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

Genomic analysis of genotype-environment interaction in age at first calving of Murrah buffaloes

Age at first calving (AFC) is a measure of sexual maturity associated with the start of productive life of dairy animals. Additionally, a lower AFC reduces the generation interval and early culling of females. However, AFC has low heritability, making it a trait highly influenced by environmental factors. In this scenario, one way to improve the reproductive performance of buffalo cows is to select robust animals according to estimated breeding value (EBV) using models that include genotype-environment interaction (GEI) with the application of reaction norm models (RNMs). This can be achieved by understanding the genomic basis related to GEI of AFC. Thus, in this study, we aimed to predict EBV considering GEI via the RNM and identify candidate genes related to this component in dairy buffaloes through genome-wide association studies (GWAS). We used 1795 AFC records from three Murrah buffalo herds and formed environmental gradients (EGs) from contemporary group solutions obtained from genetic analysis of 270-day cumulative milk yield. Heritability estimates ranged from 0.15 to 0.39 along the EG. GWAS of the RNM slope parameter identified important genomic regions. The genomic window that explained the highest percentage of genetic variance of the slope (0.67%) was located on BBU1. After functional analysis, five candidate genes were detected, involved in two biological processes. The results suggested the existence of a GEI for AFC in Murrah buffaloes, with reclassification of animals when different environmental conditions were considered. The inclusion of genomic information increased the accuracy of breeding values for the intercept and slope of the reaction norm. GWAS analysis suggested that important genes associated with the AFC reaction norm slope were possibly also involved in biological processes related to lipid metabolism and immunity.

Validation of reaction norm breeding values for robustness in Australian sheep

BACKGROUND

There can be variation between animals in how stable their genetic merit is across different environments due to genotype-by-environment (G×E) interactions. This variation could be used in breeding programs to select robust genotypes that combine high overall performance with stable genetic ranking across environments. There have been few attempts to validate breeding values for robustness in livestock, although this is a necessary step towards their implementation in selection decisions. The objective of this study was to validate breeding values for the robustness of body weight across different growth environments that were estimated using reaction norm models in sheep data.

RESULTS

Using threefold cross-validation for the progeny of 337 sires, the average correlation between single-step breeding values for the reaction norm slope and the realised robustness of progeny across different growth environments was 0.21. The correlation between breeding values for the reaction slope estimated independently in two different datasets linked by common sires was close to the expected correlation based on theory.

CONCLUSIONS

Slope estimated breeding values (EBV) obtained using reaction norm models were predictive of the phenotypic robustness of progeny across different environments and were consistent for sires with progeny in two different datasets. Selection based on reaction norm EBV could be used to increase the robustness of a population to environmental variation.

Weighted genomic prediction for growth and carcass-related traits in Nelore cattle

This study aimed to assess the impact of differential weighting in genomic regions harboring candidate causal loci on the genomic prediction accuracy and dispersion for growth and carcass-related traits in Nelore cattle. The dataset contained 168 793 phenotypic records for adjusted weight at 450 days of age (W450), 83 624 for rib eye area (REA), 24 480 for marbling (MAR) and 82 981 for subcutaneous backfat thickness (BFT) and rump fat thickness (RFT). The pedigree harbored information from 244 254 animals born between 1977 and 2016, including 6283 sires and 50 742 dams. Animals (n = 7769) were genotyped with the low-density panel (Clarifide® Nelore 3.0), and the genotypes were imputed to a panel containing 735 044 markers. A linear animal model was applied to estimate the genetic parameters and to perform the weighted single-step genome-wide association study (WssGWAS). A total of seven models for genomic prediction were evaluated combining the SNP weights obtained in the iterations of the WssGWAS and the candidate QTL. The heritability estimated for W450 (0.35) was moderate, and for carcass-related traits, the estimates were moderate for REA (0.27), MAR (0.28) and RFT (0.28), and low for BFT (0.18). The prediction accuracy for W450 incorporating reported QTL previously described in the literature along with different SNPs weights was like those described for the default ssGBLUP model. The use of the ssGWAS to weight the SNP effects displayed limited advantages for the REA prediction accuracy. Comparing the ssGBLUP with the BLUP model, a meaningful improvement in the prediction accuracy from 0.09 to 0.63 (700%) was observed for MAR. The highest prediction accuracy was obtained for BFT and RFT in all evaluated models. The application of information obtained from the WssGWAS is an alternative to reduce the genomic prediction dispersion for growth and carcass-related traits, except for MAR. Furthermore, the results obtained herein pointed out that is possible to improve the prediction accuracy and reduce the genomic prediction dispersion for growth and carcass-related traits in young animals.

Genotype-environment interaction for milk production of Gyr cattle in Brazil and Colombia

BACKGROUND

Genotype by environment interactions (G × E) can play an important role in cattle populations and should be included in breeding programs in order to select the best animals for different environments.

OBJECTIVE

The aim of this study was to investigate the G × E for milk production of Gyr cattle in Brazil and Colombia by applying a reaction norm model used genomics information, and to identify genomic regions associated with milk production in the two countries.

METHODS

The Brazilian and Colombian database included 464 animals (273 cows and 33 sires from Brazil and 158 cows from Colombia) and 27,505 SNPs. A two-trait animal model was used for milk yield adjusted to 305 days in Brazil and Colombia as a function of country of origin, which included genomic information obtained with a single-step genomic reaction norm model. The GIBBS3F90 and POSTGSf90 programs were used.

RESULTS

The results obtained indicate G × E based on the reranking of bulls between Brazil and Colombia, demonstrating environmental differences between the two countries. The findings highlight the importance of considering the environment when choosing breeding animals in order to ensure the adequate performance of their progeny. Within this context, the reranking of bulls and the different SNPs associated with milk production in the two countries suggest that G × E is an important effect that should be included in the genetic evaluation of Dairy Gyr cattle in Brazil and Colombia.

CONCLUSION

The Gyr breeding program can be optimized by choosing a selection environment that will allow maximum genetic progress in milk production in different environments within and between countries.

Transcriptome Profiling of the Liver in Nellore Cattle Phenotypically Divergent for RFI in Two Genetic Groups

The identification and selection of genetically superior animals for residual feed intake (RFI) could enhance productivity and minimize environmental impacts. The aim of this study was to use RNA-seq data to identify the differentially expressed genes (DEGs), known non-coding RNAs (ncRNAs), specific biomarkers and enriched biological processes associated with RFI of the liver in Nellore cattle in two genetic groups. In genetic group 1 (G1), 24 extreme RFI animals (12 low RFI (LRFI) versus 12 high RFI (HRFI)) were selected from a population of 60 Nellore bulls. The RNA-seq of the samples from their liver tissues was performed using an Illumina HiSeq 2000. In genetic group 2 (G2), 20 samples of liver tissue of Nellore bulls divergent for RFI (LRFI, n = 10 versus HRFI, n = 10) were selected from 83 animals. The raw data of the G2 were chosen from the ENA repository. A total of 1811 DEGs were found for the G1 and 2054 for the G2 (p-value ≤ 0.05). We detected 88 common genes in both genetic groups, of which 33 were involved in the immune response and in blocking oxidative stress. In addition, seven (B2M, ADSS, SNX2, TUBA4A, ARHGAP18, MECR, and ABCF3) possible gene biomarkers were identified through a receiver operating characteristic analysis (ROC) considering an AUC > 0.70. The B2M gene was overexpressed in the LRFI group. This gene regulates the lipid metabolism protein turnover and inhibits cell death. We also found non-coding RNAs in both groups. MIR25 was up-regulated and SNORD16 was down-regulated in the LRFI for G1. For G2, up-regulated RNase_MRP and SCARNA10 were found. We highlight MIR25 as being able to act by blocking cytotoxicity and oxidative stress and RMRP as a blocker of mitochondrial damage. The biological pathways associated with RFI of the liver in Nellore cattle in the two genetic groups were for energy metabolism, protein turnover, redox homeostasis and the immune response. The common transcripts, biomarkers and metabolic pathways found in the two genetic groups make this unprecedented work even more relevant, since the results are valid for different herds raised in different ways. The results reinforce the biological importance of these known processes but also reveal new insights into the complexity of the liver tissue transcriptome of Nellore cattle.

Heteroscedastic Reaction Norm Models Improve the Assessment of Genotype by Environment Interaction for Growth, Reproductive, and Visual Score Traits in Nellore Cattle

The assessment of the presence of genotype by environment interaction (GxE) in beef cattle is very important in tropical countries with diverse climatic conditions and production systems. The present study aimed to assess the presence of GxE by using different reaction norm models for eleven traits related to growth, reproduction, and visual score in Nellore cattle. We studied five reaction norm models (RNM), fitting a linear model considering homoscedastic residual variance (RNM_homo), and four models considering heteroskedasticity, being linear (RNM_hete), quadratic (RNM_quad), linear spline (RNM_l-l), and quadratic spline (RNM_q-q). There was the presence of GxE for age at first calving (AFC), scrotal circumference (SC), weaning to yearling weight gain (WYG), and yearling weight (YW). The best models were RNM_l-l for YW and RNM_q-q for AFC, SC, and WYG. The heritability estimates for RNM_l-l ranged from 0.07 to 0.20, 0.42 to 0.61, 0.24 to 0.42, and 0.47 to 0.63 for AFC, SC, WYG, and YW, respectively. The heteroskedasticity in reaction norm models improves the assessment of the presence of GxE for YW, WYG, AFC, and SC. Additionally, the trajectories of reaction norms for these traits seem to be affected by a non-linear component, and selecting robust animals for these traits is an alternative to increase production and reduce environmental sensitivity.

A dynamic concept of animal welfare: The role of appetitive and adverse internal and external factors and the animal’s ability to adapt to them

Animal welfare is a multifaceted issue that can be approached from different viewpoints, depending on human interests, ethical assumptions, and culture. To properly assess, safeguard and promote animal welfare, concepts are needed to serve as guidelines in any context the animal is kept in. Several different welfare concepts have been developed during the last half decade. The Five Freedoms concept has provided the basis for developing animal welfare assessment to date, and the Five Domains concept has guided those responsible for safeguarding animal welfare, while the Quality of Life concept focuses on how the individual perceives its own welfare state. This study proposes a modified and extended version of an earlier animal welfare concept - the Dynamic Animal Welfare Concept (DAWCon). Based on the adaptability of the animal, and taking the importance of positive emotional states and the dynamic nature of animal welfare into account, an individual animal is likely in a positive welfare state when it is mentally and physically capable and possesses the ability and opportunity to react adequately to sporadic or lasting appetitive and adverse internal and external stimuli, events, and conditions. Adequate reactions are elements of an animal’s normal behavior. They allow the animal to cope with and adapt to the demands of the (prevailing) environmental circumstances, enabling it to reach a state that it perceives as positive, i.e., that evokes positive emotions. This paper describes the role of internal as well as external factors in influencing welfare, each of which exerts their effects in a sporadic or lasting manner. Behavior is highlighted as a crucial read-out parameter. As most animals under human care are selected for certain traits that may affect their behavioral repertoire it is crucial to have thorough ethograms, i.e., a catalogue of specific behaviors of the species/strain/breed under study. DAWCon highlights aspects that need to be addressed when assessing welfare and may stimulate future research questions.

Weak genotype x environment interaction suggests that measuring scrotal circumference at 12 and 18 months of age is helpful to select precocious Brahman cattle

The aim of this study was to evaluate the genotype x environment interaction (GxE) for scrotal circumference (SC) measured at different ages using pedigree-based (A -1) and pedigree and genomic-based (H -1) relationship matrices. Data from 1,515 Brahman bulls, from the Cooperative Research Centre for Beef Genetic Technologies (Beef CRC) experimental dataset were used in this study. SC was adjusted to age and body weight measured at 6 months (SC6), 12 months (SC12), 18 months (SC18) and 24 months of age (SC24). Body weight (BW) measured at 6 months (BW6), 12 months (BW12), 18 months (BW18) and 24 months of age (BW24) were used as criteria to describe the environment for SC in each age. All the animals measured were genotyped using medium-density SNP chips ("50k" or "70k" SNP) and their genotype were imputed using a reference panel with 729,068 SNP. The environment gradient (EG) was obtained by standardizing the solutions of the contemporary groups obtained by Animal Model with BW as the dependent variable. Then, the reaction norms (RN) were determined through a Random Regression Model. The breeding values (EBV) were estimated using either A -1 or H -1. The rank correlation was obtained using Spearman's correlation among the EBV estimated for the traits in analysis. For SC6 and SC24, higher estimates of heritability (h²) were obtained using A -1, when compared to those observed with H -1. In those ages, the improvement of the environment decreases the h² coefficient. On the other hand, the h² for SC12 and SC18 increased as the environment became more favorable, regardless of the matrix used. The RN for SC6 and SC24 estimated using A -1 and H -1 showed a decrease of variance from the worst to the best environment, an indication of existence of GxE. On the other hand, for SC12 and SC18, there were no significant differences between the EBV estimated in the lower and in the higher environments, regardless of the kinship matrix used, suggesting absence of GxE on those ages. Spearman's correlation among EBV estimated using A -1 and H -1 in different EG were practically equal to unity for all traits evaluated. In our study, there was weak evidence of GxE effect on SC in ages suitable for selection for sexual precocity. So, the absence of GxE at 12 and 18 months means these ages are advantageous for measuring SC to selection for sexual precocity. The advantage is that no changes in classification were observed when the sires were evaluated in different environments.

Genomic analysis of the slope of the reaction norm for body weight in Australian sheep

Background

Selection of livestock based on their robustness or sensitivity to environmental variation could help improve the efficiency of production systems, particularly in the light of climate change. Genetic variation in robustness arises from genotype-by-environment (G × E) interactions, with genotypes performing differently when animals are raised in contrasted environments. Understanding the nature of this genetic variation is essential to implement strategies to improve robustness. In this study, our aim was to explore the genetics of robustness in Australian sheep to different growth environments using linear reaction norm models (RNM), with post-weaning weight records of 22,513 lambs and 60 k single nucleotide polymorphisms (SNPs). The use of scale-corrected genomic estimated breeding values (GEBV) for the slope to account for scale-type G × E interactions was also investigated.

Results

Additive genetic variance was observed for the slope of the RNM, with genetic correlations between low- and high-growth environments indicating substantial re-ranking of genotypes (0.44–0.49). The genetic variance increased from low- to high-growth environments. The heritability of post-weaning body weight ranged from 0.28 to 0.39. The genetic correlation between intercept and slope of the reaction norm for post-weaning body weight was low to moderate when based on the estimated (co)variance components but was much higher when based on back-solved SNP effects. An initial analysis suggested that a region on chromosome 11 affected both the intercept and the slope, but when the GEBV for the slope were conditioned on the GEBV for the intercept to remove the effect of scale-type G × E interactions on SNP effects for robustness, a single genomic region on chromosome 7 was found to be associated with robustness. This region included genes previously associated with growth traits and disease susceptibility in livestock.

Conclusions

This study shows a significant genetic variation in the slope of RNM that could be used for selecting for increased robustness of sheep. Both scale-type and rank-type G × E interactions contributed to variation in the slope. The correction for scale effects of GEBV for the slope should be considered when analysing robustness using RNM. Overall, robustness appears to be a highly polygenic trait.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00734-6.

Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms

BACKGROUND

There is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction (total number of piglets born, TNB; total number of piglets born alive, NBA; total number of piglets weaned, NW), growth (weaning weight, WW; off-test weight, OW), and body composition (ultrasound muscle depth, MD; ultrasound backfat thickness, BF) traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait.

RESULTS

The average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs.

CONCLUSIONS

We provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs.

Genotype-by-environment interaction in Holstein heifer fertility traits using single-step genomic reaction norm models

Background

The effect of heat stress on livestock production is a worldwide issue. Animal performance is influenced by exposure to harsh environmental conditions potentially causing genotype-by-environment interactions (G × E), especially in highproducing animals. In this context, the main objectives of this study were to (1) detect the time periods in which heifer fertility traits are more sensitive to the exposure to high environmental temperature and/or humidity, (2) investigate G × E due to heat stress in heifer fertility traits, and, (3) identify genomic regions associated with heifer fertility and heat tolerance in Holstein cattle.

Results

Phenotypic records for three heifer fertility traits (i.e., age at first calving, interval from first to last service, and conception rate at the first service) were collected, from 2005 to 2018, for 56,998 Holstein heifers raised in 15 herds in the Beijing area (China). By integrating environmental data, including hourly air temperature and relative humidity, the critical periods in which the heifers are more sensitive to heat stress were located in more than 30 days before the first service for age at first calving and interval from first to last service, or 10 days before and less than 60 days after the first service for conception rate. Using reaction norm models, significant G × E was detected for all three traits regarding both environmental gradients, proportion of days exceeding heat threshold, and minimum temperature-humidity index. Through single-step genome-wide association studies, PLAG1, AMHR2, SP1, KRT8, KRT18, MLH1, and EOMES were suggested as candidate genes for heifer fertility. The genes HCRTR1, AGRP, PC, and GUCY1B1 are strong candidates for association with heat tolerance.

Conclusions

The critical periods in which the reproductive performance of heifers is more sensitive to heat stress are trait-dependent. Thus, detailed analysis should be conducted to determine this particular period for other fertility traits. The considerable magnitude of G × E and sire re-ranking indicates the necessity to consider G × E in dairy cattle breeding schemes. This will enable selection of more heat-tolerant animals with high reproductive efficiency under harsh climatic conditions. Lastly, the candidate genes identified to be linked with response to heat stress provide a better understanding of the underlying biological mechanisms of heat tolerance in dairy cattle.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07496-3.

Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single-step genomic reaction norm model

Economically important traits are usually complex traits influenced by genes, environment and genotype-by-environment (G × E) interactions. Ignoring G × E interaction could lead to bias in the estimation of breeding values and selection decisions. A total of 1,778 pigs were genotyped using the PorcineSNP80 BeadChip. The existence of G × E interactions was investigated using a single-step reaction norm model for growth traits of days to 100 kg (AGE) and backfat thickness adjusted to 100 kg (BFT), based on a pedigree-based relationship matrix (A) or a genomic-pedigree joint relationship matrix (H). In the reaction norm model, the herd-year-season effect was measured as the environmental variable (EV). Our results showed no G × E interactions for AGE, but for BFT. For both AGE and BFT, the genomic reaction norm model (H) produced more accurate predictions than the conventional reaction norm model (A). For BFT, the accuracies were greater based on the reaction norm model than those based on the reduced model without exploiting G × E interaction, with EV ranging from 0.5 to 1, and accuracy increasing by 3.9% and 4.6% in the reaction norm model based on A and H matrices, respectively, while reaction norm model yielded approximately 8.4% and 7.9% lower accuracy for EVs ranging from 0 to 0.4, based on A and H matrices, respectively. In addition, for BFT, the highest accuracy was obtained in the BJLM6 farm for realizing directional selection. This study will help to apply G × E interactions to practical genomic selection.

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.

Evaluation of the sensitivity of pulmonary arterial pressure to elevation using a reaction norm model in Angus Cattle

Pulmonary arterial pressure (PAP) is a diagnostic measure used to determine an individual's susceptibility to developing high-altitude disease. The importance of PAP measures collected at elevations lower than the intended breeding elevation of the bulls (i.e., ≥1,520 m) is unknown. Therefore, the objective of this study was to determine the genetic relationship between PAP measures collected in a range of elevations using reaction norm models. A total of 9,177 PAP and elevation observations on purebred Angus cattle, which averaged 43.49 ± 11.32 mmHg and 1,878.6 ± 296.8 m, respectively, were used in the evaluation. The average age of the individuals in the evaluation was 434.04 ± 115.9 d. A random regression model containing the effects of sex, a linear covariate of age, a quadratic fixed covariate of elevation, and random effects consisting of a contemporary group and a linear regression of PAP on elevation was used for the evaluation of PAP. Two forms of PAP were evaluated with this model. First, to address the non-normality of the data, PAP was raised to the power of -2.6 (ptPAP) based on the results of a Box-Cox analysis. Second, raw PAP (rPAP) phenotypes were evaluated to compare the results to those obtained from the transformed data. For ptPAP, heritability ranged from 0.25 to 0.37 corresponding to elevations of 1,900 and 1,215 m, respectively. For rPAP, heritability ranged from 0.22 to 0.41 corresponding to elevations of 1,700 and 2,495 m, respectively. Generally, lower elevations corresponded to decreased heritabilities while higher elevations corresponded to increased heritability estimates. For ptPAP, genetic correlations ranged from 0.18 (elevation: 1,215 and 2,495 m) to 1.00. For rPAP, genetic correlations ranged from 0.08 (elevation: 1,215 and 2,495 m) to 1.00. In general, the closer the elevations in which PAP was measured, the greater the genetic relationship. The greater the difference in elevation between PAP measures resulted in lower genetic correlations. The rank correlation between expected progeny differences (EPD) for 1,215 and 2,495 m was 0.65 and 0.49 for the ptPAP and rPAP, respectively. These results suggested that PAP measures collected in lower elevations may be used as an indicator of high-altitude adaptability. In the estimation of EPD to rank sires for their suitability for use in high-elevation production systems, it is important to account for the relationships among varied altitudes.

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.

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.

Unraveling genetic sensitivity of beef cattle to environmental variation under tropical conditions

BACKGROUND

Selection of cattle that are less sensitive to environmental variation in unfavorable environments and more adapted to harsh conditions is of primary importance for tropical beef cattle production systems. Understanding the genetic background of sensitivity to environmental variation is necessary for developing strategies and tools to increase efficiency and sustainability of beef production. We evaluated the degree of sensitivity of beef cattle performance to environmental variation, at the animal and molecular marker levels (412 K single nucleotide polymorphisms), by fitting and comparing the results of different reaction norm models (RNM), using a comprehensive dataset of Nellore cattle raised under diverse environmental conditions.

RESULTS

Heteroscedastic RNM (with different residual variances for environmental level) provided better fit than homoscedastic RNM. In addition, spline and quadratic RNM outperformed linear RNM, which suggests the existence of a nonlinear genetic component affecting the performance of Nellore cattle. This nonlinearity indicates that within-animal sensitivity depends on the environmental gradient (EG) level and that animals may present different patterns of sensitivity according to the range of environmental variations. The spline RNM showed that sensitivity to environmental variation from harsh to average EG is lowly correlated with sensitivity from average to good EG, at both the animal and molecular marker levels. Although the genomic regions that affect sensitivity in harsher environments were not the same as those associated with less challenging environments, the candidate genes within those regions participate in common biological processes such as those related to inflammatory and immune response. Some plausible candidate genes were identified.

CONCLUSIONS

Sensitivity of tropical beef cattle to environmental variation is not continuous along the environmental gradient, which implies that animals that are less sensitive to harsher conditions are not necessarily less responsive to variations in better environmental conditions, and vice versa. The same pattern was observed at the molecular marker level, i.e. genomic regions and, consequently, candidate genes associated with sensitivity to harsh conditions were not the same as those associated with sensitivity to less challenging conditions.

Genotype by environment interaction for female fertility traits under conventional and organic production systems in Danish Holsteins

Conventional and organic production systems mainly differ in feeding strategies, outdoor and pasture access, and the use of antibiotic treatments. These environmental differences could lead to a genotype by environment interaction (G × E) and a requirement for including G × E in breeding decisions. The objectives of this study were to estimate variance components and heritabilities for conventional and organic production systems and investigate G × E under these 2 production systems for female fertility traits in Danish Holsteins. The analyzed traits included the interval from calving to first insemination (ICF), the interval from first to last insemination, number of inseminations per conception (NINS), and non-return rate within 56 d after the first insemination. Records of female fertility in heifers and the first 3 lactations in cows as well as grass ratio of feed at herd level were collected during the period from 2011 to 2016. The performances of a trait in heifers and cows (lactation 1 to 3) were considered as different traits. The (co)variance components and the resulting heritabilities and genetic correlations were estimated using 2 models. One was a bivariate model treating performances of a trait under organic and conventional production systems as 2 different traits using a reduced data set, and the other was a reaction norm model with random regression on the production system and the grass ratio of feed using a full data set. The full data set comprised records of 37,836 females from 112 organic herds and 513,599 females from 1,224 conventional herds, whereas the reduced data set comprised records from all these 112 organic herds and 92,696 females from 185 convention herds extracted from the full data set with grass ratio of feed lower than 0.20. All female fertility performances of the organic production system were superior to those of the conventional production system. Besides, heterogeneities in additive genetic variances and heritabilities were observed between conventional and organic production systems for all traits. Furthermore, genetic correlations between these 2 production systems ranged from 0.607 to 1.000 estimated from bivariate models and from 0.848 to 0.999 estimated from reaction norm models. Statistically significant G × E were observed for NINS in heifers, non-return rate within 56 d after the first insemination in heifers, and ICF from the bivariate model, and for ICF and NINS in cows from the reaction norm model.

Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model

Genotype-by-environment (G × E) interactions could play an important role in cattle populations, and it should be considered in breeding programmes to select the best sires for different environments. The objectives of this study were to study G × E interactions for female fertility traits in the Danish Holstein dairy cattle population using a reaction norm model (RNM), and to detect the particular genomic regions contributing to the performance of these traits and the G × E interactions. In total 4534 bulls were genotyped by an Illumina BovineSNP50 BeadChip. An RNM with a pedigree-based relationship matrix and a pedigree-genomic combined relationship matrix was used to explore the existence of G × E interactions. In the RNM, the environmental gradient (EG) was defined as herd effect. Further, the genomic regions affecting interval from calving to first insemination (ICF) and interval from first to last insemination (IFL) were detected using single-step genome-wide association study (ssGWAS). The genetic correlations between extreme EGs indicated that G × E interactions were sizable for ICF and IFL. The genomic RNM (pedigree-genomic combined relationship matrix) had higher prediction accuracy than the conventional RNM (pedigree-based relationship matrix). The top genomic regions affecting the slope of the reaction norm included immunity-related genes (IL17, IL17F and LIF), and growth-related genes (MC4R and LEP), while the top regions influencing the intercept of the reaction norm included fertility-related genes such as EREG, AREG and SMAD4. In conclusion, our findings validated the G × E interactions for fertility traits across different herds and were helpful in understanding the genetic background of G × E interactions for these traits.

CLUSTERS DE ROBUSTEZ COMO CRITÉRIO DE SELEÇÃO NO MELHORAMENTO GENÉTICO PARA MITIGAÇÃO DE IMPACTOS DAS MUDANÇAS CLIMÁTICAS

Mudanças climáticas são previstas para as próximas décadas e, consequentemente, seus impactos na pecuária bovina, sendo a seleção nos rebanhos uma maneira de amenizá-los. Este trabalho teve como objetivo desenvolver um sistema de seleção baseado nos parâmetros genéticos gerados por modelos de norma de reação adaptativa em bovinos da raça Nelore. Foram utilizados dados genealógicos e de crescimento fornecidos pela Associação Brasileira de Criadores de Bovinos. Definiu-se um gradiente ambiental baseado em valores médios de grupos contemporâneos padronizados. Para a predição de coeficientes das normas de reação adaptativas utilizou-se a regressão aleatória com polinômios cúbicos para pesos aos 450 dias com análise de sexos separados. Foram calculados os valores genéticos dos diferentes indivíduos em função de um gradiente ambiental utilizando o software BLUPF90. Os indivíduos foram classificados considerando coeficientes que gerassem normas com valores genéticos elevados e com menor variação ao longo do gradiente ambiental. Compensou-se, então, a elevação do valor genético e a sua robustez, criando clusters de robustez (CRs) com base na comparação direta entre os coeficientes. Os resultados da classificação mostraram que a seleção de indivíduos das classes de maior robustez devem gerar progênies com menor sensibilidade ambiental, visto que os coeficientes são componentes genéticos aditivos. Conclui-se que a seleção por clusters de robustez é uma forma de amenizar os impactos produzidos nos sistemas de produção por alterações nos ambientes de criação.