Genetic analysis of nitrogen use efficiency in Arabidopsis thaliana

Abstract

Nitrogen (N) is an abundant element on earth. It is a crucial macro-element for plant growth and development. The lack of N causes reduced shoot growth and photosynthesis, early flowering, increased lateral/primary root ratio, and anthocyanin accumulation. Farmers apply N fertilizers to deal with N shortage, however, it causes adverse effects on the environment. Plants are adapted to live under low N supply and can still sustain growth and reproduction. N use efficiency (NUE), shoot dry matter per shoot N content, is used to measure the adaptation to the N depletion. This research investigates the candidate genes, which are responsible for sub-optimal N supply adaptation by using Arabidopsis thaliana, as a model organism. 354 diverse accessions of a natural population, called as HapMap population, are screened under sub-optimal (1 mM) and optimal (5 mM) N (in the form of ammonium/nitrate) conditions. NUE, related agronomic traits (NRATs), and phenotypic plasticity are used to map quantitative trait loci (QTLs). Genome-wide association study (GWAS) is used as a mapping technique and identifies 28 QTLs. 20 accessions from HapMap population are reassessed to confirm extreme accessions in the population. Besides, positive correlation is observed between NUE and relative growth rate and between shoot N concentration and water content. Br-0 and Col-0, two distinct accessions on NUE, are selected as parental accessions for further QTL investigation using bi-parental linkage mapping. QTL mapping reveals seven loci in Br-0 and Col-0 RIL population. One locus, associated with the phenotypic plasticity of water content in GWAS and the shoot fresh weight in the bi-parental linkage mapping, is found to be co-located in both mapping results. This research indicates that the natural genetic variation in Arabidopsis thaliana has a great potential for investigation of NUE improvement in plants. Both GWAS and bi-parental linkage mapping use the natural genetic variation to identify QTLs associated with NUE, NRATs, and phenotypic plasticity.