Abstract Enteric methane emissions from ruminant livestock contribute to global warming, creating an urgent need for effective mitigation strategies that do not compromise animal productivity and welfare. Methanogenic archaea within the rumen microbiome drive enteric methane emissions. However, large-scale rumen-fluid sampling in commercial production systems is impractical, due to its invasive nature and the associated logistical challenges. This study hypothesised that rumination enables the capture of rumen microbial signals within the oral cavity and using oral microbiome profiles to provide a practical, non-invasive alternative method for proxy methane phenotyping in commercial production systems. To test the hypothesis, we estimated the oral microbiability, defined as the proportion of phenotypic variance in methane emissions explained by oral microbiome variation. Samples were collected from 209 animals across two trials in Queensland, Australia. Oral microbiome samples were obtained from all animals, with paired rumen samples in one trial, and methane emissions were measured using either the sulphur hexafluoride (SF 6 ) tracer technique or the GreenFeed system. Microbial features were characterised using taxonomic and functional annotations, and microbiability was estimated using mixed linear models incorporating microbiome-based relationship matrices. Although the small sample size limited strong conclusions, the oral microbiability estimates reported in this study were comparable to those derived from rumen samples. Functional microbial profiles generally explained a greater proportion of methane variation than taxonomic profiles, suggesting that microbial function is more closely linked to methane production than community composition alone. However, these differences were not statistically significant due to large standard errors. These findings suggest that oral microbiome sampling potentially provides a practical, minimally invasive, scalable proxy method for methane emissions of individual cattle in grazing systems, where direct methane gas measurements are labour-intensive and difficult to implement. Integrating oral microbiome profiles in the existing breeding model with the host genetics, weight and environmental factors could provide a promising pathway for enabling selection for low emissions and advancing reduced emissions livestock farming under real-world production conditions. Lay summary Cattle produce methane as part of their normal digestion and this contributes to climate change. Reducing methane emission in grazing livestock systems is therefore important. However, measuring methane from individual grazing animals is difficult, costly, and often impractical under commercial conditions. The rumen microbiome has been used as a proxy for estimating methane emissions, but collecting rumen samples is invasive and impractical for large-scale use. Because rumination transfers material from the rumen to the mouth, we investigated whether microbes found in cattle mouths could also be used to estimate how much methane an individual animal produced. We suggest that mouth-swab sampling method can be an alternative to rumen fluid sampling because it was less invasive, relatively quick and practically applicable in commercial conditions. Importantly, the microbiome explained a meaningful proportion of the between-animal variation for methane emission. This suggests that collection of mouth swabs is a potentially scalable alternative proxy method to identify cattle that naturally produce less methane. Overall, our findings support the potential use of oral ruminant microbial information to improve breeding and management strategies aimed at reducing methane emissions while maintaining productive livestock systems. Teaser Text This study demonstrates that collecting oral swabs from the mouths of grazing beef cattle could provide a scalable method to estimate individual methane emissions in commercial production systems, offering a practical alternative to invasive rumen sampling and complex gas measurement systems. These findings support the development of scalable breeding and management strategies for methane mitigation in large-scale livestock production systems.

Enteric methane emissions from ruminant livestock contribute to global warming, creating an urgent need for effective mitigation strategies that do not compromise animal productivity and welfare. Methanogenic archaea within the rumen microbiome drive enteric methane emissions. However, large-scale rumen-fluid sampling in commercial production systems is impractical, due to its invasive nature and the associated logistical challenges. This study hypothesized that rumination facilitates the capture of rumen microbial signals within the oral cavity, therefore oral microbiome profiles can be a practical alternative for explaining variation in methane emissions commercial production systems. To test the hypothesis, we estimated the oral microbiability, defined as the proportion of phenotypic variance in methane emissions explained by oral microbiome variation. Samples were collected from 209 tropical composite beef cattle across two trials in Queensland, Australia. Oral microbiome samples were obtained from all animals, with paired rumen samples in one trial, and methane emissions were measured using either the sulfur hexafluoride tracer technique or the GreenFeed system. Microbial features were characterized using taxonomic and functional annotations, and microbiability was estimated using mixed linear models incorporating microbiome-based relationship matrices. The oral microbiability reported in this study ranged from 0.27 to 0.63 with standard errors 0.12 to 0.25. Functional microbial profiles explained a numerically greater proportion of methane emission variation than taxonomic profiles in some comparisons, however, the differences were not statistically significant due to large standard errors. These findings demonstrated that oral microbiome sampling provides a practical and scalable proxy method for capturing variation in methane emissions among the individual cattle in grazing systems, where direct methane gas measurements are labor-intensive and difficult to implement. Therefore, further validation of oral microbiability against methane emissions measured in larger animal cohorts is required. Such validation would enable a more robust assessment of the predictive accuracy of oral microbiome-based models while accounting for additional sources of variation, including host genetic, environmental, and management factors, which could not be fully addressed in the present study.

BACKGROUND:Oxford Nanopore sequencing is a long-read sequencing technology that does not rely on a polymerase to generate sequence data. Sequencing library preparation methods used in Oxford Nanopore sequencing rely on the addition of a motor protein bound to an adapter sequence, which is added either using ligation-based methods (ligation sequencing kit), or transposase-based methods (rapid sequencing kit). However, these methods have enzymatic steps that may be susceptible to motif bias, including the underrepresentation of adenine-thymine (AT) sequences due to ligation and biases from transposases. This study aimed to compare the recognition motif and relative interaction frequencies of these library preparation methods and assess their effects on relative sequencing coverage, microbiome, and methylation profiles. The impacts of DNA extraction kits and basecalling models on microbiome analysis were also investigated. RESULTS:By using sequencing data generated by the ligation and rapid library kits, we identified the recognition motif (5'-TATGA-3') consistent with MuA transposase in the rapid kit and low frequencies of AT in the sequence terminus of the ligation kit. The rapid kit showed reduced yield in regions with 40-70% guanine-cytosine (GC) contents, while the ligation kit showed relatively even coverage distribution in areas with various GC contents. Due to longer reads, ligation kits showed increased taxonomic classification efficiency compared to the rapid protocols. Rumen microbial profile at different taxonomic levels and mock community profile showed significant variation due to the library preparation method used. The ligation kit outperformed the rapid kit in subsequent bacterial DNA methylation statistics, although there were no significant differences. CONCLUSIONS:Our findings indicated that careful and consistent library preparation method selection is essential for quantitative methods such as bovine-related microbiome analysis due to the systematic bias induced by the enzymatic reactions in Oxford Nanopore library preparation.

The skin is the primary feeding site of ticks that infest livestock animals such as cattle. The highly specialised functions of skin at the molecular level may be a factor contributing to variation in susceptibility to tick infestation; but these remain to be well defined. The aim of this study was to investigate the bovine skin transcriptomic profiles of tick-naïve and tick-infested cattle and to uncover the gene expression networks that influence contrasting phenotypes of host resistance to ticks. RNA-Seq data was obtained from skin of Brangus cattle with high (n = 5) and low (n = 6) host resistance at 0 and 12 weeks following artificial tick challenge with Rhipicephalus australis larvae. No differentially expressed genes were detected pre-infestation between high and low resistance groups, but at 12-weeks there were 229 differentially expressed genes (DEGs; FDR < 0.05), of which 212 were the target of at least 1866 transcription factors (TFs) expressed in skin. Regulatory impact factor (RIF) analysis identified 158 significant TFs ( P < 0.05) of which GRHL3 , and DTX1 were also DEGs in the experiment. Gene term enrichment showed the significant TFs and DEGs were enriched in processes related to immune response and biological pathways related to host response to infectious diseases. Interferon Type 1-stimulated genes, including MX2 , ISG15 , MX1 , OAS2 were upregulated in low host resistance steers after repeated tick challenge, suggesting dysregulated wound healing and chronic inflammatory skin processes contributing to host susceptibility to ticks. The present study provides an assessment of the bovine skin transcriptome before and after repeated tick challenge and shows that the up-regulation of pro-inflammatory genes is a prominent feature in the skin of tick-susceptible animals. In addition, the identification of transcription factors with high regulatory impact provides insights into the potentially meaningful gene–gene interactions involved in the variation of phenotypes of bovine host resistance to ticks.

Sugarcane smut and Pachymetra root rots are two serious diseases of sugarcane, with susceptible infected crops losing over 30% of yield. A heritable component to both diseases has been demonstrated, suggesting selection could improve disease resistance. Genomic selection could accelerate gains even further, enabling early selection of resistant seedlings for breeding and clonal propagation. In this study we evaluated four types of algorithms for genomic predictions of clonal performance for disease resistance. These algorithms were: Genomic best linear unbiased prediction (GBLUP), including extensions to model dominance and epistasis, Bayesian methods including BayesC and BayesR, Machine learning methods including random forest, multilayer perceptron (MLP), modified convolutional neural network (CNN) and attention networks designed to capture epistasis across the genome-wide markers. Simple hybrid methods, that first used BayesR/GWAS to identify a subset of 1000 markers with moderate to large marginal additive effects, then used attention networks to derive predictions from these effects and their interactions, were also developed and evaluated. The hypothesis for this approach was that using a subset of markers more likely to have an effect would enable better estimation of interaction effects than when there were an extremely large number of possible interactions, especially with our limited data set size. To evaluate the methods, we applied both random five-fold cross-validation and a structured PCA based cross-validation that separated 4702 sugarcane clones (that had disease phenotypes and genotyped for 26k genome wide SNP markers) by genomic relationship. The Bayesian methods (BayesR and BayesC) gave the highest accuracy of prediction, followed closely by hybrid methods with attention networks. The hybrid methods with attention networks gave the lowest variation in accuracy of prediction across validation folds (and lowest MSE), which may be a criteria worth considering in practical breeding programs. This suggests that hybrid methods incorporating the attention mechanism could be useful for genomic prediction of clonal performance, particularly where non-additive effects may be important.

Most genetic variants associated with fertility in mammals fall in non-coding regions of the genome and it is unclear how these variants affect fertility. Here we use genome-wide association summary statistics for Heifer puberty (pubertal or not at 600 days) from 27,707 Bos indicus, Bos taurus and crossbred cattle; multi-trait GWAS signals from 2119 indicine cattle for four fertility traits, including days to calving, age at first calving, pregnancy status, and foetus age in weeks (assessed by rectal palpation of the foetus); and expression quantitative trait locus for whole blood from 489 indicine cattle, to identify 87 putatively functional genes affecting cattle fertility. Our analysis reveals a significant overlap between the set of cattle and previously reported human fertility-related genes, impling the existence of a shared pool of genes that regulate fertility in mammals. These findings are crucial for developing approaches to improve fertility in cattle and potentially other mammals. The authors identify the genetic variants and genes associated with four fertility-related traits in a well-phenotyped cattle population.

The accuracy of genomic prediction for key traits in sugarcane remains low relative to other crops, even with growing reference sets. This is potentially due to high-level polyploidy, frequent aneuploidy and complex genetic architectures. Here we aim to assess the impact of genotype transformations typically used in polyploid crops on the accuracy of genomic prediction. We focus on the effect of using “diploid” genotype calls and aneuploidy. We simulated a sugarcane genome with multi-species ancestry and genome duplications, resulting in 96 chromosomes. We included a range of allele dosage to phenotype maps across ploidies including additive linear, dominant, non-linear sigmoid and log shaped maps. Genomic prediction accuracies were compared from six algorithms including genomic BLUP (GBLUP), extended GBLUP, BayesR, Multilayer perceptron, Convolutional neural network and Attention network. Accuracy of prediction for phenotypes controlled by a higher proportion of additive effects were more sensitive to diploid transformation and aneuploidy events. Conversely, accuracy of prediction for phenotypes involving directional dominance were less affected by diploid transformation but were still substantially affected by aneuploidy. For traits controlled by non-linear allele dosage to phenotype maps, the predictive accuracy was significantly affected by diploid transformation, but the influence of aneuploidy was not obvious. Extended GBLUP performed well and gave higher accuracies than other linear based models across most of scenarios, while the DL models were more competitive when the allele action across ploidies became more complex. Overall, our results demonstrate that genotypes that capture allele dosage should be used in genomic prediction for complex polyploids.

BACKGROUND:Genotype by environment interactions (GxE) affect a range of production traits in beef cattle. Quantifying the effect of GxE in commercial and multi-breed herds is challenging due to unknown genetic linkage between animals across environment levels. The primary aim of this study was to use multi-trait models to investigate GxE for three heifer fertility traits, corpus luteum (CL) presence, first pregnancy and second pregnancy, in a large tropical beef multibreed dataset (n = 21,037). Environmental levels were defined by two different descriptors, burden of heat load (temperature humidity index, THI) and nutritional availability (based on mean average daily gain for the herd, ADWG). To separate the effects of genetic linkage and real GxE across the environments, 1000 replicates of a simulated phenotype were generated by simulating QTL effects with no GxE onto real marker genotypes from the population, to determine the genetic correlations that could be expected across environments due to the existing genetic linkage only. Correlations from the real phenotypes were then compared to the empirical distribution under the null hypothesis from the simulated data. By adopting this approach, this study attempted to establish if low genetic correlations between environmental levels were due to GxE or insufficient genetic linkage between animals in each environmental level. RESULTS:The correlations (being less than <0.8) for the real phenotypes were indicative of GxE for CL presence between ADWG environmental levels and in pregnancy traits. However, none of the correlations for CL presence or first pregnancy between ADWG levels were below the 5th percentile value for the empirical distribution under the null hypothesis from the simulated data. Only one statistically significant (P < 0.05) indication of GxE for first pregnancy was found between THI environmental levels, where rg = 0.28 and 5th percentile value = 0.29, and this result was marginal. CONCLUSIONS:Only one case of statistically significant GxE for fertility traits was detected for first pregnancy between THI environmental levels 2 and 3. Other initial indications of GxE that were observed from the real phenotypes did not prove significant when compared to an empirical null distribution from simulated phenotypes. The lack of compelling evidence of GxE indicates that direct selection for fertility traits can be made accurately, using a single evaluation, regardless of environment.

Bovicola ovis, commonly known as the sheep-biting louse, is an ectoparasite that adversely affects the sheep industry. Sheep louse infestation lowers the quality of products, including wool and leather, causing a loss of approximately AUD 123M per annum in Australia alone. The lack of a high-quality genome assembly for the sheep-biting louse, as well as any closely related livestock lice, has hindered the development of louse research and management control tools. In this study, we present the assembly of B. ovis with a genome size of ~123 Mbp based on a nanopore long-read sequencing library and Illumina RNA sequencing, complemented with a chromosome-level scaffolding using the Pore-C multiway chromatin contact dataset. Combining multiple alignment and gene prediction tools, a comprehensive annotation on the assembled B. ovis genome was conducted and recalled 11,810 genes as well as other genomic features including orf, ssr, rRNA and tRNA. A manual curation using alignment with the available closely related louse species, Pediculus humanus, increased the number of annotated genes to 16,024. Overall, this study reported critical genetic resources and biological insights for the advancement of sheep louse research and the development of sustainable control strategies in the sheep industry.

Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such, genomic prediction in sugarcane presents an interesting case for machine learning (ML) methods, which are purportedly able to deal with high levels of complexity in prediction. Here, we investigated deep learning (DL) neural networks, including multilayer networks (MLP) and convolution neural networks (CNN), and an ensemble machine learning approach, random forest (RF), for genomic prediction in sugarcane. The data set used was 2912 sugarcane clones, scored for 26,086 genome wide single nucleotide polymorphism markers, with final assessment trial data for total cane harvested (TCH), commercial cane sugar (CCS), and fiber content (Fiber). The clones in the latest trial (2017) were used as a validation set. We compared prediction accuracy of these methods to genomic best linear unbiased prediction (GBLUP) extended to include dominance and epistatic effects. The prediction accuracies from GBLUP models were up to 0.37 for TCH, 0.43 for CCS, and 0.48 for Fiber, while the optimized ML models had prediction accuracies of 0.35 for TCH, 0.38 for CCS, and 0.48 for Fiber. Both RF and DL neural network models have comparable predictive ability with the additive GBLUP model but are less accurate than the extended GBLUP model.

Background It has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models. Results Treating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30–0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented. Conclusions When no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy.

Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo-diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele-dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non-parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%-7% for CCS and Fiber. The pseudo-diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc-cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non-additive effects might influence these traits. The prevalence of low-dosage markers in the study may have limited the benefits of approximating allele-dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo-diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.

Background Genomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies’ (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance. Results SNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays. Conclusions Here we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays.

Studies have demonstrated that structural variants (SV) play a substantial role in the evolution of species and have an impact on Mendelian traits in the genome. However, unlike small variants (< 50 bp), it has been challenging to accurately identify and genotype SV at the population scale using short-read sequencing. Long-read sequencing technologies are becoming competitively priced and can address several of the disadvantages of short-read sequencing for the discovery and genotyping of SV. In livestock species, analysis of SV at the population scale still faces challenges due to the lack of resources, high costs, technological barriers, and computational limitations. In this review, we summarize recent progress in the characterization of SV in the major livestock species, the obstacles that still need to be overcome, as well as the future directions in this growing field. It seems timely that research communities pool resources to build global population-scale long-read sequencing consortiums for the major livestock species for which the application of genomic tools has become cost-effective.

Context Genotyping-by-sequencing, the use of sequence reads to genotype single-nucleotide polymorphisms (SNPs), has seen an increase in popularity as a tool for genomic prediction. Oxford Nanopore Technologies (Nanopore) sequencing is an emerging technology that produces long sequence reads in real-time. Recent studies have established the ability for low-coverage Nanopore sequence data to be used for genomic prediction. However, the value proposition of Nanopore sequencing for individuals could be improved if both genotyping and disease diagnosis are achieved from a single sample. Aims This study aimed to demonstrate that Nanopore sequencing can be used for both rapid genotyping and as a disease diagnostic tool using the same sample in livestock. Methods Total DNA extracts from nasal swabs collected from 48 feedlot cattle presenting with clinical signs of bovine respiratory disease (BRD) were sequenced using the Nanopore PromethION sequencer. After 24 h of sequencing, genotypes were imputed and genomic estimated breeding values (GEBVs) for four traits were derived using 641 163 SNPs and corresponding SNP effects. These GEBVs were compared with GEBVs derived from SNP array genotypes and calculated using the same SNP effects. Unmapped sequence reads were classified into taxa using Kraken2 and compared with quantitative real-time polymerase chain reaction (qPCR) results for five BRD-associated pathogens of interest. Key results Sequence-derived genotypes for 46 of the 48 animals were produced in 24 h and GEBV correlations ranged between 0.92 and 0.94 for the four traits. Eleven different BRD-associated pathogens (two viruses and nine bacterial species) were detected in the samples using Nanopore sequence data. A significant (P < 0.001) relationship between Nanopore and qPCR results was observed for five overlapping species when a maximum threshold cycle was used. Conclusions The results of this study indicated that 46 cattle genomes can be multiplexed and accurately genotyped for downstream genomic prediction by using a single PromethION flow cell (ver. R9.4) in 24 h. This equates to a cost of AUD35.82 per sample for consumables. The concordance between qPCR results and pathogen proportion estimates also indicated that some pathogenic species, in particular bacterial species, can be accurately identified from the same test. Implications Using Nanopore sequencing, routine genotyping and disease detection in livestock could be combined into one cost-competitive test with a rapid turnaround time.

Context Recent advances in molecular technology have allowed us to examine the cattle genome with an accuracy never before possible. Genetic variations, both small and large, as well as the transcriptional landscape of the bovine genome, have both been explored in many studies. However, the topological configuration of the genome has not been extensively investigated, largely due to the cost of the assays required. Such assays can both identify topologically associated domains and be used for genome scaffolding. Aims This study aimed to implement a chromatin conformation capture together with long-read nanopore sequencing (Pore-C) pipeline for scaffolding a draft assembly and identifying topologically associating domains (TADs) of a Bos indicus Brahman cow. Methods Genomic DNA from a liver sample was first cross-linked to proteins, preserving the spatial proximity of loci. Restriction digestion and proximity ligation were then used to join cross-linked fragments, followed by nucleic isolation. The Pore-C DNA extracts were then prepped and sequenced on a PromethION device. Two genome assemblies were used to analyse the data, namely, one generated from sequencing of the same Brahman cow, and the other is the ARS-UCD1.2 Bos taurus assembly. The Pore-C snakemake pipeline was used to map, assign bins and scaffold the draft and current annotated bovine assemblies. The contact matrices were then used to identify TADs. Key results The study scaffolded a chromosome-level Bos indicus assembly representing 30 chromosomes. The scaffolded assembly showed a total of 215 contigs (2.6 Gbp) with N50 of 44.8 Mb. The maximum contig length was 156.8 Mb. The GC content of the scaffold assembly is 41 ± 0.02%. Over 50% of mapped chimeric reads identified for both assemblies had three or more contacts. This is the first experimental study to identify TADs in bovine species. In total, 3036 and 3094 TADs across 30 chromosomes were identified for input Brahman and ARS-UCD1.2 assemblies respectively. Conclusions The Pore-C pipeline presented herein will be a valuable approach to scaffold draft assemblies for agricultural species and understand the chromatin structure at different scales. Implications The Pore-C approach will open a new era of 3D genome-organisation studies across agriculture species.

Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.

Recent advances in sequencing technology have revolutionised access to large scale genomic data that can be assembled into a platinum quality genome. Here we present a high quality genome assembly with less than 300 gaps of a Brahman cow ( B. taurus indicus ). The assembly was generated using 195GB of PacBio and 169GB of Oxford Nanopore Technologies sequence data. The high quality genome assembly allows us to identify substantial GC content variation that is positively associated with gene rich islands, and negatively associated with genetic variation in the form of structural variants. In addition, 92371 structural variants that are segregating in the brahman population were identified. Gene ontology analysis revealed that genes with varying copy numbers were enriched for gene ontology terms related to immune function. This analysis has revealed the complex structure of the mammalian genome of an outbred species, and identifies the ability of long read data from diploid species can be used to not only assemble a high quality genome, but also discover novel genetic variation within that genome.

The northern Australia beef cattle industry operates in harsh environmental conditions which consistently suppress female fertility. To better understand the environmental effect on cattle raised extensively in northern Australia, new environmental descriptors were defined for 54 commercial herds located across the region. Three fertility traits, based on the presence of a corpus luteum at 600 d of age, indicating puberty, (CL Presence, n = 25,176), heifer pregnancy (n = 20,989) and first lactation pregnancy (n = 10,072) were recorded. Temperature, humidity, and rainfall were obtained from publicly available data based on herd location. Being pubertal at 600 d (i.e. CL Presence) increased the likelihood of success at heifer pregnancy and first lactation pregnancy (P < 0.05), underscoring the importance of early puberty in reproductive success. A temperature humidity index (THI) of 65-70 had a significant (P < 0.05) negative effect on first lactation pregnancy rate, heifer pregnancy and puberty at 600 d of age. Area under the curve of daily THI was significant (P < 0.05) and reduced the likelihood of pregnancy at first lactation and puberty at 600 days. Deviation from long-term average rainfall was not significant (P < 0.05) for any trait. Average daily weight gain had a significant and positive relationship (P < 0.05) for heifer and first lactation pregnancy. The results indicate that chronic or cumulative heat load is more determinantal to reproductive performance than acute heat stress. The reason for the lack of a clear relationship between acute heat stress and reproductive performance is unclear but may be partially explained by peak THI and peak nutrition coinciding at the same time. Sufficient evidence was found to justify the use of average daily weight gain and chronic heat load as descriptors to define an environmental gradient.

The reproductive tract metagenome plays a significant role in the various reproductive system functions, including reproductive cycles, health, and fertility. One of the major challenges in bovine vaginal metagenome studies is host DNA contamination, which limits the sequencing capacity for metagenomic content and reduces the accuracy of untargeted shotgun metagenomic profiling. This is the first study comparing the effectiveness of different host depletion and DNA extraction methods for bovine vaginal metagenomic samples. The host depletion methods evaluated were slow centrifugation (Soft-spin), NEBNext Microbiome DNA Enrichment kit (NEBNext), and propidium monoazide (PMA) treatment, while the extraction methods were DNeasy Blood and Tissue extraction (DNeasy) and QIAamp DNA Microbiome extraction (QlAamp). Soft-spin and QIAamp were the most effective host depletion method and extraction methods, respectively, in reducing the number of cattle genomic content in bovine vaginal samples. The reduced hostto-microbe ratio in the extracted DNA increased the sequencing depth for microbial reads in untargeted shotgun sequencing. Bovine vaginal samples extracted with QIAamp presented taxonomical profiles which closely resembled the mock microbial composition, especially for the recovery of Gram-positive bacteria. Additionally, samples extracted with QIAamp presented extensive functional profiles with deep coverage. Overall, a combination of Soft-spin and QIAamp provided the most robust representation of the vaginal microbial community in cattle while minimizing host DNA contamination. IMPORTANCE In addition to the host tissue collected during the sampling process, bovine vaginal samples are saturated with large amounts of extracellular DNA and secreted proteins that are essential for physiological purposes, including the reproductive cycle and immune defense. Due to the high host-to-microbe genome ratio, which hampers the sequencing efficacy for metagenome samples and the recovery of the actual metagenomic profiles, bovine vaginal samples cannot benefit from the full potential of shotgun sequencing. This is the first investigation on the most effective host depletion and extraction methods for bovine vaginal metagenomic samples. This study demonstrated an effective combination of host depletion and extraction methods, which harvested higher percentages of 16S rRNA genes and microbial reads, which subsequently led to a taxonomical profile that resembled the actual community and a functional profile with deeper coverage. A representative metagenomic profile is essential for investigating the role of the bovine vaginal metagenome for both reproductive function and susceptibility to infections.