Evidence map›Paper›PMID 34215814›Full record

ArticleScientific reports2021

Genetic basis and network underlying synergistic roots and shoots biomass accumulation revealed by genome-wide association studies in rice.

Yan Zhao, Zhigang Yin, Xueqiang Wang, Conghui Jiang, Muhammad Mahran Aslam, Fenghua Gao, Yinghua Pan, Jianyin Xie, Xiaoyang Zhu, Luhao Dong and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Genome-wide association study for biomass accumulation traits in soybean.Molecular breeding : new strategies in plant improvement · 2023
    Article
  6. Article
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

14 authors.

Yan Zhao *State Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Zhigang Yin *State Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Xueqiang Wang *State Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Conghui Jiang *State Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Muhammad Mahran AslamState Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Fenghua GaoState Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Yinghua PanGuangxi Key Laboratory of Rice Genetics and Breeding, Rice Research Institute of Guangxi Academy of Agricultural Sciences, Nanning, 530007, Guangxi, People's Republic of China.
Jianyin XieState Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Xiaoyang ZhuState Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Luhao DongState Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China.
Yanhe LiuState Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China.
Hongliang ZhangState Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Jinjie LiState Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China.
Zichao LiState Key Laboratory of Agrobiotechnology/Beijing Key Laboratory of Crop Genetic Improvement, and College of Agronomy and Biotechnology , China Agricultural University, Beijing, 100193, People's Republic of China. lizichao@cau.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic basis and network studies underlying synergistic biomass accumulation of roots and shoots (SBA) are conducive for rational design of high-biomass rice breeding. In this study, association signals for root weight, shoot weight, and the ratio of root-to-shoot mass (R/S) were identified using 666 rice accessions by genome-wide association study, together with their sub-traits, root length, root thickness and shoot length. Most association signals for root weight and shoot weight did not show association with their sub-traits. Based on the results, we proposed a top-to-bottom model for SBA, i.e. root weight, shoot weight and R/S were determined by their highest priority in contributing to biomass in the regulatory pathway, followed by a lower priority pathway for their sub-traits. Owing to 37 enriched clusters with more than two association signals identified, the relationship among the six traits could be also involved in linkage and pleiotropy. Furthermore, a discrimination of pleiotropy and LD at sequencing level using the known gene OsPTR9 for root weight, R/S and root length was provided. The results of given moderate correlation between traits and their corresponding sub-traits, and moderate additive effects between a trait and the accumulation of excellent alleles corresponding to its sub-traits supported a bottom-to-top regulation model for SBA. This model depicted each lowest-order trait (root length, root thickness and shoot length) was determined by its own regulation loci, and competition among different traits, as well as the pleiotropy and LD. All above ensure the coordinated development of each trait and the accumulation of the total biomass, although the predominant genetic basis of SBA is still indistinguishable. The presentation of the above two models and evidence of this study shed light on dissecting the genetic architecture of SBA.

Indexed as

Genome-Wide Association StudyPlant BreedingAllelesBiomassGenetic LinkageGenotypeOryzaPlant RootsPlant ShootsPolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID34215814
PMCPMC8253791

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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.