Evidence map›Paper›PMID 37223807›Full record

ArticleFrontiers in plant science2023

Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize.

Basudeb Sarkar, Yellisetty Varalaxmi, Maddi Vanaja, Nakka RaviKumar, Mathyam Prabhakar, Sushil Kumar Yadav, Mandapaka Maheswari, Vinod Kumar Singh

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
6.3field-weighted citation impact, top 3% 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 20 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Basudeb SarkarDivision of Crop Sciences, Indian Council of Agricultural Research (ICAR)-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana, India.
Yellisetty VaralaxmiDivision of Crop Sciences, Indian Council of Agricultural Research (ICAR)-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana, India.
Maddi VanajaDivision of Crop Sciences, Indian Council of Agricultural Research (ICAR)-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana, India.
Nakka RaviKumarDivision of Crop Sciences, Indian Council of Agricultural Research (ICAR)-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana, India.
Mathyam PrabhakarDivision of Crop Sciences, Indian Council of Agricultural Research (ICAR)-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana, India.
Sushil Kumar YadavDivision of Crop Sciences, Indian Council of Agricultural Research (ICAR)-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana, India.
Mandapaka MaheswariDivision of Crop Sciences, Indian Council of Agricultural Research (ICAR)-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana, India.
Vinod Kumar SinghDivision of Crop Sciences, Indian Council of Agricultural Research (ICAR)-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana, India.
Central Research Institute for Dryland Agriculture · INIndian Council of Agricultural Research · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Maize productivity is significantly impacted by drought; therefore, improvement of drought tolerance is a critical goal in maize breeding. To achieve this, a better understanding of the genetic basis of drought tolerance is necessary. Our study aimed to identify genomic regions associated with drought tolerance-related traits by phenotyping a mapping population of recombinant inbred lines (RILs) for two seasons under well-watered (WW) and water-deficit (WD) conditions. We also used single nucleotide polymorphism (SNP) genotyping through genotyping-by-sequencing to map these regions and attempted to identify candidate genes responsible for the observed phenotypic variation. Phenotyping of the RILs population revealed significant variability in most of the traits, with normal frequency distributions, indicating their polygenic nature. We generated a linkage map using 1,241 polymorphic SNPs distributed over 10 chromosomes (chrs), covering a total genetic distance of 5,471.55 cM. We identified 27 quantitative trait loci (QTLs) associated with various morphophysiological and yield-related traits, with 13 QTLs identified under WW conditions and 12 under WD conditions. We found one common major QTL (

Indexed as

candidate genesdroughtphysiological and yield traitsQTLsSNPs

Identifiers

PMID37223807
PMCPMC10200936
OpenAlexW4377940661

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LicenceCC BY
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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.