Evidence map›Paper›PMID 38745120›Full record

ArticleBMC genomics2024

Developing an SNP dataset for efficiently evaluating soybean germplasm resources using the genome sequencing data of 3,661 soybean accessions.

Yongchao Niu, Wai-Shing Yung, Ching-Ching Sze, Fuk-Ling Wong, Man-Wah Li, Gyuhwa Chung, Hon-Ming Lam

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In one paragraph

Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Yongchao NiuCentre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China.
Wai-Shing YungCentre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China.
Ching-Ching SzeCentre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China.
Fuk-Ling WongCentre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China.
Man-Wah LiCentre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China.
Gyuhwa ChungDepartment of Biotechnology, Chonnam National University, Yeosu-Si, Republic of Korea.
Hon-Ming LamCentre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China. honming@cuhk.edu.hk.

Funding

Hong Kong Research Grants Council Area of Excellence Scheme RGC-AoE scheme AoE/M-403/16The Guangdong Provincial Department of Science and Technology 2020B020220008
6 · The paper itself

Abstract

backgroundSingle nucleotide polymorphism (SNP) markers play significant roles in accelerating breeding and basic crop research. Several soybean SNP panels have been developed. However, there is still a lack of SNP panels for differentiating between wild and cultivated populations, as well as for detecting polymorphisms within both wild and cultivated populations.

resultsThis study utilized publicly available resequencing data from over 3,000 soybean accessions to identify differentiating and highly conserved SNP and insertion/deletion (InDel) markers between wild and cultivated soybean populations. Additionally, a naturally occurring mutant gene library was constructed by analyzing large-effect SNPs and InDels in the population.

conclusionThe markers obtained in this study are associated with numerous genes governing agronomic traits, thus facilitating the evaluation of soybean germplasms and the efficient differentiation between wild and cultivated soybeans. The natural mutant gene library permits the quick identification of individuals with natural mutations in functional genes, providing convenience for accelerating soybean breeding using reverse genetics.

Indexed as

Glycine maxINDEL MutationPolymorphism, Single NucleotideGene LibraryGenome, PlantPlant BreedingGenome sequencingGermplasm evaluationInDel markerInsertion/deletionLarge-effect mutation (LEM)Naturally occurring mutantSingle nucleotide polymorphismSNP markerSoybean

Identifiers

PMID38745120
PMCPMC11092025

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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.