 daughters was 0.87 for milk yield, suggesting that the reranking is unimportant. The genetic correlations suggested that GxE between the two regions is small and that the traits should not be split in the current genetic evaluation. In conclusion, no critical effect of genotype-by-environment interaction was observed, even though top bulls may differ across regions. The next step of this project is to evaluate GxE for somatic cell score and to include genomic information in the evaluations. Finally, a genome-wide association study across the two regions will be performed to investigate changes in genomic regions associated with the phenotypes.

Genetic parameter estimation for pork production and litter performance traits of Landrace, Large White, and Duroc pigs in Japan.

We estimated genetic parameters for two pork production and six litter performance traits of Landrace, Large White, and Duroc pigs reared in Japan. Pork production traits were average daily gain from birth to end of performance testing and backfat thickness at end of testing (46,042 records for Landrace, 40,467 records for Large White, and 42,920 records for Duroc). Litter performance traits were number born alive, litter size at weaning (LSW), number of piglets dead during suckling (ND), survival rate of piglets during suckling (SV), total piglet weight at weaning (TWW), and average piglet weight at weaning (AWW) (27,410, 26,716, and 12,430 records for Landrace, Large White, and Duroc, respectively). ND was calculated as the difference between LSW and litter size at start of suckling (LSS). SV was calculated as LSW/LSS. AWW was calculated as TWW/LSW. Pedigree data for Landrace, Large White, and Duroc breeds contained 50,193, 44,077, and 45,336 pigs, respectively. Trait heritability was estimated via single-trait analysis and genetic correlation between two traits was estimated via two-trait analysis. When considering the linear covariate of LSS in the statistical model for LSW and TWW, for all breeds, the heritability was estimated to be 0.4-0.5 for pork production traits and below 0.2 for litter performance traits. Estimated genetic correlation between average daily gain and backfat thickness was small, ranging from 0.057 to 0.112, and those between pork production traits and litter performance traits were negligible to moderate, ranging from -0.493 to 0.487. A wide range of genetic correlation values among the litter performance traits was estimated, while that between LSW and ND could not be obtained. The results of genetic parameter estimation were affected by whether the linear covariate of LSS was included in the statistical model for LSW and TWW or not. This finding implies the necessity of carefully interpreting the results according to the choice of statistical model. Our results could give fundamental information on simultaneously improving productivity and female reproductivity for pigs.

Testicular hypoplasia in Nellore Cattle: Genetic analysis and functional analysis of genome-wide association study results.

Characterized by the incomplete development of the germinal epithelium of the seminiferous tubules, Testicular hypoplasia (TH) leads to decreased sperm concentration, increased morphological changes in sperm and azoospermia. Economic losses resulting from the disposal of affected bulls reduce the efficiency of meat production systems. A genome-wide association study and functional analysis were performed to identify genomic windows and the underlying positional candidate genes associated with TH in Nellore cattle. Phenotypic and pedigree data from 207,195 animals and genotypes (461,057 single nucleotide polymorphism, SNP) from 17,326 sires were used in this study. TH was evaluated as a binary trait measured at 18 months of age. A possible correlated response on TH resulting from the selection for scrotal circumference was evaluated by using a two-trait analysis. Thus, estimated breeding values were calculated by fitting a linear-threshold animal model in a Bayesian approach. The SNP effects were estimated using the weighted single-step genomic BLUP method. Twelve non-overlapping windows of 20 adjacent SNP that explained more than 1% of the additive genetic variance were selected for candidate gene annotation. Functional and gene prioritization analysis of the candidate genes identified genes (KHDRBS3, GPX5, STAR, ERLIN2), which might play an important role in the expression of TH due to their known roles in the spermatogenesis process, synthesis of steroids and lipid metabolism.

Single-Step GBLUP and GWAS Analyses Suggests Implementation of Unweighted Two Trait Approach for Heat Stress in Swine.

Simple SummaryA major problem in the swine meat industry is significant weight loss due to pigs overheating. Our approach to solving this problem is breeding swine based on their ability to survive in high temperatures. We determined in this study that genetically there is a difference between the most successful animal in hot conditions and the most successful animal in cooler conditions. This can be applied to selection of breeding animals for swine companies. Additionally, we found a few genes that may be related to this heat resistance, but more studies need to be completed to validate this finding.The purpose of this study was to perform a genome-wide association study to determine the genomic regions associated with heat stress tolerance in swine. Phenotypic information on carcass weight was available for 227,043 individuals from commercial farms in North Carolina and Missouri, U.S. Individuals were from a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. Genotypic information was available for 8232 animals with 33,581 SNPs. The pedigree file contained a total of 553,448 animals. A threshold of 78 on the Temperature Humidity Index (THI) was used to signify heat stress. A two-trait analysis was used with the phenotypes heat stress (Trait One) and non-heat stress (Trait Two). Variance components were calculated via AIREML and breeding values were calculated using single step GBLUP (ssGBLUP). The heritability for Traits One and Two were calculated at 0.25 and 0.20, respectively, and the genetic correlation was calculated as 0.63. Validation was calculated for 163 genotyped sires with progeny in the last generation. The benchmark was the GEBV with complete data, and the accuracy was determined as the correlation between the GEBV of the reduced and complete data for the validation sires. Weighted ssGBLUP did not increase the accuracies. Both methods showed a maximum accuracy of 0.32 for Trait One and 0.54 for Trait Two. Manhattan Plots for Trait One, Trait Two, and the difference between the two were created from the results of the two-trait analysis. Windows explaining more than 0.8% of the genetic variance were isolated. Chromosomes 1 and 14 showed peaks in the difference between the two traits. The genetic correlation suggests a different mechanism for Hot Carcass Weight under heat stress. The GWAS results show that both traits are highly polygenic, with only a few genomic regions explaining more than 1% of variance.

Genetic parameters for body weight and milk production of dairy Gyr herds.

The aim of this study was to estimate genetic parameters for cow weight at calving (CW) and cumulative 305-day milk yield (MY305) in dairy Gyr cattle by two-trait analysis. The study used 1,847 CW records, in which 418 females presented more than one measure, and 4,048 MY305 records, wherein 1068 females provided repeated measures, from 2,339 females belonging to three herds, which calved between 1986 and 2019. Variance components were estimated by the restricted maximum likelihood method (REML) using a two-trait animal model. The model included direct additive genetic, permanent environmental and residual effects as random effects and the fixed effects of contemporary group, formed by animals that had calved on the same farm year and season, and age of cow at calving as covariates (fitted as linear and quadratic effect). The heritability estimates for CW and MY305 were 0.21 ± 0.06 and 0.29 ± 0.04, respectively, and repeatability estimates were 0.49 ± 0.03 and 0.49 ± 0.02. The genetic correlation between CW and MY305 was positive and of low magnitude (0.33 ± 0.18), indicating that selection for MY305 will cause little genetic change in the weights of dairy Gyr animals. The genetic trends of breeding values of analyzed traits showed marked genetic gains in MY305, with little changes in CW of dairy Gyr cows over the years in the herds studied, which is an important result considering the production systems adopted in the tropics.

Genetic evaluation of lactation persistency in the Gyr breed by using a two-trait random regression model

Context Lactation persistency (LP) is an economically important characteristic to include in the selection objectives of the Gyr breed. Two persistency measures were tested to establish their contribution to the genetic evaluation of milk production in this genotype. The second measure of persistency studied would be the more appropriate measure to use in the genetic evaluation of lactation persistency. Aim The aim of this work was to study LP in Gyr cows by using a random regression model (RRM) in two-trait analysis. Methods Test-day milk yields (TDMY) of the first two lactations of Gyr cows were analysed. RRM was performed by Bayesian inference using the GIBBS3F90 program. Fourth-order Legendre polynomials were used to describe the random additive genetic and permanent environmental effects of the animal. The fixed effects included in the model were contemporary group and, as covariates, age of cow at calving and the regression function according to the TDMY class of lactation as the average trajectories of lactation curve, distinguished by calving order. Two persistency measures (PSi, i.e. PS1 and PS2) were used in the analyses. Rank correlations were calculated between the predicted breeding values for PSi, 305-day lactation milk yield (MY305), and the regression coefficients a0 and a1, to determine the percentage of matching animals between rankings when different selection intensities are applied on the basis of the ranking for MY305. Key results The heritability estimates for PS1, PS2 and for MY305 were 0.19, 0.12 and 0.41 respectively, in the first lactation, and 0.43, 0.27 and 0.38 in the second lactation. The genetic correlation between MY305 and PS1 was negative and of low magnitude. The rank correlation between breeding values for PSi, obtained for bulls with at least five daughters with production records, was higher than 0.80 in the two lactation periods. Conclusions The results indicated that PS2 is the more suitable of the two persistency measures used in this work, for inclusion in genetic evaluations of lactation persistency in Brazilian Gyr cows. Implications PS2 must be included as a selection criteria in a breeding program for the Gyr breed.

Breeding value for stayability and productive traits of Saanen does obtained by survival analysis versus traditional genetic evaluation

Our objectives were (1) to evaluate how production traits (lactation length, milk production, and protein, lactose, fat and total dry extract yields) affect the permanence of Saanen goats in the herd for longer than 28 months; (2) to estimate genetic parameters for stayability (STAY28) using an Weibull proportional risk model; and (3) to evaluate the genetic associations between both stayability and production through genetic correlations and regression analysis. Two methodologies were utilized in the evaluation: (1) survival analysis using the Weibull animal model; and (2) threshold-linear model through Bayesian approach using Gibbs sampler in two-trait analysis. The data set had 3344 information. Animal effect was included as random in both methodologies. In analyses with Weibull distribution, the contemporary group effect was considered random and in the threshold-linear model was systematic. In the Weibull risk model, production traits were divided in five classes (class 0, class 1, class 2, class 3, and class 4) and the effect of parturition age was significant (P < 0.05) for all production variables. For milk production, the highest class (class 4) presented significant effect on STAY28, with reduced culling risk of does from this class in comparison with the other ones. Heritability values for STAY28 were low in both methodologies (between 0.05 and 0.09 across classes for the Weibull model and 0.07 for the threshold-linear model). Genetic correlation estimates between STAY28 and production traits were null in the threshold-linear model. Regression of breeding values of production traits as a function of STAY28 demonstrated a significant relationship between these traits. Even with slower favorable changes, the use of STAY28 for selecting animals with higher breeding values is a viable option to increase the productive lifespan of dairy goats, resulting in animals that are more productive. Therefore, the improvement of productive traits of does brings positive effects in the stay in the herd for longer than 28 months.

17 Genome-wide Association Study for Heat Stress Tolerance in Swine Using Weighted Single-step GBLUP

Abstract The purpose of this study was to perform a genome-wide association study to determine the genomic regions associated with heat stress tolerance in swine as well as analyze the accuracy of prediction. Phenotypic information on carcass weight was available for 227,043 individuals from commercial farms in North Carolina and Missouri. Individuals were a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. Genotypic information was available for 8,232 animals with 33,581 SNP. The pedigree file contained 553,448 animals. A 78 on the Temperature Humidity Index (THI) was used as a threshold for heat stress. A two-trait analysis was used with the phenotypes heat stress (trait one) and non-heat stress (trait two). Variance components were calculated via AIREML and breeding values were calculated using single step GBLUP (ssGBLUP). The heritability for trait one and two were calculated at 0.25 and 0.20, respectively, and the genetic correlation was calculated as 0.63. Validation was calculated for 163 genotyped sires with progeny in the last generation. The GEBV of complete data was used as the benchmark, and the accuracy was determined as the correlation between the GEBV of the reduced and complete data for the validation sires. Weighted ssGBLUP did not increase the accuracies, both methods showed a maximum accuracy of 0.32 for trait one and 0.54 for trait two. Manhattan Plots for trait one, trait two, and the difference between the two were created from the results of the two-trait analysis. Windows explaining around 1% of the genetic variance were identified. The only difference between the two traits was a peak at chromosome 14. The genetic correlation suggests different mechanisms for growth under heat stress. The GWAS results show that both traits are highly polygenic, with few genomic regions explaining more than 1% of variance.

Principal components for morphometric traits in Campolina horses.

Principal component analysis (PCA) was applied to evaluate the genetic variability and relationship between 15 morphometric traits in 91,483 Campolina horses, as well as to propose an index based on an aggregate genotype that promotes a particular selection objective. PCA was applied to the genetic (co)variance matrix among variables. After calculation of the principal components, the breeding values were estimated to obtain an index related to the component that explained most of the variation. The first principal component (PC1) accounted for 97.8% of the total additive genetic variance of the traits. PC1 contrasted animals in terms of body size (wither, back and croup heights, body length, and thoracic girth). PC1 traits showed higher heritabilities and positive and high genetic correlations. An index was obtained (HPC1) with the combination of the breeding values of different traits from PC1 which permitted the use of this index as an aggregate genotype to identify the best animals for selection. The second principal component (PC2) was much smaller and grouped traits related to head and neck morphometry, among others. These traits are commonly used for breed qualification, a fact explaining the small variation in this component. An evaluation of the effect of HPC1 on withers height in two-trait analysis was also made which provided positive genetic correlations of moderate to high magnitude (0.73-0.86), indicating that selection for this trait (important in Campolina horses) is accounted for in the index. The use of HCP1 could be considered as an important alternative to selection since it does not consider a single trait but rather a set of variables that capture body proportions.

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within the wheat grain ionome.

Dissection of the genetic basis of wheat ionome is crucial for understanding the physiological and biochemical processes underlying mineral accumulation in seeds, as well as for efficient crop breeding. Most of the elements essential for plants are metals stored in seeds as chelate complexes with phytic acid or sulfur-containing compounds. We assume that the involvement of phosphorus and sulfur in metal chelation is the reason for strong phenotypic correlations within ionome. Adjustment of element concentrations for the effect of variation in phosphorus and sulfur seed content resulted in drastic change of phenotypic correlations between the elements. The genetic architecture of wheat grain ionome was characterized by quantitative trait loci (QTL) analysis using a cross between durum and wild emmer wheat. QTL analysis of the adjusted traits and two-trait analysis of the initial traits paired with either P or S considerably improved QTL detection power and accuracy, resulting in the identification of 105 QTLs and 617 QTL effects for 11 elements. Candidate gene search revealed some potential functional associations between QTLs and corresponding genes within their intervals. Thus, we have shown that accounting for variation in P and S is crucial for understanding of the physiological and genetic regulation of mineral composition of wheat grain ionome and can be implemented for other plants.

Genetic evaluation of milking buffaloes (Bubalus bubalis): bull ranking

ABSTRACT The objective of this study was to evaluate genetic aspects related to production and reproductive efficiency of Murrah and Mediterranean buffaloes and their crosses. A ranking of bulls from Embrapa Eastern Amazonia was also composed to guide assisted mating. Birth records of 2,322 Murrah, Mediterranean, and crossbred buffaloes from the Embrapa Eastern Amazon herd, from 1953 to 2013, as well as information on production and reproductive traits were used. Genetic analyzes were performed in the WOMBAT software using the animal model with two-trait analysis. While heritability (h2) for total milk production (TMP) and fat milk percentage (F) were generally high, for reproductive traits h2 tended to be low. Genetic correlations for TMP with the other traits were low and negative, except for TMP with calving interval (CI) and service period (SP) in the Mediterranean breed and with age at first calving (AFC) and SP in crossbred, which were positive and high. Bull 1001 had high predicted transmitting ability (PTA) for TMP, so it should transmit a greater volume of milk to his offspring, although it had a lower PTA for F. There was sufficient variability within the herd to work with genetic management for both production and reproductive efficiency.

Selection for feed efficiency does not change the selection for growth and carcass traits in Nellore cattle.

The objectives were to conduct a genetic evaluation of residual feed intake (RFI) and residual feed intake adjusted for fat (RFIFat) and to analyse the effect of selection for these traits on growth, carcass and reproductive traits. Data from 945 Nellore bulls in seven feed efficiency tests in a feedlot were analysed. Genetic evaluation was performed using an animal model in which the feed efficiency test and age of the animal at the beginning of the test were considered as a systematic effect. Direct additive genetic and residual effects were considered as random effects. Correlations and genetic gains were estimated by two-trait analysis between feed efficiency measures (RFI and RFIFat) and other traits. Feed conversion showed low heritability (0.06), but dry matter intake (DMI), average daily gain, RFI, RFIFat, metabolic body weight and scrotal circumference measured at 450days of age (SC450) showed moderate to high heritability (0.49, 0.28, 0.33, 0.36,0.38 and 0.80, respectively). Similarly, ribeye area, backfat thickness, rump capfat thickness, marbling score and subcutaneous fat thickness also had high heritability values (0.46, 0.37, 0.57, 0.51 and 0.47, respectively). Genetic correlations between RFI and SC450 were null, and between RFIFat and SC450 were strongly positive. Genetic and phenotypic correlations of RFI and RFIFat with carcass traits were not different from zero, as correlated responses for carcass traits were also not different from zero. The Nellore selection for feed efficiency by RFI or RFIFat allows the recognition of feed efficient animals, with DMI reduction and without significant changes in growth and carcass traits. However, because of the observed results between RFIFat and SC450, selection of animals should be analysed with caution and a preselection for reproductive traits is necessary to avoid reproductive impairments in the herd.

Genetic parameters for marbling and body score in Anglonubian goats using Bayesian inference via threshold and linear models.

ObjectiveThe aim of this study was to estimate (co) variance components and genetic parameters for categorical carcass traits using Bayesian inference via mixed linear and threshold animal models in Anglonubian goats.MethodsData were obtained from Anglonubian goats reared in the Brazilian Mid-North region. The traits in study were body condition score, marbling in the rib eye, ribeye area, fat thickness of the sternum, hip height, leg perimeter, and body weight. The numerator relationship matrix contained information from 793 animals. The single- and two-trait analyses were performed to estimate (co) variance components and genetic parameters via linear and threshold animal models. For estimation of genetic parameters, chains with 2 and 4 million cycles were tested. An 1,000,000-cycle initial burn-in was considered with values taken every 250 cycles, in a total of 4,000 samples. Convergence was monitored by Geweke criteria and Monte Carlo error chain.ResultsThreshold model best fits categorical data since it is more efficient to detect genetic variability. In two-trait analysis the contribution of the increase in information and the correlations between traits contributed to increase the estimated values for (co) variance components and heritability, in comparison to single-trait analysis. Heritability estimates for the study traits were from low to moderate magnitude.ConclusionDirect selection of the continuous distribution of traits such as thickness sternal fat and hip height allows obtaining the indirect selection for marbling of ribeye.

Genetic parameter estimates for temperament, heifer rebreeding, and stayability in Nellore cattle

Abstract The aim of this study was to estimate heritability for five temperament and two reproductive traits in Nellore cattle and to estimate genetic and phenotypic correlations among them. Temperament was evaluated using the movement (MOV), tension (TEN) and crush (CS) scores (measured with animals inside the squeeze chute) as well as the flight speed (FS) and temperament score (TS). Reproductive traits included i) heifer rebreeding (HR), which evaluates heifers’ ability to become pregnant, given that they had calved once; and ii) stayability (STAY), which measures cows’ ability to calve at least 3 offspring before reaching 65 months of age. We used Bayesian inference and Gibbs sampling in a two-trait analysis to estimate genetic parameters applying a linear model for FS and threshold models for MOV, TEN, CS, TS, HR and STAY. The animal model included contemporary group as a fixed effect, direct additive genetic and residual effects as random effects, and animal age at yearling as a covariate (with linear and quadratic effects). Heritability estimates for MOV, TEN, CS, FS, TS, HR and STAY were 0.14 ± 0.04, 0.11 ± 0.03, 0.09 ± 0.03, 0.22 ± 0.02, 0.19 ± 0.04, 0.13 ± 0.02 and 0.13 ± 0.02, respectively. The genetic correlation estimates were low to moderate and the highest values (in magnitude) were − 0.19 ± 0.21 (HR-CS), − 0.21 ± 0.15 (STAY-TEN) and − 0.24 ± 0.16 (STAY-CS), indicating that the selection to improve cattle temperament does not negatively affect HR and STAY. These results indicate that all traits had sufficient genetic variability to respond to direct selection; however, given the low estimated heritability, we expect to see only long-term genetic changes. Genetic correlations showed that there is no antagonism of temperament with fertility and longevity; however, we recommend including these traits as selection criteria in Nellore breeding programs to obtain satisfactory genetic changes.

Categorical Visual Score Traits of a Nellore Beef Cattle Population

Variance components and genetic parameters of economically relevant traits in livestock, whether continuous or categorical, can be estimated by methods computationally available providing support for the selection and mating of animals in breeding programs. The objectives of this paper were to obtain and compare the variance components estimates for visual traits under continuous or categorical distribution in single-trait analysis and their correlations with continuous productive traits in two-trait analysis. Data of conformation (CONF), precocity of fat deposition (PREC) and muscling (MUSC) visual scores evaluated at 18 months of age as well as the weight at 18 months of age (YW) were collected from animals born from 2000 to 2012, in Nellore cattle herds raised in Southeastern and Central Western tropical regions of Brazil. Methods III of Henderson, Restricted Maximum Likelihood (REML), Bayesian Inference and generalized linear mixed model (GLMM) were tested. Variance components obtained from single-trait analysis were similar to those obtained from two-trait analysis. The estimates of heritability (h2) for the visual scores ranged from 0.1081 to 0.2190. Heritability estimates for traits evaluated by visual scores have moderate to high magnitude justifying the inclusion of visual scores as selection criteria in animal breeding and the selection of animals with higher scores for mating. High genetic correlations between yearling weight and morphological traits were verified. For visual scores of conformation, precocity and muscling, the most suitable model based on one-trait or two-trait analyses considered an animal model, a linear distribution of the data and the estimation method of the components of (co)variance based on Bayesian methodology.

Preweaning Calf Survival of a Nellore Beef Cattle Population

The preweaning calf survival (SW) is one of the main economic bottlenecks of beef cattle rearing systems, however there is still few quantitative studies approaching this issue. Being a binary trait, genetic parameters for SW can be estimated considering continuous or categorical data under frequentist and Bayesian methods providing support for the selection and mating of animals in breeding programs. Therefore, the objectives in this study were to obtain and compare the variance component estimates for preweaning calf survival of calves in single-trait analyses and their correlations with a continuous trait in two-trait analyses. An amount of 25 218 data of the categorical trait of calf survival until weaning (SW) and the continuous trait of weaning weight (WW) were collected between the years of 2000 and 2012 in six herds of Nellore cattle. Methods III of Henderson, Maximum Restricted Likelihood (REML), Bayesian Inference and Generalized Linear Mixed Model (GLMM) were tested. Variance components obtained in one-trait analyses were similar to those obtained in two-trait analyses. Estimates of heritability (h2) obtained with different models for SW ranged from 0.0206 to 0.2644. The comparison between different estimation methods in single or two-trait analysis models allows the conclusion that the most appropriate method for SW analysis was the Bayesian estimation under an animal model and assuming linear distribution for phenotypes of SW trait.

Estimation of genetic parameters for linear type traits in the population of sport horses in the Czech Republic

Linear profiling of horses has been used in the Czech Republic for 20 years but without preceding genetic evaluation (predictions of breeding values). The main objective of this study was to estimate genetic parameters of linear profiling of the population of sport horses in the Czech Republic. A linear profiling database was obtained for the period 1997–2015 and it comprised 12 455 horses in total and 25 traits were evaluated. The model equation included fixed effects of gender, horse age, place, classifier*year and individual. Genetic parameters were computed by a single-trait BLUP-AM analysis. The range for heritability coefficients were between 0.05 and 0.31 for linear type traits and between 0.43 and 0.67 for measured traits. Genetic correlations were computed by a two-trait analysis between the evaluated traits and they were in the range of 0.0–0.93. The estimated genetic parameters suggest that a selection programme can be based on the genetic evaluation of linear profiling, which will lead to an improvement in conformation traits according to the defined breeding objectives.