Multi-breed genomic evaluation for tropical beef cattle when no pedigree information is available

An overview of recent technological developments in bovine genomics

Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.

Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

Genome-wide association and expression quantitative trait loci in cattle reveals common genes regulating mammalian fertility

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.

Expression network analysis of bovine skin infested with Rhipicephalus australis identifies pro-inflammatory genes contributing to tick susceptibility

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.

Imputation strategies for genomic prediction using nanopore sequencing

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.