Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models

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.

Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review

Understanding the biological mechanisms underlying tick resistance in cattle holds the potential to facilitate genetic improvement through selective breeding. Genome wide association studies (GWAS) are popular in research on unraveling genetic determinants underlying complex traits such as tick resistance. To date, various studies have been published on single nucleotide polymorphisms (SNPs) associated with tick resistance in cattle. The discovery of SNPs related to tick resistance has led to the mapping of associated candidate genes. Despite the success of these studies, information on genetic determinants associated with tick resistance in cattle is still limited. This warrants the need for more studies to be conducted. In Africa, the cost of genotyping is still relatively expensive; thus, conducting GWAS is a challenge, as the minimum number of animals recommended cannot be genotyped. These population size and genotype cost challenges may be overcome through the establishment of collaborations. Thus, the current review discusses GWAS as a tool to uncover SNPs associated with tick resistance, by focusing on the study design, association analysis, factors influencing the success of GWAS, and the progress on cattle tick resistance studies.

Production, reproduction and some adaptation characteristics of Boran cattle breed under changing climate: A systematic review and meta-analysis

Introduction

Climate change affects livestock production and productivity, which could threaten livestock-based food security in pastoral and agro-pastoral production systems of the tropics and sub-tropics. Boran cattle breed is one of the hardiest Zebu cattle reared by Borana Oromo pastoralists for milk and meat production. However, there is limited comprensive information on production, reproduction and adaption traits of the Boran cattle in Ethiopia. Thus, this paper aims to compile the main production, reproduction and some adaptation traits of Boran cattle based on systematic review and meta-analysis of peer reviewed published and unpublished literature.

Methodology

A combination of systematic review and meta-analysis based on PRISMA guideline was employed. Accordingly, out of 646 recorded articles identified through database searching, 64 were found to be eligible for production, reproduction and adaptation characteristics of the Boran cattle, 28 articles were included in qualitative systematic review while 36 articles were used for quantitative meta-analysis.

Result

The Boran cattle breed has the ability to survive, produce and reproduce under high ambient temperature, utilize low quality forage resources, and resist water shortage or long watering intervals and tick infestations. The review revealed that the breed employs various adaptation responses (morphological, physiological, biochemical, metabolic, cellular and molecular responses) to cope with harsh environmental conditions including climate change, rangeland degradation, seasonal feed and water shortages and high incidences of tick infestations. The meta-analysis using a random-effects model allowed provision of pooled estimates of heritability and genetic correlations for reproduction and production traits, which could be used to solve genetic prediction equations under a population level in purebred Boran cattle. In addition, heritability and genetic-correlation estimates found in the present study suggest that there is high genetic variability for most traits in Boran cattle, and that genetic progress is possible for all studied traits in this breed.

Conclusion

The Boran cattle breed has the ability to survive, produce and reproduce under high ambient temperature, utilize low quality forage resources, and resist water shortage or long watering intervals and tick infestations. However, currently there are several challenges such as recurrent droughts, pasture deterioration and lack of systematic selection and breeding programs that play to undermine the realization of the potential of the breed. Thus, we recommend systematic selection for enhancing the reproductive and production performances without compromising the adaptation traits of the breed coupled with improved management of rangelands.

Different selection practices affect the environmental sensitivity of beef cattle

The objective of the present study was to evaluate the effects of different selection practices on the environmental sensitivity of reproductive and growth traits in males and females of three Nellore selection lines [control (NeC), selection (NeS), and traditional (NeT) lines]. Moreover, genetic trends for the intercept and slope were estimated for each line, and the possible reranking of sires was examined. A total of 8,757 records of selection weight (SW), 3,331 records of scrotal circumference (SC), and 2,311 records of days to first calving (DFC) from Nellore cattle born between 1981 and 2017 were analyzed. (Co)variance components and genetic parameters of all traits were estimated using a reaction norm model with Gibbs sampler. In all cattle lines, the mean heritability of the studied traits ranged from 0.39 to 0.75 for SW in both males and females, from 0.46 to 0.68 for SC, and from 0.06 to 0.57 for DFC along with the environmental descriptor. In all cattle lines, the genetic correlation coefficients between the intercept and slope ranged from 0.03 to 0.81 for SW, from -0.14 to 0.39 for SC, and from -0.87 to -0.42 for DFC. Genetic trends for the slope and proportion of plastic genotypes indicated that the NeS line was more responsive to environmental changes, whereas the NeC and NeT lines tended to respond more modestly. Reranking of sires was observed for all traits, specifically in the NeC and NeT lines, because of the weak correlation between the opposite extreme environments. In the NeS line, reranking of sires was observed for DFC alone. Our results indicate that the effects of genotype-environment interaction are important and should be considered in genetic evaluations of Nellore cattle. Moreover, different selection practices affected the environmental sensitivity of the Nellore selection lines tested in this study.

Random regression for modeling yield genetic trajectories in Jatropha curcas breeding

Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.

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-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines ([Formula: see text]) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy.

Bovine Immune Factors Underlying Tick Resistance: Integration and Future Directions

The mechanisms underlying tick resistance within and between cattle breeds have been studied for decades. Several previous papers on bovine immune parameters contributing to tick resistance discussed findings across DNA, RNA, protein, cellular, and tissue levels. However, the differences between bovine host species, tick species and the experimental layouts were not always taken into account. This review aims to (a) give a comprehensive summary of studies investigating immune marker differences between cattle breeds with varying degrees of tick resistance, and (b) to integrate key findings and suggest hypotheses on likely immune-regulated pathways driving resistance. Experimental issues, which may have skewed conclusions, are highlighted. In future, improved experimental strategies will enable more focused studies to identify and integrate immune markers and/or pathways. Most conclusive thus far is the involvement of histamine, granulocytes and their associated pathways in the tick-resistance mechanism. Interestingly, different immune markers might be involved in the mechanisms within a single host breed in contrast to between breeds. Also, differences are evident at each tick life stage, limiting the level to which datasets can be compared. Future studies to further elucidate immune molecule dynamics across the entire tick life cycle and in-depth investigation of promising markers and pathways on both molecular and cellular level are in dire need to obtain a scientifically sound hypothesis on the drivers of tick resistance.

Analyses of reaction norms reveal new chromosome regions associated with tick resistance in cattle

Despite single nucleotide polymorphism (SNP) availability and frequent cost reduction has allowed genome-wide association studies even in complex traits as tick resistance, the use of this information source in SNP by environment interaction context is unknown for many economically important traits in cattle. We aimed at identifying putative genomic regions explaining differences in tick resistance in Hereford and Braford cattle under SNP by environment point of view as well as to identify candidate genes derived from outliers/significant markers. The environment was defined as contemporary group means of tick counts, since they seemed to be the most appropriate entities to describe the environmental gradient in beef cattle. A total of 4363 animals having tick counts (n=10 673) originated from 197 sires and 3966 dams were used. Genotypes were acquired on 3591 of these cattle. From top 1% SNPs (410) having the greatest effects in each environment, 75 were consistently relevant in all environments, which indicated SNP by environment interaction. The outliers/significant SNPs were mapped on chromosomes 1, 2, 5, 6, 7, 9, 11, 13, 14, 15, 16, 18, 21, 23, 24, 26 and 28, and potential candidate genes were detected across environments. The presence of SNP by environment interaction for tick resistance indicates that genetic expression of resistance depends upon tick burden. Markers with major portion of genetic variance explained across environments appeared to be close to genes with different direct or indirect functions related to immune system, inflammatory process and mechanisms of tissue destruction/repair, such as energy metabolism and cell differentiation.

Evolution of phenotypic plasticity in extreme environments

Phenotypic plasticity, if adaptive, may allow species to counter the detrimental effects of extreme conditions, but the infrequent occurrence of extreme environments and/or their restriction to low-quality habitats within a species range means that they exert little direct selection on reaction norms. Plasticity could, therefore, be maladaptive under extreme environments, unless genetic correlations are strong between extreme and non-extreme environmental states, and the optimum phenotype changes smoothly with the environment. Empirical evidence suggests that populations and species from more variable environments show higher levels of plasticity that might preadapt them to extremes, but genetic variance for plastic responses can also be low, and genetic variation may not be expressed for some classes of traits under extreme conditions. Much of the empirical literature on plastic responses to extremes has not yet been linked to ecologically relevant conditions, such as asymmetrical fluctuations in the case of temperature extremes. Nevertheless, evolved plastic responses are likely to be important for natural and agricultural species increasingly exposed to climate extremes, and there is an urgent need to collect empirical information and link this to model predictions.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'.

Gene networks for total number born in pigs across divergent environments

For reproductive traits such as total number born (TNB), variance due to different environments is highly relevant in animal breeding. In this study, we aimed to perform a gene-network analysis for TNB in pigs across different environments using genomic reaction norm models. Thus, based on relevant single-nucleotide polymorphisms and linkage disequilibrium blocks across environments obtained from GWAS, different sets of candidate genes having biological roles linked to TNB were identified. Network analysis across environment levels resulted in gene interactions consistent with known mammal's fertility biology, captured relevant transcription factors for TNB biology and pointing out different sets of candidate genes for TNB in different environments. These findings may have important implication for animal production, as optimal breeding may vary depending on later environments. Based on these results, genomic diversity was identified and inferred across environments highlighting differential genetic control in each scenario.