PHENOMICS EVALUATION IN CATTLE BREEDING
Cattle Phenomics
DOI:
https://doi.org/10.26873/SVR-2047-2024Keywords:
animal performance, behavior, big data, bioinformatics, cattle breeding, genomics, phenomics, PLF, welfareAbstract
The progress of technology, namely in the realm of computer science, has resulted in the capability to handle extensive sets of information, sometimes referred to as big data, which has substantially influenced the field of genetic science. The discipline of livestock farming has witnessed the emergence of several branches of study, including genomics, proteomics, transcriptomics, and metabolomics. Using phenomics in cattle breeding has gained significant importance in augmenting production and efficiency. Scientists have created livestock phenomics platforms to enhance the welfare and productivity of animals by employing cutting-edge digital technologies and real-time sensors. Incorporating phenomics with genomics has expanded the potential for genetic assessment and breeding initiatives in the livestock sector. Phenomics allows for the assessment of the intricate reactions of animals to environmental stimuli, hence aiding in the advancement of more robust and productive cattle. In summary, of phenomics in cattle breeding has significant potential for the future of livestock production, providing prospects for better breeding objectives, promoting animal well-being, and boosting overall output.
Vrednotenje fenomike v govedoreji
Izvleček: Tehnološki napredek, zlasti na področju računalništva, je omogočil obdelavo obsežnih nizov informacij, ki se včasih imenujejo masovni podatki, kar je bistveno vplivalo na področje genetike. Na področju živinoreje se je pojavilo več raziskovalnih vej, vključno z genomiko, proteomiko, transkriptomiko in metabolomiko. Fenomika je v govedoreji pridobila velik pomen pri povečanju proizvodnje in učinkovitosti. Znanstveniki so ustvarili platforme za fenomiko v živinoreji, da bi z uporabo najsodobnejših digitalnih tehnologij in senzorjev v realnem času izboljšali dobrobit in produktivnost živali. Vključevanje fenomike z genomiko je razširilo možnosti za genetsko ocenjevanje in vzrejne iniciative v živinorejskem sektorju. Fenomika omogoča ocenjevanje zapletenih odzivov živali na okoljske dražljaje, kar pripomore k razvoju močnejšega in produktivnejšega goveda. Če povzamemo, ima fenomika v govedoreji velik potencial za prihodnost živinoreje, saj zagotavlja možnosti za boljše rejske cilje, spodbuja dobrobit živali in povečuje splošno proizvodnjo.
Ključne besede: učinkovitost živali; vedenje; masovni podatki; bioinformatika; govedoreja; genomika; fenomika; PLF; dobrobit živali
References
Abbasi K, Ali P, Barbour V, et al. Editorial: time to treat the climate and nature crisis as one indivisible global health emergency. Kafkas Univ Vet Fak Derg 2024; 30(1): 1–3. doi: 10.9775/kvfd.2023.editorial
Aldevir O, Guclu S, Dursun S, et al. The Association Between the STAT1 g.3141C>T polymorphism and reproductive performance in high-yielding Holstein-Friesian dairy cows. Large Anim Rev 2023; 29(2): 59–63.
Andersson L, Archibald AL, Bottema CD, et al. Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project. Genome Biol 2015; 16 (1): 57. doi: 10.1186/s13059-015-0622-4
Anonymous: A coordinated international action to accelerate genome to phenome. Available at: https://www.animalgenome.org/community/FAANG/; Accessed: 15.12.2023
Anonymous: Omics. Available at: https://en.wikipedia.org/wiki/Omics; Accessed: 15.12.2023
Anonymous: Phenomics. Available at: https://en.wikipedia.org/wiki/Phenomics; Accessed: 15.12.2023
Anonymous: The definition of omics. Available at: https://omics.org/What_is_omics; Accessed: 15.12.2023
Ansari S, Ghavi Hossein-Zadeh N, Shadparvar AA. Genomic predictions under different genetic architectures are impacted by mating designs. Vet Anim Sci 2024; 25: 1003730. https://doi.org/10.1016/j.vas.2024.100373
Ardicli S, Aldevir O, Aksu E, Gumen A. The variation in the beta-casein genotypes and its effect on milk yield and genomic values in Holstein-Friesian cows. Anim Biotechnol 2023; 34(8): 4116–25. doi: 10.1080/10495398.2023.2267614
Ardicli S, Aldevir Ö, Aksu E, Kucuk K, Gümen A. Associations of bovine beta-casein and kappa-casein genotypes with genomic merit in Holstein Friesian cattle. Arch Anim Breed 2024;67(1) 2024: 61–71. doi: 10.5194/aab-67-61-2024
Ardicli S, Alpay . The Effects of MC4R and CACNA2D1 gene polymorphisms on carcass traits and marbling score in turkish native cattle breeds and their crossbreds with the Holstein-Friesians. Genetika 2023; 55(2): 655–72. doi: 10.2298/GENSR2302655A
Ardicli S, Dincel D, Samli H, et al. Association of polymorphisms in lipid and energy metabolism-related genes with fattening performance in Simmental cattle. Anim Biotechnol 2023; 34(8): 3428–40. doi: 10.1080/10495398.2022.2152557
Ardicli S, Samli H, Balci F. Analysis of bovine beta-casein A1 and A2 allele frequency in Holstein-Friesian cows by Real-time PCR with fluorescent hybridization probes. Veterinarski arhiv 2023; 93 (3): 279–86. doi: 10.24099/vet.arhiv.1821
Ardicli S, Senturk N, Bozkurt B, et al. The impact of genetic variants related to the fatty acid metabolic process pathway on milk production traits in Jersey cows. Anim Biotechnol 2024; 35(1): 2396421. doi: 10.1080/10495398.2024.2396421
Baes C, Schenkel F. The future of phenomics. Anim Front 2020; 10(2):4–5. doi: 10.1093/af/vfaa013
Boichard D, Brochard M. New phenotypes for new breeding goals in dairy cattle. Animal 2012; 6(4):544–50.doi: 10.1017/S1751731112000018
Chang Y, Brito LF, Alvarenga AB, Wang Y. Incorporating temperament traits in dairy cattle breeding programs: challenges and opportunities in the phenomics era. Anim Front 2020; 10(2):29–36. doi: 10.1093/af/vfaa006
Chen CJ, Morota G, Cheng H. 124. VTag: automatic pipeline to annotate video data for pig phenomics studies. In: Veerkamp RF, eds. Proceedings of 12th World Congress on Genetics Applied to Livestock Production. Wageningen: Wageningen Academic Publishers, 2022: 545–8. doi: 10.3920/978-90-8686-940-4_124
Cobanoglu O, Ardicli S. Genetic variation at the OLR1, ANXA9, MYF5, LTF, IGF1, LGB, CSN3, PIT1, MBL1, CACNA2D1, and ABCG2 loci in turkish grey steppe, anatolian black, and eastanatolian red cattle. Turkish J Vet Anim Sci 2022; 46(3): 494–504. doi: 10.55730/1300-0128.4220
Coffey M. Dairy cows: in the age of the genotype, #phenotypeisking. Anim Front 2020; 1 (2): 19–22. doi: 10.1093/af/vfaa004
Cole JB, Eaglen SAE, Maltecca C, Mulder HA, Pryce JE. The future of phenomics in dairy cattle breeding. Anim Front 2020; 10(2): 37–44.doi: 10.1093/af/vfaa007
Daetwyler HD, Villanueva B, Woolliams JA. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 2008;3(10): e3395. doi: 10.1371/journal.pone.0003395
Dai X, Shen L. Advances and trends in omics technology development. Front Med 2022; 9: 911861. doi: 10.3389/fmed.2022.911861
Dalal N, Jalandra R, Sharma M, et al. Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives. Biomed Pharmacother 2020; 131; 110648. doi: 10.1016/j.biopha.2020.110648
Fan T, Bai W, Fu Y, et al. Study on the skeletal muscle transcriptomics of Alxa Gobi camel and Desert camel. Kafkas Univ Vet Fak Derg 2023: 29 (2): 191–9. doi: 10.9775/kvfd.2023.29002
FAO. How to Feed the World in 2050. Rim: Food and Agriculture Organization of the United Nations, 2024. https://www.fao.org/fileadmin/templates/wsfs/docs/expert_paper/How_to_Feed_the_World_in_2050.pdf (10.01.2024)
Giuffra E, Tuggle CK. Functional Annotation of Animal Genomes (FAANG): Current Achievements and Roadmap. Ann Rev Anim Biosci 2019;7(1): 65–88. doi: 10.1146/annurev-animal-020518-114913
Gökdai A, Sakarya E. Determination of goat milk cost and assessment of factors affecting the profitability of Saanen goat enterprises in Çanakkale province, Turkey. Ankara Üniv Vet Fak Derg 2022; 69(2): 123–30. doi: 10.33988/auvfd.799114
Greenwood PL, Bishop-Hurley GJ, González LA, Ingham A. Development and application of a livestock phenomics platform to enhance productivity and efficiency at pasture. Anim Product Sci 2016; 56 (8): 1299. doi: 10.1071/AN15400
Halachmi I, Guarino M, Bewley J, Pastell M. Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annu Rev Anim Biosci 2019; 7(1): 403–25. doi: 10.1146/annurev-animal-020518-114851
Hayes BJ, Duff CJ, Hine BC, Mahony TJ. Genomic estimated breeding values for bovine respiratory disease resistance in Angus feedlot cattle. J Anim Sci 2024; 102: skae 13. doi: 10.1093/jas/skae113
Houaga I, Mrode R, Opoola O, et al. Livestock phenomics and genetic evaluation approaches in Africa: current state and future perspectives. Front Genet 2023; 14: 1115973 doi: 10.3389/fgene.2023.1115973
Houle D, Govindaraju DR, Omholt S. Phenomics: the next challenge. Nat Rev Genet 2010; 11(12): 855–66. doi: 10.1038/nrg2897
Houle D. A dispatch from the multivariate frontier. J Evolut Biol 2007; 20 (1): 22–3. doi: 10.1111/j.1420-9101.2006.01237.x
Hu M, Shi L, Yi W, Li F, Yan S. Identification of genomic diversity and selection signatures in Luxi cattle using whole-genome sequencing data. Anim Biosci 2024; 37(3): 461–70. doi: 10.5713/ab.23.0304
Jin L. Welcome to the phenomics journal. Phenomics 2021;1(1): 1–2. doi: 10.1007/s43657-020-00009-4
Johnson JS, Wen H, Freitas PH, et al. Evaluating phenotypes associated with heat tolerance and identifying moderate and severe heat stress thresholds in lactating sows housed in mechanically or naturally ventilated barns during the summer under commercial conditions. J Anim Sci 2023; 101: skad129. doi: 10.1093/jas/skad129
Krupová Z, Kašná E, Zavadilová L, Krupa E, Bauer J, Wolfová M. Udder, claw, and reproductive health in genomic selection of the Czech Holstein. Animals (Basel) 2023; 14(6): 864. doi: 10.3390/ani14060864
Mat B, Cevger Y. Determination of factors affecting competitiveness through technical and economic analyses of dairy cattle enterprises in Balıkesir province. Ankara Üniv Vet Fak Derg 2022; 69(2): 163–70. doi: 10.33988/auvfd.837725
McCoy JCS, Spicer JI, Ibbini Z, Tills O. Phenomics as an approach to comparative developmental physiology. Front Physiol 2023; 14: 1229500. doi: 10.3389/fphys.2023.1229500
Motmain Z, Özdemir M, Ekinci M, Saygılı E, Bilgin E. A meta-analysis of the associations between prolactin (PRL) gene polymorphism and milk production traits in cattle. Kafkas Univ Vet Fak Derg 2022; 28 (5): 627–31, 2022. doi: 10.9775/kvfd.2022.27857
Mrode R, Ekine Dzivenu C, Marshall K, et al. Phenomics and its potential impact on livestock development in low-income countries: innovative applications of emerging related digital technology. Anim Front 2020; 10(2): 6–11. doi: 10.1093/af/vfaa002
Nye J, Zingaretti LM, Pérez-Enciso M. Estimating conformational traits in dairy cattle with deepaps: a two-step deep learning automated phenotyping and segmentation approach. Front Genet 2020; 11: 513. doi: 10.3389/fgene.2020.00513
Özbeyaz C, Kocakaya A: Genomic Evaluation in dairy cattle (a review). Lalahan Hayvancılık Araştırma Enstitüsü Dergisi 2011; 51(2): 93–104.
Özbeyaz C, Özcan M. Investigation of hereditary cholesterol deficiency (CD) in Holstein Cattle at the state farms in Türkiye. Ankara Univ Vet Fak Derg 2024; 71 (3): 321–8. doi: 10.33988/auvfd.1295330
Pérez-Enciso M, Steibel JP: Phenomes: the current frontier in animal breeding. Genet Sel Evol 2021; 53 (1): 22. doi: 10.1186/s12711-021-00618-1
RD-Connect. Omics data. Barcelona: RD-Connect, 2023. https://rd-connect.eu/what-we-do/omics/ (15.12.2023)
Rexroad C, Vallet J, Matukumalli LK. Genome to phenome: improving animal health, production, and well‑ being—a new USDA blueprint for animal genome research 2018–27. Front Genet 2019; 10: 327. doi: 10.3389/fgene.2019.00327
Sarviaho K, Uimari P, Martikainen K. Signatures of positive selection after the introduction of genomic selection in the Finnish Ayrshire population. J Dairy Sci 2024; 107(7): 4822–32. doi: 10.3168/jds.2024-24105
Saygili E, Turkyilmaz D, Ekinci K. Associations between MSTN/HaeIII polymorphism and reproductive and growth characteristics in morkaraman sheep. Kafkas Univ Vet Fak Derg 2022; 28 (6): 717–22. doi: 10.9775/kvfd.2022.27952
Seidel A, Krattenmacher N, Thaller G. Dealing with complexity of new phenotypes in modern dairy cattle breeding. Anim Front 2020; 10 (2): 23–8. doi: 10.1093/af/vfaa005
Shi L, Zhang P, Liu Q, et al. Genome-wide analysis of genetic diversity and selection signatures in zaobei beef cattle. Animals (Basel) 2023; 14(16): 2447. doi: 10.3390/ani14162447
Sızmaz Ö, Köksal BH, Yıldız G. Rumen fermentation characteristics of rams fed supplemental boric acid and humic acid diets. Ankara Üniv Vet Fak Derg 2022; 69(3): 337–40. doi: 10.33988/auvfd.1059346.
Subedi P, Moertl S, Azimzadeh O. Omics in radiation biology: surprised but not disappointed. Radiation 2022; 2 (1): 124–9. doi: 10.3390/radiation2010009
Sucu E, Ak Sonat F. Effects of algae derived pure β–glucan on in vitro rumen fermentation. Ankara Üniv Vet Fak Derg 2023; 70(4): 447–52. doi: 10.33988/ auvfd.1084176.
Sukhija N, Kanaka KK, Goli RC, et al. The flight of chicken genomics and allied omics-a mini review. Ecol Genet Genom 2023; 29: 100201. doi: 10.1016/j.egg.2023.100201
Tills O, Holmes LA, Quinn E, Everett T, Truebano M, Spicer JI. Phenomics enables the measurement of complex responses of developing animals to global environmental drivers. Sci Total Environ 2023; 858(2): 159555. doi: 10.1016/j.scitotenv.2022.159555
Vailati-Riboni M, Palombo V, Loor JJ. What are omics sciences? In: Ametaj BN, ed. Periparturient diseases of dairy cows. Berlin: Springer, 2017: 1–7. doi: 10.1007/978-3-319-43033-1_1
Vieira Ventura R, Fonseca e Silva F, Manuel Yáñez J, Brito LF. Opportunities and challenges of phenomics applied to livestock and aquaculture breeding in South America. Anim Front 2020; 10(2): 45-52. doi: 10.1093/af/vfaa008
Visser C, Van Marle-Köster E, Myburgh HC, De Freitas . Phenomics for sustainable production in the South African dairy and beef cattle industry. Anim Front 2020; 10(2): 12–18. doi: 10.1093/af/vfaa003
Wu XL, Ding X, Zhao Y, et al. Editorial: Lactation genomics and phenomics in farm animals: Where are we at? Front Genet 2023; 14: 1173595. doi: 10.3389/fgene.2023.1173595
Yan S, Nagle DG, Zhou Y, Zhang W. Application of systems biology in the research of tcm formulae. In: Zhang WD, ed. Systems biology and its application in TCM formulas research. London: Elsevier, 2018: 31–67. doi: 10.1016/B978-0-12-812744- 5.00003-5
Zlatanović Z, Hristov S, Stanković B, Cincović M, Nakov D, Bojkovski J. Influence of claw disorders on milk production in Simmental dairy cows. Kafkas Univ Vet Fak Derg 2021; 27(1): 103–10. doi: 10.9775/kvfd.2020.24839
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Afsin Kocakaya

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.