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. 2015 Aug 5:16:86.
doi: 10.1186/s12863-015-0249-1.

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

Shalabh Dixit  1 , Alexandre Grondin  2   3 , Cheng-Ruei Lee  4   5 , Amelia Henry  6 , Thomas-Mitchell Olds  7 , Arvind Kumar  8
Affiliations

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

Shalabh Dixit et al. BMC Genet. .

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.

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Figures

Fig. 1
Fig. 1
Morphological differences between rice cultivars Swarna (S) and Moroberekan (M). a Plant type, tiller and panicle number; b Stem diameter (first to fourth internode from the bottom); c Root architecture at seedling stage; d Flag leaf length and width. e Panicle architecture and grains per panicle; f Grain type and size
Fig. 2
Fig. 2
Percentage of traits showing significant variation in ANOVA across the four trait groups
Fig. 3
Fig. 3
Multi-dimensional scaling (MDS) analysis conducted using the correlation matrix of 66 traits belonging to the four different trait groups
Fig. 4
Fig. 4
PCA on trait correlations in parents and progeny for the four trait groups. Gray dots represent the genetic means of each progeny; Red and green circles represent means for Swarna and Moroberekan, respectively. Crosses (color coded as presented in the legend) indicate the loadings for each trait along the first two components, which comprise 22.7 % of the total genetic variation for all traits
Fig. 5
Fig. 5
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
Fig. 6
Fig. 6
Heat maps showing the relative contribution of univariate traits to major MVQTLs identified for the four composite traits
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