家畜研究中的表型组学:家畜表型组学研究:全球范围内数字表型和其他定量技术的瓶颈与前景》(Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on the Global Scale)。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
ACS Applied Bio Materials Pub Date : 2024-08-01 Epub Date: 2024-07-16 DOI:10.1089/omi.2024.0109
Vishwa Ranjan Upadhyay, Vikram Ramesh, Harshit Kumar, Y M Somagond, Swagatika Priyadarsini, Aruna Kuniyal, Ved Prakash, Artabandhu Sahoo
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引用次数: 0

摘要

将基因组学应用于实际生活的瓶颈还包括表型组学。这不仅适用于基因组医学和公共卫生基因组学,也适用于生态学和家畜表型组学。这篇专家叙事综述以家畜研究为背景,探讨了基因构成与不同生物水平上可观察到的表型性状之间错综复杂的关系。我们解读并强调了精确表型数据在选择性育种结果中的重要意义,并考察了表型组学的多方面应用,包括牲畜的改良、福利评估、繁殖性状和环境适应性等。由于表型性状表现出很强的相关性,因此在测量特定生物结果的同时测量表型性状,可以深入了解牲畜的表现、整体健康以及发病率和疾病等临床终点。此外,对家畜进行自动化评估还能监测不同物种的动态表型特征,有助于更深入地了解它们如何适应环境和随之而来的压力。家畜遗传改良的一个关键挑战是在没有直接测量的情况下预测个体的最佳适应性。无人机系统的时间预测可以超越基因组预测,提供牲畜的深度数据。在不久的将来,数字表型和数字生物标记可能会进一步揭示动物耐压性、适应性和福利方面错综复杂的基因问题,从而选出具有气候适应能力和高产的牲畜。因此,本专家综述深入探讨了与表型相关的挑战,并讨论了影响未来家畜生物学研究的技术进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale.

Bottlenecks in moving genomics to real-life applications also include phenomics. This is true not only for genomics medicine and public health genomics but also in ecology and livestock phenomics. This expert narrative review explores the intricate relationship between genetic makeup and observable phenotypic traits across various biological levels in the context of livestock research. We unpack and emphasize the significance of precise phenotypic data in selective breeding outcomes and examine the multifaceted applications of phenomics, ranging from improvement to assessing welfare, reproductive traits, and environmental adaptation in livestock. As phenotypic traits exhibit strong correlations, their measurement alongside specific biological outcomes provides insights into performance, overall health, and clinical endpoints like morbidity and disease. In addition, automated assessment of livestock holds potential for monitoring the dynamic phenotypic traits across various species, facilitating a deeper comprehension of how they adapt to their environment and attendant stressors. A key challenge in genetic improvement in livestock is predicting individuals with optimal fitness without direct measurement. Temporal predictions from unmanned aerial systems can surpass genomic predictions, offering in-depth data on livestock. In the near future, digital phenotyping and digital biomarkers may further unravel the genetic intricacies of stress tolerance, adaptation and welfare aspects of animals enabling the selection of climate-resilient and productive livestock. This expert review thus delves into challenges associated with phenotyping and discusses technological advancements shaping the future of biological research concerning livestock.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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