Evidence map›Paper›PMID 39062864›Full record

ArticleInternational journal of molecular sciences2024

Genetic Analysis of Soybean Flower Size Phenotypes Based on Computer Vision and Genome-Wide Association Studies.

Song Jin, Huilin Tian, Ming Ti, Jia Song, Zhenbang Hu, Zhanguo Zhang, Dawei Xin, Qingshan Chen, Rongsheng Zhu

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Plants (Basel, Switzerland) · 2026
    Article
4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Song JinCollege of Agriculture, Northeast Agricultural University, Harbin 150030, China.ORCID 0009-0007-6785-005X
Huilin TianCollege of Agriculture, Northeast Agricultural University, Harbin 150030, China.ORCID 0009-0002-9448-2233
Ming TiCollege of Agriculture, Northeast Agricultural University, Harbin 150030, China.
Jia SongCollege of Agriculture, Northeast Agricultural University, Harbin 150030, China.
Zhenbang HuCollege of Agriculture, Northeast Agricultural University, Harbin 150030, China.
Zhanguo ZhangNational Key Laboratory of Smart Farm Technolog and System, Harbin 150030, China.ORCID 0009-0007-5102-8731
Dawei XinCollege of Agriculture, Northeast Agricultural University, Harbin 150030, China.ORCID 0000-0002-1264-5135
Qingshan ChenCollege of Agriculture, Northeast Agricultural University, Harbin 150030, China.
Rongsheng ZhuNational Key Laboratory of Smart Farm Technolog and System, Harbin 150030, China.

Funding

National Key Research and Development Program of the "14th Five Year Plan" (Accurate identification of yield traits in soybean) 2021YFD120160204Natural Science Foundation of Heilongjiang Province of China LH2021C021, LH2021C026Research and Application of Key Technologies for Intelligent Farming Decision Platform, An Open Competition Project of Heilongjiang Province, China 2021ZXJ05A03
6 · The paper itself

Abstract

The dimensions of organs such as flowers, leaves, and seeds are governed by processes of cellular proliferation and expansion. In soybeans, the dimensions of these organs exhibit a strong correlation with crop yield, quality, and other phenotypic traits. Nevertheless, there exists a scarcity of research concerning the regulatory genes influencing flower size, particularly within the soybean species. In this study, 309 samples of 3 soybean types (123 cultivar, 90 landrace, and 96 wild) were re-sequenced. The microscopic phenotype of soybean flower organs was photographed using a three-eye microscope, and the phenotypic data were extracted by means of computer vision. Pearson correlation analysis was employed to assess the relationship between petal and seed phenotypes, revealing a strong correlation between the sizes of these two organs. Through GWASs, SNP loci significantly associated with flower organ size were identified. Subsequently, haplotype analysis was conducted to screen for upstream and downstream genes of these loci, thereby identifying potential candidate genes. In total, 77 significant SNPs associated with vexil petals, 562 significant SNPs associated with wing petals, and 34 significant SNPs associated with keel petals were found. Candidate genes were screened by candidate sites, and haplotype analysis was performed on the candidate genes. Finally, the present investigation yielded 25 and 10 genes of notable significance through haplotype analysis in the vexil and wing regions, respectively. Notably,

Indexed as

FlowersGenome-Wide Association StudyGlycine maxPhenotypePolymorphism, Single NucleotideHaplotypesQuantitative Trait LociSeedscomputer visiongenome-wide association studieshaplotype analysisphenomicssoybean organ development

Identifiers

PMID39062864
PMCPMC11277310

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