Bibliometric Analyzes of Some Major Effect Genes Associated with Meat Yield Traits in Livestock

Abstract

Bibliometric analysis is commonly used to guide decisions regarding research funding and the creation of research policies and offers scientists helpful information about research trends, patterns, and impact. Thus, researchers can track collaborators in this subject and find prospective scientific alliances. Additionally, researchers can develop new research themes by constantly monitoring the most recent trend study topics in this area. Therefore, we performed the comprehensive bibliometric analysis of 4085 documents scanned in the Web of Science (WoS) database in some major effect genes associated with meat yield traits in livestock between 1981 and 2023. The analysis shows that interest in this topic has lately grown. The fact that numerous scholars participated in the investigations, which major research groups conducted, demonstrates the growth of this field's collaborative working culture. The publication of studies in this field in high-impact journals such as Meat Science, Journal of Animal Science and Animal Genetics reveals the scientific impact of this field. Keywords used in studies in this field are generally related to investigating the genetic factors affecting livestock's growth, muscle development and meat quality characteristics. In country-based studies, China and the United States have the most studies in this field. The citation records of articles reveal the significant impact of this field in literature. The scientific community has recently been particularly interested in investigating some major effect genes associated with meat yield traits in livestock.

Keywords

• Bibliometrics
• Livestock
• Myostatin
• Calpastatin
• Meat Yield
• Meat Traits

References

1. Aiello D, Patel K and Lasagna E (2018). The myostatin gene: an overview of mechanisms of action and its relevance to livestock animals. Animal genetics 49(6): 505-519.
2. Akhavan P, Ebrahim NA, Fetrati MA and Pezeshkan A (2016). Major trends in knowledge management research: a bibliometric study. Scientometrics 107: 1249-1264.
3. Amirkhanov K, Igenbayev A, Nurgazezova A, Okuskhanova E, Kassymov S, Muslimova N and Yessimbekov Z (2017). Research article comparative analysis of red and white Turkey meat quality. Pakistan Journal of Nutrition 16: 412-416.
4. Amthor H, Huang R, McKinnell I, Christ B, Kambadur R, Sharma M and Patel K (2002). The regulation and action of myostatin as a negative regulator of muscle development during avian embryogenesis. Developmental biology 251(2): 241-257.
5. Aria M and Cuccurullo C (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics 11(4): 959-975.
6. Baltic MZ and Boskovic M (2015). When man met meat: meat in human nutrition from ancient times till today. Procedia Food Science 5: 6-9.
7. Bellinge R, Liberles D, Iaschi S, O'brien P and Tay G (2005). Myostatin and its implications on animal breeding: a review. Animal genetics 36(1): 1-6.
8. Boler DD and Woerner DR (2017). What is meat? A perspective from the American Meat Science Association. Animal Frontiers 7(4): 8-11.

9. Boucher D, Palin M, Castonguay F, Gariépy C and Pothier F (2006). Detection of polymorphisms in the ovine leptin (LEP) gene: Association of a single nucleotide polymorphism with muscle growth and meat quality traits. Canadian journal of animal science 86(1): 31-35.
10. Burrow H, Moore S, Johnston D, Barendse W and Bindon B (2001). Quantitative and molecular genetic influences on properties of beef: a review. Australian journal of experimental agriculture 41(7): 893-919.
11. Capper JL (2013). Should we reject animal source foods to save the planet? A review of the sustainability of global livestock production. South African Journal of Animal Science 43(3): 233-246.
12. Case L, Miller S and Wood B (2010). Factors affecting breast meat yield in turkeys. World's poultry science journal 66(2): 189-202.
13. Çelik Ş (2020). Bibliometrics Analysis and a Husbandry Practice. Journal of Multidisciplinary Engineering Science Studies (JMESS) 6(12): 3632-3641.
14. Coimbra DR, Dominski FH, Correia CK and Andrade A (2019). Scientific production in Sports Science Journals: bibliometric analysis. Revista Brasileira de Medicina do Esporte 25: 88-93.
15. Filipčík R, Falta D, Kopec T, Chládek G, Večeřa M and Rečková Z (2020). Environmental Factors and Genetic Parameters of Beef Traits in Fleckvieh Cattle Using Field and Station Testing. Animals 10(11): 2159.
16. Gibbs D, Holloway L, Gilna B and Morris C (2009). Genetic techniques for livestock breeding: Restructuring institutional relationships in agriculture. Geoforum 40(6): 1041-1049.
17. Grosso J, Balieiro JCdC, Eler JP, Ferraz JBS, Mattos E and Michelan Filho T (2010). Comparison of different models to estimate genetic parameters for carcass traits in a commercial broiler line. Genetics and Molecular Research 9(2): 908-918.
18. Gura S (2007). Livestock genetics companies. Concentration and proprietary strategies of an emerging power in the global food economy. League for Pastoral Peoples and Endogenous Livestock Development, Ober-Ramstadt, Germany.
19. Hagen I, Zadissa A, McEwan J, Veenvliet B, Hickey S, Cullen N, Morris C and Wilson T (2005). Molecular and bioinformatic strategies for gene discovery for meat traits: a reverse genetics approach. Australian Journal of Experimental Agriculture 45(8): 801-807.
20. Herrero M and Thornton PK (2013). Livestock and global change: Emerging issues for sustainable food systems. Proceedings of the National Academy of Sciences 110(52): 20878-20881.
21. Meissner H, Scholtz M and Palmer A (2013). Sustainability of the South African livestock sector towards 2050 Part 1: Worth and impact of the sector. South African Journal of Animal Science 43(3): 282-297.
22. Merigó JM and Yang JB (2017). Accounting research: A bibliometric analysis. Australian Accounting Review 27(1): 71-100.
23. Mishra D, Gunasekaran A, Papadopoulos T and Dubey R (2018). Supply chain performance measures and metrics: a bibliometric study. Benchmarking: An International Journal 25(3): 932-967.
24. Motoyama M, Sasaki K and Watanabe A (2016). Wagyu and the factors contributing to its beef quality: A Japanese industry overview. Meat Science 120: 10-18.
25. Muhuri PK, Shukla AK, Janmaijaya M and Basu A (2018). Applied soft computing: A bibliometric analysis of the publications and citations during (2004–2016). Applied Soft Computing 69: 381-392.
26. Mwangi FW, Pewan SB, Otto JR, Adegboye OA, Charmley E, Gardiner CP, Malau-Aduli BS, Kinobe RT and Malau-Aduli AE (2022). Single Nucleotide Polymorphisms in the Fatty Acid Binding Protein 4, Fatty Acid Synthase and Stearoyl-CoA Desaturase Genes Influence Carcass Characteristics of Tropical Crossbred Beef Steers. Agriculture 12(8): 1171.
27. Onder H and Tırınk C (2022). Bibliometric Analysis for Genomic Selection Studies in Animal Science. Journal of the Institute of Science and Technology, 12(3): 1849-1856.
28. Özşensoy Y and Kara H (2019). Investigation of GH and GHR Alu I gene polymorphisms on meat yields in Anatolian water buffalo breed using PCR-RFLP method. Turkish Journal of Zoology 43(6): 560-565.
29. Pereira PMdCC and Vicente AFdRB (2013). Meat nutritional composition and nutritive role in the human diet. Meat science 93(3): 586-592.
30. Pethick D, Hocquette J, Scollan N and Dunshea F (2021). Improving the nutritional, sensory and market value of meat products from sheep and cattle. Animal 15: 100356.
31. Ramiah S, Meng G and Ebrahimii M (2016). Physiological and pathophysiological aspects of peroxisome proliferator-activated receptor regulation by fatty acids in poultry species. World's Poultry Science Journal 72(3): 551-562.
32. Rauw WM, Rydhmer L, Kyriazakis I, Øverland M, Gilbert H, Dekkers JC, Hermesch S, Bouquet A, Gómez Izquierdo E and Louveau I (2020). Prospects for sustainability of pig production in relation to climate change and novel feed resources. Journal of the Science of Food and Agriculture 100(9): 3575-3586.
33. Raza SHA, Khan S, Amjadi M, Abdelnour SA, Ohran H, Alanazi KM, Abd El-Hack ME, Taha AE, Khan R and Gong C (2020). Genome-wide association studies reveal novel loci associated with carcass and body measures in beef cattle. Archives of Biochemistry and Biophysics 694: 108543.
34. Raziye I (2019). The Identification of Novel Single Nucleotide Polymorphisms in Calpain 1 (CAPN1) Gene of Japanese Quail (Coturnix coturnix japonica). Animal Production 60(1): 25-29.
35. Resurreccion A (2004). Sensory aspects of consumer choices for meat and meat products. Meat science 66(1): 11-20.
36. Samicho Z, Ab Mutalib SR and Abdullah N (2013). Amino acid composition of droughtmaster beef at various beef cuts. Agricultural Sciences 4(5B): 61.
37. Sato S, Ohtake T, Uemoto Y, Okumura Y and Kobayashi E (2012). Polymorphism of insulin‐like growth factor 1 gene is associated with breast muscle yields in chickens. Animal science journal 83(1): 1-6.
38. Singh H and Bharti J (2021). Incredibly Common Nutrient Deficiencies. EAS Journal of Nutrition and Food Sciences 3(6): 175-178.
39. Sohaib M and Jamil F (2017). An insight of meat industry in Pakistan with special reference to halal meat: a comprehensive review. Korean journal for food science of animal resources 37(3): 329.
40. Song Y, Xu L, Chen Y, Zhang L, Gao H, Zhu B, Niu H, Zhang W, Xia J and Gao X (2016). Genome-wide association study reveals the PLAG1 gene for knuckle, biceps and shank weight in Simmental beef cattle. PloS one 11(12): e0168316.
41. Strazdiņa V, Jemeļjanovs A and Šterna V (2013). Nutrition value of wild animal meat. Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences.,373-377.
42. Sweileh WM (2020). Bibliometric analysis of scientific publications on “sustainable development goals” with emphasis on “good health and well-being” goal (2015–2019). Globalization and health 16: 1-13.
43. Team RC (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www. R-project. org/.
44. Telegina E, Krivoruchko A and Yatsyk O (2018). Interrelation of the MyoD1 gene polymorphisms with indicators of meat productivity of sheep of Stavropol breed. Agrarnyy nauchnyy zhurnal (6): 21-25.
45. Testa ML, Grigioni G, Panea B and Pavan E (2021). Color and marbling as predictors of meat quality perception of Argentinian consumers. Foods 10(7): 1465.
46. Thanuskodi S (2010). Journal of Social Sciences: A bibliometric study. Journal of Social Sciences 24(2): 77-80. Thomas M, Langley B, Berry C, Sharma M, Kirk S, Bass J and Kambadur R (2000). Myostatin, a negative regulator of muscle growth, functions by inhibiting myoblast proliferation. Journal of Biological Chemistry 275(51): 40235-40243.
47. Wang T, Niu Q, Zhang T, Zheng X, Li H, Gao X, Chen Y, Gao H, Zhang L and Liu GE (2022). Cis-eQTL Analysis and Functional Validation of Candidate Genes for Carcass Yield Traits in Beef Cattle. International Journal of Molecular Sciences 23(23): 15055.
48. Yan W, Zhou H, Hu J, Luo Y and Hickford JG (2018). Variation in the FABP4 gene affects carcass and growth traits in sheep. Meat science 145: 334-339.
49. Zahra AA, Nurmandi A, Tenario CB, Rahayu R, Benectitos SH, Mina FLP and Haictin KM (2021). Bibliometric analysis of trends in theory-related policy publications. Emerging Science Journal 5(1): 96-110.
50. Zhang Z-R, Jiang X-S, Du H-R, Zhu Q, Li X-C, Yang C-W and Liu Y-P (2012). Characterization of the expression profiles of calpastatin (CAST) gene in chicken. Molecular biology reports 39: 1839-1843.