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Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification

Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populat...

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Autores principales: Anilkumar, C., Sah, Rameswar Prasad, Muhammed Azharudheen, T. P., Behera, Sasmita, Singh, Namita, Prakash, Nitish Ranjan, Sunitha, N. C., Devanna, B. N., Marndi, B. C., Patra, B. C., Nair, Sunil Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381546/
https://www.ncbi.nlm.nih.gov/pubmed/35974066
http://dx.doi.org/10.1038/s41598-022-17402-w
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author Anilkumar, C.
Sah, Rameswar Prasad
Muhammed Azharudheen, T. P.
Behera, Sasmita
Singh, Namita
Prakash, Nitish Ranjan
Sunitha, N. C.
Devanna, B. N.
Marndi, B. C.
Patra, B. C.
Nair, Sunil Kumar
author_facet Anilkumar, C.
Sah, Rameswar Prasad
Muhammed Azharudheen, T. P.
Behera, Sasmita
Singh, Namita
Prakash, Nitish Ranjan
Sunitha, N. C.
Devanna, B. N.
Marndi, B. C.
Patra, B. C.
Nair, Sunil Kumar
author_sort Anilkumar, C.
collection PubMed
description Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1, PDIL, SAUR, and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight.
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spelling pubmed-93815462022-08-18 Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification Anilkumar, C. Sah, Rameswar Prasad Muhammed Azharudheen, T. P. Behera, Sasmita Singh, Namita Prakash, Nitish Ranjan Sunitha, N. C. Devanna, B. N. Marndi, B. C. Patra, B. C. Nair, Sunil Kumar Sci Rep Article Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1, PDIL, SAUR, and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight. Nature Publishing Group UK 2022-08-16 /pmc/articles/PMC9381546/ /pubmed/35974066 http://dx.doi.org/10.1038/s41598-022-17402-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Anilkumar, C.
Sah, Rameswar Prasad
Muhammed Azharudheen, T. P.
Behera, Sasmita
Singh, Namita
Prakash, Nitish Ranjan
Sunitha, N. C.
Devanna, B. N.
Marndi, B. C.
Patra, B. C.
Nair, Sunil Kumar
Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification
title Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification
title_full Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification
title_fullStr Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification
title_full_unstemmed Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification
title_short Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification
title_sort understanding complex genetic architecture of rice grain weight through qtl-meta analysis and candidate gene identification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381546/
https://www.ncbi.nlm.nih.gov/pubmed/35974066
http://dx.doi.org/10.1038/s41598-022-17402-w
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