Evidence map›Paper›PMID 40336954›Full record

ArticleFood chemistry. Molecular sciences2025

Dissecting the genetic architecture of polygenic nutritional traits in maize through meta-QTL analysis.

Bhupender Kumar, Shrikant Yankanchi, Rakhi Singh, Pushpendra, Debjyoti Sarkar, Pardeep Kumar, Krishan Kumar, Mukesh Choudhary, Bahadur Singh Jat, H S Jat

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Article in Food chemistry. Molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Genetic Dissection and Functional Validation ofInternational journal of molecular sciences · 2026
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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Bhupender KumarICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.
Shrikant YankanchiICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.
Rakhi SinghICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.
PushpendraICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.
Debjyoti SarkarICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.
Pardeep KumarICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.
Krishan KumarICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.
Mukesh ChoudharyICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.
Bahadur Singh JatICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.
H S JatICAR-Indian Institute of Maize Research, Ludhiana, Punjab 1410045, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Maize, as a staple crop, contributes significantly to global nutritional security. However, improving its nutritional quality, including grain zinc (GZn), grain iron (GFe), kernel oil (KO), protein quality (PQ), and content (PC), is difficult due to the complex and polygenic nature of these traits. In traditional quantitative trait loci (QTLs) mapping, different populations tested across variable environments have resulted in heterogeneous findings, highlighting the challenge of QTL instability. Therefore, we tested whether Meta-QTL (MQTL) analysis enables the identification of stable QTLs with broader allelic coverage and higher mapping resolution for effective marker-assisted selection (MAS) of complex traits. A comprehensive literature search revealed 29 mapping studies encompassing 308 QTLs for the targeted traits. A total of 34 stable MQTLs were identified, with an average CI of 4.59 cM. These MQTLs were located on all ten maize chromosomes, with phenotypic variance explained (PVE %) ranging from 7.3 % (MQTL1_2) to 49.0 % (MQTL3_2). Furthermore, the analysis revealed six MAS-friendly and five hotspot MQTLs. Besides, 591 CGs were identified underlying these MQTLs, of which 14 have known roles in grain filling, metal homeostasis, and fatty acid biosynthesis in maize. In silico analysis confirmed the tissue-specific expression of these 14 CGs. MQTL analysis effectively refined the genomic regions (4.86 folds) linked with nutritional quality and identified stable MQTLs and CGs. These findings will be useful for developing nutritionally enriched varieties through MAS and genetic engineering.

Indexed as

Candidate genesMaizeMetal homeostasisNutritional quality

Identifiers

PMID40336954
PMCPMC12056801

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.