journal article·2026-04Open access

Additive and Partially Dominant Effects from Genomic Variation Contribute to Rice Heterosis

Zhiwu Dan, Yunping Chen, Wei Zhou, Yanghong Xu, Junran Huang, Yi Chen, Jizhou Meng, Guoxin Yao, Wenchao Huang

Advanced Science· Volume 13· Issue 20

Abstract

ABSTRACT Heterosis, or hybrid vigor, describes the superior performance of F 1 hybrids relative to their parents. Despite its significant importance in crop breeding, the molecular mechanisms underlying heterosis remain debated, mainly attributable to discrepancies across genotypes, traits, tissues, populations, developmental stages, growth environments, and species. This study systematically identifies heterosis‐associated genes and metabolites from parental molecular differences and functionally validates three genes to heterosis for seedling length in rice. The predominant inheritance patterns of these molecules are additive and partially dominant effects, namely at mid‐parent levels or values between mid‐parent and parental levels, respectively. These two genetic effects contribute to heterosis of 17 agronomic traits in rice, including grain yield and plant height across developmental stages. They also explain yield heterosis in diverse hybrid populations and distinct growth environments in both rice and maize, as well as biomass heterosis in Arabidopsis . Notably, additive and partially dominant effects are associated with parental genomic variants, and the number of these variants correlates significantly with heterosis. Unlike classical heterosis models primarily focused on genomic sequence variation, these findings provide quantitative insights from genomic downstream information into the molecular mechanisms of plant heterosis, highlighting their potential for improving breeding efficiency of hybrid crops.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Genetic Mapping and Diversity in Plants and Animals · Genetic Associations and Epidemiology · Genetic and phenotypic traits in livestock

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

Affiliations

State Key Laboratory of Hybrid Rice Key Laboratory for Research and Utilization of Heterosis in Indica Rice the Ministry of Agriculture The Yangtze River Valley Hybrid Rice Collaboration & Innovation Center College of Life Sciences Wuhan University Wuhan China

Funding

National Natural Science Foundation of China · China Postdoctoral Science Foundation · National Key Research and Development Program of China · Hubei Agricultural Science and Technology Innovation Center

References (90)

Show source-provided references
1. 10.1073/pnas.060562710310.1073/pnas.0605627103 ↗
2. 10.1093/genetics/33.5.43910.1093/genetics/33.5.439 ↗
3. 10.1105/tpc.110.07613310.1105/tpc.110.076133 ↗
4. 10.1093/plphys/kiae38510.1093/plphys/kiae385 ↗
5. 10.1016/j.cub.2018.06.04110.1016/j.cub.2018.06.041 ↗
6. 10.1126/science.32.827.627‐a10.1126/science.32.827.627‐a ↗
7. 10.1073/pnas.3.4.31010.1073/pnas.3.4.310 ↗
8. 10.1073/pnas.150922911210.1073/pnas.1509229112 ↗
9. 10.1126/science.28.718.454‐b10.1126/science.28.718.454‐b ↗
10. 10.1093/genetics/21.4.37510.1093/genetics/21.4.375 ↗
11. 10.1093/genetics/33.5.47710.1093/genetics/33.5.477 ↗
12. 10.1073/pnas.94.17.922610.1073/pnas.94.17.9226 ↗
13. 10.1093/genetics/145.2.45310.1093/genetics/145.2.453 ↗
14. 10.1086/28119910.1086/281199 ↗
15. 10.1038/s41588‐022‐01283‐w10.1038/s41588‐022‐01283‐w ↗
16. 10.1093/plphys/kiad07810.1093/plphys/kiad078 ↗
17. 10.1111/pbi.7048410.1111/pbi.70484 ↗
18. 10.1186/s13059‐021‐02370‐710.1186/s13059‐021‐02370‐7 ↗
19. 10.1093/genetics/iyaf04510.1093/genetics/iyaf045 ↗
20. 10.1186/s13059‐022‐02822‐810.1186/s13059‐022‐02822‐8 ↗
21. 10.1146/annurev‐arplant‐042110‐10382710.1146/annurev‐arplant‐042110‐103827 ↗
22. 10.1111/j.1365‐313X.2011.04689.x10.1111/j.1365‐313X.2011.04689.x ↗
23. 10.1038/ng.55010.1038/ng.550 ↗
24. 10.1016/j.xplc.2025.10128910.1016/j.xplc.2025.101289 ↗
25. 10.1111/pbi.1429510.1111/pbi.14295 ↗
26. 10.1038/184527a010.1038/184527a0 ↗
27. 10.3389/fpls.2015.0073810.3389/fpls.2015.00738 ↗
28. 10.1021/pr500337j10.1021/pr500337j ↗
29. 10.1016/j.molp.2018.12.01510.1016/j.molp.2018.12.015 ↗
30. 10.1093/plphys/kiab27310.1093/plphys/kiab273 ↗
31. 10.26508/lsa.20190055110.26508/lsa.201900551 ↗
32. 10.1111/pbi.1302410.1111/pbi.13024 ↗
33. 10.1038/srep2173210.1038/srep21732 ↗
34. 10.1111/tpj.7029710.1111/tpj.70297 ↗
35. 10.1104/pp.111.17830110.1104/pp.111.178301 ↗
36. 10.1111/pbi.7000410.1111/pbi.70004 ↗
37. 10.3390/ijms2313704510.3390/ijms23137045 ↗
38. 10.3390/ijms2610495410.3390/ijms26104954 ↗
39. 10.1371/journal.pgen.101167910.1371/journal.pgen.1011679 ↗
40. 10.1073/pnas.241116412210.1073/pnas.2411164122 ↗
41. 10.1105/tpc.4.11.141310.1105/tpc.4.11.1413 ↗
42. 10.1104/pp.106.07859210.1104/pp.106.078592 ↗
43. 10.1038/s41467‐019‐13110‐810.1038/s41467‐019‐13110‐8 ↗
44. 10.1371/journal.pcbi.100312310.1371/journal.pcbi.1003123 ↗
45. 10.1111/tpj.1349510.1111/tpj.13495 ↗
46. 10.1534/genetics.117.30037410.1534/genetics.117.300374 ↗
47. 10.1073/pnas.202327811810.1073/pnas.2023278118 ↗
48. 10.1371/journal.pone.009312210.1371/journal.pone.0093122 ↗
49. 10.1038/nature1976010.1038/nature19760 ↗
50. 10.1101/2024.1111.1109.62282310.1101/2024.1111.1109.622823 ↗
51. 10.1038/nbt.387010.1038/nbt.3870 ↗
52. 10.1111/jipb.1341610.1111/jipb.13416 ↗
53. 10.1111/pbi.1345810.1111/pbi.13458 ↗
54. 10.26508/lsa.20240259910.26508/lsa.202402599 ↗
55. 10.1093/molbev/msaa28310.1093/molbev/msaa283 ↗
56. 10.1038/ncomms725810.1038/ncomms7258 ↗
57. 10.1073/pnas.170542311410.1073/pnas.1705423114 ↗
58. 10.1111/tpj.1592810.1111/tpj.15928 ↗
59. 10.1073/pnas.161011511310.1073/pnas.1610115113 ↗
60. 10.1093/jxb/erab34410.1093/jxb/erab344 ↗
61. 10.1016/j.pnsc.2009.01.01110.1016/j.pnsc.2009.01.011 ↗
62. Identifying Indica‐Japonica Characteristic of Rice Breeding Materials with Indica‐Japonica Differentiation indel Markers
63. 10.1038/nmeth.331710.1038/nmeth.3317 ↗
64. 10.1093/bioinformatics/btt65610.1093/bioinformatics/btt656 ↗
65. 10.1093/nar/gkz24010.1093/nar/gkz240 ↗
66. 10.1093/nar/gky31010.1093/nar/gky310 ↗
67. 10.1093/nar/gkv46810.1093/nar/gkv468 ↗
68. 10.1093/bioinformatics/btp10110.1093/bioinformatics/btp101 ↗
69. 10.1093/bioinformatics/btt01910.1093/bioinformatics/btt019 ↗
70. 10.1038/nmeth.221210.1038/nmeth.2212 ↗
71. 10.1093/plcell/koae14010.1093/plcell/koae140 ↗
72. 10.1038/nbt.237710.1038/nbt.2377 ↗
73. 10.1021/ac500734c10.1021/ac500734c ↗
74. 10.1016/j.radonc.2018.06.02210.1016/j.radonc.2018.06.022 ↗
75. 10.1038/s41467‐019‐09550‐x10.1038/s41467‐019‐09550‐x ↗
76. 10.1007/s11306‐017‐1304‐x10.1007/s11306‐017‐1304‐x ↗
77. 10.1038/s41592‐018‐0110‐310.1038/s41592‐018‐0110‐3 ↗
78. 10.1038/s41596‐022‐00710‐w10.1038/s41596‐022‐00710‐w ↗
79. 10.1093/nar/gkab39410.1093/nar/gkab394 ↗
80. 10.1093/bioinformatics/btp32410.1093/bioinformatics/btp324 ↗
81. 10.1093/bioinformatics/btp35210.1093/bioinformatics/btp352 ↗
82. 10.1101/gr.114876.11010.1101/gr.114876.110 ↗
83. 10.1038/nmeth.136310.1038/nmeth.1363 ↗
84. 10.1093/nar/gkq60310.1093/nar/gkq603 ↗
85. 10.1101/gr.092759.10910.1101/gr.092759.109 ↗
86. 10.1093/molbev/msz31210.1093/molbev/msz312 ↗
87. 10.1093/bioinformatics/btp03310.1093/bioinformatics/btp033 ↗
88. 10.1016/S0169‐7439(01)00155‐110.1016/S0169‐7439(01)00155‐1 ↗
89. 10.1007/BF0087742810.1007/BF00877428 ↗
90. 10.1038/nmeth.281110.1038/nmeth.2811 ↗