Genome-wide interaction study reveals epistatic interactions for beef lipid-related traits in Nellore cattle.

Gene-gene interactions cause hidden genetic variation in natural populations and could be responsible for the lack of replication that is typically observed in complex traits studies. This study aimed to identify gene-gene interactions using the empirical Hilbert-Schmidt Independence Criterion method to test for epistasis in beef fatty acid profile traits of Nellore cattle. The dataset contained records from 963 bulls, genotyped using a 777 962k SNP chip. Meat samples of Longissimus muscle, were taken to measure fatty acid composition, which was quantified by gas chromatography. We chose to work with the sums of saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA), omega-3 (OM3), omega-6 (OM6), SFA:PUFA and OM3:OM6 fatty acid ratios. The SNPs in the interactions where P < 10 - 8 were mapped individually and used to search for candidate genes. Totals of 602, 3, 13, 23, 13, 215 and 169 candidate genes for SFAs, MUFAs, PUFAs, OM3s, OM6s and SFA:PUFA and OM3:OM6 ratios were identified respectively. The candidate genes found were associated with cholesterol, lipid regulation, low-density lipoprotein receptors, feed efficiency and inflammatory response. Enrichment analysis revealed 57 significant GO and 18 KEGG terms ( P < 0.05), most of them related to meat quality and complementary terms. Our results showed substantial genetic interactions associated with lipid profile, meat quality, carcass and feed efficiency traits for the first time in Nellore cattle. The knowledge of these SNP-SNP interactions could improve understanding of the genetic and physiological mechanisms that contribute to lipid-related traits and improve human health by the selection of healthier meat products.

Genome-wide scan reveals genomic regions and candidate genes underlying direct and maternal effects of preweaning calf mortality in Nellore cattle.

We conducted analysis to estimate genetic parameters and to identify genomic regions and candidate genes affecting direct and maternal effects of preweaning calf mortality (PWM) in Nellore cattle. Phenotypic records of 67,196 animals, and 8443 genotypes for 410,936 SNPs were used. Analysis were performed through the weighted single-step GBLUP approach and considering a threshold animal model via Bayesian Inference. Direct and maternal heritability estimates were of 0.2143 ± 0.0348 and 0.0137 ± 0.0066, respectively. The top 10 genomic regions accounted for 13.61 and 14.23% of the direct and maternal additive genetic variances and harbored a total of 63 and 91 positional candidate genes, respectively. Two overlapping regions on BTA2 were identified for both direct and maternal effects. Candidate genes are involved in biological mechanisms i.e. embryogenesis, immune response, feto-maternal communication, circadian rhythm, hormone alterations, myometrium adaptation, and milk secretion, which are critical for the successful calf growth and survival during preweaning period.

Genome-enabled prediction of reproductive traits in Nellore cattle using parametric models and machine learning methods.

This study aimed to assess the predictive ability of different machine learning (ML) methods for genomic prediction of reproductive traits in Nellore cattle. The studied traits were age at first calving (AFC), scrotal circumference (SC), early pregnancy (EP) and stayability (STAY). The numbers of genotyped animals and SNP markers available were 2342 and 321 419 (AFC), 4671 and 309 486 (SC), 2681 and 319 619 (STAY) and 3356 and 319 108 (EP). Predictive ability of support vector regression (SVR), Bayesian regularized artificial neural network (BRANN) and random forest (RF) were compared with results obtained using parametric models (genomic best linear unbiased predictor, GBLUP, and Bayesian least absolute shrinkage and selection operator, BLASSO). A 5-fold cross-validation strategy was performed and the average prediction accuracy (ACC) and mean squared errors (MSE) were computed. The ACC was defined as the linear correlation between predicted and observed breeding values for categorical traits (EP and STAY) and as the correlation between predicted and observed adjusted phenotypes divided by the square root of the estimated heritability for continuous traits (AFC and SC). The average ACC varied from low to moderate depending on the trait and model under consideration, ranging between 0.56 and 0.63 (AFC), 0.27 and 0.36 (SC), 0.57 and 0.67 (EP), and 0.52 and 0.62 (STAY). SVR provided slightly better accuracies than the parametric models for all traits, increasing the prediction accuracy for AFC to around 6.3 and 4.8% compared with GBLUP and BLASSO respectively. Likewise, there was an increase of 8.3% for SC, 4.5% for EP and 4.8% for STAY, comparing SVR with both GBLUP and BLASSO. In contrast, the RF and BRANN did not present competitive predictive ability compared with the parametric models. The results indicate that SVR is a suitable method for genome-enabled prediction of reproductive traits in Nellore cattle. Further, the optimal kernel bandwidth parameter in the SVR model was trait-dependent, thus, a fine-tuning for this hyper-parameter in the training phase is crucial.

Genomic reaction norm models exploiting genotype × environment interaction on sexual precocity indicator traits in Nellore cattle.

Brazilian beef cattle are raised predominantly on pasture in a wide range of environments. In this scenario, genotype by environment (G×E) interaction is an important source of phenotypic variation in the reproductive traits. Hence, the evaluation of G×E interactions for heifer's early pregnancy (HP) and scrotal circumference (SC) traits in Nellore cattle, belonging to three breeding programs, was carried out to determine the animal's sensitivity to the environmental conditions (EC). The dataset consisted of 85 874 records for HP and 151 553 records for SC, from which 1800 heifers and 3343 young bulls were genotyped with the BovineHD BeadChip. Genotypic information for 826 sires was also used in the analyses. EC levels were based on the contemporary group solutions for yearling body weight. Linear reaction norm models (RNM), using pedigree information (RNM_A) or pedigree and genomic information (RNM_H), were used to infer G×E interactions. Two validation schemes were used to assess the predictive ability, with the following training populations: (a) forward scheme-dataset was split based on year of birth from 2008 for HP and from 2011 for SC; and (b) environment-specific scheme-low EC (-3.0 and -1.5) and high EC (1.5 and 3.0). The inclusion of the H matrix in RNM increased the genetic variance of the intercept and slope by 18.55 and 23.00% on average respectively, and provided genetic parameter estimates that were more accurate than those considering pedigree only. The same trend was observed for heritability estimates, which were 0.28-0.56 for SC and 0.26-0.49 for HP, using RNM_H, and 0.26-0.52 for SC and 0.22-0.45 for HP, using RNM_A. The lowest correlation observed between unfavorable (-3.0) and favorable (3.0) EC levels were 0.30 for HP and -0.12 for SC, indicating the presence of G×E interaction. The G×E interaction effect implied differences in animals' genetic merit and re-ranking of animals on different environmental conditions. SNP marker-environment interaction was detected for Nellore sexual precocity indicator traits with changes in effect and variance across EC levels. The RNM_H captured G×E interaction effects better than RNM_A and improved the predictive ability by around 14.04% for SC and 21.31% for HP. Using the forward scheme increased the overall predictive ability for SC (20.55%) and HP (11.06%) compared with the environment-specific scheme. The results suggest that the inclusion of genomic information combined with the pedigree to assess the G×E interaction leads to more accurate variance components and genetic parameter estimates.

Method for the estimation of genetic merit of animals with uncertain paternity under Bayesian inference.

The use of controlled mating or artificial insemination is impracticable in the case of large herds, mainly because of labour costs and the need to delimit areas during the breeding period. However, the exclusion of information from animals with uncertain paternity reduces genetic progress. The objectives of this study were as follows: (i) propose an iterative empirical Bayesian procedure to implement the hierarchical animal model (ITER); (ii) calculate the posterior probabilities of paternity by the maximum likelihood method following the concepts; (iii) compare an average numerator relationship matrix (ANRM), Bayesian hierarchical (HIER) models and ITER. Records of Nellore animals born between 1984 and 2006 from the zootechnical archive of Agropecuária Jacarezinho Ltda were used. For data consistency, records of contemporary groups (CGs) with fewer than three animals and animals whose records were 3.5 standard deviations above or below the mean of their CG were eliminated. After editing the data, 62,212 animals in the file and 12,876 animals in pedigree file were maintained, respectively. Spearman and Pearson correlations between the posterior mean of the genetic effects of animals were calculated to compare the ranking of animals for selection. Simulated data were used to confirm the veracity of the model. The correlations between ITER and HIER and between ITER and ANRM were similar evaluating different files, which decreased at the same proportion when only high-ranked animals were evaluated. In conclusion, the model proposed herein is a suitable computational alternative to improve the prediction of breeding values of animals in genetic evaluations using large databases, including animals with uncertain paternity.

SNP detection using RNA-sequences of candidate genes associated with puberty in cattle.

Fertility traits, such as heifer pregnancy, are economically important in cattle production systems, and are therefore, used in genetic selection programs. The aim of this study was to identify single nucleotide polymorphisms (SNPs) using RNA-sequencing (RNA-Seq) data from ovary, uterus, endometrium, pituitary gland, hypothalamus, liver, longissimus dorsi muscle, and adipose tissue in 62 candidate genes associated with heifer puberty in cattle. RNA-Seq reads were assembled to the bovine reference genome (UMD 3.1.1) and analyzed in five cattle breeds; Brangus, Brahman, Nellore, Angus, and Holstein. Two approaches used the Brangus data for SNP discovery 1) pooling all samples, and 2) within each individual sample. These approaches revealed 1157 SNPs. These were compared with those identified in the pooled samples of the other breeds. Overall, 172 SNPs within 13 genes (CPNE5, FAM19A4, FOXN4, KLF1, LOC777593, MGC157266, NEBL, NRXN3, PEPT-1, PPP3CA, SCG5, TSG101, and TSHR) were concordant in the five breeds. Using Ensembl's Variant Effector Predictor, we determined that 12% of SNPs were in exons (71% synonymous, 29% nonsynonymous), 1% were in untranslated regions (UTRs), 86% were in introns, and 1% were in intergenic regions. Since these SNPs were discovered in RNA, the variants were predicted to be within exons or UTRs. Overall, 160 novel transcripts in 42 candidate genes and five novel genes overlapping five candidate genes were observed. In conclusion, 1157 SNPs were identified in 62 candidate genes associated with puberty in Brangus cattle, of which, 172 were concordant in the five cattle breeds. Novel transcripts and genes were also identified.

Genetic parameter estimates for prenatal and postnatal mortality in Nellore cattle.

The aim of this study was to estimate genetic parameters for prenatal (PRE) and postnatal (POS) mortality in Nellore cattle. A total of 13 141 (PRE) and 17 818 (POS) records from Nellore females were used. PRE and POS were recorded using binary scale scores: a score of '1' was given to calves that were born alive (PRE) and those that were alive at weaning (POS), and a score of '0' was given to calves that were not alive at or around birth (PRE), as well as to those weighed at birth but not at weaning (POS). The relationship matrix included 698 sires, 107 paternal grandsires and 69 maternal grandsires. Data were analysed using Bayesian inference and a sire-maternal grandsire threshold model, including contemporary groups as random effects, and the classes of dam age at the beginning of mating season (for PRE), and dam age at calving and birthweight (linear covariable) (for POS), as fixed effects. For both traits, the covariance between direct and maternal effects (rD,M ) was estimated (rD,M ≠ 0) or fixed at zero (rD,M  = 0). PRE and POS rates were 3.00 and 4.04%, respectively. Estimates of direct and maternal heritability were 0.07 and 0.17, respectively, for PRE, and 0.02 and 0.07, respectively, for POS, assuming rD,M  = 0. For rD,M  ≠ 0, these estimates were 0.07 and 0.12, respectively, for PRE, and 0.03 and 0.07, respectively, for POS. The correlation estimates between direct and maternal effects were -0.71 (PRE) and -0.33 (POS). PRE and POS show low genetic variability, indicating that these traits probably suffer major environmental influences. Additionally, our study shows that the maternal genetic component affects preweaning calf mortality twice as much (or more) as the direct genetic component. A large number of offspring per sire is necessary in progeny tests to genetically decrease calf mortality.

Random regression models for the estimation of genetic and environmental covariance functions for growth traits in Santa Ines sheep.

Polynomial functions of different orders were used to model random effects associated with weight of Santa Ines sheep from birth to 196 days. Fixed effects included in the models were contemporary groups, age of ewe at lambing, and fourth-order Legendre polynomials for age to represent the average growth curve. In the random part, functions of different orders were included to model variances associated with direct additive and maternal genetic effects and with permanent environmental effects of the animal and mother. Residual variance was fitted by a sixth-order ordinary polynomial for age. The higher the order of the functions, the better the model fit the data. According to the Akaike information criterion and likelihood ratio test, a continuous function of order, five, five, seven, and three for direct additive genetic, maternal genetic, animal permanent environmental, and maternal permanent environmental effects (k = 5573), respectively, was sufficient to model changes in (co)variances with age. However, a more parsimonious model of order three, three, five, and three (k = 3353) was suggested based on Schwarz's Bayesian information criterion for the same effects. Since it was a more flexible model, model k = 5573 provided inconsistent genetic parameter estimates when compared to the biologically expected result. Predicted breeding values obtained with models k = 3353 and k = 5573 differed, especially at young ages. Model k = 3353 adequately fit changes in variances and covariances with time, and may be used to describe changes in variances with age in the Santa Ines sheep studied.

Estimates of genetic parameters for stayability and their associations with traits of economic interest in Gir dairy cows.

The objective of the present study was to estimate genetic parameters for stayability at 60 months of age (STAY60) and its association with first lactation cumulative milk yield (P305), age at first calving (AFC), and first calving interval (FCI), in order to adopt these traits as selection criteria for longevity in Gir dairy cattle. Records for 2770 cows born between 1982 and 2008 from six herds in the Brazilian states of Minas Gerais, São Paulo, and Paraíba were analyzed. The (co)variance components were estimated by a Bayesian approach using bivariate animal models. The heritability estimates were 0.37 ± 0.09, 0.23 ± 0.04, 0.26 ± 0.06, and 0.07 ± 0.03 for STAY60, P305, AFC, and FCI, respectively. The genetic correlations of STAY60 with P305, AFC, and FCI were moderate to high, with values of 0.61 (0.17), -0.44 (0.23), and 0.88 (0.13), respectively. STAY60, P305, and AFC exhibited additive genetic variability, and these traits should be considered in selection indices. The indirect selection for longevity through the correlated responses of early-expression traits, such as milk production at first lactation, could be used to improve the ability of animals to remain in the herd.

Genetic study of skin thickness and its association with postweaning growth in Nellore cattle: estimation of the genetic parameters.

The objective of the present study was to estimate genetic parameters for skin thickness (ST) and postweaning weight gain (PWG550) in Nellore cattle. Records were obtained from 152,392 Nellore animals born between 2001 and 2011. ST was measured in the posterior region of the animal's scapula with a millimeter caliper. The animals were assigned to different contemporary groups, formed on the basis of farm, year, sex, feeding regimen at weaning, date of weaning, feeding regimen at 450 days of age, and date of weighing at 450 days of age. The genetic parameters were estimated by Bayesian analysis using the GIBBS1F90 program. The mean ST and PWG550 observed were 7.71 ± 2.04 mm and 115.95 ± 36.17 kg, respectively. The posterior mean estimates of heritability (h2) were 0.12 ± 0.02 and 0.29 ± 0.02 for ST and PWG550, respectively. The posterior mean estimates of the phenotypic, genetic, and environmental correlations between the traits were 0.16 ± 0.0, 0.17 ± 0.02, and 0.17 ± 0.09, respectively. The traits ST and PWG550 showed sufficient additive genetic variance to be used as selection criteria in breeding programs. The low genetic correlation obtained indicates that genes favoring the expression of one trait may not influence the other. Consequently, a selection favoring ST would be less efficient in increasing PWG550.

Inbreeding depression in Zebu cattle traits.

The productivity of herds may be negatively affected by inbreeding depression, and it is important to know how intense is this effect on the livestock performance. We performed a comprehensive analysis involving five Zebu breeds reared in Brazil to estimate inbreeding depression in productive and reproductive traits. Inbreeding depression was estimated for 13 traits by including the individual inbreeding rate as a linear covariate in the standard genetic evaluation models. For all breeds and for almost all traits (no effect was observed on gestation length), the performance of the animals was compromised by an increase in inbreeding. The average inbreeding depression was -0.222% and -0.859% per 1% of inbreeding for linear regression coefficients scaled on the percentage of mean (ßm ) and standard deviation (ßσ ), respectively. The means for ßm (and ßσ ) were -0.269% (-1.202%) for weight/growth traits and -0.174% (-0.546%) for reproductive traits. Hence, inbreeding depression is more pronounced in weight/growth traits than in reproductive traits. These findings highlight the need for the management of inbreeding in the respective breeding programmes of the breeds studied here.

Short communication: Variable number of tandem repeat polymorphisms in DGAT1 gene of buffaloes (Bubalus bubalis) is associated with milk constituents.

The diacylglycerol-O-transferase 1 gene is a positional and functional candidate for milk composition traits. The objective of this study was to evaluate the segregation of the variable number of tandem repeat polymorphisms in the regulatory region of diacylglycerol-O-transferase 1 gene in a water buffalo herd, and to assess the association of this mutation with milk production traits. For this purpose, 196 Murrah buffalo cows were genotyped by PCR. The association of the marker with total milk, fat, and protein yields at 305 d of lactation, milk fat and protein percentage, and somatic cell scores were evaluated by single-trait analyses using a generalized mixed model. Two segregating alleles were identified in the population. The allele with 2 repeats affected fat percentage favorably. The present results suggest that this polymorphism is an interesting marker to include in the genetic evaluation of buffaloes.

Random regression analysis of test-day milk yields in the first and second lactations of Brazilian Gyr cows.

The objective of the present study was to estimate the genetic parameters for test-day milk yields (TDMY) in the first and second lactations using random regression models (RRM) in order to contribute to the application of these models in genetic evaluation of milk yield in Gyr cattle. A total of 53,328 TDMY records from 7118 lactations of 5853 Gyr cows were analyzed. The model included the direct additive, permanent environmental, and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cows at calving were included as fixed effects. A random regression model fitting fourth-order Legendre polynomials for additive genetic and permanent environmental effects, with five classes of residual variance, was applied. In the first lactation, the heritabilities increased from early lactation (0.26) until TDMY3 (0.38), followed by a decrease until the end of lactation. In the second lactation, the estimates increased from the first (0.29) to the fifth test day (0.36), with a slight decrease thereafter, and again increased on the last two test days (0.34 and 0.41). There were positive and high genetic correlations estimated between first-lactation TDMY and the remaining TDMY of the two lactations. The moderate heritability estimates, as well as the high genetic correlations between half the first-lactation TDMY and all TDMY of the two lactations, suggest that the selection based only on first lactation TDMY is the best selection strategy to increase milk production across first and second lactations of Gyr cows.

Multivariate analysis of test-day and total milk yield in goats.

The objective of this study was to estimate genetic parameters for 305-day cumulative milk yield (MY305) and its association with test-day milk yield (TDMY) in Saanen and Alpine goats in order to provide information that allows the use of TDMY as selection criteria. This was done using standard multi-trait and reduced rank models. Data from 1157 lactations, including the first three kiddings, and 5435 test-day records from 683 Saanen and 449 Alpine goats were used. MY305 was analyzed together with TDMY by multi-trait analysis, from the first to tenth test-day, using records of the first three lactations as repeated measures. Three multi-trait models were used: a standard (SM) and two reduced rank models that fitted the first two (PC2) and three (PC3) genetic principal components. Akaike and Schwarz Bayesian information criteria were used to compare models. Heritability for TDMY estimated with the SM ranged from 0.20 to 0.66, whereas the range calculated from the PC2 model was 0.16 to 0.63. Genetic correlations between TDMY and MY305 were positive and moderate to high, ranging from 0.56 to 0.98 when estimated with the SM, and 0.91 to 1.00 when estimated with the PC2. The standard multi-trait model produced estimates that were more accurate than the reduced rank models. Although the SM provided the worst fit according to the two model selection criteria, it was the best in this dataset.

Heritability of predicted daily enteric methane emissions from growing Nellore cattle.

The objective of this study was to estimate the heritability of predicted daily enteric methane emissions (PME) from growing Nellore cattle. Dry matter intake (DMI) records of 955 Nellore animals that were born between 2004 and 2013, which were obtained in a postweaning performance test lasting 83 ± 15 days, were used. The PME of each animal, obtained as MJ/day and converted to g/day, was estimated using three equations: PME1 (MJ/day) = 2.29 + 0.647 x DMI (kg/day), PME2 (MJ/day) = 3.96 + 0.561 x DMI (kg/day), and PME3 (MJ/day) = 4.41 + 0.50 x DMI (kg/day). The heritability (h2) of PME obtained using the three equations was identical to the h2 of DMI, regardless of whether the model included the effect of mid-test weight (h2 = 0.32 ± 0.069) or not (h2 = 0.48 ± 0.069). The equations were based exclusively on variations in DMI, and detected variations in this trait without taking into consideration individual differences in enteric methane emission caused by differences in fermentation and digestion capacity. Therefore, prediction equations of enteric methane emission from DMI are not adequate to estimate differences between animals.

Genetic association of growth traits with carcass and meat traits in Nellore cattle.

The aim of this study was to estimate genetic and phenotypic associations of growth traits with carcass and meat traits in Nellore cattle. Data from male and female animals were used for weaning weight (WW; N = 241,416), yearling weight (YW, N = 126,596), weight gain from weaning to yearling (GWY, N = 78,687), and yearling hip height (YHH, N = 90,720), respectively; 877 male animals were used for hot carcass weight (HCW) and 884 for longissimus muscle area (LMA), backfat thickness (BT), marbling score (MS), and shear force (SF). The variance components were estimated by the restricted maximum likelihood method using three-trait animal models that included WW. The model for WW included direct and maternal additive genetic, maternal permanent environmental, and residual effects as random effects; contemporary group as fixed effects; and age of dam at calving and age of animal as covariates (linear and quadratic effects). For the other traits, maternal effects and the effect of age of dam at calving were excluded from the model. Heritability ranged from 0.10 ± 0.12 (LMA) to 0.44 ± 0.007 (YW). Genetic correlations ranged from -0.40 ± 0.38 (WW x LMA) to 0.55 ± 0.10 (HCW x YW). Growth, carcass, and meat traits have sufficient genetic variability to be included as selection criteria in animal breeding programs.

Study of lipid metabolism-related genes as candidate genes of sexual precocity in Nellore cattle.

The objective of this study was to identify associations between known polymorphisms in genes related to adipose tissue and sexual precocity in Nellore cattle. A total of 1689 precocious and non-precocious heifers belonging to farms participating in Conexão Delta G breeding program were studied. SNPs from the Illumina High-Density Bovine SNP BeadChip were used. This chip contains 777,000 SNPs located within the region of the candidate genes at a distance of up to 5 kb, considering that linkage disequilibrium (LD) exists at this distance. Linear models were used for statistical analysis. The fastPHASE and GenomeStudio programs were used for haplotype reconstruction and LD analysis based on r2 statistics. Fifty-seven candidate genes and 443 SNPs were analyzed: among the latter, 370 SNPs formed 83 haplotypes, while the remaining SNPs were studied separately. Statistical analysis showed that only three haplotypes, one haplotype consisting of two SNPs located in the FABP4 gene and two haplotypes consisting of four and two SNPs located in the PPP3CA gene, had a significant effect on sexual precocity at P < 0.05. It can be concluded that the FABP4 and PPP3CA genes influence sexual precocity and may therefore be used in selection programs designed to improve sexual precocity in Nellore cattle.

Reaction norms for the study of genotype-environment interaction for growth and indicator traits of sexual precocity in Nellore cattle.

The objective of this study was to quantify the magnitude of genotype-environment interaction (GxE) effects on age at first calving (AFC), scrotal circumference (SC), and yearling weight (YW) in Nellore cattle using reaction norms. For the study, 89,152 weight records of female and male Nellore animals obtained at yearling age were used. Genetic parameters were estimated with a single-trait random-regression model using Legendre polynomials as base functions. The heritability estimates were of low to medium magnitude for AFC (0.05 to 0.47) and of medium to high magnitude for SC (0.32 to 0.51) and YW (0.13 to 0.72), and increased as the environmental gradient became more favorable. The genetic correlation estimates ranged from 0.25 to 1.0 for AFC, from 0.71 to 1.0 for SC, and from 0.42 to 1.0 for YW. High Spearman correlation coefficients were obtained for the three traits, ranging from 0.97 to 0.99. The reaction norms along the environmental gradient of 10 sires each with the highest or lowest breeding value for YW predicted by single-trait analysis demonstrated more plastic phenotypes for YW and more robust phenotypes for SC. The effect of GxE was most important for YW and AFC with respect to SC. When animals are selected for higher SC or YW or lower AFC, considering or not the GxE effect, it is expected that the same animals will be selected. The reaction norms obtained based on sire breeding values along the environmental gradient showed that animals with extreme breeding values respond differently as environmental conditions improve.

Associations between single nucleotide polymorphisms and carcass traits in Nellore cattle using high-density panels.

The objective of this study was to evaluate associations between single nucleotide polymorphism (SNP) markers and carcass traits measured postmortem in Nellore cattle. Records of loin eye area (LEA) and backfat thickness (BF) from 740 males and records of hot carcass weight (HCW) from 726 males were analyzed. All of the animals were genotyped using the BovineHD BeadChip. Association analyses were performed by the restricted maximum likelihood method that considered one SNP at a time. Significant SNPs were identified on chromosomes 2 and 6 for LEA and on chromosomes 7, 1, and 2 for BF. For HCW, associations with SNPs were found on chromosomes 13, 14, and 28, in addition to genome regions that were directly related to this trait, such as the EFCAB8 and VSTM2L genes, and to bone development (RHOU). Some SNPs were located in very close proximity to genes involved in basal metabolism (BLCAP, NNAT, CTNNBL1, TGM2, and LOC100296770) and the immune system (BPI).

Genetic associations between temperament and performance traits in Nellore beef cattle.

The aim of this study was to estimate genetic associations between temperament and performance traits. Temperament was evaluated in yearling male and female Nellore cattle, using four traits: temperament score (TS), for assessing animals' reactions in a corral pen (n = 25,691); movement score (MOV), for animals' movements recorded inside the crush; crush score (CS), for animal's general reactivity inside the crush; and flight speed (FS), for the speed (in m/s) at which the animals exited the crush (n = 11,697, for the last three methods); for all the temperament traits, lower scores indicate animals with calmer temperament. Performance traits were visual scores for conformation (C), finishing precocity (P) and muscling (M) evaluated at yearlings, and average daily gain (ADG) was estimated from weaning to yearling. Bayesian inference using Gibbs sampling was applied to estimate (co)variance components and genetic and phenotypic parameters. Heritability estimates for the temperament traits ranged from 0.07 (CS) to 0.28 (FS). Genetic correlations of the temperament traits with ADG and C, P and M were negative and ranged from -0.02 to -0.31. Phenotypic correlations were negative and consistently lower than the genetic, ranging from -0.08 to -0.02. It was concluded that the temperament traits assessed had favourable genetic correlation estimates with the performance traits studied. However, indirect responses in temperament when selecting for higher ADG and visual scoring system of C, P and M, will be low.