Evidence map›Paper›PMID 42168869›Full record

ArticleBMC plant biology2026

Genome-wide association analysis and molecular marker development for effective tiller number in rice natural population.

Bin Liang, Jintian Chen, Beibei Su, Xianjun Zeng, Fenyun Zhang, Shunwu Yu, Lei Wang, Zhening Miao, Shimei Long, Xiaosong Ma and 1 more

Abstract read
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In one paragraph

Article in BMC plant biology, 2026. 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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0citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Bin LiangShanghai Agrobiological Gene Center, Shanghai, 201106, China.
Jintian ChenShanghai Agrobiological Gene Center, Shanghai, 201106, China.
Beibei SuShanghai Agrobiological Gene Center, Shanghai, 201106, China.
Xianjun ZengShanghai Agrobiological Gene Center, Shanghai, 201106, China.
Fenyun ZhangShanghai Agrobiological Gene Center, Shanghai, 201106, China.
Shunwu YuShanghai Agrobiological Gene Center, Shanghai, 201106, China.
Lei WangShanghai Agrobiological Gene Center, Shanghai, 201106, China.
Zhening MiaoShanghai Agrobiological Gene Center, Shanghai, 201106, China.
Shimei LongShanghai Agrobiological Gene Center, Shanghai, 201106, China.
Xiaosong MaShanghai Agrobiological Gene Center, Shanghai, 201106, China. mxs09@sagc.org.cn.
Hongyan LiuShanghai Agrobiological Gene Center, Shanghai, 201106, China. lhy@sagc.org.cn.

Funding

National Natural Science Foundation 32401788Research Foundation of Yunnan Seed laboratory 202205AR070001-11Shanghai Municipal Science and Technology Commission 25N32800400Shanghai Natural Science Foundation 24ZR1462500Shanghai Natural Science Foundation 24ZR1462600
6 · The paper itself

Abstract

Effective tiller number (ETN) has a pivotal role in determination of rice (Oryza sativa L.) grain yield. ETN is a complex quantitative trait regulated by both genetic and environmental factors. Multiple tillering-related genes have been cloned previously, few of them have been utilized in practical breeding programs. In this study, a genome-wide association study (GWAS) for ETN was conducted on 270 rice accessions with 1,019,883 SNPs. Total of 214 SNPs were detected on eight rice chromosomes for ETN at a significance level of -log10(P) > 6.0. 12 QTLs associated with ETN were identified, including three known functional genes (e.g., D10, D27, Stvb-i) and one novel gene OsIAA3. Compared to wild-type plants, OsIAA3-overexpressing plants could significantly increase ETN in rice. In the natural population, haplotype analysis was conducted for D10, D27, Stvb-i, and OsIAA3, and their favorable alleles were identified. Molecular markers were also developed and validated within the natural population and bi-parent population, and the validation results demonstrated that the developed molecular markers can be utilized for marker-assisted selection breeding. This study not only enhances our understanding of the genetic regulatory basis of rice ETN but also provides excellent germplasm and favorable haplotypes that serve as valuable breeding resources and genetic material for improving ETN and yield in rice.

Indexed as

Genome-Wide Association StudyOryzaGenetic MarkersHaplotypesPlant BreedingPolymorphism, Single NucleotideQuantitative Trait LociGenetic MarkersEffective tiller numberGWASHaplotype analysisMolecular markersOryza sativa L

Identifiers

PMID42168869
PMCPMC13410611

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.