Genotype–environment interactions affecting preflowering physiological and morphological traits ofBrassica rapagrown in two watering regimes

Abstract

Plant growth and productivity are greatly affected by drought, which is likely to become more threatening with the predicted global temperature increase. Understanding the genetic architecture of complex quantitative traits and their interaction with water availability may lead to improved crop adaptation to a wide range of environments. Here, the genetic basis of 20 physiological and morphological traits is explored by describing plant performance and growth in a Brassica rapa recombinant inbred line (RIL) population grown on a sandy substrate supplemented with nutrient solution, under control and drought conditions. Altogether, 54 quantitative trait loci (QTL) were identified, of which many colocated in 11 QTL clusters. Seventeen QTL showed significant QTL–environment interaction (Q×E), indicating genetic variation for phenotypic plasticity. Of the measured traits, only hypocotyl length did not show significant genotype–environment interaction (G×E) in both environments in all experiments. Correlation analysis showed that, in the control environment, stomatal conductance was positively correlated with total leaf dry weight (DW) and aboveground DW, whereas in the drought environment, stomatal conductance showed a significant negative correlation with total leaf DW and aboveground DW. This correlation was explained by antagonistic fitness effects in the drought environment, controlled by a QTL cluster on chromosome A7. These results demonstrate that Q×E is an important component of the genetic variance and can play a great role in improving drought tolerance in future breeding programmes.

Use of genotype‐environment interactions to elucidate the pattern of maize root plasticity to nitrogen deficiency

“…RDW2, RDW3, and RDW5 can used to improve root size during maker‐assisted breeding under low nitrogen condition. Plant can maintain trade off at the phenotypic level by antagonistic pleiotropy in different environment (Anderson et al ; Des Maraiset al ; El‐Soda et al ), these two QTLs (TRL4 and PRL4) with antagonistic pleiotropic effect can be helpful to breed new maize variety with high adaptability to different nitrogen levels.…”

Comparative QTL analysis for yield components and morphological traits in maize (<i>Zea mays</i> L.) under water-stressed and well-watered conditions

“…However, a limited number of QTLs for EHPH, GW, and KR have been identified under different levels of water availability (Chang et al 2017, Dong et al 2015, Ku et al 2015, and the genetic mechanisms underlying these three traits remains poorly understood, thus warranting in-depth investigations. Furthermore, a better understanding of the genotype-by-environment (G × E) interaction will provide a foundation for the genetic improvement and optimization of genotypes across different environments (EI-Soda et al 2014).…”

Association mapping reveals the genetic architecture of tomato response to water deficit: focus on major fruit quality traits

“…The first one consists of computing the effects of a given QTL across the environmental conditions using multivariate QTL mapping models (van Eeuwijk et al , 2010; El-Soda et al , 2014 b ). The second one uses the construction of composite variables measuring phenotypic plasticity and univariate mapping models (El-Soda et al , 2014 a ). With both approaches, QTLs can be classified according to the prevalence of their effect under the different conditions.…”