Evidence map›Paper›PMID 36616273›Full record

ArticlePlants (Basel, Switzerland)2022

Identification of Genomic Regions Associated with High Grain Zn Content in Polished Rice Using Genotyping-by-Sequencing (GBS).

Goparaju Anurag Uttam, Karre Suman, Veerendra Jaldhani, Pulagam Madhu Babu, Durbha Sanjeeva Rao, Raman Meenakshi Sundaram, Chirravuri Naga Neeraja

Open access · goldAbstract read
In one paragraph

Article in Plants (Basel, Switzerland), 2022. 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
1.5field-weighted citation impact, top 16% 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

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. Genomic prediction and QTL analysis for grain Zn content and yield inJournal of plant biochemistry and biotechnology · 2024
    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

7 authors at 1 institution in 1 country.

Goparaju Anurag UttamDepartment of Biotechnology, Crop Improvement Section, ICAR-Indian Institute of Rice Research (IIRR), Hyderabad 500030, India.
Karre SumanDepartment of Biotechnology, Crop Improvement Section, ICAR-Indian Institute of Rice Research (IIRR), Hyderabad 500030, India.ORCID 0000-0002-3711-7881
Veerendra JaldhaniDepartment of Biotechnology, Crop Improvement Section, ICAR-Indian Institute of Rice Research (IIRR), Hyderabad 500030, India.ORCID 0000-0002-5206-7309
Pulagam Madhu BabuDepartment of Biotechnology, Crop Improvement Section, ICAR-Indian Institute of Rice Research (IIRR), Hyderabad 500030, India.
Durbha Sanjeeva RaoDepartment of Biotechnology, Crop Improvement Section, ICAR-Indian Institute of Rice Research (IIRR), Hyderabad 500030, India.
Raman Meenakshi SundaramDepartment of Biotechnology, Crop Improvement Section, ICAR-Indian Institute of Rice Research (IIRR), Hyderabad 500030, India.
Chirravuri Naga NeerajaDepartment of Biotechnology, Crop Improvement Section, ICAR-Indian Institute of Rice Research (IIRR), Hyderabad 500030, India.ORCID 0000-0002-2418-986X
Indian Institute of Rice Research · IN

Funding

DST-SERB PDF/2016/003924
6 · The paper itself

Abstract

Globally, micronutrient (iron and zinc) enriched rice has been a sustainable and cost-effective solution to overcome malnutrition or hidden hunger. Understanding the genetic basis and identifying the genomic regions for grain zinc (Zn) across diverse genetic backgrounds is an important step to develop biofortified rice varieties. In this case, an RIL population (306 RILs) obtained from a cross between the high-yielding rice variety MTU1010 and the high-zinc rice variety Ranbir Basmati was utilized to pinpoint the genomic region(s) and QTL(s) responsible for grain zinc (Zn) content. A total of 2746 SNP markers spanning a genetic distance of 2445 cM were employed for quantitative trait loci (QTL) analysis, which resulted in the identification of 47 QTLs for mineral (Zn and Fe) and agronomic traits with 3.5-36.0% phenotypic variance explained (PVE) over the seasons. On Chr02, consistent QTLs for grain Zn polished (qZnPR.2.1) and Zn brown (qZnBR.2.2) were identified. On Chr09, two additional reliable QTLs for grain Zn brown (qZnBR.9.1 and qZnBR.9.2) were identified. The major-effect QTLs identified in this study were associated with few key genes related to Zn and Fe transporter activity. The genomic regions, candidate genes, and molecular markers associated with these major QTLs will be useful for genomic-assisted breeding for developing Zn-biofortified varieties.

Indexed as

biofortificationcandidate genesGenotyping-by-Sequence (GBS)grain zincquantitative trait locirice

Identifiers

PMID36616273
PMCPMC9824299
OpenAlexW4312226479

What OpenQuestion holds

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LicenceCC BY
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.