Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions
Abstract Meta-QTL (MQTL) analysis is a robust approach for genetic dissection of complex quantitative traits. Rice varieties adapted to non-flooded cultivation are highly desirable in breeding programs due to the water deficit global problem. In order to identify stable QTLs for major agronomic trai...
Guardado en:
Autores principales: | , , , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/3e3b9ef58e374fcda410a37c79b62762 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:doaj.org-article:3e3b9ef58e374fcda410a37c79b62762 |
---|---|
record_format |
dspace |
spelling |
oai:doaj.org-article:3e3b9ef58e374fcda410a37c79b627622021-12-02T17:04:05ZMeta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions10.1038/s41598-021-86259-22045-2322https://doaj.org/article/3e3b9ef58e374fcda410a37c79b627622021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86259-2https://doaj.org/toc/2045-2322Abstract Meta-QTL (MQTL) analysis is a robust approach for genetic dissection of complex quantitative traits. Rice varieties adapted to non-flooded cultivation are highly desirable in breeding programs due to the water deficit global problem. In order to identify stable QTLs for major agronomic traits under water deficit conditions, we performed a comprehensive MQTL analysis on 563 QTLs from 67 rice populations published from 2001 to 2019. Yield and yield-related traits including grain weight, heading date, plant height, tiller number as well as root architecture-related traits including root dry weight, root length, root number, root thickness, the ratio of deep rooting and plant water content under water deficit condition were investigated. A total of 61 stable MQTLs over different genetic backgrounds and environments were identified. The average confidence interval of MQTLs was considerably refined compared to the initial QTLs, resulted in the identification of some well-known functionally characterized genes and several putative novel CGs for investigated traits. Ortho-MQTL mining based on genomic collinearity between rice and maize allowed identification of five ortho-MQTLs between these two cereals. The results can help breeders to improve yield under water deficit conditions.Bahman KhahaniElahe TavakolVahid ShariatiLaura RossiniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021) |
institution |
DOAJ |
collection |
DOAJ |
language |
EN |
topic |
Medicine R Science Q |
spellingShingle |
Medicine R Science Q Bahman Khahani Elahe Tavakol Vahid Shariati Laura Rossini Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions |
description |
Abstract Meta-QTL (MQTL) analysis is a robust approach for genetic dissection of complex quantitative traits. Rice varieties adapted to non-flooded cultivation are highly desirable in breeding programs due to the water deficit global problem. In order to identify stable QTLs for major agronomic traits under water deficit conditions, we performed a comprehensive MQTL analysis on 563 QTLs from 67 rice populations published from 2001 to 2019. Yield and yield-related traits including grain weight, heading date, plant height, tiller number as well as root architecture-related traits including root dry weight, root length, root number, root thickness, the ratio of deep rooting and plant water content under water deficit condition were investigated. A total of 61 stable MQTLs over different genetic backgrounds and environments were identified. The average confidence interval of MQTLs was considerably refined compared to the initial QTLs, resulted in the identification of some well-known functionally characterized genes and several putative novel CGs for investigated traits. Ortho-MQTL mining based on genomic collinearity between rice and maize allowed identification of five ortho-MQTLs between these two cereals. The results can help breeders to improve yield under water deficit conditions. |
format |
article |
author |
Bahman Khahani Elahe Tavakol Vahid Shariati Laura Rossini |
author_facet |
Bahman Khahani Elahe Tavakol Vahid Shariati Laura Rossini |
author_sort |
Bahman Khahani |
title |
Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions |
title_short |
Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions |
title_full |
Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions |
title_fullStr |
Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions |
title_full_unstemmed |
Meta-QTL and ortho-MQTL analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions |
title_sort |
meta-qtl and ortho-mqtl analyses identified genomic regions controlling rice yield, yield-related traits and root architecture under water deficit conditions |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/3e3b9ef58e374fcda410a37c79b62762 |
work_keys_str_mv |
AT bahmankhahani metaqtlandorthomqtlanalysesidentifiedgenomicregionscontrollingriceyieldyieldrelatedtraitsandrootarchitectureunderwaterdeficitconditions AT elahetavakol metaqtlandorthomqtlanalysesidentifiedgenomicregionscontrollingriceyieldyieldrelatedtraitsandrootarchitectureunderwaterdeficitconditions AT vahidshariati metaqtlandorthomqtlanalysesidentifiedgenomicregionscontrollingriceyieldyieldrelatedtraitsandrootarchitectureunderwaterdeficitconditions AT laurarossini metaqtlandorthomqtlanalysesidentifiedgenomicregionscontrollingriceyieldyieldrelatedtraitsandrootarchitectureunderwaterdeficitconditions |
_version_ |
1718381843024183296 |