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Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping

Mineral malnutrition is a major problem in many rice-consuming countries. It is essential to know the genetic mechanisms of accumulation of mineral elements in the rice grain to provide future solutions for this issue. This study was conducted to identify the genetic basis of six mineral elements (C...

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Autores principales: Islam, A. S. M. Faridul, Mustahsan, Wardah, Tabien, Rodante, Awika, Joseph M., Septiningsih, Endang M., Thomson, Michael J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777918/
https://www.ncbi.nlm.nih.gov/pubmed/36553597
http://dx.doi.org/10.3390/genes13122330
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author Islam, A. S. M. Faridul
Mustahsan, Wardah
Tabien, Rodante
Awika, Joseph M.
Septiningsih, Endang M.
Thomson, Michael J.
author_facet Islam, A. S. M. Faridul
Mustahsan, Wardah
Tabien, Rodante
Awika, Joseph M.
Septiningsih, Endang M.
Thomson, Michael J.
author_sort Islam, A. S. M. Faridul
collection PubMed
description Mineral malnutrition is a major problem in many rice-consuming countries. It is essential to know the genetic mechanisms of accumulation of mineral elements in the rice grain to provide future solutions for this issue. This study was conducted to identify the genetic basis of six mineral elements (Cu, Fe, K, Mg, Mn, and Zn) by using three models for single-locus and six models for multi-locus analysis of a genome-wide association study (GWAS) using 174 diverse rice accessions and 6565 SNP markers. To declare a SNP as significant, −log10(P) ≥ 3.0 and 15% FDR significance cut-off values were used for single-locus models, while LOD ≥ 3.0 was used for multi-locus models. Using these criteria, 147 SNPs were detected by one or two GWAS methods at −log10(P) ≥ 3.0, 48 of which met the 15% FDR significance cut-off value. Single-locus models outperformed multi-locus models before applying multi-test correction, but once applied, multi-locus models performed better. While 14 (~29%) of the identified quantitative trait loci (QTLs) after multiple test correction co-located with previously reported genes/QTLs and marker associations, another 34 trait-associated SNPs were novel. After mining genes within 250 kb of the 48 significant SNP loci, in silico and gene enrichment analyses were conducted to predict their potential functions. These shortlisted genes with their functions could guide future experimental validation, helping us to understand the complex molecular mechanisms controlling rice grain mineral elements.
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spelling pubmed-97779182022-12-23 Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping Islam, A. S. M. Faridul Mustahsan, Wardah Tabien, Rodante Awika, Joseph M. Septiningsih, Endang M. Thomson, Michael J. Genes (Basel) Article Mineral malnutrition is a major problem in many rice-consuming countries. It is essential to know the genetic mechanisms of accumulation of mineral elements in the rice grain to provide future solutions for this issue. This study was conducted to identify the genetic basis of six mineral elements (Cu, Fe, K, Mg, Mn, and Zn) by using three models for single-locus and six models for multi-locus analysis of a genome-wide association study (GWAS) using 174 diverse rice accessions and 6565 SNP markers. To declare a SNP as significant, −log10(P) ≥ 3.0 and 15% FDR significance cut-off values were used for single-locus models, while LOD ≥ 3.0 was used for multi-locus models. Using these criteria, 147 SNPs were detected by one or two GWAS methods at −log10(P) ≥ 3.0, 48 of which met the 15% FDR significance cut-off value. Single-locus models outperformed multi-locus models before applying multi-test correction, but once applied, multi-locus models performed better. While 14 (~29%) of the identified quantitative trait loci (QTLs) after multiple test correction co-located with previously reported genes/QTLs and marker associations, another 34 trait-associated SNPs were novel. After mining genes within 250 kb of the 48 significant SNP loci, in silico and gene enrichment analyses were conducted to predict their potential functions. These shortlisted genes with their functions could guide future experimental validation, helping us to understand the complex molecular mechanisms controlling rice grain mineral elements. MDPI 2022-12-10 /pmc/articles/PMC9777918/ /pubmed/36553597 http://dx.doi.org/10.3390/genes13122330 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Islam, A. S. M. Faridul
Mustahsan, Wardah
Tabien, Rodante
Awika, Joseph M.
Septiningsih, Endang M.
Thomson, Michael J.
Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping
title Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping
title_full Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping
title_fullStr Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping
title_full_unstemmed Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping
title_short Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping
title_sort identifying the genetic basis of mineral elements in rice grain using genome-wide association mapping
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777918/
https://www.ncbi.nlm.nih.gov/pubmed/36553597
http://dx.doi.org/10.3390/genes13122330
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