Discovery of Genomic Regions and Candidate Genes Controlling Root Development Using a Recombinant Inbred Line Population in Rapeseed (Brassica napus L.)

Abstract

Marker-assisted selection enables breeders to quickly select excellent root architectural variations, which play an essential role in plant productivity. Here, ten root-related and shoot biomass traits of a new F6 recombinant inbred line (RIL) population were investigated under hydroponics and resulted in high heritabilities from 0.61 to 0.83. A high-density linkage map of the RIL population was constructed using a Brassica napus 50k Illumina single nucleotide polymorphism (SNP) array. A total of 86 quantitative trait loci (QTLs) explaining 4.16–14.1% of the phenotypic variances were detected and integrated into eight stable QTL clusters, which were repeatedly detected in different experiments. The codominant markers were developed to be tightly linked with three major QTL clusters, qcA09-2, qcC08-2, and qcC08-3, which controlled both root-related and shoot biomass traits and had phenotypic contributions greater than 10%. Among these, qcA09-2, renamed RT.A09, was further fine-mapped to a 129-kb interval with 19 annotated genes in the B. napus reference genome. By integrating the results of real-time PCR and comparative sequencing, five genes with expression differences and/or amino acid differences were identified as important candidate genes for RT.A09. Our findings laid the foundation for revealing the molecular mechanism of root development and developed valuable markers for root genetic improvement in rapeseed.

Quantitative trait loci mapping reveals important genomic regions controlling root architecture and shoot biomass under nitrogen, phosphorus, and potassium stress in rapeseed (Brassica napus L.)

“…Significant and strong correlations were observed among the investigated traits under LN/LP/LK conditions ( Figure 2A ). Consistent with the correlations under the control condition ( Kuang et al, 2022 ), RFW revealed significant and positive correlations with the other six BT, SFW ( r = 0.66, 0.62, and 0.65, P < 0.01), SDW ( r = 0.64, 0.50, 0.55, P < 0.01), RDW ( r = 0.74, 0.76, 0.45, P < 0.01), TDW ( r = 0.67, 0.59, 0.68, P < 0.01), and TFW ( r = 0.75, 0.78, 0.75, P < 0.01), as well as the four RMT, TRL ( r = 0.60, 0.79 and 0.65, P < 0.01), TSA ( r = 0.73, 0.87, 0.72, P < 0.01), TRV ( r = 0.72, 0.79, 0.68., P < 0.01), TNR ( r = 0.37, 0.17, 0.35, P < 0.01), under LN/LP/LK conditions, respectively. SFW also displayed positive and significant correlation with the four RMT, TRL ( r = 0.30, 0.51, 0.41, P < 0.01), TSA ( r = 0.44, 0.58, 0.49, P < 0.01), TRV ( r = 0.50, 0.53, 0.48, P < 0.01), TNR ( r = 0.28, 0.13, 0.33, P < 0.01).…”

“…The F 1 generation plants were self-pollinated to obtain F 2 generation seeds, and the F 2 generation was continuously selfed by the single seed descent method. F 2:6 seeds were obtained as the recombined inbred line population, and 236 lines were randomly selected from the (F 2:6 ) generation and used for this experiment ( Kuang et al, 2022 ). The two parents were used for the pilot experiment first to find the suitable concentration of nitrogen, phosphorus and potassium, the results of which were used for low-NPK treatment for screening the RIL population.…”

“…For phenotypic and QTL analysis, all 12 investigated traits were represented by the best linear unbiased estimation (BLUE) value of three plants per genotype under NPK-stress only since the results of QTL mapping under normal conditions were shown in the previous study ( Kuang et al, 2022 ). The “ PerformanceAnalytics ” package in R software was used to calculate Pearson correlation at a significance level of P < 0.05.…”

“…For all traits under stress treatments, analysis of variance (ANOVA) and broad-sense heritability ( h 2 ) were performed with QRL IciMapping 4.1 1 with the ANOVA function. A total of three phenotypic datasets (LN-BLUE, LP-BLUE, and LK-BLUE) of each trial were used to map QTLs based on the construction of a genetic linkage map of the RIL population using a Brassica 50K SNP Chip ( Kuang et al, 2022 ). QTL mapping was performed with the help of the software Windows QTL Cartographer 2.5, using the composite interval mapping (CIM) approach ( Wang et al, 2012b ).…”

Deciphering the Genetic Basis of Root and Biomass Traits in Rapeseed (Brassica napus L.) through the Integration of GWAS and RNA-Seq under Nitrogen Stress

“…Correlations between root and biomass traits demonstrate the balance of root and shoot organs and resource partitioning between above- and below-ground plant tissues [ 44 ]. In the present study, a high correlation between shoot and root weights observed might be due to the supply of nutrients from roots to the shoot parts, as reported in rapeseed [ 25 , 26 , 30 , 43 , 45 ]. Furthermore, the strong correlation between root and biomass traits aids in successful soil exploration by intercepting nutrients and communicating stress signals [ 46 ].…”