Evidence map›Paper›PMID 39399540›Full record

ArticleFrontiers in plant science2024

Identification of candidate genes and development of KASP markers for soybean shade-tolerance using GWAS.

Qianru Jia, Shengyan Hu, Xihuan Li, Libin Wei, Qiong Wang, Wei Zhang, Hongmei Zhang, Xiaoqing Liu, Xin Chen, Xuejun Wang and 1 more

Abstract read
In one paragraph

Article in Frontiers in plant science, 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

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

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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

2 citing papers in PubMed.

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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

11 authors.

Qianru Jia *Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
Shengyan Hu *Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
Xihuan LiInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
Libin WeiJiangsu Yanjiang Institute of Agricultural Sciences, Nantong, China.
Qiong WangInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
Wei ZhangInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
Hongmei ZhangInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
Xiaoqing LiuInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
Xin ChenInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
Xuejun WangJiangsu Yanjiang Institute of Agricultural Sciences, Nantong, China.
Huatao ChenInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Shade has a direct impact on photosynthesis and production of plants. Exposure to shade significantly reduces crops yields. Identifying shade-tolerant genomic loci and soybean varieties is crucial for improving soybean yields. In this study, we applied a shade treatment (30% light reduction) to a natural soybean population consisting of 264 accessions, and measured several traits, including the first pod height, plant height, pod number per plant, grain weight per plant, branch number, and main stem node number. Additionally, we performed GWAS on these six traits with and without shade treatment, as well as on the shade tolerance coefficients (STCs) of the six traits. As a result, we identified five shade-tolerance varieties, 733 SNPs and four candidate genes over two years. Furthermore, we developed four kompetitive allele-specific PCR (KASP) makers for the STC of S18_1766721, S09_48870909, S19_49517336, S18_3429732. This study provides valuable genetic resources for breeding soybean shade tolerance and offers new insights into the theoretical research on soybean shade tolerance.

Indexed as

GWASKASPshade toleranceshade tolerance coefficientsoybean

Identifiers

PMID39399540
PMCPMC11466877

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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.