journal article·2026-06-12Open access

The Spatiotemporal Genetic Architecture of Seed Vigor in Upland Cotton

Luyao Wang, Jie Dai, Ting Zhao, Shengjun Zhao, Hongyu Wu, Jiaying Pan, Yupeng Hao, Ke Nie, Li Yu, Yumeng Zhu, Xiangyi Zhao, Kai Huang, Mengke Zhang, Yongyan Zhao, Yaping Jiang, Siyuan Wang, Yiling Pan, Yubing Chen, Yilai Lin, Wanghong Shi, Yayuan Deng, Jin Han, Jie Li, Xuan Long, Junbin Wang, Zhiyuan Zhang, Zegang Han, Zhanfeng Si, Lei Ju, Jun Li, Tianzhen Zhang, Ji Zhou, Xueying Guan

Abstract

ABSTRACT Seed vigor underpins uniform crop establishment, but its dynamic genetics are understudied. Combining high‐resolution temporal phenotyping and genomics in upland cotton, we used the SeedRanger platform to record 17 image‐based traits every 30 min over 120 h, revealing stage‐specific heritability and identifying 541 seed‐vigor loci. These loci show extensive pleiotropy and temporal coordination, forming a genetic network that preserves developmental continuity; 8.9% overlap regions under domestication selection, indicating concurrent optimization with fiber yield. Functional validation of FLA2 , a candidate gene underlying a dynamic QTL, implicates auxin‐mediated control of radicle elongation and cotyledon development. This temporal framework exposes dynamic genetic architecture and breeding targets for high‐vigor crops.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Research in Cotton Cultivation · Lipid metabolism and biosynthesis · Plant Molecular Biology Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Cotton and Cash Crops Innovation Group Hainan Institute of Zhejiang University Yazhou Bay Science and Technology City Sanya Hainan China

Engineering Research Centre of Cotton Ministry of Education / College of Agriculture Xinjiang Agricultural University Urumqi China

Tropical Crops Genetic Resources Institute Chinese Academy of Tropical Agricultural Sciences/Key Laboratory of Crop Gene Resources and Germplasm Enhancement in Southern China Ministry of Agriculture Haikou China

State Key Laboratory of Plant Trait Design CAS Center for Excellence in Molecular Plant Sciences (CEMPS) Shanghai Institute of Plant Physiology and Ecology (SIPPE) Chinese Academy of Sciences (CAS) Shanghai China

Zhejiang Provincial Key Laboratory of Crop Genetic Resources The Advanced Seed Institute Plant Precision Breeding Academy College of Agriculture and Biotechnology Zhejiang University Hangzhou China

Data Sciences Department Crop Science Centre National Institute of Agricultural Botany (NIAB) University of Cambridge Cambridge UK

Funding

National Key Research and Development Program of China · National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities

References (82)

Show source-provided references
1. Seed Aging/Repair Hypothesis Leads to New Testing Methods
2. 10.15258/sst.2022.50.1.s.0410.15258/sst.2022.50.1.s.04 ↗
3. 10.1093/jxb/erv49010.1093/jxb/erv490 ↗
4. 10.1111/j.1365‐313X.2008.03465.x10.1111/j.1365‐313X.2008.03465.x ↗
5. 10.1093/plphys/kiac12510.1093/plphys/kiac125 ↗
6. Integrative QTL Identification, Fine Mapping and Candidate Gene Analysis of a Major Locus qLTG3a for Seed Low‐Temperature Germinability in Rice10.1186/s12284-021-00544-2 ↗
7. 10.1146/annurev‐arplant‐042811‐10555010.1146/annurev‐arplant‐042811‐105550 ↗
8. 10.3390/s1905109010.3390/s19051090 ↗
9. Insights Into Chloroplast Biogenesis and Development10.1016/j.bbabio.2015.02.003 ↗
10. Roberts E. H. · 197210.1007/978-94-009-5685-8 ↗
11. 10.1104/pp.19.0033810.1104/pp.19.00338 ↗
12. Seed Germination and Vigor: Ensuring Crop Sustainability in a Changing Climate10.1038/s41437-022-00497-2 ↗
13. Bewley J. D. · 2012
14. 10.1111/nph.1673610.1111/nph.16736 ↗
15. The Ohio State University Seed Vigor Imaging System (SVIS) for Soybean and Corn Seedlings
16. 10.1104/pp.16.0112210.1104/pp.16.01122 ↗
17. SAPL®: A Free Software for Determining the Physiological Potential in Soybean Seeds10.1590/1983-40632018v4852340 ↗
18. 10.1104/pp.112.20512010.1104/pp.112.205120 ↗
19. 10.1017/S096025851800023510.1017/S0960258518000235 ↗
20. 10.1016/j.jare.2023.03.00610.1016/j.jare.2023.03.006 ↗
21. 10.1038/s41588‐018‐0229‐210.1038/s41588‐018‐0229‐2 ↗
22. 10.1109/TSMC.1979.431007610.1109/TSMC.1979.4310076 ↗
23. 10.13031/2013.2783810.13031/2013.27838 ↗
24. 10.1590/0103‐9016‐2015‐000710.1590/0103‐9016‐2015‐0007 ↗
25. 10.2298/GENSR1001103M10.2298/GENSR1001103M ↗
26. 10.1038/s41588‐019‐0530‐810.1038/s41588‐019‐0530‐8 ↗
27. 10.1073/pnas.181579011610.1073/pnas.1815790116 ↗
28. Integration of eQTL and Machine Learning to Dissect Causal Genes With Pleiotropic Effects in Genetic Regulation Networks of Seed Cotton Yield10.1016/j.celrep.2023.113111 ↗
29. 10.1111/j.1744‐7909.2012.01155.x10.1111/j.1744‐7909.2012.01155.x ↗
30. 10.1104/pp.17.0098010.1104/pp.17.00980 ↗
31. 10.1093/pcp/pcn07910.1093/pcp/pcn079 ↗
32. Smith C. W. · 1999
33. 10.1038/ncomms608710.1038/ncomms6087 ↗
34. 10.1093/jxb/ery37310.1093/jxb/ery373 ↗
35. 10.1038/ncomms1052710.1038/ncomms10527 ↗
36. 10.1093/jxb/erv10010.1093/jxb/erv100 ↗
37. 10.1038/ncomms1334210.1038/ncomms13342 ↗
38. 10.1016/j.molp.2018.03.01810.1016/j.molp.2018.03.018 ↗
39. 10.1093/jxb/eraa28010.1093/jxb/eraa280 ↗
40. 10.1093/genetics/150.3.125710.1093/genetics/150.3.1257 ↗
41. 10.1111/pbi.1343110.1111/pbi.13431 ↗
42. 10.1093/g3journal/jkad11610.1093/g3journal/jkad116 ↗
43. 10.1105/tpc.114.12960110.1105/tpc.114.129601 ↗
44. 10.1371/journal.pone.009704710.1371/journal.pone.0097047 ↗
45. 10.1186/s12870‐017‐1085‐410.1186/s12870‐017‐1085‐4 ↗
46. 10.1186/s12870‐019‐1723‐010.1186/s12870‐019‐1723‐0 ↗
47. 10.1371/journal.pone.010825510.1371/journal.pone.0108255 ↗
48. Genetic Variation of Growth Dynamics in Maize (Zea mays L.) Revealed Through Automated Non‐invasive Phenotyping10.1111/tpj.13390 ↗
49. 10.1534/g3.119.40075710.1534/g3.119.400757 ↗
50. 10.1111/pbi.1343710.1111/pbi.13437 ↗
51. 10.1186/s13007‐017‐0173‐710.1186/s13007‐017‐0173‐7 ↗
52. 10.1534/genetics.116.19871310.1534/genetics.116.198713 ↗
53. Interactive Machine Learning for Soybean Seed and Seedling Quality Classification10.1038/s41598-020-68273-y ↗
54. 10.1111/jipb.1338010.1111/jipb.13380 ↗
55. Time‐Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling10.34133/plantphenomics.0158 ↗
56. Spatiotemporal Limitations in Plant Biology Research10.1016/j.tplants.2021.10.005 ↗
57. Artificial Intelligence‐based Tools for next‐generation Seed Quality Analysis10.1016/j.cropd.2024.100094 ↗
58. 10.5937/JPEA1801040M10.5937/JPEA1801040M ↗
59. 10.3389/fpls.2021.73866010.3389/fpls.2021.738660 ↗
60. 10.1631/jzus.B100023810.1631/jzus.B1000238 ↗
61. 10.1038/s41588‐019‐0371‐510.1038/s41588‐019‐0371‐5 ↗
62. 10.1093/bioinformatics/btu17010.1093/bioinformatics/btu170 ↗
63. 10.1093/bioinformatics/btp32410.1093/bioinformatics/btp324 ↗
64. 10.1093/bioinformatics/btp35210.1093/bioinformatics/btp352 ↗
65. 10.1093/bioinformatics/btw04410.1093/bioinformatics/btw044 ↗
66. 10.1093/bioinformatics/btr33010.1093/bioinformatics/btr330 ↗
67. 10.1093/nar/gkq60310.1093/nar/gkq603 ↗
68. 10.1093/nar/gkw37010.1093/nar/gkw370 ↗
69. 10.1016/j.ajhg.2010.11.01110.1016/j.ajhg.2010.11.011 ↗
70. 10.1073/pnas.160274311310.1073/pnas.1602743113 ↗
71. 10.1038/ng.54810.1038/ng.548 ↗
72. 10.1007/s00439‐011‐1118‐210.1007/s00439‐011‐1118‐2 ↗
73. Second‐generation PLINK: Rising to the Challenge of Larger and Richer Datasets10.1186/s13742-015-0047-8 ↗
74. 10.1016/j.molp.2017.01.00310.1016/j.molp.2017.01.003 ↗
75. 10.1111/jipb.1342710.1111/jipb.13427 ↗
76. 10.1007/s11427‐018‐9402‐910.1007/s11427‐018‐9402‐9 ↗
77. 10.1038/s41587‐019‐0201‐410.1038/s41587‐019‐0201‐4 ↗
78. 10.1038/nbt.162110.1038/nbt.1621 ↗
79. 10.1186/s13059‐014‐0550‐810.1186/s13059‐014‐0550‐8 ↗
80. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data
81. 10.1038/ng.71510.1038/ng.715 ↗
82. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index10.1038/ng.686 ↗