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-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.

Differential expression of immune response genes associated with subclinical mastitis in dairy buffaloes.

Buffalo milk production has become of significant importance on the world scale, however, there are few studies involving biotechnological tools specifically for buffalo. To verify the effects caused by subclinical mastitis on the components of milk and to study the innate immune system in the udder of dairy buffaloes with subclinical mastitis, we evaluated the levels of expression of the lactoferrin (LTF), tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1ß), interleukin-8 (IL-8), and toll-like receptors 2 (TLR-2) and 4 (TLR-4) genes in buffaloes with and without subclinical mastitis. Milk samples were collected for the determination of milk components: somatic cell score (SCS), fat, protein, lactose, total solids and solids-not-fat (SNF), as well as for RNA extraction of milk cells, complementary DNA synthesis, and expression profile quantification by quantitative real-time PCR. For gene expression, the ΔΔCt was estimated using contrasts of the target genes expression adjusted for the expression of the housekeeping genes between both groups. Linear regression analysis was performed to determine the relationship between the genes studied and the milk components. Subclinical mastitis induced changes in the fat, lactose and SNF in milk of buffaloes, and the messenger RNA abundance was upregulated for TLR-2, TLR-4, TNF-α, IL-1ß and IL-8 genes in milk cells of buffaloes with subclinical mastitis, whereas the LTF gene was not differentially expressed. Results of linear regression analysis showed that TLR-2 gene expression most explains the variation in SCS, and the change in a unit of ΔCt of the TNF-α gene would result in a higher increase in SCS. The study of these immune function genes that are active in the mammary gland is important to characterize the action mechanism of the innate immunity that occurs in subclinical mastitis in dairy buffaloes and may aid the development of strategies to preserve the health of the udder.

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.

Reaction norm for yearling weight in beef cattle using single-step genomic evaluation.

When the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G × E). The main objective of this study was to evaluate the existence of G × E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals' future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments.

Genome-wide association study for growth traits in Nelore cattle.

The objective of this study was to investigate the association of single nucleotide polymorphisms (SNPs) with birth weight, weight gain from birth to weaning and from weaning to yearling, yearling height and cow weight in Nelore cattle. Data from 5064 animals participating in the DeltaGen and PAINT breeding programs were used. The animals were genotyped with a panel of 777 962 SNPs (Illumina BovineHD BeadChip) and 412 993 SNPs remained after quality control analysis of the genomic data. A genome-wide association study was performed using a single-step methodology. The analyses were processed with the BLUPF90 family of programs. When applied to a genome-wide association studies, the single-step GBLUP methodology is an iterative process that estimates weights for the SNPs. The weights of SNPs were included in all analyses by iteratively applying the single-step GBLUP methodology and repeated twice so that the effect of the SNP and the effect of the animal were recalculated in order to increase the weight of SNPs with large effects and to reduce the weight of those with small effects. The genome-wide association results are reported based on the proportion of variance explained by windows of 50 adjacent SNPs. Considering the two iterations, only windows with an additive genetic variance >1.5% were presented in the results. Associations were observed with birth weight on BTA 14, with weight gain from birth to weaning on BTA 5 and 29, with weight gain from weaning to yearling on BTA 11, and with yearling height on BTA 8, showing the genes TMEM68 (transmembrane protein 8B) associated with birth weight and yearling height, XKR4 (XK, Kell blood group complex subunit-related family, member 4) associated with birth weight, NPR2 (natriuretic peptide receptor B) associated with yearling height, and REG3G (regenerating islet-derived 3-gamma) associated with weight gain from weaning to yearling. These genes play an important role in feed intake, weight gain and the regulation of skeletal growth.

Evaluation of longevity modeling censored records in Nellore.

The aim of the present study was to evaluate the prediction ability of models that cope with longevity phenotypic expression as uncensored and censored in Nellore cattle. Longevity was defined as the difference between the dates of last weaned calf and cow birth. There were information of 77 353 females, being 61 097 cows with uncensored phenotypic information and 16 256 cows with censored records. These data were analyzed considering three different models: (1) Gaussian linear model (LM), in which only uncensored records were considered; and two models that consider both uncensored and censored records: (2) Censored Gaussian linear model (CLM); and (3) Weibull frailty hazard model (WM). For the model prediction ability comparisons, the data set was randomly divided into training and validation sets, containing 80% and 20% of the records, respectively. There were considered 10 repetitions applying the following restrictions: (a) at least three animals per contemporary group in the training set; and (b) sires with more than 10 progenies with uncensored records (352 sires) should have daughters in the training and validation sets. The variance components estimated using the whole data set in each model were used as true values in the prediction of breeding values of the animals in the training set. The WM model showed the best prediction ability, providing the lowest χ 2 average and the highest number of sets in which a model had the smallest value of χ 2 statistics. The CLM and LM models showed prediction abilities 2.6% and 3.7% less efficient than WM, respectively. In addition, the accuracies of sire breeding values for LM and CLM were lower than those obtained for WM. The percentages of bulls in common, considering only 10% of sires with the highest breeding values, were around 75% and 54%, respectively, between LM-CLM and LM-WM models, considering all sires, and 75% between LM-CLM and LM-WM, when only sires with more than 10 progenies with uncensored records were taken into account. These results are indicative of reranking of animals in terms of genetic merit between LM, CLM and WM. The model in which censored records of longevity were excluded from the analysis showed the lowest prediction ability. The WM provides the best predictive performance, therefore this model would be recommended to perform genetic evaluation of longevity in this population.

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.

Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population.

Animal feeding is the most important economic component of beef production systems. Selection for feed efficiency has not been effective mainly due to difficult and high costs to obtain the phenotypes. The application of genomic selection using SNP can decrease the cost of animal evaluation as well as the generation interval. The objective of this study was to compare methods for genomic evaluation of feed efficiency traits using different cross-validation layouts in an experimental beef cattle population genotyped for a high-density SNP panel (BovineHD BeadChip assay 700k, Illumina Inc., San Diego, CA). After quality control, a total of 437,197 SNP genotypes were available for 761 Nelore animals from the Institute of Animal Science, Sertãozinho, São Paulo, Brazil. The studied traits were residual feed intake, feed conversion ratio, ADG, and DMI. Methods of analysis were traditional BLUP, single-step genomic BLUP (ssGBLUP), genomic BLUP (GBLUP), and a Bayesian regression method (BayesCπ). Direct genomic values (DGV) from the last 2 methods were compared directly or in an index that combines DGV with parent average. Three cross-validation approaches were used to validate the models: 1) YOUNG, in which the partition into training and testing sets was based on year of birth and testing animals were born after 2010; 2) UNREL, in which the data set was split into 3 less related subsets and the validation was done in each subset a time; and 3) RANDOM, in which the data set was randomly divided into 4 subsets (considering the contemporary groups) and the validation was done in each subset at a time. On average, the RANDOM design provided the most accurate predictions. Average accuracies ranged from 0.10 to 0.58 using BLUP, from 0.09 to 0.48 using GBLUP, from 0.06 to 0.49 using BayesCπ, and from 0.22 to 0.49 using ssGBLUP. The most accurate and consistent predictions were obtained using ssGBLUP for all analyzed traits. The ssGBLUP seems to be more suitable to obtain genomic predictions for feed efficiency traits on an experimental population of genotyped animals.

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.

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.

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.

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.

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).

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.