Analysis of the Effectiveness of Marker-Assisted Selection (MAS) in Breeding High-Yielding Plants

  • Nur Nazhifah Adyputri Departemen of Biology, Faculty of Mathematic and Natural Sciences Makassar State University, Makassar, Indonesia
  • Nanda Adrian Mursalim Departemen of Biology, Faculty of Mathematic and Natural Sciences Makassar State University, Makassar, Indonesia
  • Sri Mardawiyah Departemen of Biology, Faculty of Mathematic and Natural Sciences Makassar State University, Makassar, Indonesia
  • Yusminah Hala Departemen of Biology, Faculty of Mathematic and Natural Sciences Makassar State University, Makassar, Indonesia
Keywords: Marker-Assisted Selection (MAS); Plant Productivity; Molecular Breeding; Genetic Markers

Abstract

Global food security demands more precise and efficient plant breeding strategies to develop high-yielding varieties. Marker-Assisted Selection (MAS) is a molecular approach that utilizes genetic markers such as SSRs, SNPs, and InDels to identify superior genotypes from early growth stages. This study employs a Systematic Literature Review (SLR) method following PRISMA guidelines to analyze the effectiveness of MAS in enhancing crop productivity compared to conventional methods. Review results indicate that MAS can accelerate breeding cycles, improve selection accuracy, and support strategies such as foreground selection, background selection, and gene pyramiding across various commodities, including rice, maize, wheat, soybean, and alfalfa. Significant success is observed in improved disease resistance, abiotic stress tolerance, and yield stability. However, the implementation of MAS still faces challenges, including high operational costs, limited laboratory infrastructure, the complexity of quantitative traits, and the need for marker re-validation across different genetic backgrounds. Future prospects suggest that the integration of MAS with genomic selection, artificial intelligence, and CRISPR-Cas9 technology will strengthen data-driven predictive breeding systems. Consequently, MAS can be viewed as a strategic solution to accelerate the development of high-yielding superior varieties and support the global food security agenda.

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References

Abbas, B., Mawikere, NL, Dasnarebo, SSH, Yuliati, TI, Kurung, S., Rumi, HS (2025). Textbook of Biotechnological Perspectives in Plant Breeding. Cirebon: Greenbook Publishing Indonesia.

Arockiasamy, S., Kumpatla, J., Hadole, S., Yepuri, V., Patil, MS., Shrivastava, V., Rao, J., Kancharla, N., Jalali, S., Varshney, A., Madan, NS, Pothakani, S., Nair, V., Peyyala, S., Kumar, A., Gomaruri, A. NS, Pachiyannan, J., Seelamanthula, S., … Dasgupta, S. (2021). Breeding and biotechnological efforts in Jatropha curcas L. for sustainable yields. Oil Crop Science, 6(4), 180–191. https://doi.org/10.1016/j.ocsci.2021.10.004

Baidyussen, A., Khassanova, G., Utebayev, M., Jatayev, S., Kushanova, R., Khalbayeva, B., Amangeldiyeva, A., Yerzhebayeva, Y., Bulatova, K., Schramm, C., Anderson, P., Jenkins, C. L. D., Soole, K. L., & Shavrukov, Y. (2024). Assessment of molecular markers and marker-assisted selection for drought tolerance in barley (Hordeum vulgare L.). Journal of Integrative Agriculture, 23(1), 20–38. https://doi.org/10.1016/j.jia.2023.06.012

Borella, J., Brasileiro, B. P., de Azeredo, A. A. C., Ruaro, L., de Oliveira, R. A., & Bespalhok Filho, J. C. (2022). Resistance to orange rust associated with the G1 molecular marker in parents of Brazilian RB sugarcane varieties. Genetics and Molecular Research, 21(1). https://doi.org/10.4238/gmr18980

Cahyo, AN (2023). The Importance of Marker-Assisted Selection Application in the Selection of Drought-Tolerant Rubber Clones. Warta Perkaretan, 42(2). https://doi.org/10.22302/ppk.wp.v42i2.939

Chen, Y., Zhou, Y., Yan, W., Li, Q., Liang, X., Wang, J., Cao, L., Zhang, X., Liu, Y., Mao, W., Luo, C., Li, Development of near isogenic lines for the glabrous gene Cmgl by marker assisted selection and transcriptome analysis in melon. Scientia Horticulturae, 353, 114505. https://doi.org/10.1016/j.scienta.2025.114505

Fitriyah, F & Siregar, HA (2025). Transforming Oil Palm Plant Breeding Through Genomic Selection and Artificial Intelligence Towards Data-Driven Smart Breeding. Warta PPKS, 30(3), 197-210. https://doi.org/10.22302/iopri.war.warta.v30i3.242

Hafez, M., Mohamed, A. E., Rashad, M., & Popova, A. I. (2021). The efficiency of application of bacterial and humic preparations to enhance of wheat (Triticum aestivum L.) plant productivity in the arid regions of Egypt. Biotechnology Reports, 29, 1-5. https://doi.org/10.1016/j.btre.2020.e00584

Han, G., Yan, H., Li, L., & An, D. (2025). Advancing wheat breeding using rye: A key contribution to wheat breeding history. Trends in Biotechnology, 43(9), 2170–2182. https://doi.org/10.1016/j.tibtech.2025.03.008

Lin, F., Jing, D., Zhang, J., Sun, Y., Du, L., Li, C., Lan, Y., & Zhou, T. (2024). Introgression of OsAP47 by marker-assisted selection enhanced resistance against southern rice black-streaked dwarf virus disease. Virology, 594, 110060. https://doi.org/10.1016/j.virol.2024.110060

Luca, L. P., Guardio, M. D., Bennici, S., Ferlito, F., Nicolosi, E., Malfa, S. L., Gentile, A., Distefano, G. (2024). Development of An Efficient Molecular-Marker Assisted Selection Strategy For Berry Color In Grapevine. Scentia Horticulturae, 337, 1-8. https://doi.org/10.1016/j.scienta.2024.113522

Mahmoud, A., Qi, R., Chi, X., Liao, N., Malangisha, G. K., Ali, A., Moustafa-Farag, M., Yang, J., Zhang, M., & Hu, Z. (2024). Integrated Bulk Segregant Analysis, Fine Mapping, and Transcriptome Revealed QTLs and Candidate Genes Associated with Drought Adaptation in Wild Watermelon. International Journal of Molecular Sciences, 25(1). https://doi.org/10.3390/ijms25010065

McLeod, L., Barchi, L., Tumino, G., Tripodi, P., Salinier, J., Gros, J., Boyaci, H.F., Özalp, R., Borovsky, Y., Schafleitner, R., Barchenger, D.W., Finkers, R., Brouwer, M., Stein, N., Rabance-Wallace, G.M., Giuliano, G.M. Voorrips, R.E., Paran, I., & Lefebvre, V. (2023). Multi-environment association study highlights candidate genes for robust agronomic quantitative trait loci in a novel worldwide Capsicum core collection. Plant Journal , 116(5), 1508–1528. https://doi.org/10.1111/tpj.16425

Manisa, T., Zuyasna, Saniaty, A., Sapareng, S. (2026). Introduction to Plant Genetics. Padang: CV Hey Publishing Indonesia

Ndlovu, N., Kachapur, R. M., Beyene, Y., Das, B., Ogugo, V., Makumbi, D., Spillane, C., McKeown, P. C., Boddupalli, P. M., & Gowda, M. S. (2024). Linkage mapping and genomic prediction of grain quality traits in tropical maize (Zea mays L.). Frontiers in Genetics, 15. https://doi.org/10.3389/fgene.2024.1353289

Noweiska, A., Bobrowska, R., Spychała, J., Tomkowiak, A., & Kwiatek, M. T. (2023). Multiplex PCR assay for the simultaneous identification of race specific and non-specific leaf resistance genes in wheat (Triticum aestivum L.). Journal of Applied Genetics, 64(1), 55–64. 10.1007/s13353-022-00745-5

Prasetya, MGA, Hayati, SA, Khuzniah, A., Hamdiyah, Aulia, A., & Sasmita, TD (2025). Integrating Molecular Breeding, Plant Biotechnology, and Genetic Resource Management to Support Food Security under Climate Change: A Systematic Literature Review. Indonesian Journal of Multidisciplinary Studies, 1(3)

Prayoga, GI, Sari, S., Dono, D., & Carsono, N. (2022). Correlation Analysis of Simple Sequence Repeats Molecular Markers with Brown Planthopper Resistance Characteristics in Five Rice Cultivars. Journal of Agriculture, 33(3), 331-341. https://doi.org/10.24198/agrikultura.v33i3.41024

Rajput, R., Boone, B. A., Mandlik, R., Islam, M. T., Qi, Y., Wang, J., Barrangou, R., Sozzani, R., Eckert, C. A., Tuskan, G. A., Chen, J. G., & Yang, X. (2025). Multigene engineering in plants: Technologies, applications, and future prospects. Biotechnology Advances, 85, 1 - 17.https://doi.org/10.1016/j.biotechadv.2025.108697

Sagwal, V., Sihag, P., Singh, Y., Mehla, S., Kapoor, P., Balyan, P., Kumar, A., Mir, R. R., Dhankher, O. P., & Kumar, U. (2022). Development and characterization of nitrogen and phosphorus use efficiency responsive genic and miRNA derived SSR markers in wheat. Heredity, 128(6), 391–401. https://doi.org/10.1038/s41437-022-00506-4

Sathishkumar, R., Darpan Mohanrao, M., Geethanjali, S., Santha Lakshmi Prasad, M., & Senthilvel, S. (2025). A simple and cost-effective SNP genotyping assay for marker-assisted selection of wilt resistance in castor breeding. Industrial Crops & Products, 226, 1-11. https://doi.org/10.1016/j.indcrop.2025.120693

Schneider, H. M., Ben-Gal, A., Furtado, B., Cicek, N., Dalmaris, E., Atzori, G., & Bazihizina, N. (2026). Going underground: The importance of soil heterogeneity in shaping plant productivity and responses to saline soils. Environmental and Experimental Botany, 243, 1 - 10. https://doi.org/10.1016/j.envexpbot.2026.106321

Shen, J., Ye, Y., Zhou, Y., & Rong, X. (2026). Genomic SSR markers elucidate genetic architecture and phenotypic trait associations in Rhododendron Linnaeus. Journal of Genetic Engineering and Biotechnology, 24(1), 1-11. https://doi.org/10.1016/j.jgeb.2025.100631

Singh, A. K., Revathi, P., Srinivas Prasad, M., Sundaram, R. M., Hari Prasad, A. S., Senguttuvel, P., Kempa Raju, K. B., & Sruthi, K. (2023). Improving blast resistance of maintainer line DRR 9B by transferring broad spectrum resistance gene Pi2 by marker assisted selection in rice. Physiology and Molecular Biology of Plants, 29(2), 253–262. https://doi.org/10.1007/s12298-023-01291-y

Singh, G. P. I., Singh, N., Ellur, R. K., Balamurugan, A., Prakash, G., Rathour, R., Mondal, K. K., Bhowmick, P. K., Gopala Krishnan, S., Nagarajan, M., Seth, R. K., Vinod, K. K., Singh, V. P., Bollinedi, H., & Singh, A. K. (2023). Genetic Enhancement for Biotic Stress Resistance in Basmati Rice through Marker-Assisted Backcross Breeding. International Journal of Molecular Sciences, 24(22).1 - 18. https://doi.org/10.3390/ijms242216081

Singh, L., Pierce, C. A., Santantonio, N., Steiner, R. L., Miller, D., Reich, J., & Ray, I. M. (2022). Validation of DNA marker-assisted selection for forage biomass productivity under deficit irrigation in alfalfa. Plant Genome, 15(1). 1-14. https://doi.org/10.1002/tpg2.20195

Varshney, R. K., Mohan, S. M., Gaur, P. M., Gangarao, N. V. P. R., Pandey, M. K., Bohra, A., Sawargaonkar, S. L., Chitikineni, A., Kimurto, P. K., Janila, P., Saxena, K. B., Fikre, A., Sharma, M., Rathore, A., Pratap, A., Tripathi, S., Datta, S., Chaturvedi, S. K., Mallikarjuna, N., ... & Gowda, C. L. L. (2013). Achievements and prospects of genomics-assisted breeding in three legume crops of the semi-arid tropics. Biotechnology Advances, 31(7), 1120–1134. https://doi.org/10.1016/j.biotechadv.2013.01.001

Zhou, W., Ouyang, H., Yan, Z., Song, J., Li, Y., Tang, Z., & Zhao, C. (2026). Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean (Glycine max) yield case study. Plant Genome, 19(1). 1-25. https://doi.org/10.1002/tpg2.70178

Published
2026-08-12
How to Cite
Adyputri, N. N., Mursalim, N. A., Mardawiyah, S., & Hala, Y. (2026). Analysis of the Effectiveness of Marker-Assisted Selection (MAS) in Breeding High-Yielding Plants. BIOPENDIX: Jurnal Biologi, Pendidikan Dan Terapan, 13(2), 125-134. https://doi.org/10.30598/biopendixvol13issue2page125-134