Evidence map›Paper›PMID 39610887›Full record

ArticleFrontiers in plant science2024

Identification of QTL-allele systems of seed size and oil content for simultaneous genomic improvement in Northeast China soybeans.

Jianbo He, Lianshun Fu, Xiaoshuai Hao, Yicun Wu, Mengfan Wang, Qi Zhang, Weidan Feng, Mengmeng Fu, Yanping Wang, Haixiang Ren and 3 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

3 citing papers in PubMed.

  1. Article
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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.

Jianbo HeSoybean Research Institute & MARA National Center for Soybean Improvement & MARA Key Laboratory of Biology and Genetic Improvement of Soybean & State Key Laboratory of Crop Genetics and Germplasm Enhancement and Utilization & State Innovation Platform for Integrated Production and Education in Soybean Bio-Breeding & Zhongshan Biological Breeding Laboratory & Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
Lianshun FuSoybean Research Institute, Tieling Academy of Agricultural Sciences, Tieling, China.
Xiaoshuai HaoSoybean Research Institute & MARA National Center for Soybean Improvement & MARA Key Laboratory of Biology and Genetic Improvement of Soybean & State Key Laboratory of Crop Genetics and Germplasm Enhancement and Utilization & State Innovation Platform for Integrated Production and Education in Soybean Bio-Breeding & Zhongshan Biological Breeding Laboratory & Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
Yicun WuSoybean Research Institute & MARA National Center for Soybean Improvement & MARA Key Laboratory of Biology and Genetic Improvement of Soybean & State Key Laboratory of Crop Genetics and Germplasm Enhancement and Utilization & State Innovation Platform for Integrated Production and Education in Soybean Bio-Breeding & Zhongshan Biological Breeding Laboratory & Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
Mengfan WangSoybean Research Institute & MARA National Center for Soybean Improvement & MARA Key Laboratory of Biology and Genetic Improvement of Soybean & State Key Laboratory of Crop Genetics and Germplasm Enhancement and Utilization & State Innovation Platform for Integrated Production and Education in Soybean Bio-Breeding & Zhongshan Biological Breeding Laboratory & Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
Qi ZhangSoybean Research Institute & MARA National Center for Soybean Improvement & MARA Key Laboratory of Biology and Genetic Improvement of Soybean & State Key Laboratory of Crop Genetics and Germplasm Enhancement and Utilization & State Innovation Platform for Integrated Production and Education in Soybean Bio-Breeding & Zhongshan Biological Breeding Laboratory & Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
Weidan FengSoybean Research Institute & MARA National Center for Soybean Improvement & MARA Key Laboratory of Biology and Genetic Improvement of Soybean & State Key Laboratory of Crop Genetics and Germplasm Enhancement and Utilization & State Innovation Platform for Integrated Production and Education in Soybean Bio-Breeding & Zhongshan Biological Breeding Laboratory & Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
Mengmeng FuSoybean Research Institute & MARA National Center for Soybean Improvement & MARA Key Laboratory of Biology and Genetic Improvement of Soybean & State Key Laboratory of Crop Genetics and Germplasm Enhancement and Utilization & State Innovation Platform for Integrated Production and Education in Soybean Bio-Breeding & Zhongshan Biological Breeding Laboratory & Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
Yanping WangMudanjiang Research and Development Center for Soybean & Mudanjiang Experiment Station of the National Center for Soybean Improvement, Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, Mudanjiang, China.
Haixiang RenMudanjiang Research and Development Center for Soybean & Mudanjiang Experiment Station of the National Center for Soybean Improvement, Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, Mudanjiang, China.
Weiguang DuMudanjiang Research and Development Center for Soybean & Mudanjiang Experiment Station of the National Center for Soybean Improvement, Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, Mudanjiang, China.
Wubin WangSoybean Research Institute & MARA National Center for Soybean Improvement & MARA Key Laboratory of Biology and Genetic Improvement of Soybean & State Key Laboratory of Crop Genetics and Germplasm Enhancement and Utilization & State Innovation Platform for Integrated Production and Education in Soybean Bio-Breeding & Zhongshan Biological Breeding Laboratory & Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
Junyi GaiSoybean Research Institute & MARA National Center for Soybean Improvement & MARA Key Laboratory of Biology and Genetic Improvement of Soybean & State Key Laboratory of Crop Genetics and Germplasm Enhancement and Utilization & State Innovation Platform for Integrated Production and Education in Soybean Bio-Breeding & Zhongshan Biological Breeding Laboratory & Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Northeast China (NEC) is the major production area for soybeans in China, whereas its soybean germplasm has played key roles in world soybean production, especially in the Americas. For plant breeding, genomic selection involves two stages, cross design and progeny selection, with the former determining the latter's potential. In NEC, one of the major breeding purposes is for 100-seed weight (100SW) and seed oil content (SOC). A diverse sample with 361 NEC soybean germplasm accessions was evaluated for their 100SW and SOC in Tieling, Liaoning, China. Both traits exhibited significant phenotypic, genotypic, and G × E variation, with a trait heritability of 82.38% and 86.26%, respectively. A restricted two-stage multi-locus genome-wide association study (RTM-GWAS) with 15,501 SNPLDB (SNP linkage disequilibrium block) markers identified 80 and 92 QTLs with 230 and 299 alleles for 100SW and SOC, respectively. Corresponding to some increase of the two traits, almost all the alleles in the early maturity groups (MG 0 + 00 + 000) were inherited from the late MGs (MG I+II+III), indicating that genetic recombination was the major motivator in addition to a few allele emergence and some allele exclusion fluctuations among early MGs. Using the 95th percentile as indicator, the prediction of recombination potentials showed that 30.43 g 100SW and 27.73% SOC might be achieved, respectively. Three strategies of simultaneous genomic improvement of both traits in designing optimal crosses, namely, 100SW-first, SOC-first, and 100SW-SOC-balance, were proved to be efficient. Thus, the optimal cross design could be extended to multiple traits based on a relatively thorough identification of the QTL-alleles using RTM-GWAS.

Indexed as

100-seed weight (100SW)evolutionary dynamicsNortheast China soybean germplasm population (NECSGP)recombination potential predictionRTM-GWASseed oil content (SOC)simultaneous genomic cross design for traits

Identifiers

PMID39610887
PMCPMC11602309

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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.