QTL Modelling: An Adaptation Option in Spring Wheat for Drought Stress

Abstract

A project was executed to study genotype to phenotype relationships through QTL analysis in a recombinant inbred population of 77 lines and its integration in crop simulation modeling. RILs were generated from a cross between wheat cultivar Opata and SH-349. At two leaves stage drought was imposed using gravimetric method for drought maintenance at 40 % of field capacity and control was maintained at 100 % field capacity. At three phenological stages viz. jointing, flag leaf and anthesis; photosynthetic rate, stomatal conductance, transpiration rate, stomatal resistance were determined and chlorophyll content was measured. The RILs under study exhibited high phenotypic variation under drought stress. The physiological and phenological data was used to parameterize and validate Agricultural Production Systems Simulator (APSIM); a crop growth and development modeling tool. It was noted that APSIM predicted the phenology of all the 77 RILs with R2 value ranging from 0.72 to 0.98. The same mapping population was used for QTL mapping using computational approaches with observed data and simulated data from crop simulation model APSIM. In linkage group 1 a single QTL controlling 13 physiological traits and another QTL controlling a single trait for phenology was found. In linkage group 2 one QTL controlling 7 phenological traits was mapped. The QTLs which were mapped with real data were the same as with simulated data. This indicated that the simulated data with crop models under different environmental scenarios could be efficiently used for QTL mapping reducing the environmental contribution in G x E complex and suggesting the QTLs with more precision. Photosynthetic attributes of these RILs under drought stress at different phenological stages suggests complex physiological aspects critical for coping moisture stress and provides a strong basis for their utilization in wheat cultivar improvement for drought stress adaptation under changing climatic scenarios.