Genetics of robustness under nitrogen‐ and water‐deficient conditions in field‐grown lettuce. Issue 3 (26th April 2021) - Macias‐González, M.; Truco, M. J.; Smith, R.; Cahn, M.; Simko, I.; Hayes, R.; Michelmore, R. W.
Abstract: Robustness is the measure of the ability to perform well across multiple environments. The genetics of robustness in lettuce ( Lactuca sativa L.) under changing nitrogen (N) application was studied by two parameters obtained from linear regression analysis. Robustness in changing water application was assessed by two parameters: relative efficiency component (REC) and susceptibility component (SC). The efficiency, sensitivity, and the effect of reduced irrigation on these parameters was assessed for 50 lines of lettuce in 2011. Efficiency, sensitivity, and plant N uptake and utilization were important traits in robustness under changing N application. Eight cultivars were re‐evaluated in 2012, 2014, and 2016 under high‐ and low‐N treatments in order to estimate the repeatability of efficiency and sensitivity. Repeatability was greater for efficiency than sensitivity. In addition, the genetics of efficiency, sensitivity, REC, SC, nutrient concentration, nutrient content, dry‐to‐fresh biomass percentage, bolting, and shoot biomass production were studied by quantitative trait locus (QTL) analysis of a recombinant inbred line (RIL) population derived from the cultivars Grand Rapids and Iceberg. The population was phenotyped in three experiments and genotyped by sequencing. Major clusters of QTL for these traits were identified in linkage groups (LGs) 3, 7, and 9. Minor‐effect QTL for sensitivity were identified in LGs 3 and 4. Intermediate‐effect QTL for SC were identified in LGs 4 and 7. These results provide evidence that the components of robustness under changing N (efficiency and sensitivity) and water (REC and SC) availability are genetically independent and can be improved through breeding. Core Ideas: Supra‐optimal use of N fertilizer and water can pollute the environment. A regression analysis of yield and N content was used to identify QTL for robustness. A principal component analysis was used to assess robustness under limited water conditions. Significant QTL were identified that determined robustness under low N and low water. Improving efficiency can be the most efficient way to improve robustness under low N.