Progress and opportunities through use of genomics in animal production

Application of generalized additive models to explore minerals in sheep milk

This study explores the complex relationships among ovine milk minerals (Ca, P, Na, K, Mg, K, Cl, respectively) and different factors employing generalized additive mixed models (GAMM). The GAMM included milk yield (MY), parity, and breed as parametric terms, and casein, fat, lactose, pH, SCS, DIM, and sampling day as smooth functions. The objectives were to investigate how these factors could affect minerals in sheep milk and to assess whether their patterns change over time and across different concentrations of major milk components. The GAMM identified distinct patterns in the mineral concentrations between Comisana and Massese breeds, with the Massese ewes having less P, Mg, K, and Cl compared with the Comisana. Moreover, these minerals were also affected by DIM; Mg, S, and Cl were influenced by parity; and P, K, and Na changed across MY levels. Regarding milk components, all the minerals were affected by casein, fat, and lactose concentrations (excluding P for fat). Milk pH was important for Ca, K, Na, and Cl, whereas SCS affected the variability of all minerals except Ca. This study provided valuable insights on the variability of macrominerals in sheep milk, by using GAMM and examining the trajectory of each element across factors as breed, MY, parity, and DIM, as well as across various concentrations of major milk components and their interactions. The dynamic nature of milk mineral content was evident through temporal variability, likely driven by dietary changes, environmental fluctuations, and physiological adaptations, as well as synergistic and antagonistic interactions between milk components and fixed factors. These findings enhance understanding of mineral composition in sheep milk, providing a comprehensive framework for future research on milk quality, animal health, and cheesemaking properties.

Transforming beef quality through healthy breeding: a strategy to reduce carcinogenic compounds and enhance human health: a review

The presence of carcinogenic substances in beef poses a significant risk to public health, with far-reaching implications for consumer safety and the meat production industry. Despite advancements in food safety measures, traditional breeding methods have proven inadequate in addressing these risks, revealing a substantial gap in knowledge. This review aims to fill this gap by evaluating the potential of healthy breeding techniques to significantly reduce the levels of carcinogenic compounds in beef. We focus on elucidating the molecular pathways that contribute to the formation of key carcinogens, such as heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs), while exploring the transformative capabilities of advanced genomic technologies. These technologies include genomic selection, CRISPR/Cas9, base editing, prime editing, and artificial intelligence-driven predictive models. Additionally, we examine multi-omics approaches to gain new insights into the genetic and environmental factors influencing carcinogen formation. Our findings suggest that healthy breeding strategies could markedly enhance meat quality, thereby offering a unique opportunity to improve public health outcomes. The integration of these innovative technologies into breeding programs not only provides a pathway to safer beef production but also fosters sustainable livestock management practices. The improvement of these strategies, along with careful consideration of ethical and regulatory challenges, will be crucial for their effective implementation and broader impact.

Development and Application of a 40 K Liquid Capture Chip for Beef Cattle

The availability of genome sequences and single-nucleotide polymorphism (SNP) chips allows us to investigate the various genomic characteristics of species by exploring genetic diversity to aid genetic selection. The SNP chip is a cost-effective genotyping platform essential for molecular breeding of livestock. In this study, we developed a liquid SNP capture chip suitable for five Hubei (China) indigenous beef cattle breeds based on whole-genome sequencing datasets. The panel consisted of 42,686 SNPs (~40 K). These SNPs were evenly distributed on each bovine chromosome, with the majority of SNPs having minor allele frequencies >0.05 and located within intergenic regions. The performance evaluation of this SNP chip panel was proceeded by genotyping 200 individuals, revealing that this panel has a high SNP call rate of 99.48%. The SNP chip panel was further used to examine the population structure of a beef cattle population with 205 individuals and demonstrated the ability to differentiate between foreign and indigenous cattle breeds. The SNP chip was also used to determine the runs of homozygosity (ROH) within a local Hubei beef cattle population of 195 individuals. We identified 2547 ROH and several genes associated with economically important traits in the study population. Our findings demonstrate that this chip not only contributes to the understanding of the genetic characteristics of local beef cattle breeds but also provides valuable genetic information for future breeding programs, thereby improving their production efficiency and economic value.

Genomics identifies koala populations at risk across eastern Australia

Koalas are an iconic, endangered, Australian marsupial. Disease, habitat destruction, and catastrophic mega-fires have reduced koalas to remnant patches of their former range. With increased likelihood of extreme weather events and ongoing habitat clearing across Australia, koala populations are vulnerable to further declines and isolation. Small, isolated populations are considered at risk when there is increased inbreeding, erosion of genomic diversity, and loss of adaptive potential, all of which reduce their ability to respond to prevailing threats. Here, we characterized the current genomic landscape of koalas using data from The Koala Genome Survey, a joint initiative between the Australian Federal and New South Wales Governments that aimed to provide a future-proofed baseline genomic dataset across the koala's range in eastern Australia. We identified several regions of the continent where koalas have low genomic diversity and high inbreeding, as measured by runs of homozygosity. These populations included coastal sites along southeast Queensland and northern and mid-coast New South Wales, as well as southern New South Wales and Victoria. Analysis of genomic vulnerability to future climates revealed that northern koala populations were more at risk due to the extreme expected changes in this region, but that the adaptation required was minimal compared with other species. Our genomic analyses indicate that continued development, particularly linear infrastructure along coastal sites, and resultant habitat destruction are causing isolation and subsequent genomic erosion across many koala populations. Habitat protection and the formation of corridors must be employed for all koala populations to maintain current levels of diversity. For highly isolated koala populations, active management may be the only way to improve genomic diversity in the short term. If koalas are to be conserved for future generations, reversing their genomic isolation must be a priority in conservation planning.

The Evolution and Role of Molecular Tools in Measuring Diversity and Genomic Selection in Livestock Populations (Traditional and Up-to-Date Insights): A Comprehensive Exploration

Distinctive molecular approaches and tools, particularly high-throughput SNP genotyping, have been applied to determine and discover SNPs, potential genes of interest, indicators of evolutionary selection, genetic abnormalities, molecular indicators, and loci associated with quantitative traits (QTLs) in various livestock species. These methods have also been used to obtain whole-genome sequencing (WGS) data, enabling the implementation of genomic selection. Genomic selection allows for selection decisions based on genomic-estimated breeding values (GEBV). The estimation of GEBV relies on the calculation of SNP effects using prediction equations derived from a subset of individuals in the reference population who possess both SNP genotypes and phenotypes for target traits. Compared to traditional methods, modern genomic selection methods offer advantages for sex-limited traits, low heritability traits, late-measured traits, and the potential to increase genetic gain by reducing generation intervals. The current availability of high-density genotyping and next-generation sequencing data allow for genome-wide scans for selection. This investigation provides an overview of the essential role of advanced molecular tools in studying genetic diversity and implementing genomic selection. It also highlights the significance of adaptive selection in light of new high-throughput genomic technologies and the establishment of selective comparisons between different genomes. Moreover, this investigation presents candidate genes and QTLs associated with various traits in different livestock species, such as body conformation, meat production and quality, carcass characteristics and composition, milk yield and composition, fertility, fiber production and characteristics, and disease resistance.

Integrating Genomics and Transcriptomics to Identify Candidate Genes for Egg Production in Taihe Black-Bone Silky Fowls (Gallus gallus domesticus Brisson)

The Taihe Black-Bone Silky Fowl (Gallus gallus domesticus Brisson) possesses significant value in terms of consumption, medicinal applications, and ornamental appeal, representing a precious genetic resource and traditional Chinese medicinal material. However, considerable variation exists within populations regarding egg-laying performance. This study integrates a whole-genome selection signal analysis (SSA) with a transcriptome analysis to identify genes associated with egg-laying traits in Taihe Black-Bone Silky Fowls. We identified 31 candidate genes under selection from the high-yield chicken (HC) and low-yield chicken (LC) groups. Additionally, through RNA-seq analysis, 257 common differentially expressed genes (DEGs) were identified from four comparative groups. Two overlapping genes-LPL and SETBP1-were found in both the selected gene and DEG lists. These selected genes and DEGs were enriched in pathways related to ovarian development, including the lysosome pathway, the ECM-receptor interaction pathway, the TGF-beta signaling pathway, the Wnt signaling pathway, the PPAR signaling pathway, and the glycerolipid metabolism pathway. These research findings contribute to the breeding of Taihe Black-Bone Silky Fowls with high egg production traits and provide a theoretical foundation for exploring the regulatory mechanisms of avian reproduction.

Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

The increasing global population drives a rising demand for food, particularly fish as a preferred protein source, straining capture fisheries. Overfishing has depleted wild stocks, emphasizing the need for advanced aquaculture technologies. Unlike agriculture, aquaculture has not seen substantial technological advancements. Artificial Intelligence (AI) tools like Internet of Things (IoT), machine learning, cameras, and algorithms offer solutions to reduce human intervention, enhance productivity, and monitor fish health, feed optimization, and water resource management. However, challenges such as data collection, standardization, model accuracy, interpretability, and integration with existing aquaculture systems persist. This review explores the adoption of AI techniques and tools to advance the aquaculture industry and bridge the gap between food supply and demand.

Global status of gene edited animals for agricultural applications

Gene editing (GnEd) involves using a site-directed nuclease to introduce a double-strand break (DSB) at a targeted location in the genome. A literature search was performed on the use of GnEd in animals for agricultural applications. Data was extracted from 212 peer-reviewed articles that described the production of at least one living animal employing GnEd technologies for agricultural purposes. The most common GnEd system reported was CRISPR/Cas9, and the most frequent type of edit was the unguided insertion or deletion resulting from the repair of the targeted DSB leading to a knock-out (KO) mutation. Animal groups included in the reviewed papers were ruminants (cattle, sheep, goats, n=63); monogastrics (pigs and rabbits, n=60); avian (chicken, duck, quail, n=17); aquatic (many species, n=65), and insects (honeybee, silkworm, n=7). Yield (32%), followed by reproduction (21%) and disease resistance (17%) were the most commonly targeted traits. Over half of the reviewed papers had Chinese first-authorship. Several countries, including Argentina, Australia, Brazil, Colombia and Japan, have adopted a regulatory policy that considers KO mutations introduced following GnEd DSB repair as akin to natural genetic variation, and therefore treat these GnEd animals analogously to those produced using conventional breeding. This approach has resulted in a non-GMO determination for a small number of GnEd food animal applications, including three species of GnEd KO fast-growing fish, (red sea bream, olive flounder and tiger pufferfish in Japan), KO fish and cattle in Argentina and Brazil, and porcine reproductive and respiratory syndrome (PRRS) virus disease-resistant KO pigs in Colombia.

Genetic selection for reduced health treatment costs in Holstein cows: implications from a long-term study

The objective of this study was to estimate genetic parameters of health treatment cost of Holstein cows from producer-recorded health treatments in 8 herds over an 8-yr period of time. Genetic parameters of health treatment cost were estimated in first (n = 2,214), second (n = 1,487) and third (n = 800) parities of US Holstein cows. The health treatments were uniformly defined and consistently recorded by 8 high-performance dairy herds in Minnesota from 2008 to 2015. A fixed treatment cost was assigned to 14 types of health treatments, and the cost included the mean veterinary expense obtained from the veterinary clinics that serviced the 8 herds, pharmaceuticals, and labor cost. The labor cost was $18/h, and the time incurred for each type of health treatment was determined from interviews with the herd owners. The 14 types of health treatment costs were partitioned into 5 categories: mastitis (including mastitis diagnostic test), reproduction (cystic ovary, retained placenta, and metritis), lameness (hoof treatments), metabolic (milk fever, displaced abomasum, ketosis, and digestive), and miscellaneous (respiratory, injury, and other). Health treatment cost for each cow was summed by category within lactation and also across categories within lactation. The estimates of heritability for health treatment cost were 0.13, 0.04, 0.10, 0.12, and 0.04 for the mastitis, reproduction, lameness, metabolic, and miscellaneous categories, respectively, in first parity. Genetic correlations between categories of health treatment cost in first parity were greatest for mastitis and reproduction (r = 0.85); however, phenotypic correlations between all categories were small (r < 0.16). Total health treatment cost had a large genetic correlation with somatic cell score (0.93) and 305-d milk production (0.44) in first parity; however, the genetic correlation (-0.60) between total health treatment cost and udder depth in first parity indicated a genetic relationship exists between shallow udders and less total health treatment cost. Total health treatment cost across categories had a heritability estimate of 0.25 in first parity, 0.16 in second parity, and 0.17 in third parity. Consequently, genetic selection for reduced health treatment cost should be possible by using producer-recorded health treatment records supplemented with treatment costs.