Chakraborty Dibyendu, Sharma Neelesh, Kour Savleen, Sodhi Simrinder Singh, Gupta Mukesh Kumar, Lee Sung Jin, Son Young Ok
Division of Animal Genetics and Breeding, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Ranbir Singh Pura, India.
Division of Veterinary Medicine, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Ranbir Singh Pura, India.
Front Genet. 2022 Jun 2;13:774113. doi: 10.3389/fgene.2022.774113. eCollection 2022.
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 techniques and the availability of 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 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, 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.
传统的动物选择和育种方法基于动物的表型表现。这些方法存在局限性,特别是对于限性性状和在生命周期后期表达的性状(如胴体性状)。因此,遗传进展缓慢,世代间隔较长。随着高通量技术的出现以及技术和复杂分析软件包的可用性,已经开发出了几种有前景的工具和方法来估计动物的实际遗传潜力。现在已经能够收集和访问包含不同基因组学、转录组学、蛋白质组学、代谢组学和表型组学数据以及动物水平数据(如寿命、行为、适应性等)的大型复杂数据集,这为更好地理解调节动物实际性能的机制提供了新机会。生物学、生物信息学、统计学和计算生物学等多个领域的技术成本和专业知识在某些情况下限制了这些技术的应用。群体规模和准确的表型数据记录是制定合适的选择和育种策略的其他重大限制因素。尽管如此,这些技术可以通过在生命早期协助选择遗传上优良、无疾病的动物来估计更准确的育种值(BVs),并提高遗传进展,从而提高动物的生产力和盈利能力。本文综述了各种组学技术及其在动物遗传选择和育种决策中的局限性。