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Upadhyay VR, Ramesh V, Kumar H, Somagond YM, Priyadarsini S, Kuniyal A, Prakash V, Sahoo A. Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024. [PMID: 39012961 DOI: 10.1089/omi.2024.0109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
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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Affiliation(s)
| | - Vikram Ramesh
- ICAR-National Research Centre on Mithun, Medziphema, Nagaland, India
| | - Harshit Kumar
- ICAR-National Research Centre on Mithun, Medziphema, Nagaland, India
| | - Y M Somagond
- ICAR-National Research Centre on Mithun, Medziphema, Nagaland, India
| | | | - Aruna Kuniyal
- ICAR-National Research Centre on Camel, Bikaner, Rajasthan, India
| | - Ved Prakash
- ICAR-National Research Centre on Camel, Bikaner, Rajasthan, India
| | - Artabandhu Sahoo
- ICAR-National Research Centre on Camel, Bikaner, Rajasthan, India
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Hollifield MK, Chen CY, Psota E, Holl J, Lourenco D, Misztal I. Estimating genetic parameters of digital behavior traits and their relationship with production traits in purebred pigs. Genet Sel Evol 2024; 56:29. [PMID: 38627636 PMCID: PMC11022375 DOI: 10.1186/s12711-024-00902-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/08/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND With the introduction of digital phenotyping and high-throughput data, traits that were previously difficult or impossible to measure directly have become easily accessible, offering the opportunity to enhance the efficiency and rate of genetic gain in animal production. It is of interest to assess how behavioral traits are indirectly related to the production traits during the performance testing period. The aim of this study was to assess the quality of behavior data extracted from day-wise video recordings and estimate the genetic parameters of behavior traits and their phenotypic and genetic correlations with production traits in pigs. Behavior was recorded for 70 days after on-test at about 10 weeks of age and ended at off-test for 2008 female purebred pigs, totaling 119,812 day-wise records. Behavior traits included time spent eating, drinking, laterally lying, sternally lying, sitting, standing, and meters of distance traveled. A quality control procedure was created for algorithm training and adjustment, standardizing recording hours, removing culled animals, and filtering unrealistic records. RESULTS Production traits included average daily gain (ADG), back fat thickness (BF), and loin depth (LD). Single-trait linear models were used to estimate heritabilities of the behavior traits and two-trait linear models were used to estimate genetic correlations between behavior and production traits. The results indicated that all behavior traits are heritable, with heritability estimates ranging from 0.19 to 0.57, and showed low-to-moderate phenotypic and genetic correlations with production traits. Two-trait linear models were also used to compare traits at different intervals of the recording period. To analyze the redundancies in behavior data during the recording period, the averages of various recording time intervals for the behavior and production traits were compared. Overall, the average of the 55- to 68-day recording interval had the strongest phenotypic and genetic correlation estimates with the production traits. CONCLUSIONS Digital phenotyping is a new and low-cost method to record behavior phenotypes, but thorough data cleaning procedures are needed. Evaluating behavioral traits at different time intervals offers a deeper insight into their changes throughout the growth periods and their relationship with production traits, which may be recorded at a less frequent basis.
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Affiliation(s)
| | | | | | | | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Ignacy Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
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Ghaderi Zefreh M, Doeschl-Wilson AB, Riggio V, Matika O, Pong-Wong R. Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals. Front Genet 2023; 14:1127530. [PMID: 37252663 PMCID: PMC10213464 DOI: 10.3389/fgene.2023.1127530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/02/2023] [Indexed: 05/31/2023] Open
Abstract
Sustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simulations of sheep populations to assess the effect of genomic data, different genetic evaluation models and phenotyping strategies on prediction accuracies and bias for production potential and resilience. In addition, we also assessed the effect of different selection strategies on the improvement of these traits. Results show that estimation of both traits greatly benefits from taking repeated measurements and from using genomic information. However, the prediction accuracy for production potential is compromised, and resilience estimates tends to be upwards biased, when families are clustered in groups even when genomic information is used. The prediction accuracy was also found to be lower for both traits, resilience and production potential, when the environment challenge levels are unknown. Nevertheless, we observe that genetic gain in both traits can be achieved even in the case of unknown environmental challenge, when families are distributed across a large range of environments. Simultaneous genetic improvement in both traits however greatly benefits from the use of genomic evaluation, reaction norm models and phenotyping in a wide range of environments. Using models without the reaction norm in scenarios where there is a trade-off between resilience and production potential, and phenotypes are collected from a narrow range of environments may result in a loss for one trait. The study demonstrates that genomic selection coupled with reaction-norm models offers great opportunities to simultaneously improve productivity and resilience of farmed animals even in the case of a trade-off.
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Affiliation(s)
- Masoud Ghaderi Zefreh
- The Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Valentina Riggio
- The Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United Kingdom
| | - Oswald Matika
- The Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Tropical Livestock Genetics and Health (CTLGH), The Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United Kingdom
| | - Ricardo Pong-Wong
- The Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United Kingdom
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Ruban S, Danshyn V, Matvieiev М, Borshch OO, Borshch OV, Korol-Bezpala L. Characteristics of Lactation Curve and Reproduction in Dairy Cattle. ACTA UNIVERSITATIS AGRICULTURAE ET SILVICULTURAE MENDELIANAE BRUNENSIS 2023. [DOI: 10.11118/actaun.2022.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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Menendez HM, Brennan JR, Gaillard C, Ehlert K, Quintana J, Neethirajan S, Remus A, Jacobs M, Teixeira IAMA, Turner BL, Tedeschi LO. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and Challenges of Confined and Extensive Precision Livestock Production. J Anim Sci 2022; 100:6577180. [PMID: 35511692 PMCID: PMC9171331 DOI: 10.1093/jas/skac160] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/28/2022] [Indexed: 11/18/2022] Open
Abstract
Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.
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Affiliation(s)
- H M Menendez
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - J R Brennan
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - C Gaillard
- Institut Agro, PEGASE, INRAE, 35590 Saint Gilles, France
| | - K Ehlert
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - J Quintana
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - Suresh Neethirajan
- Farmworx, Adaptation Physiology, Animal Sciences Group, Wageningen University, 6700 AH, The Netherlands
| | - A Remus
- Sherbrooke Research and Development Centre, 2000 College Street, Sherbrooke, QC J1M 1Z3, Canada
| | - M Jacobs
- FR Analytics B.V., 7642 AP Wierden, The Netherlands
| | - I A M A Teixeira
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Twin Falls, ID 83301, USA
| | - B L Turner
- Department of Agriculture, Agribusiness, and Environmental Science, and King Ranch® Institute for Ranch Management, Texas A&M University-Kingsville, 700 University Blvd MSC 228, Kingsville, TX 78363, USA
| | - L O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
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