ArticleFrontiers in genetics2021
Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- Univariate and multivariate QTL analyses provide insights into genetic architecture of variation of key volatile aroma compounds in cucumber (Cucumis sativus L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- A Genome‑wide Association Study of the Grain Ionome in Rice Oryza Sativa Ssp. Japonica under Two Diverse Water Management Systems.Rice (New York, N.Y.) · 2025Article
- Rice Adaptation to Abiotic Stresses Caused by Soil Inorganic Elements.International journal of molecular sciences · 2025Review
- Multi-Experiment and Multi-Locus Genome-Wide Association Mapping for Grain Arsenic in Rice Population.Plant direct · 2025Article
- A chloroplast localized heavy metal-associated domain containing protein regulates grain calcium accumulation in rice.Nature communications · 2024Article
- Relationships Among Arsenic-Related Traits, Including Rice Grain Arsenic Concentration and Straighthead Resistance, as Revealed by Genome-Wide Association.Frontiers in genetics · 2021Article
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rice provides more than one fifth of daily calories for half of the world's human population, and is a major dietary source of both essential mineral nutrients and toxic elements. Rice grains are generally poor in some essential nutrients but may contain unsafe levels of some toxic elements under certain conditions. Identification of quantitative trait loci (QTLs) controlling the concentrations of mineral nutrients and toxic trace metals (the ionome) in rice will facilitate development of nutritionally improved rice varieties. However, QTL analyses have traditionally considered each element separately without considering their interrelatedness. In this study, we performed principal component analysis (PCA) and multivariate QTL analyses to identify the genetic loci controlling the covariance among mineral elements in the rice ionome. We resequenced the whole genomes of a rice recombinant inbred line (RIL) population, and performed univariate and multivariate QTL analyses for the concentrations of 16 elements in grains, shoots and roots of the RIL population grown in different conditions. We identified a total of 167 unique elemental QTLs based on analyses of individual elemental concentrations as separate traits, 53 QTLs controlling covariance among elemental concentrations within a single environment/tissue (PC-QTLs), and 152 QTLs which determined covariation among elements across environments/tissues (aPC-QTLs). The candidate genes underlying the QTL clusters with elemental QTLs, PC-QTLs and aPC-QTLs co-localized were identified, including
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