Limits...
Understanding rice adaptation to varying agro-ecosystems: trait interactions and quantitative trait loci.

Dixit S, Grondin A, Lee CR, Henry A, Olds TM, Kumar A - BMC Genet. (2015)

Bottom Line: Rice requires better adaptation across a wide range of environments and cultivation practices to adjust to climate change.This study provides a wider picture of the genetics and physiology of adaptation of rice to wide range of environments.With a complete understanding of the processes and relationships between traits and trait groups, marker-assisted breeding can be used more efficiently to develop plant types that can combine all or most of the beneficial traits and show high stability across environments, ecosystems, and cultivation practices.

View Article: PubMed Central - PubMed

Affiliation: International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines. s.dixit@irri.org.

ABSTRACT

Background: Interaction and genetic control for traits influencing the adaptation of the rice crop to varying environments was studied in a mapping population derived from parents (Moroberekan and Swarna) contrasting for drought tolerance, yield potential, lodging resistance, and adaptation to dry direct seeding. A BC2F3-derived mapping population for traits related to these four trait groups was phenotyped to understand the interactions among traits and to map and align QTLs using composite interval mapping (CIM). The study also aimed to identify QTLs for the four trait groups as composite traits using multivariate least square interval mapping (MLSIM) to further understand the genetic control of these traits.

Results: Significant correlations between drought- and yield-related traits at seedling and reproductive stages respectively with traits for adaptation to dry direct-seeded conditions were observed. CIM and MLSIM methods were applied to identify QTLs for univariate and composite traits. QTL clusters showing alignment of QTLs for several traits within and across trait groups were detected at chromosomes 3, 4, and 7 through CIM. The largest number of QTLs related to traits belonging to all four trait groups were identified on chromosome 3 close to the qDTY 3.2 locus. These included QTLs for traits such as bleeding rate, shoot biomass, stem strength, and spikelet fertility. Multivariate QTLs were identified at loci supported by univariate QTLs such as on chromosomes 3 and 4 as well as at distinctly different loci on chromosome 8 which were undetected through CIM.

Conclusion: Rice requires better adaptation across a wide range of environments and cultivation practices to adjust to climate change. Understanding the genetics and trade-offs related to each of these environments and cultivation practices thus becomes highly important to develop varieties with stability of yield across them. This study provides a wider picture of the genetics and physiology of adaptation of rice to wide range of environments. With a complete understanding of the processes and relationships between traits and trait groups, marker-assisted breeding can be used more efficiently to develop plant types that can combine all or most of the beneficial traits and show high stability across environments, ecosystems, and cultivation practices.

No MeSH data available.


Related in: MedlinePlus

Circle plot showing the location of QTLs affecting single and composite traits identified through CIM and MLSIM analysis respectively. Colored bars showing the twelve rice chromosomes form the outermost circle, marker names (starting with the term ‘id/wd/ud’ followed by the number) and positions (cM) are presented along the chromosomes. Colored concentric circles sequentially from the center represent the QTLs for drought tolerance (CIM), QTLs for drought tolerance (MLSIM), QTLs for yield potential (CIM), QTLs for yield potential (MLSIM), QTLs for lodging resistance (CIM), QTLs for lodging resistance (MLSIM), QTLs for adaptation to direct seeding (CIM) and QTLs for adaptation to direct seeding (MLSIM). Horizontal bars within the rings represent the QTL span while vertical lines represent the peak position. The intensity of color of QTL bars shows the amount of variance explained by the QTL with color intensity increasing with QTL effect
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Fig5: Circle plot showing the location of QTLs affecting single and composite traits identified through CIM and MLSIM analysis respectively. Colored bars showing the twelve rice chromosomes form the outermost circle, marker names (starting with the term ‘id/wd/ud’ followed by the number) and positions (cM) are presented along the chromosomes. Colored concentric circles sequentially from the center represent the QTLs for drought tolerance (CIM), QTLs for drought tolerance (MLSIM), QTLs for yield potential (CIM), QTLs for yield potential (MLSIM), QTLs for lodging resistance (CIM), QTLs for lodging resistance (MLSIM), QTLs for adaptation to direct seeding (CIM) and QTLs for adaptation to direct seeding (MLSIM). Horizontal bars within the rings represent the QTL span while vertical lines represent the peak position. The intensity of color of QTL bars shows the amount of variance explained by the QTL with color intensity increasing with QTL effect

Mentions: A total of 49 QTLs were identified through CIM analysis for the four trait groups (Additional file 9). The QTLs were distributed across nearly all chromosomes with the highest densities observed on chromosomes 3, 4, and 7 (Fig. 5). In particular, QTLs for traits across all four trait groups were identified on chromosome 3 close to the qDTY3.2 region. For the drought tolerance trait group, QTLs were seen for traits related to drought and grain yield. However, higher numbers of QTLs were identified for drought-related traits as compared to yield-related traits. QTL clusters were observed at chromosome 3 at the qDTY3.2 region, including a QTL for grain yield under drought. However, the yield-enhancing allele in this case came from the susceptible parent. While this QTL is known to affect the flowering time along with its effect on grain yield under drought, the staggered seeding of the progeny from different maturity groups may explain Swarna’s contribution of the yield-enhancing allele at this locus. The advantage of having the Moroberekan allele at this locus can be seen through its effect on several other drought-related traits affecting plant function (Additional file 9). Apart from chromosome 3, another QTL cluster was observed at chromosome 7 where root mass, sap from the root system, and canopy temperature-related QTLs were identified (Additional file 9, Fig. 5). Other QTLs on chromosome 1, 4, and 9 were identified for root mass density, nodal root number, and panicle length at harvest. Similar to drought tolerance, QTLs for traits related to yield potential were contributed by both parents. However, QTLs for grain yield per se were not identified. The high-yielding parent Swarna contributed to QTLs for number of panicles and tillers at harvest on chromosomes 3 and 4, respectively. It also contributed to two QTLs on chromosomes 3 and 12 for plant height. The donor parent Moroberekan also contributed to several QTLs related to yield potential, including QTLs for shoot biomass, harvest index, and panicle length. Two major QTL clusters were identified on chromosomes 3 and 4 for traits related to lodging resistance. QTLs for the two major lodging-related traits – stem strength and diameter – were also located in these QTL clusters. The QTLs on chromosome 3 were contributed by Swarna while those on chromosome 4 were contributed by Moroberekan. Both QTL clusters showed consistent effects on lodging- related traits under upland direct-seeded and lowland transplanted conditions. QTLs were also identified for traits related to adaptation to direct seeding. In particular, QTLs for seedling emergence contributed by Moroberekan and Swarna were observed on chromosomes 1 and 3, respectively. Apart from this, some of the yield-related QTLs identified under transplanted lowland conditions also showed an effect under direct-seeded conditions. These included QTLs related to flowering time, plant height, and panicle length. A QTL for grain weight was also identified on chromosome 10.Fig. 5


Understanding rice adaptation to varying agro-ecosystems: trait interactions and quantitative trait loci.

Dixit S, Grondin A, Lee CR, Henry A, Olds TM, Kumar A - BMC Genet. (2015)

Circle plot showing the location of QTLs affecting single and composite traits identified through CIM and MLSIM analysis respectively. Colored bars showing the twelve rice chromosomes form the outermost circle, marker names (starting with the term ‘id/wd/ud’ followed by the number) and positions (cM) are presented along the chromosomes. Colored concentric circles sequentially from the center represent the QTLs for drought tolerance (CIM), QTLs for drought tolerance (MLSIM), QTLs for yield potential (CIM), QTLs for yield potential (MLSIM), QTLs for lodging resistance (CIM), QTLs for lodging resistance (MLSIM), QTLs for adaptation to direct seeding (CIM) and QTLs for adaptation to direct seeding (MLSIM). Horizontal bars within the rings represent the QTL span while vertical lines represent the peak position. The intensity of color of QTL bars shows the amount of variance explained by the QTL with color intensity increasing with QTL effect
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526302&req=5

Fig5: Circle plot showing the location of QTLs affecting single and composite traits identified through CIM and MLSIM analysis respectively. Colored bars showing the twelve rice chromosomes form the outermost circle, marker names (starting with the term ‘id/wd/ud’ followed by the number) and positions (cM) are presented along the chromosomes. Colored concentric circles sequentially from the center represent the QTLs for drought tolerance (CIM), QTLs for drought tolerance (MLSIM), QTLs for yield potential (CIM), QTLs for yield potential (MLSIM), QTLs for lodging resistance (CIM), QTLs for lodging resistance (MLSIM), QTLs for adaptation to direct seeding (CIM) and QTLs for adaptation to direct seeding (MLSIM). Horizontal bars within the rings represent the QTL span while vertical lines represent the peak position. The intensity of color of QTL bars shows the amount of variance explained by the QTL with color intensity increasing with QTL effect
Mentions: A total of 49 QTLs were identified through CIM analysis for the four trait groups (Additional file 9). The QTLs were distributed across nearly all chromosomes with the highest densities observed on chromosomes 3, 4, and 7 (Fig. 5). In particular, QTLs for traits across all four trait groups were identified on chromosome 3 close to the qDTY3.2 region. For the drought tolerance trait group, QTLs were seen for traits related to drought and grain yield. However, higher numbers of QTLs were identified for drought-related traits as compared to yield-related traits. QTL clusters were observed at chromosome 3 at the qDTY3.2 region, including a QTL for grain yield under drought. However, the yield-enhancing allele in this case came from the susceptible parent. While this QTL is known to affect the flowering time along with its effect on grain yield under drought, the staggered seeding of the progeny from different maturity groups may explain Swarna’s contribution of the yield-enhancing allele at this locus. The advantage of having the Moroberekan allele at this locus can be seen through its effect on several other drought-related traits affecting plant function (Additional file 9). Apart from chromosome 3, another QTL cluster was observed at chromosome 7 where root mass, sap from the root system, and canopy temperature-related QTLs were identified (Additional file 9, Fig. 5). Other QTLs on chromosome 1, 4, and 9 were identified for root mass density, nodal root number, and panicle length at harvest. Similar to drought tolerance, QTLs for traits related to yield potential were contributed by both parents. However, QTLs for grain yield per se were not identified. The high-yielding parent Swarna contributed to QTLs for number of panicles and tillers at harvest on chromosomes 3 and 4, respectively. It also contributed to two QTLs on chromosomes 3 and 12 for plant height. The donor parent Moroberekan also contributed to several QTLs related to yield potential, including QTLs for shoot biomass, harvest index, and panicle length. Two major QTL clusters were identified on chromosomes 3 and 4 for traits related to lodging resistance. QTLs for the two major lodging-related traits – stem strength and diameter – were also located in these QTL clusters. The QTLs on chromosome 3 were contributed by Swarna while those on chromosome 4 were contributed by Moroberekan. Both QTL clusters showed consistent effects on lodging- related traits under upland direct-seeded and lowland transplanted conditions. QTLs were also identified for traits related to adaptation to direct seeding. In particular, QTLs for seedling emergence contributed by Moroberekan and Swarna were observed on chromosomes 1 and 3, respectively. Apart from this, some of the yield-related QTLs identified under transplanted lowland conditions also showed an effect under direct-seeded conditions. These included QTLs related to flowering time, plant height, and panicle length. A QTL for grain weight was also identified on chromosome 10.Fig. 5

Bottom Line: Rice requires better adaptation across a wide range of environments and cultivation practices to adjust to climate change.This study provides a wider picture of the genetics and physiology of adaptation of rice to wide range of environments.With a complete understanding of the processes and relationships between traits and trait groups, marker-assisted breeding can be used more efficiently to develop plant types that can combine all or most of the beneficial traits and show high stability across environments, ecosystems, and cultivation practices.

View Article: PubMed Central - PubMed

Affiliation: International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines. s.dixit@irri.org.

ABSTRACT

Background: Interaction and genetic control for traits influencing the adaptation of the rice crop to varying environments was studied in a mapping population derived from parents (Moroberekan and Swarna) contrasting for drought tolerance, yield potential, lodging resistance, and adaptation to dry direct seeding. A BC2F3-derived mapping population for traits related to these four trait groups was phenotyped to understand the interactions among traits and to map and align QTLs using composite interval mapping (CIM). The study also aimed to identify QTLs for the four trait groups as composite traits using multivariate least square interval mapping (MLSIM) to further understand the genetic control of these traits.

Results: Significant correlations between drought- and yield-related traits at seedling and reproductive stages respectively with traits for adaptation to dry direct-seeded conditions were observed. CIM and MLSIM methods were applied to identify QTLs for univariate and composite traits. QTL clusters showing alignment of QTLs for several traits within and across trait groups were detected at chromosomes 3, 4, and 7 through CIM. The largest number of QTLs related to traits belonging to all four trait groups were identified on chromosome 3 close to the qDTY 3.2 locus. These included QTLs for traits such as bleeding rate, shoot biomass, stem strength, and spikelet fertility. Multivariate QTLs were identified at loci supported by univariate QTLs such as on chromosomes 3 and 4 as well as at distinctly different loci on chromosome 8 which were undetected through CIM.

Conclusion: Rice requires better adaptation across a wide range of environments and cultivation practices to adjust to climate change. Understanding the genetics and trade-offs related to each of these environments and cultivation practices thus becomes highly important to develop varieties with stability of yield across them. This study provides a wider picture of the genetics and physiology of adaptation of rice to wide range of environments. With a complete understanding of the processes and relationships between traits and trait groups, marker-assisted breeding can be used more efficiently to develop plant types that can combine all or most of the beneficial traits and show high stability across environments, ecosystems, and cultivation practices.

No MeSH data available.


Related in: MedlinePlus