Pre-DUS screening of elite maize inbreds via SSR markers and ideotype selection
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Abstract. Mitreka RF, Atmojo ED, Asyari SND, Arifin AG, Saptadi D, Sugiharto AN. 2026. Pre-DUS screening of elite maize inbreds via SSR markers and ideotype selection. Biodiversitas 27 (4): d270433. https://doi.org/10.13057/biodiv/d270433. Maize is a key cereal crop in Indonesia, but traditional pre-DUS evaluation relies heavily on morphological traits, which are often confounded by environmental influences. This limitation hinders precise genetic differentiation and efficient selection of elite inbred lines. This study aimed to support pre-DUS differentiation by integrating Simple Sequence Repeat markers with quantitative phenotypic traits and multivariate selection. Twelve elite maize inbred lines were evaluated under field conditions using a randomized complete block design; nine quantitative traits were recorded, and 10 SSR markers were used for genotyping. Our findings present three parallel outputs: phenotypic performance, molecular characterization, and multivariate selection outcomes. Phenotypically, integrated analysis revealed significant variation, with JUNRT1 and JUNRT3 demonstrating superior performance in key yield components (kernel weight, ear diameter, dehusked ear weight, and potential yield) alongside earlier flowering. Molecularly, SSR markers provided evidence consistent with genetic differentiation and predominantly single-fragment patterns in bulked SSR profiles for JUNRT1 and JUNRT3, complementing phenotypic data for pre-DUS characterization. Through multivariate analysis, Factor Analysis effectively reduced nine quantitative traits into three meaningful components: flowering time, yield components, and ear/kernel attributes. Subsequently, the Multi-Trait Genotype-Ideotype Distance Index simultaneously favored early maturity and high yield components, ranking JUNRT1 and JUNRT3 among the top lines. Collectively, this integrated morpho-molecular workflow provides screening-level evidence for pre-DUS characterization and prioritization of candidate parents for subsequent multi-location testing and potential hybrid development. It is crucial to reiterate that these outcomes are intended to support, not replace, formal DUS testing. However, single location-season phenotyping and bulked SSR profiles cannot confirm uniformity or stability under formal DUS requirements.
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References
Abo-Hamed S, Elghareeb EM, El-Shahaby O, Ibraheem F. 2025. Monitoring dynamics in ear-leaf physiology during maize grain filling: Genotype and nitrogen impact on source-sink relations and yield. Acta Physiol Plant 47: 34. https://doi.org/10.1007/s11738-025-03775-8.
Akbari A, Bahmani K, Kazan M, Bilgin ÖF, Rahimi J, Darbandi AI, Kafkas NE, Kafkas S. 2025. Analysis of fennel breeding populations based on Distinctness, Uniformity, and Stability (DUS) testing via morphological descriptors and DNA molecular markers. Genet Resour Crop Evol 72: 325-342. https://doi.org/10.1007/s10722-024-01934-3.
Baduwal P, Chand H, Kayastha P, Lamichhane P, Pandey B, Barsha KC, Magar BR, Bhandari J, Khanal S. 2022. Correlation analysis of maize (Zea mays L.) genotypes: A review. Intl J Environ Agric Biotechnol 7 (6): 153-157. https://doi.org/10.22161/ijeab.76.17.
Borrás L, Vitantonio-Mazzini LN. 2018. Maize reproductive development and kernel set under limited plant growth environments. J Exp Bot 69 (13): 3235-3243. https://doi.org/10.1093/jxb/erx452.
Bouchetat F, Ghanai R, Himour S, Bouaroudj S, Benfkih LA. 2023. Effect of genotype by environment interactions on quality parameters and grain yield of durum wheat. Biodiversitas 24 (10): 5565-5571. https://doi.org/10.13057/biodiv/d241038.
Cai H, Lu Y, Liu G, Zhang S, Jia H, You A, Jiao C. 2020. Genetic diversity analysis of hybrid rice parental lines and genetic purity assessment of hybrid seeds of China. J Agric Sci 12 (5): 37-47. https://doi.org/10.5539/jas.v12n5p37.
Carnimeo ESG, Bertasello LET, Dutra SMF, Moro GV. 2020. Principal component analysis for selection of superior maize genotypes. Científica 48 (4): 357-362. https://doi.org/10.15361/1984-5529.2020v48n4p357-362.
Chanda R, Mukanga M, Mwala M, Osiru DS, MacRobert J. 2014. A comparative analysis of Distinctness, Uniformity and Stability (DUS) data in discriminating selected Southern African maize (Zea mays L.) inbred lines. Afr J Agric Res 9 (41): 3056-3076. https://doi.org/10.5897/ajar2014.8755.
Chen J, Zhang L, Liu S, Li Z, Huang R, Li Y, Cheng H, Li X, Zhou B, Wu S, Chen W, Wu J, Ding J. 2016. The genetic basis of natural variation in kernel size and related traits using a four-way cross population in maize. PLoS One 11 (4): e0153428. https://doi.org/10.1371/journal.pone.0153428.
De Pasqual C, Suisto K, Kirvesoja J, Gordon S, Ketola T, Mappes J. 2022. Heterozygote advantage and pleiotropy contribute to intraspecific color trait variability. Evolution 76 (10): 2389-2403. https://doi.org/10.1111/evo.14597.
Devasree S, Ganesan KN, Ravikesavan R, Senthil N, Paranidharan V. 2020. Relationship between yield and its component traits for enhancing grain yield in single cross hybrids of maize (Zea mays L.). Electron J Plant Breed 11 (3): 796-802. https://doi.org/10.37992/2020.1103.131.
Dwivedi SL, Reynolds MP, Ortiz R. 2021. Mitigating tradeoffs in plant breeding. iScience 24 (9): 102965. https://doi.org/10.1016/j.isci.2021.102965.
Ehemba GL, Zoclanclounon YAB, Gueye M, Diatta C, Diatta MBE, Thiaw C, Tine AK, Baldé AB, Kanfany G. 2019. Evaluation of extra-early maturing maize genotypes for grain yield and stability in the groundnut basin agro-ecological zone of Senegal. Agric Food Sci Res 6 (1): 79-84. https://doi.org/10.20448/journal.512.2019.61.79.84.
Fernández JA, Messina CD, Salinas A, Prasad PVV, Nippert JB, Ciampitti IA. 2022. Kernel weight contribution to yield genetic gain of maize: A global review and US case studies. J Exp Bot 73 (11): 3597-3609. https://doi.org/10.1093/jxb/erac103.
Figàs MR, Prohens J, Casanova C, Fernández-de-Córdova P, Soler S. 2018. Variation of morphological descriptors for the evaluation of tomato germplasm and their stability across different growing conditions. Sci Hortic 238: 107-115. https://doi.org/10.1016/j.scienta.2018.04.039.
Gamea HA, Darwich MM, Aboyousef HA. 2018. Combining ability for some inbred lines in half-diallel crosses of maize under two different locations conditions. Arch Agric Sci J 1 (3): 14-25. https://doi.org/10.21608/aasj.2018.29102.
Gedil M, Menkir A. 2019. An integrated molecular and conventional breeding scheme for enhancing genetic gain in maize in Africa. Front Plant Sci 10: 1430. https://doi.org/10.3389/fpls.2019.01430.
Genievskaya Y, Abugalieva S, Turuspekov Y. 2025. Identification of QTLs associated with grain yield-related traits of spring barley. BMC Plant Biol 25: 554. https://doi.org/10.1186/s12870-025-06588-6.
Handayani CO, Sukaharjo S, Zu’amah H, Dewi T. 2023. Distribution characteristics, sources analysis, and health risks assessment of heavy metals in farmland soil in Batu City, East Java. Jurnal Teknologi Lingkungan 24 (2): 166-175. https://doi.org/10.55981/jtl.2023.291. [Indonesian]
Jahnke G, Smidla J, Poczai P. 2022. MolMarker: A simple tool for DNA fingerprinting studies and polymorphic information content calculation. Diversity 14 (6): 497. https://doi.org/10.3390/d14060497.
Josia C, Mashingaidze K, Amelework AB, Kondwakwenda A, Musvosvi C, Sibiya J. 2021. SNP-based assessment of genetic purity and diversity in maize hybrid breeding. PLoS One 16 (8): e0249505. https://doi.org/10.1371/journal.pone.0249505.
Kandel M, Ghimire SK, Ojha BR, Shrestha J. 2018. Correlation and path coefficient analysis for grain yield and its attributing traits of maize inbred lines (Zea mays L.) under heat stress condition. Intl J Agric Environ Food Sci 2 (4): 124-130. https://doi.org/10.31015/jaefs.18021.
Kovinčić A, Markovic K, Ristic D, Babic V, Petrovic T, Zivanovic T, Kravic N. 2023. Efficiency of biological typing methods in maize hybrid genetic purity estimation. Genes 14 (6): 1195. https://doi.org/10.3390/genes14061195.
Kumar M, Nagar KK, Pawar SV, Pawar B, Gadekar DA. 2024. Unravelling of DNA profiling in wheat genotypes using microsatellite marker. Intl J Adv Biochem Res 8 (10S): 944-949. https://doi.org/10.33545/26174693.2024.v8.i10sk.2611.
Kyi S, Win KK, Than H, Win S, Htwe NM. 2021. Effect of different growing seasons on correlation and path coefficients of yield and yield components in maize (Zea mays L.). Intl J Adv Res 9 (1): 243-250. https://doi.org/10.21474/ijar01/12301.
Labroo MR, Studer AJ, Rutkoski JE. 2021. Heterosis and hybrid crop breeding: A multidisciplinary review. Front Genet 12: 643761. https://doi.org/10.3389/fgene.2021.643761.
Liu G, Yang Y, Guo X, Liu W, Xie R, Ming B, Xue J, Wang K, Li S, Hou P. 2023. A global analysis of dry matter accumulation and allocation for maize yield breakthrough from 1.0 to 25.0 Mg ha⁻¹. Resour Conserv Recycl 188: 106656. https://doi.org/10.1016/j.resconrec.2022.106656.
Masha JC, Muhammed N, Njung’e V, Oyoo ME, Miheso M. 2022. Improved SSRs-based genetic diversity assessment of coconuts (Cocos nucifera L.) along the coast of Kenya. Afr J Biotechnol 21 (11): 510-521. https://doi.org/10.5897/ajb2020.17212.
Nagesh P, Takalkar SA, Mohan SM, Naidu PB, Lohithaswa CH, Kachapur RM, Kuchanur P, Injeti SK, Singh NK, Kanwade DG, Shrestha J, Vivek BS. 2025. Genotype and environmental interactions in maize (Zea mays L.) across regions of India: Implications for hybrid testing locations in South Asia. Aust J Crop Sci 19 (7): 773-783. https://doi.org/10.21475/ajcs.25.19.07.p330.
Nei M. 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89 (3): 583-590. https://doi.org/10.1093/genetics/89.3.583.
Olivoto T, Diel MI, Schmidt D, Lúcio AD. 2022. MGIDI: A powerful tool to analyze plant multivariate data. Plant Methods 18: 121. https://doi.org/10.1186/s13007-022-00952-5.
Olivoto T, Nardino M. 2021. MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics 37 (10): 1383-1389. https://doi.org/10.1093/bioinformatics/btaa981.
Prasanna BM, Cairns JE, Zaidi PH, Beyene Y, Makumbi D, Gowda M, Magorokosho C, Zaman-Allah M, Olsen M, Das A, Worku M, Gethi J, Vivek BS, Nair SK, Rashid Z, Vinayan MT, Issa AB, Vicente FS, Dhliwayo T, Zhang X. 2021. Beat the stress: Breeding for climate resilience in maize for the tropical rainfed environments. Theor Appl Genet 134: 1729-1752. https://doi.org/10.1007/s00122-021-03773-7.
Saidi A, Sarvmeili J, Pouresmael M. 2022. Genetic diversity study in lentil (Lens culinaris Medik.) germplasm: A comparison of CAAT Box Derived Polymorphism (CBDP) and Simple Sequence Repeat (SSR) markers. Biologia 77: 2793-2803. https://doi.org/10.1007/s11756-022-01089-5.
Seck F, Covarrubias-Pazaran G, Gueye T, Bartholomé J. 2023. Realized genetic gain in rice: Achievements from breeding programs. Rice 16: 61. https://doi.org/10.1186/s12284-023-00677-6.
Simmons CR, Weers BP, Reimann KS, Abbitt SE, Frank MJ, Wang W, Wu J, Shen B, Habben JE. 2020. Maize BIG GRAIN1 homolog overexpression increases maize grain yield. Plant Biotechnol J 18 (11): 2304-2315. https://doi.org/10.1111/pbi.13392.
Singamsetti A, Shahi JP, Zaidi PH, Seetharam K. 2024. Study on applicability of Genotype × Yield × Trait (GYT) biplots over Genotype × Trait (GT) biplots in selection of maize hybrids across soil moisture regimes. Indian J Agric Res 58 (6): 1145-1151. https://doi.org/10.18805/ijare.a-5850.
Swarup S, Cargill EJ, Crosby K, Flagel L, Kniskern J, Glenn KC. 2020. Genetic diversity is indispensable for plant breeding to improve crops. Crop Sci 61 (2): 839-852. https://doi.org/10.1002/csc2.20377.
Syahruddin K, Azrai M, Nur A, Abid M, Wu WZ. 2020. A review of maize production and breeding in Indonesia. IOP Conf Ser: Earth Environ Sci 484: 012040. https://doi.org/10.1088/1755-1315/484/1/012040.
Yadav VK, Singh IS. 2010. Comparative evaluation of maize inbred lines (Zea mays L.) according to DUS testing using morphological, physiological and molecular markers. Agric Sci 1 (3): 131-142. https://doi.org/10.4236/as.2010.13016.
Yan W, Rajcan I. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci 42 (1): 11-20. https://doi.org/10.2135/cropsci2002.1100.
Yang CJ, Russell J, Ramsay L, Thomas W, Powell W, Mackay I. 2021a. Overcoming barriers to the registration of new plant varieties under the DUS system. Commun Biol 4: 302. https://doi.org/10.1038/s42003-021-01840-9.
Yang Y, Tian H, Wang R, Wang L, Yi H, Liu Y, Xu L, Fan Y, Zhao J, Wang F. 2021b. Variety discrimination power: An appraisal index for loci combination screening applied to plant variety discrimination. Front Plant Sci 12: 566796. https://doi.org/10.3389/fpls.2021.566796.
Zhao J, Ren B, Zhao B, Liu P, Zhang J. 2022. Yield of summer maize hybrids with different growth duration determined by light and temperature resource use efficiency from silking to physiological maturity stage. Front Plant Sci 13: 992311. https://doi.org/10.3389/fpls.2022.992311.