Evidence map›Paper›PMID 37309355›Full record

ArticleMolecular breeding : new strategies in plant improvement2021

Exploring genetic architecture for pod-related traits in soybean using image-based phenotyping.

Fangguo Chang, Wenhuan Lv, Peiyun Lv, Yuntao Xiao, Wenliang Yan, Shu Chen, Lingyi Zheng, Ping Xie, Ling Wang, Benjamin Karikari and 3 more

Open access · greenAbstract read
In one paragraph

Article in Molecular breeding : new strategies in plant improvement, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.1field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

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

13 authors at 3 institutions in 3 countries.

Fangguo ChangNational Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Wenhuan LvNational Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Peiyun LvNational Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Yuntao XiaoNational Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Wenliang YanNational Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Shu ChenCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210095 Jiangsu China.
Lingyi ZhengNational Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Ping XieNational Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Ling WangNational Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Benjamin KarikariDepartment of Crop Science, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, P. O. Box TL, 1882 Tamale, Ghana.
Salah Fatouh Abou-ElwafaAgronomy Departments, Faculty of Agriculture, Assiut University, Asyut, 71526 Egypt.
Haiyan JiangCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210095 Jiangsu China.
Tuanjie ZhaoNational Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Nanjing Agricultural University · CNAssiut University · EGUniversity for Development Studies · GH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mature pod color (PC) and pod size (PS) served as important characteristics are used in the soybean breeding programs. However, manual phenotyping of such complex traits is time-consuming, laborious, and expensive for breeders. Here, we collected pod images from two different populations, namely, a soybean association panel (SAP) consisting of 187 accessions and an inter-specific recombinant inbred line (RIL) population containing 284 RILs. An image-based phenotyping method was developed and used to extract the pod color- and size-related parameters from images. Genome-wide association study (GWAS) and linkage mapping were performed to decipher the genetic control of pod color- and size-related traits across 2 successive years. Both populations exhibited wide phenotypic variations and continuous distribution in pod color- and size-related traits, indicating quantitative polygenic inheritance of these traits. GWAS and linkage mapping approaches identified the two major quantitative trait loci (QTL) underlying the pod color parameters, i.e., Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01223-2.

Indexed as

GWASImage analysisPod colorPod sizeQTL mappingSoybean

Identifiers

PMID37309355
PMCPMC10236113
OpenAlexW3150983431

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.