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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

Multi-dimensional scaling (MDS) analysis conducted using the correlation matrix of 66 traits belonging to the four different trait groups
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Fig3: Multi-dimensional scaling (MDS) analysis conducted using the correlation matrix of 66 traits belonging to the four different trait groups

Mentions: Correlations among the traits belonging to the four different trait groups are presented in Additional file 6. The analysis showed higher levels of correlations within trait groups as compared to those across trait groups. In general, higher levels of correlation were observed between traits related to yield potential, lodging resistance, and adaptation to direct seeding while drought tolerance-related traits showed lower correlation with the other three trait groups. The multidimensional scaling (MDS) analysis divided the traits into three distinct clusters based on the correlations between them (Fig. 3). Cluster 1 specifically constituted of drought-related traits, cluster 2 contained most of the lodging-related traits and some traits related to yield potential and adaptation to direct seeding, and cluster 3 contained correlated traits across all four trait groups. Most of the traits related to adaptation to direct seeding belonged to this cluster. Interestingly, some of the root-related traits measured under drought stress grouped with cluster 3, showing the importance of these traits under direct-seeded conditions. Principal component analysis (PCA) was conducted to further examine the relationships among traits. The first two components together explained 22.7 % of the genetic trait variation, showing a mild level of genetic correlation among the traits (Fig. 4). Components 1–8 together explained 50.1 % of the variation while components 1–20 explained 75.5 % of the variation (Additional file 7). This can be attributed to the large number and diversity of traits. The PCA may explain higher percentage variations if traits belonging to each trait group are analyzed separately. However, analyzing them together allowed us to view the pattern of arrangement for all four trait groups simultaneously on PC1 and PC2. The PCA further resolved the trait groups along the two axes, and a clearer grouping of traits within each trait group was observed. The progenies were distributed almost evenly across the four quadrants; however, a large difference in the positioning of parents Moroberekan and Swarna was observed, where Moroberekan was at the positive side of the two axes and Swarna was at the negative side. In order to further understand the effect of the individual traits on yield stability across lowland drought stress and non-stress and direct-seeded non-stress conditions, we calculated the percentage difference for each trait for 25 lines with highest mean yield and 25 with lowest mean yield across the three experiments (Additional file 8). Differences ranged from positive to negative in the trait groups except for traits related to yield potential where high-yielding lines had higher means for all traits. The analysis also showed the magnitude and direction of effect of different traits on yield stability across ecosystems. While the two groups of lines were highly contrasting for some drought-related traits such as bleeding rate, reduction of NDVI, and leaf: stem ratio, they showed very little difference for the other traits such as stem strength and stem diameter. However, a large proportion of traits across these trait groups showed intermediate level differences, indicating their importance in determining yield stability along with the traits that showed larger differences.Fig. 3


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)

Multi-dimensional scaling (MDS) analysis conducted using the correlation matrix of 66 traits belonging to the four different trait groups
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Multi-dimensional scaling (MDS) analysis conducted using the correlation matrix of 66 traits belonging to the four different trait groups
Mentions: Correlations among the traits belonging to the four different trait groups are presented in Additional file 6. The analysis showed higher levels of correlations within trait groups as compared to those across trait groups. In general, higher levels of correlation were observed between traits related to yield potential, lodging resistance, and adaptation to direct seeding while drought tolerance-related traits showed lower correlation with the other three trait groups. The multidimensional scaling (MDS) analysis divided the traits into three distinct clusters based on the correlations between them (Fig. 3). Cluster 1 specifically constituted of drought-related traits, cluster 2 contained most of the lodging-related traits and some traits related to yield potential and adaptation to direct seeding, and cluster 3 contained correlated traits across all four trait groups. Most of the traits related to adaptation to direct seeding belonged to this cluster. Interestingly, some of the root-related traits measured under drought stress grouped with cluster 3, showing the importance of these traits under direct-seeded conditions. Principal component analysis (PCA) was conducted to further examine the relationships among traits. The first two components together explained 22.7 % of the genetic trait variation, showing a mild level of genetic correlation among the traits (Fig. 4). Components 1–8 together explained 50.1 % of the variation while components 1–20 explained 75.5 % of the variation (Additional file 7). This can be attributed to the large number and diversity of traits. The PCA may explain higher percentage variations if traits belonging to each trait group are analyzed separately. However, analyzing them together allowed us to view the pattern of arrangement for all four trait groups simultaneously on PC1 and PC2. The PCA further resolved the trait groups along the two axes, and a clearer grouping of traits within each trait group was observed. The progenies were distributed almost evenly across the four quadrants; however, a large difference in the positioning of parents Moroberekan and Swarna was observed, where Moroberekan was at the positive side of the two axes and Swarna was at the negative side. In order to further understand the effect of the individual traits on yield stability across lowland drought stress and non-stress and direct-seeded non-stress conditions, we calculated the percentage difference for each trait for 25 lines with highest mean yield and 25 with lowest mean yield across the three experiments (Additional file 8). Differences ranged from positive to negative in the trait groups except for traits related to yield potential where high-yielding lines had higher means for all traits. The analysis also showed the magnitude and direction of effect of different traits on yield stability across ecosystems. While the two groups of lines were highly contrasting for some drought-related traits such as bleeding rate, reduction of NDVI, and leaf: stem ratio, they showed very little difference for the other traits such as stem strength and stem diameter. However, a large proportion of traits across these trait groups showed intermediate level differences, indicating their importance in determining yield stability along with the traits that showed larger differences.Fig. 3

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