Genetic Dissection of Yield-Related Traits in a Set of Maize Recombinant Inbred Lines Under Multiple Environments
Abstract
1. Introduction
2. Materials and Methods
2.1. Plant Materials and Field Experiments
2.2. Phenotypic Measurements and Analysis
2.3. Genetic Map and QTL Mapping
2.4. QTL Epistasis Effect Analysis
3. Results
3.1. Phenotypic Evaluation of Yield-Related Traits Across Two Plant Densities
3.2. QTL Mapping of Yield-Related Traits in the RIL Population
3.3. Epistatic Interactions Under Contrasting Planting Densities
3.4. Analysis of QTL Clusters Associated with Yield-Related Traits
4. Discussion
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ranum, P.; Pea-Rosas, J.P.; Garcia-Casal, M.N. Global maize production, utilization, and consumption. Ann. N. Y. Acad. Sci. 2014, 1312, 105–112. [Google Scholar] [CrossRef] [PubMed]
- FAO. The Future of Food and Agriculture—Trends and Challenges, Summary version; FAO: Rome, Italy, 2017. [Google Scholar]
- Qin, X.; Feng, F.; Li, Y.; Xu, S.; Siddique, K.H.M.; Liao, Y.; Lübberstedt, T. Maize yield improvements in China: Past trends and future directions. Plant Breed. 2016, 135, 166–176. [Google Scholar] [CrossRef]
- Gonzalez, V.H.; Tollenaar, M.; Bowman, A.; Good, B.; Lee, E.A. Maize Yield Potential and Density Tolerance. Crop Sci. 2018, 58, 472–485. [Google Scholar] [CrossRef]
- Li, Y.; Tao, F. Changes in maize traits and yield under the cultivar, environment and management interactions across China’s Maize Belt in the past two decades. Eur. J. Agron. 2023, 151, 127008. [Google Scholar] [CrossRef]
- Liu, G.; Yang, H.; Xie, R.; Yang, Y.; Li, S. Genetic gains in maize yield and related traits for high-yielding cultivars released during 1980s to 2010s in China. Field Crops Res. 2021, 270, 108223. [Google Scholar] [CrossRef]
- Hou, P.; Liu, Y.; Liu, W.; Liu, G.; Li, S. How to increase maize production without extra nitrogen input. Resour. Conserv. Recycl. 2020, 160, 104913. [Google Scholar] [CrossRef]
- Chaudhry, A.; Chen, Z.; Gallavotti, A. Hormonal influence on maize inflorescence development and reproduction. Plant Reprod. 2024, 37, 393–407. [Google Scholar] [CrossRef]
- Bolduc, N.; Hake, S. The maize transcription factor KNOTTED1 directly regulates the gibberellin catabolism gene ga2ox1. Plant Cell 2009, 21, 1647–1658. [Google Scholar] [CrossRef]
- Bolduc, N.; Yilmaz, A.; Mejia-Guerra, M.K.; Morohashi, K.; O′Connor, D.; Grotewold, E.; Hake, S. Unraveling the KNOTTED1 regulatory network in maize meristems. Genes Dev. 2012, 26, 1685–1690. [Google Scholar] [CrossRef]
- Phillips, K.A.; Skirpan, A.L.; Liu, X.; Christensen, A.; Slewinski, T.L.; Hudson, C.; Barazesh, S.; Cohen, J.D.; Malcomber, S.; Mcsteen, P. vanishing tassel2 Encodes a Grass-Specific Tryptophan Aminotransferase Required for Vegetative and Reproductive Development in Maize. Plant Cell 2011, 23, 550–566. [Google Scholar] [CrossRef]
- Zhao, Y. Auxin Biosynthesis: A Simple Two-Step Pathway Converts Tryptophan to Indole-3-Acetic Acid in Plants. Mol. Plant 2012, 5, 334–338. [Google Scholar] [CrossRef]
- Martin, A.; Lee, J.; Kichey, T.; Gerentes, D.; Zivy, M.; Tatout, C.; Dubois, F.; Balliau, T.; Valot, B.T.; Davanture, M. Two Cytosolic Glutamine Synthetase Isoforms of Maize Are Specifically Involved in the Control of Grain Production. Plant Cell 2006, 18, 3252–3274. [Google Scholar] [CrossRef] [PubMed]
- Cui, H.; Camberato, J.J.; Jin, L.; Zhang, J. Effects of shading on spike differentiation and grain yield formation of summer maize in the field. Int. J. Biometeorol. 2015, 59, 1189–1200. [Google Scholar] [CrossRef] [PubMed]
- Song, Z.; Guo, J.; Zhang, Z.; Kou, T.; Deng, A.; Zheng, C.; Ren, J.; Zhang, W. Impacts of planting systems on soil moisture, soil temperature and corn yield in rainfed area of Northeast China. Eur. J. Agron. 2013, 50, 66–74. [Google Scholar] [CrossRef]
- Zhang, M.; Chen, T.; Hojatollah, L.; Feng, X.-m.; Cao, T.-h. How plant density affects maize spike differentiation, kernel set, and grain yield formation in Northeast China? J. Integr. Agric. 2018, 17, 1745–1757. [Google Scholar] [CrossRef]
- Duvick, D.N. Genetic progress in yield of United States maize (Zea mays L.). Maydica 2005, 50, 193–202. [Google Scholar]
- Mansfield, B. Survey of Plant Density Tolerance in US Maize Germplasm. Crop Sci. 2012, 54, 157–173. [Google Scholar] [CrossRef]
- Ming, B.; Xie, R.; Hou, P.; Li, L.; Wang, K.; Li, S. Changes of Maize Planting Density in China. Sci. Agric. Sin. 2017, 50, 1960–1972. [Google Scholar]
- Ci, X.; Li, M.; Xu, J.; Lu, Z.; Bai, P.; Ru, G.; Liang, X.; Zhang, D.; Li, X.; Bai, L. Trends of grain yield and plant traits in Chinese maize cultivars from the 1950s to the 2000s. Euphytica 2012, 185, 395–406. [Google Scholar] [CrossRef]
- Liu, Z.; Yang, X.; Hubbard, K.G.; Lin, X. Maize potential yields and yield gaps in the changing climate of northeast China. Glob. Change Biol. 2012, 18, 3441–3454. [Google Scholar] [CrossRef]
- Chen, X.; Chen, F.; Chen, Y.; Gao, Q.; Yang, X.; Yuan, L.; Zhang, F.; Mi, G. Modern maize hybrids in Northeast China exhibit increased yield potential and resource use efficiency despite adverse climate change. Glob. Change Biol. 2013, 19, 923–936. [Google Scholar] [CrossRef]
- Hashemi, A.M.; Herbert, S.J.; Putnam, D.H. Yield Response of Corn to Crowding Stress. Agron. J. 2005, 97, 839–846. [Google Scholar] [CrossRef]
- Pan, L.; Yin, Z.; Huang, Y.; Chen, J.; Zhu, L.; Zhao, Y.; Guo, J. QTL for maize grain yield identified by QTL mapping in six environments and consensus loci for grain weight detected by meta-analysis. Plant Breed. 2017, 136, 820–833. [Google Scholar] [CrossRef]
- Chang, L.; He, K.; Liu, J. Decoding the genetic basis of ear-related traits in maize (Zea mays L.) using linkage mapping, association mapping and genomic prediction. Plant Breed. 2024, 143, 840–856. [Google Scholar] [CrossRef]
- Dong, Z.; Wang, Y.; Bao, J.; Li, Y.n.; Yin, Z.; Long, Y.; Wan, X. The Genetic Structures and Molecular Mechanisms Underlying Ear Traits in Maize (Zea mays L.). Cells 2023, 12, 1900. [Google Scholar] [CrossRef]
- Xiao, Y.; Tong, H.; Yang, X.; Yan, J. Genome-wide dissection of the maize ear genetic architecture using multiple populations. New Phytol. 2015, 210, 1095–1106. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.; Ran, F.; Yin, X.; Jiang, F.; Bi, Y.; Shaw, R.K.; Fan, X. Genome-Wide Association Studies on the Kernel Row Number in a Multi-Parent Maize Population. Int. J. Mol. Sci. 2024, 25, 3377. [Google Scholar] [CrossRef] [PubMed]
- Li, T.; Yang, H.; Zhang, X.; Zhu, L.; Zhang, J.; Wei, N.; Li, R.; Dong, Y.; Feng, Z.; Zhang, X.; et al. Genetic architecture of ear traits based on association mapping and co-expression networks in maize inbred lines and hybrids. Mol. Breed. 2023, 43, 78. [Google Scholar] [CrossRef]
- Gonzalo, M.; Holland, J.B.; Vyn, T.J.; McIntyre, L.M. Direct mapping of density response in a population of B73 x Mo17 recombinant inbred lines of maize (Zea mays L.). Heredity 2010, 104, 583–599. [Google Scholar] [CrossRef]
- Guo, J.; Chen, Z.; Liu, Z.; Wang, B.; Song, W.; Li, W.; Chen, J.; Dai, J.; Lai, J. Identification of genetic factors affecting plant density response through QTL mapping of yield component traits in maize (Zea mays L.). Euphytica 2011, 182, 409–422. [Google Scholar] [CrossRef]
- Yi, Q.; Liu, Y.; Hou, X.; Zhang, X.; Zhang, J.; Liu, H.; Hu, Y.; Yu, G.; Li, Y.; Wang, Y.; et al. Quantitative trait loci mapping for yield-related traits under low and high planting densities in maize (Zea mays). Plant Breed. 2020, 139, 227–240. [Google Scholar] [CrossRef]
- Pei, Y.; Deng, Y.; Zhang, H.; Zhang, Z.; Liu, J.; Chen, Z.; Cai, D.; Li, K.; Du, Y.; Zang, J.; et al. EAR APICAL DEGENERATION1 regulates maize ear development by maintaining malate supply for apical inflorescence. Plant Cell 2022, 34, 2222–2241. [Google Scholar] [CrossRef] [PubMed]
- Chen, W.; Chen, L.; Zhang, X.; Yang, N.; Guo, J.; Wang, M.; Ji, S.; Zhao, X.; Yin, P.; Cai, L. Convergent selection of a WD40 protein that enhances grain yield in maize and rice. Science 2022, 375, eabg7985. [Google Scholar] [CrossRef] [PubMed]
- Wang, H.; Huang, Y.; Li, Y.; Cui, Y.; Xiang, X.; Zhu, Y.; Wang, Q.; Wang, X.; Ma, G.; Xiao, Q.; et al. An ARF gene mutation creates flint kernel architecture in dent maize. Nat. Commun. 2024, 15, 2565. [Google Scholar] [CrossRef]
- Irfan, M.; Ting, Z.T.; Yang, W.; Chunyu, Z.; Lin, F. Modification of CTAB protocol for maize genomic DNA extraction. Res. J. Biotechnol. 2013, 8, 41–45. [Google Scholar]
- Li, H.; Ribaut, J.M.; Li, Z.; Wang, J. Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theor. Appl. Genet. 2008, 116, 243–260. [Google Scholar] [CrossRef]
- Wang, D.L.; Zhu, J.; Li, Z.K.L.; Paterson, A.H. Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches. Theor. Appl. Genet. 1999, 99, 1255–1264. [Google Scholar] [CrossRef]
- Yang, J.; Hu, C.; Hu, H.; Yu, R.; Xia, Z.; Ye, X.; Zhu, J. QTLNetwork: Mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 2008, 24, 721–723. [Google Scholar] [CrossRef]
- Malcomber, S.T.; Kellogg, E.A. Evolution of unisexual flowers in grasses (Poaceae) and the putative sex-determination gene, TASSELSEED2 (TS2). New Phytol. 2010, 170, 885–899. [Google Scholar] [CrossRef]
- Yu, J.; Song, G.; Guo, W.; Le, L.; Xu, F.; Wang, T.; Wang, F.; Wu, Y.; Gu, X.; Pu, L. ZmBELL10 interacts with other ZmBELLs and recognizes specific motifs for transcriptional activation to modulate internode patterning in maize. New Phytol. 2023, 240, 577–596. [Google Scholar] [CrossRef]
- Bai, F.; Reinheimer, R.; Durantini, D.; Kellogg, E.A.; Schmidt, R.J. TCP transcription factor, BRANCH ANGLE DEFECTIVE 1 (BAD1), is required for normal tassel branch angle formation in maize. Proc. Natl. Acad. Sci. USA 2012, 109, 12225–12230. [Google Scholar] [CrossRef]
- Hay, A. The Dominant Mutant Wavy auricle in blade1 Disrupts Patterning in a Lateral Domain of the Maize Leaf. Plant Physiol. 2004, 135, 300–308. [Google Scholar] [CrossRef]
- Taguchi-Shiobara, F. The fasciated ear2 gene encodes a leucine-rich repeat receptor-like protein that regulates shoot meristem proliferation in maize. Genes Dev. 2001, 15, 2755–2766. [Google Scholar] [CrossRef] [PubMed]
- Zebosi, B.; Vollbrecht, E.; Best, N.B. Brassinosteroid biosynthesis and signaling: Conserved and diversified functions of core genes across multiple plant species. Plant Commun. 2024, 5, 100982. [Google Scholar] [CrossRef]
- Yang, Y.; Mu, J.; Hao, X.; Yang, K.; Cao, Z.; Feng, J.; Li, R.; Zhang, N.; Zhou, G.; Kong, Y. Identification and Analysis of the Mechanism of Stem Mechanical Strength Enhancement for Maize Inbred Lines QY1. Int. J. Mol. Sci. 2024, 25, 8195. [Google Scholar] [CrossRef]
- Li, Y.; Wang, J.; Zhong, S.; Huo, Q.; Wang, Q.; Shi, Y.; Liu, H.; Liu, J.; Song, Y.; Fang, X. positively controls maize yield by increasing leaf number above the ear. Nat. Commun. 2025, 16, 1147. [Google Scholar] [CrossRef]
- Sigmon, B.; Vollbrecht, E. Evidence of selection at the ramosa1 locus during maize domestication. Mol. Ecol. 2010, 19, 1296–1311. [Google Scholar] [CrossRef]
- Kong, D.; Li, C.; Xue, W.; Wei, H.; Ding, H.; Hu, G.-H.; Zhang, X.; Zhang, G.; Zou, T.; Xian, Y.; et al. UB2/UB3/TSH4-anchored transcriptional networks regulate early maize inflorescence development in response to simulated shade. Plant Cell 2022, 35, 717–737. [Google Scholar] [CrossRef]
- Satoh-Nagasawa, N.; Nagasawa, N.; Malcomber, S.; Sakai, H.; Jackson, D. A trehalose metabolic enzyme controls inflorescence architecture in maize. Nature 2006, 441, 227–230. [Google Scholar] [CrossRef]
- Qian, B.; Wang, Q.; Zhang, C.; Guo, J.; Yu, Z.; Han, J.; Xia, H.; Zhao, R.; Yin, Y. Exploring the Roles of TALE Gene Family in Maize Drought Stress Responses. Agronomy 2024, 14, 1267. [Google Scholar] [CrossRef]
- Chen, L.Q.; Luo, J.H.; Cui, Z.H.; Xue, M.; Wang, L.; Zhang, X.Y.; Pawlowski, W.P.; He, Y. ATX3, ATX4, and ATX5 Encode Putative H3K4 Methyltransferases and Are Critical for Plant Development. Plant Physiol. 2017, 174, 1795–1806. [Google Scholar] [CrossRef] [PubMed]
- Bomblies, K.; Doebley, J.F. Pleiotropic Effects of the Duplicate Maize FLORICAULA/LEAFY Genes zfl1 and zfl2 on Traits Under Selection During Maize Domestication. Genetics 2006, 172, 519–531. [Google Scholar] [CrossRef]
- Duvick, D.N. The Contribution of Breeding to Yield Advances in Maize (Zea mays L.). Adv. Agron. 2005, 86, 83–145. [Google Scholar]
- Wang, H.; Liang, Q.J.; Li, K.; Hu, X.; Hu, Y.; Wang, H.; Liu, Z.; Huang, C. QTL analysis of ear leaf traits in maize (Zea mays L.) under different planting densities. Crop J. 2017, 5, 387–395. [Google Scholar] [CrossRef]
- Zhang, L.; Ren, Z.; Zhou, J.; Guo, S.; Zhu, Y. Dissection of the genetic architecture underlying the plant density response by mapping plant height-related traits in maize (Zea mays L.). Mol. Genet. Genom. MGG 2015, 290, 1223–1233. [Google Scholar]
- Mukri, G.; Shilpa, K.; Gadag, R.N.; Bhat, J.S.; Singh, C.; Gupta, N.C.; Prabha, C.; Patil, S.P. Designed and validated novel allele-specific primer to differentiate Kernel Row Number (KRN) in tropical field corn. PLoS ONE 2023, 18, e0284277. [Google Scholar] [CrossRef] [PubMed]
Trait | Plant Density | PH1219 | ZM058 | RIL Population | |||||
---|---|---|---|---|---|---|---|---|---|
Mean ± SD | Range | Skewness | Kurtosis | CV (%) | H2 (%) | ||||
BEL | LPD | 1.79 ± 0.06 | 0.23 ± 0.01 | 1.11 ± 0.72 | 0–4.48 | 0.96 | 1.18 | 65.45 | 81.54 |
HPD | 2.25 ± 0.15 | 0.37 ± 0.05 | 1.16 ± 0.71 | 0–5.62 | 0.82 | 1.46 | 61.21 | 80.15 | |
CD | LPD | 1.81 ± 0.07 | 2.45 ± 0.11 | 2.19 ± 0.22 | 1.54–2.98 | 0.41 | 0.38 | 10.05 | 93.01 |
HPD | 1.71 ± 0.04 | 2.35 ± 0.07 | 2.17 ± 0.22 | 1.61–3.01 | 0.53 | 0.48 | 10.14 | 92.70 | |
ED | LPD | 3.18 ± 0.11 | 4.03 ± 0.13 | 3.99 ± 0.31 | 2.67–4.91 | −0.19 | 0.29 | 7.77 | 90.44 |
HPD | 3.14 ± 0.07 | 3.99 ± 0.09 | 3.95 ± 0.31 | 2.94–4.81 | 0 | −0.14 | 7.59 | 90.45 | |
EL | LPD | 9.45 ± 0.49 | 14.53 ± 0.59 | 14.26 ± 1.62 | 7.99–19.95 | −0.15 | 0.77 | 11.36 | 87.27 |
HPD | 9.33 ± 0.55 | 14.39 ± 0.46 | 13.93 ± 1.59 | 7.81–19.28 | −0.15 | 0.4 | 11.41 | 87.54 | |
KNR | LPD | 13.51 ± 0.81 | 25.71 ± 0.68 | 25.99 ± 4.73 | 8.26–41.98 | −0.13 | 0.24 | 18.20 | 87.89 |
HPD | 12.59 ± 0.76 | 25.17 ± 0.36 | 25.23 ± 4.64 | 10.44–42.33 | 0.01 | 0.09 | 18.39 | 86.71 | |
KRN | LPD | 13.63 ± 0.18 | 14.95 ± 0.41 | 15.61 ± 1.91 | 10.32–21.36 | 0.23 | −0.41 | 12.24 | 94.23 |
HPD | 13.48 ± 0.35 | 14.72 ± 0.29 | 15.55 ± 1.88 | 11.06–21.69 | 0.31 | −0.27 | 12.09 | 92.60 |
Trait | Name | Chr. | Interval (Mb) | LOD | PVE Range (%) | ADD Range | Environment |
---|---|---|---|---|---|---|---|
BEL | qBEL1-1 | 1 | 26.91–35.00 | 3.92 | 7.88 | −0.26 | E10 |
qBEL1-2 | 1 | 233.517–233.518 | 2.50 | 2.23 | 0.15 | E5 | |
qBEL2-1 | 2 | 25.93–37.39 | 3.09 | 2.92–7.73 | 0.18–0.22 | E1/E4/E5 | |
qBEL2-2 | 2 | 213.75–216.33 | 3.63 | 6.90 | −0.22 | E6 | |
qBEL2-3 | 2 | 223.09–224.39 | 2.63 | 6.68 | 0.18 | E9 | |
qBEL4 | 4 | 176.00–177.07 | 3.04 | 6.87 | −0.20 | E11 | |
qBEL5 | 5 | 9.83–68.68 | 2.93 | 14.06 | 0.42 | E5 | |
qBEL6 | 6 | 36.48–40.05 | 3.26 | 5.84 | 0.20 | E6 | |
qBEL7-1 | 7 | 13.97–16.20 | 4.49 | 3.97 | −0.22 | E5 | |
qBEL7-2 | 7 | 162.09–174.14 | 3.54 | 5.48–10.72 | 0.22−0.25 | E1/E6/E10/E12 | |
qBEL8 | 8 | 142.20–149.42 | 3.88 | 6.61 | 0.24 | E6 | |
qBEL9 | 9 | 103.15–110.48 | 2.59 | 6.04 | −0.17 | E7 | |
qBEL10 | 10 | 26.14–65.43 | 4.91 | 8.35 | 0.28 | E10 | |
CD | qCD1-1 | 1 | 7.24–10.82 | 3.93 | 6.54–8.17 | −0.06 to −0.07 | E3/E7 |
qCD1-2 | 1 | 40.23–43.50 | 3.07 | 6.06 | 0.04 | E8 | |
qCD1-3 | 1 | 83.44–85.03 | 3.5 | 5.15–7.79 | 0.05−0.07 | E3/E7 | |
qCD4-1 | 4 | 47.23–48.44 | 2.79 | 5.30 | 0.04 | E3 | |
qCD4-2 | 4 | 163.42–166.94 | 2.95 | 4.52–7.28 | −0.05 to −0.06 | E1/E8 | |
qCD4-3 | 4 | 230.03–231.88 | 3.94 | 7.27 | −0.07 | E12 | |
qCD5 | 5 | 189.86–198.87 | 4.69 | 7.48 | 0.07 | E1 | |
qCD6 | 6 | 162.88–163.20 | 2.52 | 5.83 | −0.05 | E2 | |
qCD7 | 7 | 138.41–164.83 | 3.30 | 4.69–5.28 | 0.05–0.07 | E1/E7 | |
qCD8-1 | 8 | 72.69–99.70 | 4.04 | 6.57–6.89 | −0.06 to −0.07 | E1/E6 | |
qCD8-2 | 8 | 139.81–141.13 | 2.74 | 5.08 | −0.06 | E12 | |
qCD10 | 10 | 142.93–144.36 | 2.86 | 5.19 | 0.05 | E7 | |
ED | qED1-1 | 1 | 40.23–43.50 | 2.69 | 5.28 | 0.08 | E8 |
qED1-2 | 1 | 79.45–85.03 | 3.20 | 7.36 | 0.08 | E7 | |
qED1-3 | 1 | 286.60–288.77 | 3.22 | 7.70 | −0.08 | E9 | |
qED2 | 2 | 49.03–56.07 | 3.33 | 9.01 | 0.08 | E1 | |
qED3-1 | 3 | 3.50–5.32 | 3.10 | 8.13 | 0.09 | E3 | |
qED3-2 | 3 | 88.33–104.92 | 3.24 | 7.28 | 0.08 | E7 | |
qED3-3 | 3 | 197.47–198.60 | 2.73 | 4.50 | 0.08 | E2 | |
qED3-4 | 3 | 214.20–214.45 | 3.82 | 7.72 | −0.08 | E5 | |
qED4-1 | 4 | 149.08–151.11 | 2.58 | 5.18 | 0.08 | E5 | |
qED4-2 | 4 | 163.42–166.94 | 2.96 | 7.38 | −0.10 | E8 | |
qED5 | 5 | 40.26–132.69 | 3.31 | 10.59 | 0.14 | E6 | |
qED8 | 8 | 13.79–15.99 | 2.68 | 2.86 | 0.08 | E6 | |
qED10 | 10 | 138.80–140.93 | 2.79 | 6.66 | –0.10 | E2 | |
EL | qEL1 | 1 | 15.35–23.61 | 2.94 | 4.89–6.17 | −0.45 to −0.49 | E3/E6 |
qEL2 | 2 | 45.01–46.79 | 3.27 | 5.63 | 0.47 | E1 | |
qEL3-1 | 3 | 47.83–57.99 | 3.33 | 6.46–7.21 | −0.38 to −0.45 | E3/E7 | |
qEL3-2 | 3 | 101.20–106.14 | 3.68 | 6.67 | −0.45 | E5 | |
qEL3-3 | 3 | 158.55–180.31 | 2.88 | 5.37–8.34 | −0.46 to −0.48 | E10/E11 | |
qEL3-4 | 3 | 196.78–201.76 | 3.50 | 7.36–8.73 | −0.41 to −0.52 | E4/E5/E12 | |
qEL4-1 | 4 | 37.26–38.32 | 3.68 | 8.06 | −0.43 | E7 | |
qEL4-2 | 4 | 85.70–126.63 | 4.38 | 7.77 | −0.53 | E1 | |
qEL5 | 5 | 14.77–16.45 | 2.63 | 5.40 | 0.45 | E3 | |
qEL6-1 | 6 | 37.90–74.20 | 4.77 | 8.81 | −0.53 | E5 | |
qEL6-2 | 6 | 107.27–122.11 | 3.50 | 6.45–10.26 | −0.26 to −0.58 | E4/E8/E10/E11 | |
qEL6-3 | 6 | 165.00–167.6 | 2.65 | 5.68 | −0.36 | E7 | |
qEL7 | 7 | 137.55–138.41 | 3.16 | 5.49 | 0.43 | E6 | |
qEL8 | 8 | 119.58–144.13 | 3.33 | 6.68–8.17 | −0.41 to −0.49 | E6/E12 | |
qEL9 | 9 | 40.45–154.11 | 2.86 | 8.41 | 0.74 | E1 | |
KNR | qKNR2-1 | 2 | 0.14–1.28 | 2.69 | 4.89 | −0.95 | E6 |
qKNR2-2 | 2 | 9.98–10.22 | 2.76 | 6.03 | −1.15 | E7 | |
qKNR3 | 3 | 165.49–218.41 | 3.82 | 4.92–9.52 | −0.96 to −1.34 | E4/E5/E6/E7/E10/E11/E12 | |
qKNR4-1 | 4 | 11.09–12.22 | 3.29 | 7.87 | 1.29 | E2 | |
qKNR4-2 | 4 | 157.64–158.53 | 3.11 | 5.19 | −1.04 | E5 | |
qKNR6 | 6 | 65.43–96.10 | 3.10 | 4.56–9.45 | −0.95 to −1.28 | E5/E10/E11 | |
qKNR7-1 | 7 | 133.08–139.17 | 4.54 | 7.73 | 1.27 | E5 | |
qKNR7-2 | 7 | 163.00–164.83 | 4.87 | 9.13 | −1.34 | E5 | |
qKNR8-1 | 8 | 2.76–4.17 | 3.04 | 7.56 | −1.05 | E5 | |
qKNR8-2 | 8 | 119.58–149.42 | 3.87 | 6.64–12.22 | −1.13 to −1.54 | E4/E6/E10/E12 | |
qKNR8-3 | 8 | 163.29–164.96 | 3.10 | 8.40 | −1.13 | E1 | |
KRN | qKRN1-1 | 1 | 37.85–40.23 | 4.10 | 9.03–10.32 | 0.57–0.58 | E1/E7 |
qKRN1-2 | 1 | 81.98–82.03 | 3.40 | 4.17 | 0.43 | E11 | |
qKRN1-3 | 1 | 260.21–263.27 | 2.67 | 3.15 | 0.37 | E5 | |
qKRN2-1 | 2 | 46.79–52.38 | 2.59 | 4.06 | 0.51 | E12 | |
qKRN2-2 | 2 | 199.05–207.81 | 3.62 | 4.57–9.53 | 0.51–0.61 | E1/E2/E6/E7 | |
qKRN3-1 | 3 | 21.76–27.77 | 8.77 | 8.46–14.94 | 0.56–0.94 | E3/E5/E6/E11 | |
qKRN3-2 | 3 | 33.78–44.22 | 15.60 | 13.18 | 1.06 | E4 | |
qKRN3-3 | 3 | 59.05–114.81 | 6.62 | 5.88–8.30 | −0.50 to −0.80 | E4/E5/E6/E11 | |
qKRN4-1 | 4 | 24.49–30.96 | 5.86 | 11.04–12.19 | 0.65–0.72 | E2/E8 | |
qKRN4-2 | 4 | 84.05–87.83 | 2.80 | 2.27 | 0.47 | E4 | |
qKRN4-3 | 4 | 143.12–163.42 | 5.34 | 5.00–9.90 | 0.51–0.72 | E5/E10/E11/E12 | |
qKRN5-1 | 5 | 155.03–214.16 | 3.36 | 8.57–10.91 | −0.70 to −0.74 | E8/E10 | |
qKRN5-2 | 5 | 177.17–189.86 | 4.51 | 10.63 | 0.62 | E3 | |
qKRN8-1 | 8 | 15.99–16.35 | 4.09 | 4.14 | −0.50 | E6 | |
qKRN8-2 | 8 | 135.25–165.28 | 3.66 | 3.64–9.28 | −0.39 to −0.69 | E5/E11/E12 | |
qKRN9 | 9 | 5.67–7.46 | 5.06 | 7.89 | 0.63 | E10 | |
qKRN10 | 10 | 147.95–148.36 | 3.51 | 5.16 | 0.49 | E8 |
QTL Cluster | Chr. | Interval (Mb) | Physical Length (Mb) | Bin (B73 RefGen_v3) | No. of QTLs | Integrated QTLs | Associated Gene |
---|---|---|---|---|---|---|---|
QC1 | 1 | 37.85–43.50 | 5.65 | 1.03 | 3 | qCD1-2, qED1-1, qKRN1-1 | TS2 [40] |
QC2 | 1 | 79.45–85.03 | 5.58 | 1.05 | 3 | qCD1-3, qED1-2, qKRN1-2 | TALE5 [41] |
QC3 | 2 | 45.01–56.07 | 11.06 | 2.07 | 3 | qED2, qEL2, qKRN2-1 | |
QC4 | 2 | 199.05–224.39 | 25.34 | 2.09–2.10 | 3 | qBEL2-2, qBEL2-3, qKRN2-2 | BAD1 [42], WAB1 [43] |
QC5 | 4 | 143.12–177.07 | 33.95 | 4.06–4.08 | 6 | qBEL4, qCD4-2, qED4-1, qED4-2, qKNR4-2, qKRN4-3 | FEA2 [44], NATL1 [45], XTH32 [46], TU1 [47] |
QC6 | 7 | 133.08–139.76 | 6.68 | 7.03 | 3 | qCD7, qEL7, qKNR7-1 | RA1 [48], TSH4 [49] |
QC7 | 7 | 162.08–174.14 | 12.06 | 7.04 | 3 | qBEL7-2, qCD7, qKNR7-2 | RA3 [50] |
QC8 | 8 | 119.58–149.42 | 29.84 | 8.04–8.06 | 5 | qBEL8, qCD8-2, qEL8, qKNR8-2, qKRN8-2 | TALE33 [51], ATX5 [52] |
QC9 | 10 | 138.80–148.36 | 9.56 | 10.06 | 3 | qCD10, qED10, qKRN10 | ZFL1 [53] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, D.; Zeng, W.; Han, Z.; Shang, J.; An, T.; Li, Y.; Xu, Y.; Wang, F.; Jin, X.; Fan, J.; et al. Genetic Dissection of Yield-Related Traits in a Set of Maize Recombinant Inbred Lines Under Multiple Environments. Agronomy 2025, 15, 2109. https://doi.org/10.3390/agronomy15092109
Li D, Zeng W, Han Z, Shang J, An T, Li Y, Xu Y, Wang F, Jin X, Fan J, et al. Genetic Dissection of Yield-Related Traits in a Set of Maize Recombinant Inbred Lines Under Multiple Environments. Agronomy. 2025; 15(9):2109. https://doi.org/10.3390/agronomy15092109
Chicago/Turabian StyleLi, Donglin, Weiwei Zeng, Zhongmin Han, Jiawei Shang, Tai An, Yuan Li, Yuan Xu, Fengyu Wang, Xiaochun Jin, Jinsheng Fan, and et al. 2025. "Genetic Dissection of Yield-Related Traits in a Set of Maize Recombinant Inbred Lines Under Multiple Environments" Agronomy 15, no. 9: 2109. https://doi.org/10.3390/agronomy15092109
APA StyleLi, D., Zeng, W., Han, Z., Shang, J., An, T., Li, Y., Xu, Y., Wang, F., Jin, X., Fan, J., Qi, J., Wang, R., Li, L., Fan, K., Sun, D., & Lu, Y. (2025). Genetic Dissection of Yield-Related Traits in a Set of Maize Recombinant Inbred Lines Under Multiple Environments. Agronomy, 15(9), 2109. https://doi.org/10.3390/agronomy15092109