Evidence map›Paper›PMID 36011264›Full record

ArticleGenes2022

Identification of Heat-Tolerant Genes in Non-Reference Sequences in Rice by Integrating Pan-Genome, Transcriptomics, and QTLs.

Samuel Tareke Woldegiorgis, Ti Wu, Linghui Gao, Yunxia Huang, Yingjie Zheng, Fuxiang Qiu, Shichang Xu, Huan Tao, Andrew Harrison, Wei Liu and 1 more

Open access · goldAbstract read
In one paragraph

Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Plant Adaptation and Tolerance to Heat Stress: Advance Approaches and Future Aspects.Combinatorial chemistry & high throughput screening · 2024
    Review
  5. Biotechnological Advances to Improve Abiotic Stress Tolerance in Crops.International journal of molecular sciences · 2022
    Review
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 at 2 institutions in 2 countries.

Samuel Tareke WoldegiorgisCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.ORCID 0000-0001-8127-0907
Ti WuCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Linghui GaoCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Yunxia HuangCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Yingjie ZhengCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Fuxiang QiuCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Shichang XuCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Huan TaoCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Andrew HarrisonDepartment of Mathematical Sciences, University of Essex, Colchester, CO4 3SQ, UK.
Wei LiuCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.ORCID 0000-0001-5659-6139
Huaqin HeCollege of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Fujian Agriculture and Forestry University · CNUniversity of Essex · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The availability of large-scale genomic data resources makes it very convenient to mine and analyze genes that are related to important agricultural traits in rice. Pan-genomes have been constructed to provide insight into the genome diversity and functionality of different plants, which can be used in genome-assisted crop improvement. Thus, a pan-genome comprising all genetic elements is crucial for comprehensive variation study among the heat-resistant and -susceptible rice varieties. In this study, a rice pan-genome was firstly constructed by using 45 heat-tolerant and 15 heat-sensitive rice varieties. A total of 38,998 pan-genome genes were identified, including 37,859 genes in the reference and 1141 in the non-reference contigs. Genomic variation analysis demonstrated that a total of 76,435 SNPs were detected and identified as the heat-tolerance-related SNPs, which were specifically present in the highly heat-resistant rice cultivars and located in the genic regions or within 2 kbp upstream and downstream of the genes. Meanwhile, 3214 upregulated and 2212 downregulated genes with heat stress tolerance-related SNPs were detected in one or multiple RNA-seq datasets of rice under heat stress, among which 24 were located in the non-reference contigs of the rice pan-genome. We then mapped the DEGs with heat stress tolerance-related SNPs to the heat stress-resistant QTL regions. A total of 1677 DEGs, including 990 upregulated and 687 downregulated genes, were mapped to the 46 heat stress-resistant QTL regions, in which 2 upregulated genes with heat stress tolerance-related SNPs were identified in the non-reference sequences. This pan-genome resource is an important step towards the effective and efficient genetic improvement of heat stress resistance in rice to help meet the rapidly growing needs for improved rice productivity under different environmental stresses. These findings provide further insight into the functional validation of a number of non-reference genes and, especially, the two genes identified in the heat stress-resistant QTLs in rice.

Indexed as

OryzaThermotoleranceGenes, PlantQuantitative Trait LociTranscriptomeheat stresspan-genomepresence/absence variation (PAV)rice (Oryza sativa L.)single nucleotide polymorphisms (SNPs)

Identifiers

PMID36011264
PMCPMC9407402
OpenAlexW4288445916

What OpenQuestion holds

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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.