ArticleScientific reports2021
Genomic analysis of ionome-related QTLs in Arabidopsis thaliana.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Combination of meta-analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean.The plant genome · 2023Pooled it
- Genetic Variation and Quantitative Trait Loci Analysis of the Maize Ionome in Response to Phosphorus Fertilisation.Plant, cell & environment · 2025Article
- Comparison of linkage and association mapping in MAGIC lines identifies AtMTP3 as a new gene controlling natural variation in leaf zinc concentration in Arabidopsis.Journal of experimental botany · 2025Article
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- Molecular characterization of QTL for grain zinc and iron concentrations in wheat landrace Chinese Spring.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Article
- Genome-wide screening of meta-QTL and candidate genes controlling yield and yield-related traits in barley (Hordeum vulgare L.).PloS one · 2024Article
- Meta-QTLs, ortho-MetaQTLs and candidate genes for grain Fe and Zn contents in wheat (Physiology and molecular biology of plants : an international journal of functional plant biology · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ionome contributes to maintain cell integrity and acts as cofactors for catalyzing regulatory pathways. Identifying ionome contributing genomic regions provides a practical framework to dissect the genetic architecture of ionomic traits for use in biofortification. Meta-QTL (MQTL) analysis is a robust method to discover stable genomic regions for traits regardless of the genetic background. This study used information of 483 QTLs for ionomic traits identified from 12 populations for MQTL analysis in Arabidopsis thaliana. The selected QTLs were projected onto the newly constructed genetic consensus map and 33 MQTLs distributed on A. thaliana chromosomes were identified. The average confidence interval (CI) of the drafted MQTLs was 1.30 cM, reduced eight folds from a mean CI of 10.88 cM for the original QTLs. Four MQTLs were considered as stable MQTLs over different genetic backgrounds and environments. In parallel to the gene density over the A. thaliana genome, the genomic distribution of MQTLs over the genetic and physical maps indicated the highest density at non- and sub-telomeric chromosomal regions, respectively. Several candidate genes identified in the MQTLs intervals were associated with ion transportation, tolerance, and homeostasis. The genomic context of the identified MQTLs suggested nine chromosomal regions for Zn, Mn, and Fe control. The QTLs for potassium (K) and phosphorus (P) were the most frequently co-located with Zn (78.3%), Mn (76.2%), and Fe (88.2% and 70.6%) QTLs. The current MQTL analysis demonstrates that meta-QTL analysis is cheaper than, and as informative as genome-wide association study (GWAS) in refining the known QTLs.
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