Evidence map›Paper›PMID 33243137›Full record

ArticleBMC genetics2020

Genetic analysis of rice seedling traits related to machine transplanting under different seeding densities.

Dan Zhu, Yuping Zhang, Jing Xiang, Yaliang Wang, Defeng Zhu, Yikai Zhang, Huizhe Chen

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In one paragraph

Article in BMC genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Dan ZhuState Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China.
Yuping ZhangState Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China.
Jing XiangState Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China.
Yaliang WangState Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China.
Defeng ZhuState Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China.
Yikai ZhangState Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China.
Huizhe ChenState Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China. chenhuizhe@163.com.ORCID 0000-0003-0984-9927

Funding

National Natural Science Foundation of China 31701374
6 · The paper itself

Abstract

backgroundDue to the diversity of rice varieties and cropping systems in China, the limitation of seeding density and seedling quality makes it hard to improve machine-transplanted efficiency. Previous studies have shown that indica and japonica varieties varied in machine transplanting efficiency and optimal seeding density. In this study, a RIL population derived from '9311' and 'Nipponbare' were performed to explore the seedling traits variations and the genetic mechanism under three seeding densities.

resultsThe parents and RIL population exhibited similar trends as the seeding density increased, including seedling height and first leaf sheath length increases, shoot dry weight and root dry weight decreases. Among the 37 QTLs for six traits detected under the three seeding densities, 12 QTLs were detected in both three seeding densities. Five QTL hotspots identified clustered within genomic regions on chromosomes 1, 2, 4, 6 and 11. Specific QTLs such as qRDW

conclusionsOur results revealed the differences between indica rice and japonica rice seedling traits in response to seeding density. Several QTL hotspots involved in different traits and specific QTLs (such as qRDW

Indexed as

PhenotypeQuantitative Trait LociAgricultureChinaEpistasis, GeneticGenotypeOryzaSeedlingsAdditive and epistasis effectMachine transplantingQTL mappingRice seedling traitSeeding density

Identifiers

PMID33243137
PMCPMC7690112

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