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

Authors

Shalabh Dixit, International Rice Research Institute
Alexandre Grondin, University of Nebraska-Lincoln
Cheng-Ruei Lee, Gregor Mendel Institute of Molecular Plant Biology
Amelia Henry, International Rice Research Institute
Thomas-Mitchell Olds, Duke University
Arvind Kumar, International Rice Research InstituteFollow

Document Type

Article

Date of this Version

2015

Citation

Dixit et al. BMC Genetics (2015) 16:86 DOI 10.1186/s12863-015-0249-1

Comments

© 2015 Dixit et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.

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.