Evidence map›Paper›PMID 38575927›Full record

SynthesisBMC genomics2024

Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice.

Kelvin Dodzi Aloryi, Nnaemeka Emmanuel Okpala, Hong Guo, Benjamin Karikari, Aduragbemi Amo, Semiu Folaniyi Bello, Dinesh Kumar Saini, Selorm Akaba, Xiaohai Tian

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in BMC genomics, 2024. 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
4.8field-weighted citation impact, top 6% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

9 authors at 7 institutions in 5 countries.

Kelvin Dodzi Aloryi *Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China.
Nnaemeka Emmanuel Okpala *Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China.
Hong GuoUniversity of Chinese Academy of Sciences, 100049, Beijing, China.
Benjamin KarikariDépartement de phytologie, Université Laval, Québec, QC, Canada.
Aduragbemi AmoDepartment of Horticultural Sciences, Texas A&M University, College Station, TX, USA.
Semiu Folaniyi BelloDepartment of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China.
Dinesh Kumar SainiDepartment of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
Selorm AkabaSchool of Agriculture, University of Cape Coast, Cape Coast, Ghana.
Xiaohai TianHubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China. xiaohait@sina.com.ORCID http://orcid.org/0000-0001-9242-289X
Yangtze University · CNSouth China Agricultural University · CNTexas A&M University · USTexas Tech University · USUniversité Laval · CAUniversity of Cape Coast · GHUniversity of Chinese Academy of Sciences · CN

Funding

Hubei Provincial Natural Science Foundation of China 2023AFA022Key R&D Project in Hubei Province, China 2020BBB060the National Key Research and Development Program of China 2018YFD0301306
6 · The paper itself

Abstract

backgroundDue to rising costs, water shortages, and labour shortages, farmers across the globe now prefer a direct seeding approach. However, submergence stress remains a major bottleneck limiting the success of this approach in rice cultivation. The merger of accumulated rice genetic resources provides an opportunity to detect key genomic loci and candidate genes that influence the flooding tolerance of rice.

resultsIn the present study, a whole-genome meta-analysis was conducted on 120 quantitative trait loci (QTL) obtained from 16 independent QTL studies reported from 2004 to 2023. These QTL were confined to 18 meta-QTL (MQTL), and ten MQTL were successfully validated by independent genome-wide association studies from diverse natural populations. The mean confidence interval (CI) of the identified MQTL was 3.44 times narrower than the mean CI of the initial QTL. Moreover, four core MQTL loci with genetic distance less than 2 cM were obtained. By combining differentially expressed genes (DEG) from two transcriptome datasets with 858 candidate genes identified in the core MQTL regions, we found 38 common differentially expressed candidate genes (DECGs). In silico expression analysis of these DECGs led to the identification of 21 genes with high expression in embryo and coleoptile under submerged conditions. These DECGs encode proteins with known functions involved in submergence tolerance including WRKY, F-box, zinc fingers, glycosyltransferase, protein kinase, cytochrome P450, PP2C, hypoxia-responsive family, and DUF domain. By haplotype analysis, the 21 DECGs demonstrated distinct genetic differentiation and substantial genetic distance mainly between indica and japonica subspecies. Further, the MQTL7.1 was successfully validated using flanked marker S2329 on a set of genotypes with phenotypic variation.

conclusionThis study provides a new perspective on understanding the genetic basis of submergence tolerance in rice. The identified MQTL and novel candidate genes lay the foundation for marker-assisted breeding/engineering of flooding-tolerant cultivars conducive to direct seeding.

Indexed as

OryzaChromosome MappingGene Expression ProfilingGenome-Wide Association StudyGenomicsPlant BreedingDifferentially expressed candidate genes (DECGs)Haplotype analysisMarker-assisted breedingMeta-analysisMQTLSubmergence tolerance

Identifiers

PMID38575927
PMCPMC10993490
OpenAlexW4393943380

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.