Applications of Omics Technology for Livestock Selection and Improvement

Abstract

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

Development of Microsatellite Markers Based on Transcriptome Sequencing and Evaluation of Genetic Diversity in Swimming Crab (Portunus trituberculatus)

“…In recent years, transcriptome sequencing (RNA-seq) is widely used in the study of species genetics because of its wide dynamic range, precise, sensitivity, unbiased quantification of transcripts, and comprehensive coverage of all expressed sequences in a given tissue sample ( Chakraborty et al, 2022 ). Now, RNA-seq is a very updated and efficient method for discovering new genes, expression pattern identification, and development of SSR markers with higher throughput and much lower cost ( Li et al, 2018 ; Tulsania et al, 2020 ).…”

Biochemical profiling of metabolomics in heavy metal-intoxicated impaired metabolism and its amelioration using plant-based bioactive compound

“…Metabolomics is one of the branches of “omics” science. The omics science has several branches, including genomics, proteomics, transcriptomics, inomics, phenomics, and metabolomics ( Chakraborty et al, 2022 ). The definition of metabolomics is the quantitative and qualitative characterization of smaller molecules that show some changes in response to stimuli from external and internal sources, having a molecular weight of less than 1,000–1,500 Da ( Belhaj et al, 2021 ).…”

“…When Pb enters the body, it interacts with proteins and inhibits calcium action, amide, and sulfhydryl enzymes. The main route of its toxicity is oxidative stress in the liver, resulting in the suppression of antioxidant enzymes such as catalase, superoxide dismutase ( Chakraborty et al, 2022 ), glutathione reductase (GR), and glutathione peroxidase (GPx) ( Ramah et al, 2019 ; Rozier and Liebelt, 2019 ). The GSH molecule has the sulfhydryl group–SH in its structure to whom Pb has a greater affinity ( Vacchi-Suzzi et al, 2018 ; Balali-Mood et al, 2021 ).…”

Editorial: Omics applied to livestock genetics

“…For instance, the Functional Annotation of Animal Genomes (FAANG) project has generated datasets to decipher the function of genome segments, and it has analyzed samples from approximately 15 species, including pigs, cattle, sheep, and salmon ( Giuffra et al, 2019 ). Moreover, the “omics” approaches can be holistically applied to improve animal breeding strategies based on biology-driven genomic predictions, besides a better understanding of the genomic background of phenotypic variability in livestock systems ( Chakraborty et al, 2022 ).…”