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Banerjee P, Diniz WJS. Advancing Dairy and Beef Genetics Through Genomic Technologies. Vet Clin North Am Food Anim Pract 2024:S0749-0720(24)00033-1. [PMID: 39181791 DOI: 10.1016/j.cvfa.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2024] Open
Abstract
The US beef and dairy industries have made remarkable advances in sustainability and productivity through technological advancements, including selective breeding. Yet, challenges persist due to the complex nature of quantitative traits. While the beef industry has progressed in adopting genomic technologies, the availability of phenotypic data remains an obstacle. To meet the need for sustainable production systems, novel traits are being targeted for selection. Additionally, emerging approaches such as genome editing and high-throughput phenotyping hold promise for further genetic progress. Future research should address the challenges of translating functional genomic findings into practical applications, while simultaneously harnessing analytical methods.
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Affiliation(s)
- Priyanka Banerjee
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA
| | - Wellison J S Diniz
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA.
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Mitsunaga TM, Nery Garcia BL, Pereira LBR, Costa YCB, da Silva RF, Delbem ACB, dos Santos MV. Current Trends in Artificial Intelligence and Bovine Mastitis Research: A Bibliometric Review Approach. Animals (Basel) 2024; 14:2023. [PMID: 39061485 PMCID: PMC11273831 DOI: 10.3390/ani14142023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 07/08/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area.
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Affiliation(s)
- Thatiane Mendes Mitsunaga
- Luiz de Queiroz College of Agriculture—ESALQ, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-900, SP, Brazil;
| | - Breno Luis Nery Garcia
- School of Veterinary Medicine and Animal Science, University of São Paulo, Pirassununga 13635-900, SP, Brazil; (B.L.N.G.); (L.B.R.P.)
| | - Ligia Beatriz Rizzanti Pereira
- School of Veterinary Medicine and Animal Science, University of São Paulo, Pirassununga 13635-900, SP, Brazil; (B.L.N.G.); (L.B.R.P.)
| | | | - Roberto Fray da Silva
- Biosystems Engineering Department, Luiz de Queiroz College of Agriculture—ESALQ, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-900, SP, Brazil;
- Center for Artificial Intelligence—C4AI, University of Sao Paulo, Av. Prof. Lúcio Martins Rodrigues, 370-Butantã, São Paulo 05508-020, SP, Brazil;
| | - Alexandre Cláudio Botazzo Delbem
- Center for Artificial Intelligence—C4AI, University of Sao Paulo, Av. Prof. Lúcio Martins Rodrigues, 370-Butantã, São Paulo 05508-020, SP, Brazil;
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13560-970, SP, Brazil
| | - Marcos Veiga dos Santos
- School of Veterinary Medicine and Animal Science, University of São Paulo, Pirassununga 13635-900, SP, Brazil; (B.L.N.G.); (L.B.R.P.)
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3
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Nascimento BM, Cavani L, Caputo MJ, Marinho MN, Borchers MR, Wallace RL, Santos JEP, White HM, Peñagaricano F, Weigel KA. Genetic relationships between behavioral traits and feed efficiency traits in lactating Holstein cows. J Dairy Sci 2024:S0022-0302(24)00835-X. [PMID: 38825121 DOI: 10.3168/jds.2023-24526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 04/17/2024] [Indexed: 06/04/2024]
Abstract
The evaluation of dairy cow feed efficiency using residual feed intake accounts for known energy sinks. However, behavioral traits may also contribute to the variation in feed efficiency. Our objective was to estimate the heritability and repeatability of behavioral traits and their genetic correlations with feed efficiency and its components in lactating Holstein cows. The first data set consisted of 36,075 daily rumination and lying time records collected using a SMARTBOW ear tag accelerometer (Zoetis, Parsippany, NJ) and 6,371 weekly feed efficiency records of 728 cows from the University of Wisconsin-Madison. The second data set consisted of 59,155 daily activity records, measured as number of steps, recorded by pedometers (AfiAct; S.A.E. Afikim, Kibbutz Afikim, Israel), and 8,626 weekly feed efficiency records of 635 cows from the University of Florida. Feed efficiency and its components included dry matter intake, change in body weight, metabolic body weight, secreted milk energy, and residual feed intake. The statistical models included the fixed effect of cohort, lactation number, and days in milk, and the random effects of animal and permanent environment. Heritability estimates for behavioral traits using daily records were 0.19 ± 0.06 for rumination and activity, and 0.37 ± 0.07 for lying time. Repeatability estimates for behavioral traits using daily data ranged from 0.56 ± 0.02 for activity to 0.62 ± 0.01 for lying time. Both heritability and repeatability estimates were larger when weekly records instead of daily records were used. Rumination and activity had positive genetic correlations with residual feed intake (0.40 ± 0.19 and 0.31 ± 0.22, respectively) while lying time had a negative genetic correlation with this residual feed intake (-0.27 ± 0.11). These results indicate that more efficient cows tend to spend more time lying and less time active. Additionally, less efficient cows tend to eat more and therefore also tend to ruminate longer. Overall, sensor-based behavioral traits are heritable and genetically correlated with feed efficiency and its components and, therefore, they could be used as indicators to identify feed efficient cows within the herd.
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Affiliation(s)
- Bárbara M Nascimento
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706.
| | - Ligia Cavani
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Malia J Caputo
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Mariana N Marinho
- Department of Animal Sciences, University of Florida, Gainesville, FL 32611
| | | | | | - José E P Santos
- Department of Animal Sciences, University of Florida, Gainesville, FL 32611
| | - Heather M White
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Kent A Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
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Mancin E, Maltecca C, Huang YJ, Mantovani R, Tiezzi F. A first characterization of the microbiota-resilience link in swine. MICROBIOME 2024; 12:53. [PMID: 38486255 PMCID: PMC10941389 DOI: 10.1186/s40168-024-01771-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/30/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes. RESULTS This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience. CONCLUSION Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations. Video Abstract.
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Affiliation(s)
- Enrico Mancin
- Department of Agronomy, Animals and Environment, (DAFNAE), Food, Natural Resources, University of Padova, Viale del Università 14, 35020, Legnaro (Padova), Italy
| | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144, Firenze, Italy
| | - Yi Jian Huang
- Smithfield Premium Genetics, Rose Hill, NC, 28458, USA
| | - Roberto Mantovani
- Department of Agronomy, Animals and Environment, (DAFNAE), Food, Natural Resources, University of Padova, Viale del Università 14, 35020, Legnaro (Padova), Italy
| | - Francesco Tiezzi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144, Firenze, Italy.
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Tuggle CK, Clarke JL, Murdoch BM, Lyons E, Scott NM, Beneš B, Campbell JD, Chung H, Daigle CL, Das Choudhury S, Dekkers JCM, Dórea JRR, Ertl DS, Feldman M, Fragomeni BO, Fulton JE, Guadagno CR, Hagen DE, Hess AS, Kramer LM, Lawrence-Dill CJ, Lipka AE, Lübberstedt T, McCarthy FM, McKay SD, Murray SC, Riggs PK, Rowan TN, Sheehan MJ, Steibel JP, Thompson AM, Thornton KJ, Van Tassell CP, Schnable PS. Current challenges and future of agricultural genomes to phenomes in the USA. Genome Biol 2024; 25:8. [PMID: 38172911 PMCID: PMC10763150 DOI: 10.1186/s13059-023-03155-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (phenomics). Identifying the most critical opportunities and challenges in genome to phenome (G2P) research is the focus of this paper. Previously (Genome Biol, 23(1):1-11, 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with USA federal government agencies expand public-private partnerships, and engage with external stakeholders to achieve a shared vision of future the AG2PI. Acting on this latter step, AG2PI organized the "Thinking Big: Visualizing the Future of AG2PI" two-day workshop held September 9-10, 2022, in Ames, Iowa, co-hosted with the United State Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA). During the meeting, attendees were asked to use their experience and curiosity to review the current status of agricultural genome to phenome (AG2P) work and envision the future of the AG2P field. The topic summaries composing this paper are distilled from two 1.5-h small group discussions. Challenges and solutions identified across multiple topics at the workshop were explored. We end our discussion with a vision for the future of agricultural progress, identifying two areas of innovation needed: (1) innovate in genetic improvement methods development and evaluation and (2) innovate in agricultural research processes to solve societal problems. To address these needs, we then provide six specific goals that we recommend be implemented immediately in support of advancing AG2P research.
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Tang M. Analysis and evaluate of agricultural resources using data analytic methods. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:627-649. [PMID: 38303437 DOI: 10.3934/mbe.2024027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
In the agricultural sector, farmers and agribusiness are confronted with a multitude of complex choices every day. These selections are influenced by multiple variables that significantly affect their outcomes. The primary source of revenue for a good deal of individuals is derived from the agricultural sector. The provision of precise and punctual predictions on crop yields has significant importance in facilitating informed investment choices and shaping agricultural policies. One of the challenges encountered is the presence of old or incomplete data about the accessibility of resources. This represents a significant obstacle in accurately ascertaining the present state of affairs. The process of evaluating becomes complex as a result of the diverse range of soil conditions and climatic factors. This research introduces a novel approach called Enhanced Gravitational Search Optimized based Gated Recurrent Unit (EGSO-GRU) for the purpose of calculating crop production. The dataset was first gathered and pre-processed using a normalization method. Enhanced independent component analyses (EICA) have been employed for the purpose of extracting features. To determine the suggest method achievement with regard to accuracy (95.89%), specificity (92.4%), MSE (0.071), RMSE (0.210) and MAE (0.199). The proposed method achieved greater crop prediction accuracy, outperforming the majority of the existing models. The necessity of this progress is vital to the successful operation of crops. The concept signifies a technological advancement aimed at optimizing agricultural resources, hence fostering enhanced productivity and long-term sustainability within the farming industry.
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Affiliation(s)
- Min Tang
- School of Marxism, Xi'an Jiaotong University, Xi'an 710049, China
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Saravanan KA, Panigrahi M, Kumar H, Nayak SS, Rajawat D, Bhushan B, Dutt T. Progress and future perspectives of livestock genomics in India: a mini review. Anim Biotechnol 2023; 34:1979-1987. [PMID: 35369840 DOI: 10.1080/10495398.2022.2056046] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
The field of genetics has evolved a lot after the emergence of molecular and advanced genomic technologies. The advent of Next Generation Sequencing, SNP genotyping platforms and simultaneous reduction in the cost of sequencing had opened the door to genomic research in farm animals. There are various applications of genomics in livestock, such as the use of genomic data: (i) to investigate genetic diversity and breed composition/population structure (ii) to identify genetic variants and QTLs related to economically important and ecological traits, genome-wide association studies (GWAS) and genomic signatures of selection; (iii) to enhance breeding programs by genomic selection. Compared to traditional methods, genomic selection is expected to improve selection response by increasing selection accuracy and reducing the generation interval due to early selection. Genomic selection (GS) in developed countries has led to rapid genetic gains, especially in dairy cattle, due to a well-established genetic evaluation system. Indian livestock system is still lagging behind developed nations in adopting these technologies. This review discusses the current status, challenges, and future perspectives of livestock genomics in India.
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Affiliation(s)
- K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Bareilly, UP, India
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Neethirajan S. Digital Phenotyping: A Game Changer for the Broiler Industry. Animals (Basel) 2023; 13:2585. [PMID: 37627376 PMCID: PMC10451972 DOI: 10.3390/ani13162585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/04/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
In response to escalating global demand for poultry, the industry grapples with an array of intricate challenges, from enhancing productivity to improving animal welfare and attenuating environmental impacts. This comprehensive review explores the transformative potential of digital phenotyping, an emergent technological innovation at the cusp of dramatically reshaping broiler production. The central aim of this study is to critically examine digital phenotyping as a pivotal solution to these multidimensional industry conundrums. Our investigation spotlights the profound implications of 'digital twins' in the burgeoning field of broiler genomics, where the production of exact digital counterparts of physical entities accelerates genomics research and its practical applications. Further, this review probes into the ongoing advancements in the research and development of a context-sensitive, multimodal digital phenotyping platform, custom-built to monitor broiler health. This paper critically evaluates this platform's potential in revolutionizing health monitoring, fortifying the resilience of broiler production, and fostering a harmonious balance between productivity and sustainability. Subsequently, the paper provides a rigorous assessment of the unique challenges that may surface during the integration of digital phenotyping within the industry. These span from technical and economic impediments to ethical deliberations, thus offering a comprehensive perspective. The paper concludes by highlighting the game-changing potential of digital phenotyping in the broiler industry and identifying potential future directions for the field, underlining the significance of continued research and development in unlocking digital phenotyping's full potential. In doing so, it charts a course towards a more robust, sustainable, and productive broiler industry. The insights garnered from this study hold substantial value for a broad spectrum of stakeholders in the broiler industry, setting the stage for an imminent technological evolution in poultry production.
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Affiliation(s)
- Suresh Neethirajan
- Department of Animal Science and Aquaculture, Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
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Neethirajan S. Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. SENSORS (BASEL, SWITZERLAND) 2023; 23:7045. [PMID: 37631580 PMCID: PMC10458494 DOI: 10.3390/s23167045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023]
Abstract
This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in 'shy feeder' cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector.
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Affiliation(s)
- Suresh Neethirajan
- Department of Animal Science and Aquaculture, Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
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Marrella MA, Biase FH. A multi-omics analysis identifies molecular features associated with fertility in heifers (Bos taurus). Sci Rep 2023; 13:12664. [PMID: 37542054 PMCID: PMC10403585 DOI: 10.1038/s41598-023-39858-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/01/2023] [Indexed: 08/06/2023] Open
Abstract
Infertility or subfertility is a critical barrier to sustainable cattle production, including in heifers. The development of heifers that do not produce a calf within an optimum window of time is a critical factor for the profitability and sustainability of the cattle industry. In parallel, heifers are an excellent biomedical model for understanding the underlying etiology of infertility because well-nourished heifers can still be infertile, mostly because of inherent physiological and genetic causes. Using a high-density single nucleotide polymorphism (SNP) chip, we collected genotypic data, which were analyzed using an association analysis in PLINK with Fisher's exact test. We also produced quantitative transcriptome data and proteome data. Transcriptome data were analyzed using the quasi-likelihood test followed by the Wald's test, and the likelihood test and proteome data were analyzed using a generalized mixed model and Student's t-test. We identified two SNPs significantly associated with heifer fertility (rs110918927, chr12: 85648422, P = 6.7 × 10-7; and rs109366560, chr11:37666527, P = 2.6 × 10-5). We identified two genes with differential transcript abundance (eFDR ≤ 0.002) between the two groups (Fertile and Sub-Fertile): Adipocyte Plasma Membrane Associated Protein (APMAP, 1.16 greater abundance in the Fertile group) and Dynein Axonemal Intermediate Chain 7 (DNAI7, 1.23 greater abundance in the Sub-Fertile group). Our analysis revealed that the protein Alpha-ketoglutarate-dependent dioxygenase FTO was more abundant in the plasma collected from Fertile heifers relative to their Sub-Fertile counterparts (FDR < 0.05). Lastly, an integrative analysis of the three datasets identified a series of molecular features (SNPs, gene transcripts, and proteins) that discriminated 21 out of 22 heifers correctly based on their fertility category. Our multi-omics analyses confirm the complex nature of female fertility. Very importantly, our results also highlight differences in the molecular profile of heifers associated with fertility that transcend the constraints of breed-specific genetic background.
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Affiliation(s)
- Mackenzie A Marrella
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Fernando H Biase
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
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Cole J, Makanjuola B, Rochus C, van Staaveren N, Baes C. The effects of breeding and selection on lactation in dairy cattle. Anim Front 2023; 13:55-63. [PMID: 37324206 PMCID: PMC10266753 DOI: 10.1093/af/vfad044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023] Open
Affiliation(s)
- John B Cole
- URUS Group LP, Madison, WI 53718
- Department of Animal Sciences, University of Florida, Gainesville
- Department of Animal Science, North Carolina State University, Raleigh
| | - Bayode O Makanjuola
- Centre for Genetic Improvement of Livestock, University of Guelph, N1G 2W4, Canada
| | - Christina M Rochus
- Centre for Genetic Improvement of Livestock, University of Guelph, N1G 2W4, Canada
| | - Nienke van Staaveren
- Centre for Genetic Improvement of Livestock, University of Guelph, N1G 2W4, Canada
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Lafontaine S, Labrecque R, Blondin P, Cue RI, Sirard MA. Comparison of cattle derived from in vitro fertilization, multiple ovulation embryo transfer, and artificial insemination for milk production and fertility traits. J Dairy Sci 2023; 106:4380-4396. [PMID: 37028966 DOI: 10.3168/jds.2022-22736] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/19/2022] [Indexed: 04/09/2023]
Abstract
The use of assisted-reproduction technologies such as in vitro fertilization (IVF) is increasing, particularly in dairy cattle. The question of consequences in later life has not yet been directly addressed by studies on large animal populations. Studies on rodents and early data from humans and cattle suggest that in vitro manipulation of gametes and embryos could result in long-term alteration of metabolism, growth, and fertility. Our goal was to better describe these presumed consequences in the population of dairy cows produced by IVF in Québec (Canada) and to compare them to animals conceived by artificial insemination (AI) or multiple ovulation embryo transfer (MOET). To do so, we leveraged a large phenotypic database (2.5 million animals and 4.5 million lactations) from milk records in Québec aggregated by Lactanet (Sainte-Anne-de-Bellevue, QC, Canada) and spanning 2012 to 2019. We identified 304,163, 12,993, and 732 cows conceived by AI, MOET, and IVF, respectively, for a total of 317,888 Holstein animals from which we retrieved information for 576,448, 24,192, and 1,299 lactations (total = 601,939), respectively. Genetic energy-corrected milk yield (GECM) and Lifetime Performance Index (LPI) of the parents of cows were used to normalize for genetic potential across animals. When compared with the general Holstein population, MOET and IVF cows outperformed AI cows. However, when comparing those same MOET and IVF cows with only herdmates and accounting for their higher GECM in the models, we found no statistical difference between the conception methods for milk production across the first 3 lactations. We also found that the rate of Lifetime Performance Index improvement of the IVF population during the 2012 to 2019 period was less than the rate observed in the AI population. Fertility analysis revealed that MOET and IVF cows also scored 1 point lower than their parents on the daughter fertility index and had a longer interval from first service to conception, with an average of 35.52 d compared with 32.45 for MOET and 31.87 for AI animals. These results highlight the challenges of elite genetic improvement while attesting to the progress the industry has made in minimizing epigenetic disturbance during embryo production. Nonetheless, additional work is required to ensure that IVF animals can maintain their performance and fertility potential.
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Affiliation(s)
- Simon Lafontaine
- Centre de recherche en reproduction, développement et santé intergénérationnelle (CRDSI), Département des Sciences Animales, Faculté des sciences de l'agriculture et de l'alimentation, Université Laval, Québec, QC G1K 7P4, Canada
| | - Rémi Labrecque
- SEMEX Boviteq, 3450 Rue Sicotte, Saint-Hyacinthe, QC J2S 2M2, Canada
| | - Patrick Blondin
- SEMEX Boviteq, 3450 Rue Sicotte, Saint-Hyacinthe, QC J2S 2M2, Canada
| | - Roger I Cue
- Department of Animal Science, McGill University, Montréal, QC H9X 3V9, Canada
| | - Marc-André Sirard
- Centre de recherche en reproduction, développement et santé intergénérationnelle (CRDSI), Département des Sciences Animales, Faculté des sciences de l'agriculture et de l'alimentation, Université Laval, Québec, QC G1K 7P4, Canada.
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13
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Liang M, Cao S, Deng T, Du L, Li K, An B, Du Y, Xu L, Zhang L, Gao X, Li J, Guo P, Gao H. MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits. Brief Bioinform 2023; 24:7031157. [PMID: 36752363 DOI: 10.1093/bib/bbad043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/13/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023] Open
Abstract
Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.
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Affiliation(s)
- Mang Liang
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Sheng Cao
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Tianyu Deng
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Lili Du
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Keanning Li
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Bingxing An
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Yueying Du
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Lingyang Xu
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Lupei Zhang
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Xue Gao
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Junya Li
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | | | - Huijiang Gao
- Chinese Academy of Agricultural Sciences Institute of Animal Science
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14
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Doornweerd JE, Kootstra G, Veerkamp RF, de Klerk B, Fodor I, van der Sluis M, Bouwman AC, Ellen ED. Passive radio frequency identification and video tracking for the determination of location and movement of broilers. Poult Sci 2023; 102:102412. [PMID: 36621101 PMCID: PMC9841275 DOI: 10.1016/j.psj.2022.102412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/28/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
Phenotypes on individual animals are required for breeding programs to be able to select for traits. However, phenotyping individual animals can be difficult and time-consuming, especially for traits related to health, welfare, and performance. Individual broiler behavior could serve as a proxy for these traits when recorded automatically and reliably on many animals. Sensors could record individual broiler behavior, yet different sensors can differ in their assessment. In this study a comparison was made between a passive radio frequency identification (RFID) system (grid of antennas underneath the pen) and video tracking for the determination of location and movement of 3 color-marked broilers at d 18. Furthermore, a systems comparison of derived behavioral metrics such as space usage, locomotion activity and apparent feeding and drinking behavior was made. Color-marked broilers simplified the computer vision task for YOLOv5 to detect, track, and identify the animals. Animal locations derived from the RFID-system and based on video were largely in agreement. Most location differences (77.5%) were within the mean radius of the antennas' enclosing circle (≤128 px, 28.15 cm), and 95.3% of the differences were within a one antenna difference (≤256 px, 56.30 cm). Animal movement was not always registered by the RFID-system whereas video was sensitive to detection noise and the animal's behavior (e.g., pecking). The method used to determine location and the systems' sensitivities to movement led to differences in behavioral metrics. Behavioral metrics derived from video are likely more accurate than RFID-system derived behavioral metrics. However, at present, only the RFID-system can provide individual identification for non-color marked broilers. A combination of verifiable and detailed video with the unique identification of RFID could make it possible to identify, describe, and quantify a wide range of individual broiler behaviors.
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Affiliation(s)
- J E Doornweerd
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands.
| | - G Kootstra
- Farm Technology, Wageningen University & Research, 6700 AA Wageningen, the Netherlands
| | - R F Veerkamp
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| | - B de Klerk
- Research & Development, Cobb Europe BV, 5831 GH Boxmeer, the Netherlands
| | - I Fodor
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| | - M van der Sluis
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| | - A C Bouwman
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| | - E D Ellen
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
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15
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Kaur U, Malacco VMR, Bai H, Price TP, Datta A, Xin L, Sen S, Nawrocki RA, Chiu G, Sundaram S, Min BC, Daniels KM, White RR, Donkin SS, Brito LF, Voyles RM. Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming. J Anim Sci 2023; 101:skad206. [PMID: 37335911 PMCID: PMC10370899 DOI: 10.1093/jas/skad206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/17/2023] [Indexed: 06/21/2023] Open
Abstract
Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.
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Affiliation(s)
- Upinder Kaur
- School of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA
| | - Victor M R Malacco
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Huiwen Bai
- School of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA
| | - Tanner P Price
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Arunashish Datta
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Lei Xin
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Shreyas Sen
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Robert A Nawrocki
- School of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA
| | - George Chiu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Shreyas Sundaram
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Byung-Cheol Min
- Department of Computer and Information Technology, West Lafayette, IN, 47907, USA
| | - Kristy M Daniels
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Robin R White
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Shawn S Donkin
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Richard M Voyles
- School of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA
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16
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Determination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures. Vet Sci 2023; 10:vetsci10010032. [PMID: 36669033 PMCID: PMC9866369 DOI: 10.3390/vetsci10010032] [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: 11/14/2022] [Revised: 12/20/2022] [Accepted: 12/29/2022] [Indexed: 01/04/2023] Open
Abstract
Deep learning algorithms can now be used to identify, locate, and count items in an image thanks to advancements in image processing technology. The successful application of image processing technology in different fields has attracted much attention in the field of agriculture in recent years. This research was done to ascertain the number of indigestible cereal grains in animal feces using an image processing method. In this study, a regression-based way of object counting was used to predict the number of cereal grains in the feces. For this purpose, we have developed two different neural network architectures based upon Fully Convolutional Regression Networks (FCRN) and U-Net. The images used in the study were obtained from three different dairy cows enterprises operating in Nigde Province. The dataset consists of the 277 distinct dropping images of dairy cows in the farm. According to findings of the study, both models yielded quite acceptable prediction accuracy with U-Net providing slightly better prediction with a MAE value of 16.69 in the best case, compared to 23.65 MAE value of FCRN with the same batch.
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17
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Invited Review: Genetic decision tools for increasing cow efficiency and sustainability in forage-based beef systems. APPLIED ANIMAL SCIENCE 2022. [DOI: 10.15232/aas.2022-02306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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18
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Jones HE, Wilson PB. Progress and opportunities through use of genomics in animal production. Trends Genet 2022; 38:1228-1252. [PMID: 35945076 DOI: 10.1016/j.tig.2022.06.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 06/08/2022] [Accepted: 06/17/2022] [Indexed: 01/24/2023]
Abstract
The rearing of farmed animals is a vital component of global food production systems, but its impact on the environment, human health, animal welfare, and biodiversity is being increasingly challenged. Developments in genetic and genomic technologies have had a key role in improving the productivity of farmed animals for decades. Advances in genome sequencing, annotation, and editing offer a means not only to continue that trend, but also, when combined with advanced data collection, analytics, cloud computing, appropriate infrastructure, and regulation, to take precision livestock farming (PLF) and conservation to an advanced level. Such an approach could generate substantial additional benefits in terms of reducing use of resources, health treatments, and environmental impact, while also improving animal health and welfare.
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Affiliation(s)
- Huw E Jones
- UK Genetics for Livestock and Equines (UKGLE) Committee, Department for Environment, Food and Rural Affairs, Nobel House, 17 Smith Square, London, SW1P 3JR, UK; Nottingham Trent University, Brackenhurst Campus, Brackenhurst Lane, Southwell, NG25 0QF, UK.
| | - Philippe B Wilson
- UK Genetics for Livestock and Equines (UKGLE) Committee, Department for Environment, Food and Rural Affairs, Nobel House, 17 Smith Square, London, SW1P 3JR, UK; Nottingham Trent University, Brackenhurst Campus, Brackenhurst Lane, Southwell, NG25 0QF, UK
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19
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Woodward-Greene MJ, Kinser JM, Sonstegard TS, Sölkner J, Vaisman II, Van Tassell CP. PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements. PLoS One 2022; 17:e0275821. [PMID: 36227957 PMCID: PMC9560539 DOI: 10.1371/journal.pone.0275821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/24/2022] [Indexed: 11/25/2022] Open
Abstract
Computer vision is a tool that could provide livestock producers with digital body measures and records that are important for animal health and production, namely body height and length, and chest girth. However, to build these tools, the scarcity of labeled training data sets with uniform images (pose, lighting) that also represent real-world livestock can be a challenge. Collecting images in a standard way, with manual image labeling is the gold standard to create such training data, but the time and cost can be prohibitive. We introduce the PreciseEdge image segmentation algorithm to address these issues by employing a standard image collection protocol with a semi-automated image labeling method, and a highly precise image segmentation for automated body measurement extraction directly from each image. These elements, from image collection to extraction are designed to work together to yield values highly correlated to real-world body measurements. PreciseEdge adds a brief preprocessing step inspired by chromakey to a modified GrabCut procedure to generate image masks for data extraction (body measurements) directly from the images. Three hundred RGB (red, green, blue) image samples were collected uniformly per the African Goat Improvement Network Image Collection Protocol (AGIN-ICP), which prescribes camera distance, poses, a blue backdrop, and a custom AGIN-ICP calibration sign. Images were taken in natural settings outdoors and in barns under high and low light, using a Ricoh digital camera producing JPG images (converted to PNG prior to processing). The rear and side AGIN-ICP poses were used for this study. PreciseEdge and GrabCut image segmentation methods were compared for differences in user input required to segment the images. The initial bounding box image output was captured for visual comparison. Automated digital body measurements extracted were compared to manual measures for each method. Both methods allow additional optional refinement (mouse strokes) to aid the segmentation algorithm. These optional mouse strokes were captured automatically and compared. Stroke count distributions for both methods were not normally distributed per Kolmogorov-Smirnov tests. Non-parametric Wilcoxon tests showed the distributions were different (p< 0.001) and the GrabCut stroke count was significantly higher (p = 5.115 e-49), with a mean of 577.08 (std 248.45) versus 221.57 (std 149.45) with PreciseEdge. Digital body measures were highly correlated to manual height, length, and girth measures, (0.931, 0.943, 0.893) for PreciseEdge and (0.936, 0. 944, 0.869) for GrabCut (Pearson correlation coefficient). PreciseEdge image segmentation allowed for masks yielding accurate digital body measurements highly correlated to manual, real-world measurements with over 38% less user input for an efficient, reliable, non-invasive alternative to livestock hand-held direct measuring tools.
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Affiliation(s)
- M. Jennifer Woodward-Greene
- USDA-ARS-NEA Animal Genomics and Improvement Laboratory, Beltsville, MD, United States of America
- Bioinformatics and Computational Biology Department, George Mason University, Manassas, VA, United States of America
| | - Jason M. Kinser
- Department of Computational and Data Sciences, George Mason University, Fairfax, VA, United States of America
| | - Tad S. Sonstegard
- Recombinetics at Acceligen, St. Paul, Minnesota, United States of America
| | - Johann Sölkner
- BOKU University of Natural Resources and Life Sciences, Vienna, Austria
| | - Iosif I. Vaisman
- Bioinformatics and Computational Biology Department, George Mason University, Manassas, VA, United States of America
| | - Curtis P. Van Tassell
- USDA-ARS-NEA Animal Genomics and Improvement Laboratory, Beltsville, MD, United States of America
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20
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Keel BN, Lindholm-Perry AK. Recent developments and future directions in meta-analysis of differential gene expression in livestock RNA-Seq. Front Genet 2022; 13:983043. [PMID: 36199583 PMCID: PMC9527320 DOI: 10.3389/fgene.2022.983043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Decreases in the costs of high-throughput sequencing technologies have led to continually increasing numbers of livestock RNA-Seq studies in the last decade. Although the number of studies has increased dramatically, most livestock RNA-Seq experiments are limited by cost to a small number of biological replicates. Meta-analysis procedures can be used to integrate and jointly analyze data from multiple independent studies. Meta-analyses increase the sample size, which in turn increase both statistical power and robustness of the results. In this work, we discuss cutting edge approaches to combining results from multiple independent RNA-Seq studies to improve livestock transcriptomics research. We review currently published RNA-Seq meta-analyses in livestock, describe many of the key issues specific to RNA-Seq meta-analysis in livestock species, and discuss future perspectives.
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21
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Challenging Sustainable and Innovative Technologies in Cheese Production: A Review. Processes (Basel) 2022. [DOI: 10.3390/pr10030529] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
It is well known that cheese yield and quality are affected by animal genetics, milk quality (chemical, physical, and microbiological), production technology, and the type of rennet and dairy cultures used in production. Major differences in the same type of cheese (i.e., hard cheese) are caused by the rennet and dairy cultures, which affect the ripening process. This review aims to explore current technological advancements in animal genetics, methods for the isolation and production of rennet and dairy cultures, along with possible applications of microencapsulation in rennet and dairy culture production, as well as the challenge posed to current dairy technologies by the preservation of biodiversity. Based on the reviewed scientific literature, it can be concluded that innovative approaches and the described techniques can significantly improve cheese production.
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22
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Bai X, Plastow GS. Breeding for disease resilience: opportunities to manage polymicrobial challenge and improve commercial performance in the pig industry. CABI AGRICULTURE AND BIOSCIENCE 2022; 3:6. [PMID: 35072100 PMCID: PMC8761052 DOI: 10.1186/s43170-022-00073-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 01/06/2022] [Indexed: 05/31/2023]
Abstract
Disease resilience, defined as an animal's ability to maintain productive performance in the face of infection, provides opportunities to manage the polymicrobial challenge common in pig production. Disease resilience can deliver a number of benefits, including more sustainable production as well as improved animal health and the potential for reduced antimicrobial use. However, little progress has been made to date in the application of disease resilience in breeding programs due to a number of factors, including (1) confusion around definitions of disease resilience and its component traits disease resistance and tolerance, and (2) the difficulty in characterizing such a complex trait consisting of multiple biological functions and dynamic elements of rates of response and recovery from infection. Accordingly, this review refines the definitions of disease resistance, tolerance, and resilience based on previous studies to help improve the understanding and application of these breeding goals and traits under different scenarios. We also describe and summarize results from a "natural disease challenge model" designed to provide inputs for selection of disease resilience. The next steps for managing polymicrobial challenges faced by the pig industry will include the development of large-scale multi-omics data, new phenotyping technologies, and mathematical and statistical methods adapted to these data. Genome editing to produce pigs resistant to major diseases may complement selection for disease resilience along with continued efforts in the more traditional areas of biosecurity, vaccination and treatment. Altogether genomic approaches provide exciting opportunities for the pig industry to overcome the challenges provided by hard-to-manage diseases as well as new environmental challenges associated with climate change.
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Affiliation(s)
- Xuechun Bai
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB Canada
| | - Graham S. Plastow
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB Canada
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23
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Tuggle CK, Clarke J, Dekkers JCM, Ertl D, Lawrence-Dill CJ, Lyons E, Murdoch BM, Scott NM, Schnable PS. The Agricultural Genome to Phenome Initiative (AG2PI): creating a shared vision across crop and livestock research communities. Genome Biol 2022; 23:3. [PMID: 34980221 PMCID: PMC8722016 DOI: 10.1186/s13059-021-02570-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
| | | | | | - David Ertl
- Iowa Corn Growers Association, Johnston, USA
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24
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Cabrera VE, Fadul-Pacheco L. Future of dairy farming from the Dairy Brain perspective: Data integration, analytics, and applications. Int Dairy J 2021. [DOI: 10.1016/j.idairyj.2021.105069] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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25
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Spleen proteome profiling of dairy goats infected with C. pseudotuberculosis by TMT-based quantitative proteomics approach. J Proteomics 2021; 248:104352. [PMID: 34411763 DOI: 10.1016/j.jprot.2021.104352] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 08/03/2021] [Accepted: 08/09/2021] [Indexed: 12/27/2022]
Abstract
Corynebacterium pseudotuberculosis (C.pseudotuberculosis) is a zoonotic pathogen that can cause cheese lymphadenitis in goats. In order to obtain detailed information about the pathogenesis and host immune response of goats infected with C.pseudotuberculosis, we used tandem mass tag (TMT)-labeling proteomic analysis to detect differentially expressed proteins (DEPs) in dairy goats infected with C.pseudotuberculosis, and confirmed the altered proteins with western blot. A total of 6611 trusted proteins were identified, and 126 proteins were differentially abundant. Gene ontology (GO) analysis showed that all DEPs were annotated as biological processes, cell composition, and molecular functions. Biological processes mainly involve acute inflammation and immune response; cell components mainly involve extracellular areas and high-density lipoprotein particles; molecular functions are mainly antigen binding, ferric iron binding, and iron ion binding. KEGG analysis showed that a total of 102 pathways were significantly enriched, mainly lysosomes, phagosomes, and mineral absorption pathways. Our findings provided the relevant knowledge of spleen protein levels in goats infected with C.pseudotuberculosis and revealed the complex molecular pathways and immune response mechanisms in the process of C.pseudotuberculosis infection. SIGNIFICANCE: C.pseudotuberculosis is the most fatal infectious disease in dairy goats, causing huge economic losses. However, the molecular pathways and immune response mechanisms of C.pseudotuberculosis infection in goats remain unclear. Therefore, we conducted a comparative quantitative proteomics study on dairy goats infected with C.pseudotuberculosis. The results provide a basis for better understanding the complexity of C.pseudotuberculosis infection, reveal the complex molecular pathways and immune response mechanisms in C.pseudotuberculosis infection, and provide some clues for identifying potential therapeutic targets. This is the first report to show that C.pseudotuberculosis infection in dairy goats can disrupt the immune response mechanism and lead to massive immune cell death. The study provided new findings on the interaction between C.pseudotuberculosis and the host.
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26
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Dillon JA, Stackhouse-Lawson KR, Thoma GJ, Gunter SA, Rotz CA, Kebreab E, Riley DG, Tedeschi LO, Villalba J, Mitloehner F, Hristov AN, Archibeque SL, Ritten JP, Mueller ND. Current state of enteric methane and the carbon footprint of beef and dairy cattle in the United States. Anim Front 2021; 11:57-68. [PMID: 34513270 DOI: 10.1093/af/vfab043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
- Jasmine A Dillon
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
| | | | - Greg J Thoma
- Ralph E. Martin Department of Chemical Engineering, University of Arkansas, Fayetteville, AR, USA
| | - Stacey A Gunter
- Southern Plains Range Research Station, USDA Agricultural Research Service, Woodward, OK, USA
| | - C Alan Rotz
- Pasture Systems and Watershed Management Research Unit, USDA Agricultural Research Service, University Park, PA, USA
| | - Ermias Kebreab
- Department of Animal Science, University of California-Davis, Davis, CA, USA
| | - David G Riley
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Luis O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Juan Villalba
- Department of Wildland Resources, Utah State University, Logan, UT, USA
| | - Frank Mitloehner
- Department of Animal Science, University of California-Davis, Davis, CA, USA
| | - Alexander N Hristov
- Department of Animal Science, The Pennsylvania State University, University Park, PA, USA
| | - Shawn L Archibeque
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
| | - John P Ritten
- Department of Agricultural and Applied Economics, University of Wyoming, Laramie, WY, USA
| | - Nathaniel D Mueller
- Department of Ecosystem Science & Sustainability, Colorado State University, Fort Collins, CO, USA.,Department of Crop & Soil Sciences, Colorado State University, Fort Collins, CO, USA
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27
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New Technology Tools and Life Cycle Analysis (LCA) Applied to a Sustainable Livestock Production. EUROBIOTECH JOURNAL 2021. [DOI: 10.2478/ebtj-2021-0022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Agriculture 4.0, a combination of mechanical innovation and information and communication technologies (ICT) using precision farming, omics technologies and advanced waste treatment techniques, can be used to enhance the biological potential of animal and crop productions and reduce livestock gaseous emissions. In addition to animal proteins being excellent nutritional ingredients for the human diet, there is a growing concern regarding the amount of energy spent converting vegetable crops into animal protein and the relevant environmental impacts. Using the value chain analysis derived from the neoclassic production theory extended to industrial processing and the market, the hypothesis to be tested concerns the sustainability and convenience of different protein sources. The methodology implies the use of life cycle analysis (LCA) to evaluate the efficiency of different livestock diet ingredients. The use of feeding products depend upon various factors, including cost reduction, consumer acceptance, incumbent industry response, civil society support, policy consensus, lower depletion of natural resources, improved sustainable agri-food supply chain and LCA. EU policy makers should be aware of these changes in livestock and market chains and act proactively to encourage the use of alternative animal proteins.
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28
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Runcie DE, Qu J, Cheng H, Crawford L. MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits. Genome Biol 2021; 22:213. [PMID: 34301310 PMCID: PMC8299638 DOI: 10.1186/s13059-021-02416-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 06/23/2021] [Indexed: 12/21/2022] Open
Abstract
Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.
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Affiliation(s)
- Daniel E. Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA USA
| | - Jiayi Qu
- Department of Plant Sciences, University of California Davis, Davis, CA USA
| | - Hao Cheng
- Department of Plant Sciences, University of California Davis, Davis, CA USA
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Xie L, Qin J, Rao L, Tang X, Cui D, Chen L, Xu W, Xiao S, Zhang Z, Huang L. Accurate prediction and genome-wide association analysis of digital intramuscular fat content in longissimus muscle of pigs. Anim Genet 2021; 52:633-644. [PMID: 34291482 DOI: 10.1111/age.13121] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 11/30/2022]
Abstract
Intramuscular fat (IMF) content is a critical indicator of pork quality that affects directly the purchasing desire of consumers. However, to measure IMF content is both laborious and costly, preventing our understanding of its genetic determinants and improvement. In the present study, we constructed an accurate and fast image acquisition and analysis system, to extract and calculate the digital IMF content, the proportion of fat areas in the image (PFAI) of the longissimus muscle of 1709 animals from multiple pig populations. PFAI was highly significantly correlated with marbling scores (MS; 0.95, r2 = 0.90), and also with IMF contents chemically defined for 80 samples (0.79, r2 = 0.63; more accurate than direct analysis between IMF contents and MS). The processing time for one image is only 2.31 s. Genome-wide association analysis on PFAI for all 1709 animals identified 14 suggestive significant SNPs and 1 genome-wide significant SNP. On MS, we identified nine suggestive significant SNPs, and seven of them were also identified in PFAI. Furthermore, the significance (-log P) values of the seven common SNPs are higher in PFAI than in MS. Novel candidate genes of biological importance for IMF content were also discovered. Our imaging systems developed for prediction of digital IMF content is closer to IMF measured by Soxhlet extraction and slightly more accurate than MS. It can achieve fast and high-throughput IMF phenotype, which can be used in improvement of pork quality.
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Affiliation(s)
- L Xie
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - J Qin
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - L Rao
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - X Tang
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - D Cui
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - L Chen
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - W Xu
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - S Xiao
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - Z Zhang
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
| | - L Huang
- National Key Laboratory for Swine Genetic Improvement and Production Technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi Province, 330045, China
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Herrera-Uribe J, Wiarda JE, Sivasankaran SK, Daharsh L, Liu H, Byrne KA, Smith TPL, Lunney JK, Loving CL, Tuggle CK. Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing. Front Genet 2021; 12:689406. [PMID: 34249103 PMCID: PMC8261551 DOI: 10.3389/fgene.2021.689406] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/18/2021] [Indexed: 01/03/2023] Open
Abstract
Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.
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Affiliation(s)
- Juber Herrera-Uribe
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Jayne E. Wiarda
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States
- Immunobiology Graduate Program, Iowa State University, Ames, IA, United States
- Oak Ridge Institute for Science and Education, Agricultural Research Service Participation Program, Oak Ridge, TN, United States
| | - Sathesh K. Sivasankaran
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States
- Genome Informatics Facility, Iowa State University, Ames, IA, United States
| | - Lance Daharsh
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Haibo Liu
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Kristen A. Byrne
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States
| | | | - Joan K. Lunney
- USDA-ARS, Beltsville Agricultural Research Center, Animal Parasitic Diseases Laboratory, Beltsville, MD, United States
| | - Crystal L. Loving
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States
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Marsh JI, Hu H, Gill M, Batley J, Edwards D. Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1677-1690. [PMID: 33852055 DOI: 10.1007/s00122-021-03820-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 03/18/2021] [Indexed: 05/05/2023]
Abstract
Safeguarding crop yields in a changing climate requires bioinformatics advances in harnessing data from vast phenomics and genomics datasets to translate research findings into climate smart crops in the field. Climate change and an additional 3 billion mouths to feed by 2050 raise serious concerns over global food security. Crop breeding and land management strategies will need to evolve to maximize the utilization of finite resources in coming years. High-throughput phenotyping and genomics technologies are providing researchers with the information required to guide and inform the breeding of climate smart crops adapted to the environment. Bioinformatics has a fundamental role to play in integrating and exploiting this fast accumulating wealth of data, through association studies to detect genomic targets underlying key adaptive climate-resilient traits. These data provide tools for breeders to tailor crops to their environment and can be introduced using advanced selection or genome editing methods. To effectively translate research into the field, genomic and phenomic information will need to be integrated into comprehensive clade-specific databases and platforms alongside accessible tools that can be used by breeders to inform the selection of climate adaptive traits. Here we discuss the role of bioinformatics in extracting, analysing, integrating and managing genomic and phenomic data to improve climate resilience in crops, including current, emerging and potential approaches, applications and bottlenecks in the research and breeding pipeline.
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Affiliation(s)
- Jacob I Marsh
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia
| | - Haifei Hu
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia
| | - Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia.
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Cole JB, Dürr JW, Nicolazzi EL. Invited review: The future of selection decisions and breeding programs: What are we breeding for, and who decides? J Dairy Sci 2021; 104:5111-5124. [PMID: 33714581 DOI: 10.3168/jds.2020-19777] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 01/03/2021] [Indexed: 01/23/2023]
Abstract
Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. Breeding programs seek to identify genetically superior parents of the next generation, typically as a function of an index that combines information about many economically important traits into a single number. In the United States, the data that drive this system are collected through the national dairy herd improvement program that began more than a century ago. The resulting information about animal performance, pedigree, and genotype is used to compute genomic evaluations for comparing and ranking animals for selection. However, the full expression of genetic potential requires that animals are placed in environments that can support such performance. The Agricultural Research Service of the US Department of Agriculture and the Council on Dairy Cattle Breeding collaborate to deliver state-of-the-art genomic evaluations to the dairy industry. Today, most breeding stock are selected and marketed using the net merit dollars (NM$) selection index, which evolved from 2 traits in 1926 (milk and fat yield) to a combination of 36 individual traits following the last NM$ update in 2018. Updates to NM$ require the estimation of many different values, and it can be difficult to achieve consensus from stakeholders on what should be added to, or removed from, the index at each review, and how those traits should be weighted. Over time, the majority of the emphasis in the index has shifted from yield traits to fertility, health, and fitness traits. Phenotypes for some of these new traits are difficult or expensive to measure, or require changes to on-farm habits that have not been widely adopted. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. There is also a need to capture more detailed data about the environment in which animals perform, including information about feeding, housing, milking systems, and infectious and parasitic load. However, many challenges accompany these new technologies, including a lack of standardization or validation, need for high-speed internet connections, increased computational requirements, and interpretations that are often not backed by direct observations of biological phenomena. This work will describe how US selection objectives are developed, as well as discuss opportunities and challenges associated with new technologies for measuring and recording animal performance.
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Affiliation(s)
- John B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture (USDA), Beltsville, MD 20705-2350.
| | - João W Dürr
- Council on Dairy Cattle Breeding, 4201 Northview Drive, Suite 302, Bowie, MD 20716
| | - Ezequiel L Nicolazzi
- Council on Dairy Cattle Breeding, 4201 Northview Drive, Suite 302, Bowie, MD 20716
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34
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Pérez-Enciso M, Steibel JP. Phenomes: the current frontier in animal breeding. Genet Sel Evol 2021; 53:22. [PMID: 33673800 PMCID: PMC7934239 DOI: 10.1186/s12711-021-00618-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/22/2021] [Indexed: 12/13/2022] Open
Abstract
Improvements in genomic technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. However, only marginal gains in genetic prediction accuracy can now be expected by increasing marker density up to sequence, unless causative mutations are identified. We argue that some of the most scientifically disrupting and industry-relevant challenges relate to ‘phenomics’ instead of ‘genomics’. Thanks to developments in sensor technology and artificial intelligence, there is a wide range of analytical tools that are already available and many more will be developed. We can now address some of the pressing societal demands on the industry, such as animal welfare concerns or efficiency in the use of resources. From the statistical and computational point of view, phenomics raises two important issues that require further work: penalization and dimension reduction. This will be complicated by the inherent heterogeneity and ‘missingness’ of the data. Overall, we can expect that precision livestock technologies will make it possible to collect hundreds of traits on a continuous basis from large numbers of animals. Perhaps the main revolution will come from redesigning animal breeding schemes to explicitly allow for high-dimensional phenomics. In the meantime, phenomics data will definitely enlighten our knowledge on the biological basis of phenotypes.
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Affiliation(s)
- Miguel Pérez-Enciso
- ICREA, Passeig de Lluís Companys 23, 08010, Barcelona, Spain. .,Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Bellaterra, 08193, Barcelona, Spain.
| | - Juan P Steibel
- Department of Animal Science, Michigan State University, East Lansing, MI, 48824, USA.,Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, 48824, USA
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Morota G, Cheng H, Cook D, Tanaka E. ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1. J Anim Sci 2021; 99:skaa402. [PMID: 33626150 PMCID: PMC7904041 DOI: 10.1093/jas/skaa402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 12/15/2020] [Indexed: 12/19/2022] Open
Abstract
Statistical graphics, and data visualization, play an essential but under-utilized, role for data analysis in animal science, and also to visually illustrate the concepts, ideas, or outputs of research and in curricula. The recent rise in web technologies and ubiquitous availability of web browsers enables easier sharing of interactive and dynamic graphics. Interactivity and dynamic feedback enhance human-computer interaction and data exploration. Web applications such as decision support systems coupled with multimedia tools synergize with interactive and dynamic graphics. However, the importance of graphics for effectively communicating data, understanding data uncertainty, and the state of the field of interactive and dynamic graphics is underappreciated in animal science. To address this gap, we describe the current state of graphical methodology and technology that might be more broadly adopted. This includes an explanation of a conceptual framework for effective graphics construction. The ideas and technology are illustrated using publicly available animal datasets. We foresee that many new types of big and complex data being generated in precision livestock farming create exciting opportunities for applying interactive and dynamic graphics to improve data analysis and make data-supported decisions.
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Affiliation(s)
- Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA
- Center for Advanced Innovation in Agriculture, Virginia Polytechnic Institute and State University, Blacksburg, VA
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, CA
| | - Dianne Cook
- Department of Econometrics and Business Statistics, Monash University, Clayton, VIC, Australia
| | - Emi Tanaka
- Department of Econometrics and Business Statistics, Monash University, Clayton, VIC, Australia
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36
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Clark EL, Archibald AL, Daetwyler HD, Groenen MAM, Harrison PW, Houston RD, Kühn C, Lien S, Macqueen DJ, Reecy JM, Robledo D, Watson M, Tuggle CK, Giuffra E. From FAANG to fork: application of highly annotated genomes to improve farmed animal production. Genome Biol 2020; 21:285. [PMID: 33234160 PMCID: PMC7686664 DOI: 10.1186/s13059-020-02197-8] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 11/05/2020] [Indexed: 12/27/2022] Open
Affiliation(s)
- Emily L Clark
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, EH25 9RG, UK.
| | - Alan L Archibald
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Hans D Daetwyler
- Agriculture Victoria, AgriBio Centre for AgriBioscience, Bundoora, Victoria, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, 3083, Australia
| | - Martien A M Groenen
- Animal Breeding and Genomics Centre, Wageningen University and Research, 6708 PB, Wageningen, The Netherlands
| | - Peter W Harrison
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Ross D Houston
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Christa Kühn
- Leibniz Institute for Farm Animal Biology (FBN), Institute of Genome Biology, Genome Physiology Unit, Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany.,Faculty of Agricultural and Environmental Sciences, University Rostock, Justus-von-Liebig-Weg 6, 18059, Rostock, Germany
| | - Sigbjørn Lien
- Centre for Integrative Genetics (CIGENE), Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, NO-1432, Ås, Norway
| | - Daniel J Macqueen
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - James M Reecy
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Diego Robledo
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Mick Watson
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, EH25 9RG, UK
| | | | - Elisabetta Giuffra
- Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
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Hansen PJ. The incompletely fulfilled promise of embryo transfer in cattle-why aren't pregnancy rates greater and what can we do about it? J Anim Sci 2020; 98:skaa288. [PMID: 33141879 PMCID: PMC7608916 DOI: 10.1093/jas/skaa288] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 08/27/2020] [Indexed: 12/11/2022] Open
Abstract
Typically, bovine embryos are transferred into recipient females about day 7 after estrus or anticipated ovulation, when the embryo has reached the blastocyst stage of development. All the biological and technical causes for failure of a female to produce a blastocyst 7 d after natural or artificial insemination (AI) are avoided when a blastocyst-stage embryo is transferred into the female. It is reasonable to expect, therefore, that pregnancy success would be higher for embryo transfer (ET) recipients than for inseminated females. This expectation is not usually met unless the recipient is exposed to heat stress or is classified as a repeat-breeder female. Rather, pregnancy success is generally similar for ET and AI. The implication is that either one or more of the technical aspects of ET have not yet been optimized or that underlying female fertility that causes an embryo to die before day 7 also causes it to die later in pregnancy. Improvements in pregnancy success after ET will depend upon making a better embryo, improving uterine receptivity, and forging new tools for production and transfer of embryos. Key to accelerating progress in improving pregnancy rates will be the identification of phenotypes or phenomes that allow the prediction of embryo competence for survival and maternal capacity to support embryonic development.
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Affiliation(s)
- Peter J Hansen
- Department of Animal Sciences, D.H. Barron Reproductive and Perinatal Biology Research Program, and Genetics Institute, University of Florida, Gainesville, FL
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38
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Fernandes AFA, Dórea JRR, Rosa GJDM. Image Analysis and Computer Vision Applications in Animal Sciences: An Overview. Front Vet Sci 2020; 7:551269. [PMID: 33195522 PMCID: PMC7609414 DOI: 10.3389/fvets.2020.551269] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 09/15/2020] [Indexed: 11/13/2022] Open
Abstract
Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.
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Affiliation(s)
| | | | - Guilherme Jordão de Magalhães Rosa
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States
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39
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Knap PW, Doeschl-Wilson A. Why breed disease-resilient livestock, and how? Genet Sel Evol 2020; 52:60. [PMID: 33054713 PMCID: PMC7557066 DOI: 10.1186/s12711-020-00580-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 10/01/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Fighting and controlling epidemic and endemic diseases represents a considerable cost to livestock production. Much research is dedicated to breeding disease resilient livestock, but this is not yet a common objective in practical breeding programs. In this paper, we investigate how future breeding programs may benefit from recent research on disease resilience. MAIN BODY We define disease resilience in terms of its component traits resistance (R: the ability of a host animal to limit within-host pathogen load (PL)) and tolerance (T: the ability of an infected host to limit the damage caused by a given PL), and model the host's production performance as a reaction norm on PL, depending on R and T. Based on this, we derive equations for the economic values of resilience and its component traits. A case study on porcine respiratory and reproductive syndrome (PRRS) in pigs illustrates that the economic value of increasing production in infectious conditions through selection for R and T can be more than three times higher than by selection for production in disease-free conditions. Although this reaction norm model of resilience is helpful for quantifying its relationship to its component traits, its parameters are difficult and expensive to quantify. We consider the consequences of ignoring R and T in breeding programs that measure resilience as production in infectious conditions with unknown PL-particularly, the risk that the genetic correlation between R and T is unfavourable (antagonistic) and that a trade-off between them neutralizes the resilience improvement. We describe four approaches to avoid such antagonisms: (1) by producing sufficient PL records to estimate this correlation and check for antagonisms-if found, continue routine PL recording, and if not found, shift to cheaper proxies for PL; (2) by selection on quantitative trait loci (QTL) known to influence both R and T in favourable ways; (3) by rapidly modifying towards near-complete resistance or tolerance, (4) by re-defining resilience as the animal's capacity to resist (or recover from) the perturbation caused by an infection, measured as temporal deviations of production traits in within-host longitudinal data series. CONCLUSIONS All four alternatives offer promising options for genetic improvement of disease resilience, and most rely on technological and methodological developments and innovation in automated data generation.
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Affiliation(s)
| | - Andrea Doeschl-Wilson
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush Estate, Edinburgh, EH25 9RG Scotland, UK
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40
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Hu G, Do DN, Gray J, Miar Y. Selection for Favorable Health Traits: A Potential Approach to Cope with Diseases in Farm Animals. Animals (Basel) 2020; 10:E1717. [PMID: 32971980 PMCID: PMC7552752 DOI: 10.3390/ani10091717] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 09/21/2020] [Indexed: 12/17/2022] Open
Abstract
Disease is a global problem for animal farming industries causing tremendous economic losses (>USD 220 billion over the last decade) and serious animal welfare issues. The limitations and deficiencies of current non-selection disease control methods (e.g., vaccination, treatment, eradication strategy, genome editing, and probiotics) make it difficult to effectively, economically, and permanently eliminate the adverse influences of disease in the farm animals. These limitations and deficiencies drive animal breeders to be more concerned and committed to dealing with health problems in farm animals by selecting animals with favorable health traits. Both genetic selection and genomic selection contribute to improving the health of farm animals by selecting certain health traits (e.g., disease tolerance, disease resistance, and immune response), although both of them face some challenges. The objective of this review was to comprehensively review the potential of selecting health traits in coping with issues caused by diseases in farm animals. Within this review, we highlighted that selecting health traits can be applied as a method of disease control to help animal agriculture industries to cope with the adverse influences caused by diseases in farm animals. Certainly, the genetic/genomic selection solution cannot solve all the disease problems in farm animals. Therefore, management, vaccination, culling, medical treatment, and other measures must accompany selection solution to reduce the adverse impact of farm animal diseases on profitability and animal welfare.
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Affiliation(s)
| | | | | | - Younes Miar
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS B2N 5E3, Canada; (G.H.); (D.N.D.); (J.G.)
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Neethirajan S. Transforming the Adaptation Physiology of Farm Animals through Sensors. Animals (Basel) 2020; 10:E1512. [PMID: 32859060 PMCID: PMC7552204 DOI: 10.3390/ani10091512] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 08/23/2020] [Accepted: 08/25/2020] [Indexed: 12/20/2022] Open
Abstract
Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, color, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.
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Brito LF, Oliveira HR, McConn BR, Schinckel AP, Arrazola A, Marchant-Forde JN, Johnson JS. Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding. Front Genet 2020; 11:793. [PMID: 32849798 PMCID: PMC7411239 DOI: 10.3389/fgene.2020.00793] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 07/03/2020] [Indexed: 12/13/2022] Open
Abstract
Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.
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Affiliation(s)
- Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Betty R. McConn
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States
| | - Allan P. Schinckel
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Aitor Arrazola
- Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, United States
| | | | - Jay S. Johnson
- USDA-ARS Livestock Behavior Research Unit, West Lafayette, IN, United States
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43
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Fu M, Su H, Su Z, Yin Z, Jin J, Wang L, Zhang Q, Xu X. Transcriptome analysis of Corynebacterium pseudotuberculosis-infected spleen of dairy goats. Microb Pathog 2020; 147:104370. [PMID: 32653437 DOI: 10.1016/j.micpath.2020.104370] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 06/26/2020] [Accepted: 06/29/2020] [Indexed: 02/06/2023]
Abstract
Caseous lymphadenitis is a chronic disease of goats caused by Corynebacterium pseudotuberculosis (C.pseudotuberculosis) which causes great harm to the dairy goats industry. In order to obtain detailed information about the pathogenesis and host immune response in C.pseudotuberculosis-infected goats, in this study, the gene expression difference of spleen tissue after infection with C.pseudotuberculosis was analyzed by high-throughput sequencing. Transcripts obtained over 412 700 462 clean reads after reassembly were 21 343 genes detected, of which 14 720 were known genes and 7623 new genes were predicted. There were 448 up-regulated and 519 down-regulated differentially expressed genes (DEGs). Gene Ontology (GO) analysis indicated that all of the DEGs were annotated into biological process, cellular component and molecular function. Most of these unigenes are annotated in cellular processes, the cell and binding. KEGG analysis of the DEGs showed that a total of 8733 DEGs unigenes were annotated into 459 pathways classified into 6 main categories. Most of these annotated unigenes were related to immune system response to the infectious diseases pathways. In addition, 14 DEGs were verified by quantitative real-time PCR. As the first, in vivo, RNAseq analysis of dairy goats and C.pseudotuberculosis infection, this study provides knowledge about the transcriptomics of spleen in C.pseudotuberculosis-infected goats, from which a complex molecular pathways and immune response mechanism are involved in C.pseudotuberculosis infection.
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Affiliation(s)
- Mingzhe Fu
- College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Hong Su
- College of Animal Medicine, Xinjiang Agricultural University, Urumqi, 830000, China
| | - Zhanqiang Su
- College of Animal Medicine, Xinjiang Agricultural University, Urumqi, 830000, China
| | - Zheng Yin
- College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Jian Jin
- College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Lixiang Wang
- College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Qi Zhang
- College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, 712100, China.
| | - Xingang Xu
- College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, 712100, China.
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Cole JB, Eaglen SAE, Maltecca C, Mulder HA, Pryce JE. The future of phenomics in dairy cattle breeding. Anim Front 2020; 10:37-44. [PMID: 32257602 DOI: 10.1093/af/vfaa007] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Affiliation(s)
- John B Cole
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD
| | | | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC
| | - Han A Mulder
- Department of Animal Sciences, Wageningen University and Research Animal Breeding and Genomics, Wageningen, The Netherlands
| | - Jennie E Pryce
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia
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