Single nucleotide polymorphism markers and their applications for cattle production in selective breeding: A review for meat production traits https://doi.org/10.12982/VIS.2026.020

Main Article Content

Izzati Izamin
Luqman Abu Bakar
Mohd Farhan Hanif Reduan
Abubakar Muhammad Wakil
Norhidayah Noordin

Abstract

The United Nations projects the global population to reach 8.7 billion by 2030 and 10.2 billion by 2050, intensifying the demand for sustainable food security. Cattle, a cornerstone of meat production, critical to meeting this challenge. Advances in livestock genomics have revolutionized selective breeding by integrating single nucleotide polymorphisms (SNPs) into marker-assisted selection (MAS) and genomic selection. This review synthesizes the role of SNP markers in enhancing economically important growth trait related to meat production across Bos taurus, Bos indicus, and other indigenous cattle breeds. We examine SNP discovery methods, such as genome-wide association studies (GWAS) and high-throughput genotyping, and their application in identifying key genes associated with the trait. Emerging technologies, such as CRISPR-based editing guided by SNP data, are also explored. By addressing research gaps, particularly in indigenous breeds, this review highlights SNPs’ potential to optimize cattle production and advance global food security.


 

Article Details

How to Cite
Izamin, I., Abu Bakar, L., Reduan, M. F. H., Muhammad Wakil, A., & Norhidayah Noordin. (2025). Single nucleotide polymorphism markers and their applications for cattle production in selective breeding: A review for meat production traits: https://doi.org/10.12982/VIS.2026.020. Veterinary Integrative Sciences, 24(1), 1–12. retrieved from https://he02.tci-thaijo.org/index.php/vis/article/view/264610
Section
Review Article

References

Alqudah, A.M., Sallam, A., Stephen Baenziger, P., Börner, A., 2020. GWAS: Fast-forwarding gene identification and characterization in temperate cereals: lessons from barley - A review. J. Adv. Res. 22, 119-135.

Al-Samarai, F., Al-Kazaz, A., 2015. Molecular markers: an introduction and applications. Eur. J. Mol. Biotechnol. 9(3), 118-130.

Beuzen, N., Stear, M., Chang, K., 2000. Molecular markers and their use in animal breeding. Vet. J. 160, 42-52.

Bolormaa, S., Pryce, J.E., Reverter, A., Zhang, Y., Barendse, W., Kemper, K., Tier, B., Savin, K., Hayes, B.J., Goddard, M.E., 2014. A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genet. 10, e1004198.

Børsting, C., Morling, N., 2013. Single-nucleotide polymorphisms. In: Siegel, J.A., Saukko, P.J., Houck, M.M. (Eds.), Encyclopedia of forensic sciences, (2nd edition). Academic Press, Cambridge, pp. 233-238.

Casas, E., Shackelford, S., Keele, J., Koohmaraie, M., Smith, T., Stone, R., 2003. Detection of quantitative trait loci for growth and carcass composition in cattle. J. Anim. Sci. 81, 2976-2983.

Cortes, O., Cañon, J., Gama, L.T., 2022. Applications of microsatellites and single nucleotide polymorphisms for the genetic characterization of cattle and small ruminants: an overview. Ruminants. 2, 456-470.

Dekkers, J.C.M., Werf, H.J.V.d., 2007. Strategies, limitations and opportunities for marker-assisted selection in livestock. FAO, Rome, pp. 167-184.

Dhutmal, R., Mundhe, A., More, A., 2018. Molecular marker techniques: a review. Int. J. Curr. Microbiol. Appl. Sci. 6, 816-825.

Digesa, S., 2024. Effect of the genotype by environment interaction on the productive and reproductive performance of livestock in Ethiopia: a review. Glob. Res. Environ. Sustain. 2, 77-90.

Farchi, S., De Sario, M., Lapucci, E., Davoli, M., Michelozzi, P., 2017. Meat consumption reduction in Italian regions: Health co-benefits and decreases in GHG emissions. PLos ONE. 12, e0182960.

Fathoni, A., Maharani, D., Aji, R., Choiri, R., Sumadi, S., 2019. Polymorphism of the SNP g. 1180 C> T in leptin gene and its association with growth traits and linear body measurement in Kebumen Ongole Grade cattle. J. Indonesian Trop. Anim. Agric. 44, 125-134.

Flint, J., Mackay, T.F., 2009. Genetic architecture of quantitative traits in mice, flies, and humans. Genome. Res. 19, 723-733.

Gao, Y.Y., Cheng, G., Cheng, Z.X., Bao, C., Yamada, T., Cao, G. F., Bao, S.Q., Schreurs, N.M., Zan, L.S., Tong, B., 2022. Association of variants in FABP4, FASN, SCD, SREBP1 and TCAP genes with intramuscular fat, carcass traits and body size in Chinese Qinchuan cattle. Meat Sci. 192, 108882.

Garmyn, A., 2020. Consumer preferences and acceptance of meat products. Foods. 9, 708.

Goodwin, S., McPherson, J.D., McCombie, W.R., 2016. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333-351.

Goszczynski, D.E., Papaleo-Mazzucco, J., Ripoli, M.V., Villarreal, E.L., Rogberg-Muñoz, A., Mezzadra, C.A., Melucci, L.M., Giovambattista, G., 2017. Genetic variation in FABP4 and evaluation of its effects on beef cattle fat content. Anim. Biotechnol. 28, 211-219.

Huang, C.W., Lin, Y., Ding, S., Lo, L., Wang, P., Lin, E., Liu, F., Lu, Y., 2015. Efficient SNP discovery by combining microarray and lab-on-a-chip data for animal breeding and selection. Microarrays. 4, 570-595.

Huang, Y.Z., Shi, Q.T., Shi, S.Y., Yang, P., Zhang, Z.J., Lyu, S.J., Chen, F.Y., Xu, J.W., Liu, X., Li, Z., Ru, B., Cai, C., Xie, J., Lei, C., Chen, H., Xu, Z., Wang, E., 2022. Association between copy number variation of SERPINA3-1 gene and growth traits in Chinese cattle. Anim. Biotechnol. 34(4), 1524-1531.

Jones, S.R., 2003. An introduction to power and sample size estimation. J. Emerg. Med. 20, 453-458.

Katare, B., Wang, H., Lawing, J., Hao, N., Park, T., Wetzstein, M., 2020. Toward optimal meat consumption. Am. J. Agric. Econ. 102, 662-680.

Kirsanova, N., Sukhoedova, A., Pleskacheva, M., Soltynskaya, I., Timofeeva, I., Prasolova, О., Krylova, E., 2019. Pyrosequencing: its potential and limitations in diagnosis of inherited diseases in cattle. Vet. Sci. 43-48.

Kwok, P.Y., Duan, S., 2003. SNP discovery by direct DNA sequencing. Methods. Mol. Biol. 212, 71-84.

Long, J.A., 2020. The ‘omics’ revolution: Use of genomic, transcriptomic, proteomic and metabolomic tools to predict male reproductive traits that impact fertility in livestock and poultry. Anim. Reprod. Sci. 220, 106354.

Malau-Aduli, A., Edriss, M., Siebert, B., Bottema, C., Pitchford, W., 2000. Breed differences and genetic parameters for melting point, marbling score and fatty acid composition of lot‐fed cattle. J. Anim. Physiol. Anim. Nutr. 83, 95-105.

Marigorta, U.M., Rodríguez, J.A., Gibson, G., Navarro, A., 2018. Replicability and prediction: lessons and challenges from GWAS. Trends. Genet. 34, 504-517.

Meuwissen, T., Hayes, B., Goddard, M., 2016. Genomic selection: A paradigm shift in animal breeding. Anim. Front. 6, 6-14.

Meuwissen, T.H., Hayes, B.J., Goddard, M., 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157, 1819-1829.

Mishra, S.P., Mishra, C., Mishra, D.P., Rosalin B.P., Bhuyan, C., 2017. Application of advanced molecular marker technique for improvement of animal: A critical review. J. Entomol. Zool. Stud. 5, 1283-1295

Motoyama, M., Sasaki, K., Watanabe, A., 2016. Wagyu and the factors contributing to its beef quality: a Japanese industry overview. Meat Sci. 120, 10-18.

Mukhopadhyay, C.S., Kumar, A., Deb, R., 2020. Chapter 1 - Cattle genomics: Genome projects, current status, and future applications. In: Malik, Y.S., Barh, D., Azevedo, V., Khurana, S.M.P. (Eds.), Genomics and biotechnological advances in veterinary, poultry, and fisheries. Academic Press, Cambridge, pp. 3-28.

Obari, C., Rochus, C., Schenkel, F., Miglior, F., Baes, C., 2022. PSXII-21 the impact of genomic selection on Canadian Holstein cattle population structure. J. Anim. Sci. 100, 207-208.

Pal, A., Chakravarty, A.K., 2020. Chapter 21 - Advanced breeding techniques. In: Pal, A., Chakravarty, A.K. (Eds.), Genetics and breeding for disease resistance of livestock. Academic Press, Cambridge, pp. 317-350.

Paula, M.C.d., Martins, E.N., Silva, L.O.C.d., Oliveira, C.A.L.d., Valotto, A.A., Ribas, N.P., 2009. Genotype × environment interaction for milk yield of Holstein cows among dairy production units in the state of Paraná. Rev. Bras. Zootec. 38, 467-473.

Pogorzelski, G., Pogorzelska, E., Pogorzelski, P., Półtorak, A., Hocquette, J.F., Wierzbicka, A., 2021. Towards an integration of pre- and post-slaughter factors affecting the eating quality of beef. Livest. Sci. 255, 104795.

Politi, C., Roumeliotis, S., Tripepi, G., Spoto, B., 2023. Sample size calculation in genetic association studies: a practical approach. Life. 13, 235.

Raza, S.H.A., Khan, S., Amjadi, M., Abdelnour, S.A., Ohran, H., Alanazi, K.M., Abd El-Hack, M.E., Taha, A.E., Khan, R., Gong, C., Schreurs, N.M., Zhao, C., Wei, D., Zan, L., 2020. Genome-wide association studies reveal novel loci associated with carcass and body measures in beef cattle. Arch. Biochem. Biophys. 694, 108543.

Roy, R., Taourit, S., Zaragoza, P., Eggen, A., Rodellar, C., 2005. Genomic structure and alternative transcript of bovine fatty acid synthase gene (FASN): comparative analysis of the FASN gene between monogastric and ruminant species. Cytogenet. Genome. Res. 111, 65-73.

Sabir, J., Mutwakil, M., El-Hanafy, A., Al-Hejin, A., Sadek, M.A., Abou-Alsoud, M., Qureshi, M., Saini, K., Ahmed, M., 2014. Applying molecular tools for improving livestock performance: From DNA markers to next generation sequencing technologies. J. Food Agric. Environ. 12, 541-553.

Sahana, G., Cai, Z., Sanchez, M.P., Bouwman, A.C., Boichard, D., 2023. Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle. J. Dairy Sci. 106, 5218-5241.

Sampath, H., Ntambi, J.M., 2006. Stearoyl-coenzyme A desaturase 1, sterol regulatory element binding protein-1c and peroxisome proliferator-activated receptor-alpha: independent and interactive roles in the regulation of lipid metabolism. Curr. Opin. Clin. Nutr. Metab. Care. 9, 84-88.

Santana, M.H.A., Gomes, R.C., Ozawa, G.M., Fukumasu, H., Silva, S.L., Leme, P.R., Rossi Junior, P., Pires, P.R.L., Alexandre, P.A., Oliveira, P.S., Meirelles, F.V., Ferraz, J.B.S., 2014. Single nucleotide polymorphisms in genes linked to ion transport and regulation of appetite and their associations with weight gain, feed efficiency and intake of Nellore cattle. Livest. Sci. 165, 33-36.

Schmid, M., Bennewitz, J., 2017. Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs. Arch. Anim. Breed. 60, 335-346.

Sharma, H., Gajbhiye, P., Ahlawat, A., Solanki, G., Sindhal, D., Kalma, R., Singh, V., Savsani, G.C.A.H., 2016. SNP: A potential molecular marker for livestock improvement. Adv. Life Sci. 5(18), 7227-7235.

Singh, B.D., Singh, A.K., 2015. High-throughput SNP genotyping. In: Singh, B.D., Singh, A.K. (Eds.), Marker-assisted plant breeding: principles and practices. Springer, New Delhi, pp. 367-400.

Singh, U., Deb, R., Alyethodi, R.R., Alex, R., Kumar, S., Chakraborty, S., Dhama, K., Sharma, A., 2014. Molecular markers and their applications in cattle genetic research: A review. Biomark. Genom. Med. 6, 49-58.

United Nations Department of Economic Social Affairs, 2021. World population prospects 2017 - volume I: comprehensive tables, United Nations, New York.

Usman, T., Qureshi, M., Yu, Y., Wang, Y., 2013. Influence of various environmental factors on dairy production and adaptability of Holstein cattle maintained under tropical and subtropical conditions. Adv. Environ. Biol. 7, 366-372.

Van Eenennaam, A., Werf, J., Goddard, M., 2011. The value of using DNA markers for beef bull selection in the seedstock sector. J. Anim. Sci. 89, 307-320.

Vignal, A., Milan, D., SanCristobal, M., Eggen, A., 2002. A review on SNP and other types of molecular markers and their use in animal genetics. Genet. Sel. Evol. 34, 275-305.

Von Berg, J., Ten Dam, M., Van Der Laan, S.W., De Ridder, J., 2022. PolarMorphism enables discovery of shared genetic variants across multiple traits from GWAS summary statistics. Bioinformatics. 38, i212-i219.

Wakchaure, R., Ganguly, S., Praveen, K., 2016. Genotype x environment interaction in animal breeding: a review. Biodivers. Conserv. Chang. Clim. 3, 60-73.

Wang, S., Cai, X., Xue, K., Chen, H., 2011. Polymorphisms of MRF4 and H-FABP genes association with growth traits in Qinchuan cattle and related hybrids. Mol. Biol. Rep. 38, 1013-1020.

Watanabe, K., Stringer, S., Frei, O., Umićević Mirkov, M., de Leeuw, C., Polderman, T.J.C., van der Sluis, S., Andreassen, O.A., Neale, B.M., Posthuma, D., 2019. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339-1348.

Wiggans, G.R., Cole, J.B., Hubbard, S.M., Sonstegard, T.S., 2017. Genomic selection in dairy cattle: The USDA experience. Annu. Rev. Anim. Biosci. 5, 309-327.

Xiang, R., Macleod, I.M., Bolormaa, S., Goddard, M.E., 2017. Genome-wide comparative analyses of correlated and uncorrelated phenotypes identify major pleiotropic variants in dairy cattle. Sci. Rep. 7(1), 9248.

Xu, J.Y., Xu, G.B., Chen, S.L., 2009. A new method for SNP discovery. Biotechnol. Tech. 46, 201-208.

Xu, Y., Liu, X., Fu, J., Wang, H., Wang, J., Huang, C., Prasanna, B.M., Olsen, M.S., Wang, G., Zhang, A., 2020. Enhancing genetic gain through genomic selection: from livestock to plants. Plant Commun. 1, 100005.

Yang, W., Zhang, T., Song, X., Dong, G., Xu, L., Jiang, F., 2022. SNP-target genes interaction perturbing the cancer risk in the post-GWAS. Cancers. 14, 5636.

Zalewska, M., Puppel, K., Sakowski, T., 2021. Associations between gene polymorphisms and selected meat traits in cattle — A review. Anim. Biosci. 34, 1425-1438.

Zhang, S., Knight, T.J., Reecy, J.M., Beitz, D.C., 2008. DNA polymorphisms in bovine fatty acid synthase are associated with beef fatty acid composition. Anim. Genet. 39, 62-70.