Background: In tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies; including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved. Methods: Genotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density (6 K, 50 K and 800 K), analysis model, and inclusion of pre-selected WGS variants on prediction accuracy. Results: In all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants (identified using meta-analysis COJO by chromosome) were included. Conclusions: While BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants.

Many of the world’s agriculturally important plant and animal populations consist of hybrids of subspecies. Cattle in tropical and sub-tropical regions for example, originate from two subspecies, Bos taurus indicus (Bos indicus) and Bos taurus taurus (Bos taurus). Methods to derive the underlying genetic architecture for these two subspecies are essential to develop accurate genomic predictions in these hybrid populations. We propose a novel method to achieve this. First, we use haplotypes to assign SNP alleles to ancestral subspecies of origin in a multi-breed and multi-subspecies population. Then we use a BayesR framework to allow SNP alleles originating from the different subspecies differing effects. Applying this method in a composite population of B. indicus and B. taurus hybrids, our results show that there are underlying genomic differences between the two subspecies, and these effects are not identified in multi-breed genomic evaluations that do not account for subspecies of origin effects. The method slightly improved the accuracy of genomic prediction. More significantly, by allocating SNP alleles to ancestral subspecies of origin, we were able to identify four SNP with high posterior probabilities of inclusion that have not been previously associated with cattle fertility and were close to genes associated with fertility in other species. These results show that haplotypes can be used to trace subspecies of origin through the genome of this hybrid population and, in conjunction with our novel Bayesian analysis, subspecies SNP allele allocation can be used to increase the accuracy of QTL association mapping in genetically diverse populations.

Context: Beef cattle breeds in Australia can broadly be broken up into two subspecies, namely, Bos indicus and Bos taurus. Due to the time since divergence between the subspecies, it is likely that mutations affecting quantitative traits have developed independently in each. Aims: We hypothesise that this will affect the prediction accuracy of genomic selection of admixed and composite populations that include both ancestral subspecies. Our study investigates methods to quantify population stratification in a multibreed population of tropically adapted heifers, with the aim of improving prediction accuracy of genomic selection for reproductive maturity score. Methods: We used genotypes and reproductive maturity phenotypes from 3695 tropically adapted heifers from three purebred populations, namely, Brahman, Santa Gertrudis and Droughtmaster. Two of these breeds, Santa Gertrudis and Droughtmaster, are stabilised composites of varying B. indicus × B. taurus ancestry, and the third breed, Brahman, has predominately B. indicus ancestry. Genotypes were imputed to three marker-panel densities and population stratification was accounted for in genomic relationship matrices by using breed-specific allele frequencies when calculating the genomic relationships among animals. Prediction accuracy and bias were determined using a five-fold cross validation of randomly selected multibreed cohorts. Key Results: Our results showed that the use of breed-adjusted genomic relationship matrices did not improve either prediction accuracy or bias for a lowly heritable trait such as reproductive maturity score. However, using breed-adjusted genomic relationship matrices allowed the capture of a higher proportion of additive genetic effects when estimating variance components. Conclusions: These findings suggest that, despite seeing no improvement in prediction accuracy, it may still be beneficial to use breed-adjusted genomic relationship matrices in multibreed populations to improve the estimation of variance components. Implications: As such, genomic evaluations using breed-adjusted genomic relationship matrices may be beneficial in multibreed populations.

Globally agricultural emissions contribute 10%–12% of all anthropogenic greenhouse gas emissions. Ruminant livestock account for a considerable proportion of these emissions, with cattle grazing in tropical and subtropical regions representing a major source. Enteric methane production has climatic implications and represents a loss of energy from the animal that could be directed toward growth. This study focuses on cattle grazing tropical and subtropical rangelands in Australia, aiming to identify the factors that influence the recording of methane emissions in these environments. Short-term methane production was recorded from 453 mixed-breed cattle across four trial sites utilizing GreenFeed units (GFU) (C-Lock, USA). Data was generated and combined from several sources, including GFU, weather data, forage samples, and estimated forage biomass. Using a linear mixed model, the significance and variance of each factor on methane production was evaluated. Factors identified as explaining most of the variation for methane production included GFU (P < 0.001), total visitation (P < 0.001), daily visitation (P < 0.001), trial (P < 0.001), hour of visitation (P = 0.03), air temperature (P < 0.001), initial weight (P < 0.001), visit duration (P < 0.001), and forage biomass (P < 0.001). Four factors were tested and found to be not significant in explaining variation in methane production, including Bos taurus indicus (BI) content (P = 0.99), sex (P = 0.06), rain (P = 0.07), and temperature heat index (P = 0.2). A moderate coefficient of variation (CV) of 23.2% was observed in the raw observations of methane production. This study highlights important factors to consider when utilizing GFU for methane measurement of grazing beef cattle. This study provides a foundation for future studies aiming to quantify individual animal methane emissions for use in genetic evaluation programs to reduce industry emissions and improve the sustainability of beef production in tropical and subtropical regions.

The natural abundance of nitrogen isotopes (δ¹⁵N) in animal tissues is related to the ability of cattle to lose less nitrogen in the urine and has been used to estimate nitrogen use efficiency and feed efficiency. Carbon isotope enrichment (δ¹³C) has also been used to estimate feed conversion ratio and grazing behavior in beef cattle. There are significant variations between the individual beef cattle in the same environment for δ¹⁵N and δ¹³C of the tail hair. Thus, the objective was to determine the heritability of δ¹⁵N and δ¹³C in the tail hair of tropically adapted beef cattle to further validate their use in genetic breeding programs. A total of 497 Bos indicus steers (269 Brahman and 228 Droughtmaster) were used, and the steers had reliable pedigree information for three generations as they were part of the beef cattle genetic evaluation system in Australia. Two single, mixed breed, contemporary groups of steers are used including the steers weaned in 2019 (n = 254) and steers weaned in 2020 (n = 243). Samples of tail switch hair representing hair segments growing during the dry season were collected and analyzed for δ¹⁵N and δ¹³C. Fixed effects fitted in the linear regression model were age, breed, year (as contemporary groups), and average daily gain during the different seasons. The heritabilities of δ¹⁵N and δ¹³C in the tail hair were estimated at 0.43 ± 0.14, and 0.41 ± 0.15, respectively, which indicates both are moderately heritable traits. The genetic correlation between δ¹⁵N and δ¹³C was negative at -0.78 ± 0.16, and the phenotypic one was -0.40 ± 0.04. The negative correlation can be explained by the contrasting isotope fractionation in the urine of cattle, as urine has less δ¹⁵N and more δ¹³C than the diet. The findings of this study have shown for the first time that both δ¹⁵N and δ¹³C in the tail hair of cattle are heritable traits. These results support the feasibility of utilizing tail hair isotopes as a promising approach for identifying cattle with greater nitrogen use efficiency, which is a key trait in low-protein diets. Moreover, genetic selection for nitrogen-efficient cattle can reduce the environmental impact of beef production.