Evidence map›Paper›PMID 39070774›Full record

ArticleMolecular breeding : new strategies in plant improvement2024

QTL detection and candidate gene identification of

Haifeng Guo, Yongmei Guo, Yawen Zeng, Andong Zou, Najeeb Ullah Khan, Yunsong Gu, Jin Li, Xingming Sun, Zhanying Zhang, Hongliang Zhang and 6 more

Abstract read
In one paragraph

Article in Molecular breeding : new strategies in plant improvement, 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
–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

1 citing paper in PubMed.

  1. 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

16 authors.

Haifeng Guo *Beijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.
Yongmei Guo *Institute of Food Crop Research, Yunnan Academy of Agricultural Sciences, Kunming, 650205 China.
Yawen ZengBiotechnology and Germplasm Resources Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650205 China.
Andong ZouBeijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.
Najeeb Ullah KhanBeijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.
Yunsong GuBeijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.
Jin LiBeijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.
Xingming SunBeijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.
Zhanying ZhangBeijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.
Hongliang ZhangBeijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.
Youliang PengMinistry of Agriculture Key Laboratory of Pest Monitoring and Green Management, College of Plant Protection, China Agricultural University, Beijing, 100193 China.
Huahui LiInstitute of Food Crop Research, Yunnan Academy of Agricultural Sciences, Kunming, 650205 China.
Zhigang WuInstitute of Food Crop Research, Yunnan Academy of Agricultural Sciences, Kunming, 650205 China.
Pingrong YuanInstitute of Food Crop Research, Yunnan Academy of Agricultural Sciences, Kunming, 650205 China.
Jinjie LiBeijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.
Zichao LiBeijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China.ORCID 0000-0002-3186-1132

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cold stress is one of the main abiotic stresses that affects rice growth and production worldwide. Dissection of the genetic basis is important for genetic improvement of cold tolerance in rice. In this study, a new source of cold-tolerant accession from the Yunnan plateau, Lijiangxiaoheigu, was used as the donor parent and crossed with a cold-sensitive cultivar, Deyou17, to develop recombinant inbred lines (RILs) for quantitative trait locus (QTL) analysis for cold tolerance at the early seedling and booting stages in rice. In total, three QTLs for cold tolerance at the early seedling stage on chromosomes 2 and 7, and four QTLs at the booting stage on chromosomes 1, 3, 5, and 7, were identified. Haplotype and linear regression analyses showed that QTL pyramiding based on the additive effect of these favorable loci has good potential for cold tolerance breeding. Effect assessment in the RIL and BC Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01488-3.

Indexed as

Candidate geneCold toleranceQTL analysisQTL pyramidingRice

Identifiers

PMID39070774
PMCPMC11282035

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.