Evidence map›Paper›PMID 38459066›Full record

SynthesisScientific reports2024

Meta-analysis of identified genomic regions and candidate genes underlying salinity tolerance in rice (Oryza sativa L.).

Pratik Satasiya, Sanyam Patel, Ritesh Patel, Om Prakash Raigar, Kaushal Modha, Vipul Parekh, Haimil Joshi, Vipul Patel, Ankit Chaudhary, Deepak Sharma and 1 more

Erratum issuedOpen access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
5.0field-weighted citation impact, top 5% 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, 9 citations in OpenAlex.

  1. Article
  2. Meta-QTLs and candidate genes for kernel protein content in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  3. Frontiers in plant science · 2026
    Article
  4. Review
  5. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Pratik SatasiyaDepartment of Genetics and Plant Breeding, N. M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India.
Sanyam PatelDepartment of Genetics and Plant Breeding, N. M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India.
Ritesh PatelDepartment of Genetics and Plant Breeding, N. M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India.
Om Prakash RaigarSchool of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, Punjab, India.
Kaushal ModhaDepartment of Genetics and Plant Breeding, N. M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India.
Vipul ParekhDepartment of Biotechnology, College of Forestry, Navsari Agricultural University, Navsari, Gujarat, India.
Haimil JoshiCoastal Soil Salinity Research Station Danti-Umbharat, Navsari Agricultural University, Navsari, Gujarat, India.
Vipul PatelRegional Rice Research Station, Vyara, Navsari Agricultural University, Navsari, Gujarat, India.
Ankit ChaudharyKishorbhai Institute of Agriculture Sciences and Research Centre, Uka Tarsadia University, Bardoli, Gujarat, India. ankitc034@gmail.com.ORCID http://orcid.org/0000-0002-2043-5576
Deepak SharmaDepartment of Genetics and Plant Breeding, N. M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India.
Maulik PrajapatiDepartment of Genetics and Plant Breeding, N. M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India.
Navsari Agricultural University · INPunjab Agricultural University · INUka Tarsadia University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rice output has grown globally, yet abiotic factors are still a key cause for worry. Salinity stress seems to have the more impact on crop production out of all abiotic stresses. Currently one of the most significant challenges in paddy breeding for salinity tolerance with the help of QTLs, is to determine the QTLs having the best chance of improving salinity tolerance with the least amount of background noise from the tolerant parent. Minimizing the size of the QTL confidence interval (CI) is essential in order to primarily include the genes responsible for salinity stress tolerance. By considering that, a genome-wide meta-QTL analysis on 768 QTLs from 35 rice populations published from 2001 to 2022 was conducted to identify consensus regions and the candidate genes underlying those regions responsible for the salinity tolerance, as it reduces the confidence interval (CI) to many folds from the initial QTL studies. In the present investigation, a total of 65 MQTLs were extracted with an average CI reduced from 17.35 to 1.66 cM including the smallest of 0.01 cM. Identification of the MQTLs for individual traits and then classifying the target traits into correlated morphological, physiological and biochemical aspects, resulted in more efficient interpretation of the salinity tolerance, identifying the candidate genes and to understand the salinity tolerance mechanism as a whole. The results of this study have a huge potential to improve the rice genotypes for salinity tolerance with the help of MAS and MABC.

Indexed as

OryzaGenomicsPlant BreedingQuantitative Trait LociSalinitySalt ToleranceCandidate genesConfidence intervalConsensus mapGenome-wide meta-QTLQTLSalinity stressSalinity tolerance

Identifiers

PMID38459066
PMCPMC10923909
OpenAlexW4392597387

What Socratic 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 Socratic graph.