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The Genetic Architecture of Maize (Zea mays L.) Kernel Weight Determination

Individual kernel weight is an important trait for maize yield determination. We have identified genomic regions controlling this trait by using the B73xMo17 population; however, the effect of genetic background on control of this complex trait and its physiological components is not yet known. The...

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Autores principales: Prado, Santiago Alvarez, López, César G., Senior, M. Lynn, Borrás, Lucas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4169153/
https://www.ncbi.nlm.nih.gov/pubmed/25237113
http://dx.doi.org/10.1534/g3.114.013243
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author Prado, Santiago Alvarez
López, César G.
Senior, M. Lynn
Borrás, Lucas
author_facet Prado, Santiago Alvarez
López, César G.
Senior, M. Lynn
Borrás, Lucas
author_sort Prado, Santiago Alvarez
collection PubMed
description Individual kernel weight is an important trait for maize yield determination. We have identified genomic regions controlling this trait by using the B73xMo17 population; however, the effect of genetic background on control of this complex trait and its physiological components is not yet known. The objective of this study was to understand how genetic background affected our previous results. Two nested stable recombinant inbred line populations (N209xMo17 and R18xMo17) were designed for this purpose. A total of 408 recombinant inbred lines were genotyped and phenotyped at two environments for kernel weight and five other traits related to kernel growth and development. All traits showed very high and significant (P < 0.001) phenotypic variability and medium-to-high heritability (0.60−0.90). When N209xMo17 and R18xMo17 were analyzed separately, a total of 23 environmentally stable quantitative trait loci (QTL) and five epistatic interactions were detected for N209xMo17. For R18xMo17, 59 environmentally stable QTL and 17 epistatic interactions were detected. A joint analysis detected 14 stable QTL regardless of the genetic background. Between 57 and 83% of detected QTL were population specific, denoting medium-to-high genetic background effects. This percentage was dependent on the trait. A meta-analysis including our previous B73xMo17 results identified five relevant genomic regions deserving further characterization. In summary, our grain filling traits were dominated by small additive QTL with several epistatic and few environmental interactions and medium-to-high genetic background effects. This study demonstrates that the number of detected QTL and additive effects for different physiologically related grain filling traits need to be understood relative to the specific germplasm.
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spelling pubmed-41691532014-09-24 The Genetic Architecture of Maize (Zea mays L.) Kernel Weight Determination Prado, Santiago Alvarez López, César G. Senior, M. Lynn Borrás, Lucas G3 (Bethesda) Multiparental Populations Individual kernel weight is an important trait for maize yield determination. We have identified genomic regions controlling this trait by using the B73xMo17 population; however, the effect of genetic background on control of this complex trait and its physiological components is not yet known. The objective of this study was to understand how genetic background affected our previous results. Two nested stable recombinant inbred line populations (N209xMo17 and R18xMo17) were designed for this purpose. A total of 408 recombinant inbred lines were genotyped and phenotyped at two environments for kernel weight and five other traits related to kernel growth and development. All traits showed very high and significant (P < 0.001) phenotypic variability and medium-to-high heritability (0.60−0.90). When N209xMo17 and R18xMo17 were analyzed separately, a total of 23 environmentally stable quantitative trait loci (QTL) and five epistatic interactions were detected for N209xMo17. For R18xMo17, 59 environmentally stable QTL and 17 epistatic interactions were detected. A joint analysis detected 14 stable QTL regardless of the genetic background. Between 57 and 83% of detected QTL were population specific, denoting medium-to-high genetic background effects. This percentage was dependent on the trait. A meta-analysis including our previous B73xMo17 results identified five relevant genomic regions deserving further characterization. In summary, our grain filling traits were dominated by small additive QTL with several epistatic and few environmental interactions and medium-to-high genetic background effects. This study demonstrates that the number of detected QTL and additive effects for different physiologically related grain filling traits need to be understood relative to the specific germplasm. Genetics Society of America 2014-09-01 /pmc/articles/PMC4169153/ /pubmed/25237113 http://dx.doi.org/10.1534/g3.114.013243 Text en Copyright © 2014 Alvarez Prado et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Multiparental Populations
Prado, Santiago Alvarez
López, César G.
Senior, M. Lynn
Borrás, Lucas
The Genetic Architecture of Maize (Zea mays L.) Kernel Weight Determination
title The Genetic Architecture of Maize (Zea mays L.) Kernel Weight Determination
title_full The Genetic Architecture of Maize (Zea mays L.) Kernel Weight Determination
title_fullStr The Genetic Architecture of Maize (Zea mays L.) Kernel Weight Determination
title_full_unstemmed The Genetic Architecture of Maize (Zea mays L.) Kernel Weight Determination
title_short The Genetic Architecture of Maize (Zea mays L.) Kernel Weight Determination
title_sort genetic architecture of maize (zea mays l.) kernel weight determination
topic Multiparental Populations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4169153/
https://www.ncbi.nlm.nih.gov/pubmed/25237113
http://dx.doi.org/10.1534/g3.114.013243
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