Dataset Information

Identification and application of markers closely linked to the restorer gene (Rfm) in rapeseed (Brassica napus L.).

ABSTRACT: The Mutsu-Isuzu cytoplasmic male sterility (MI CMS) system is one of the three-line hybrid systems used in China. As we know, the hybrid system is tightly associated with the yield variation in F1 heterosis, while the restorer gene for the MI CMS (Rfm) has not been finely mapped for further application in marker-assisted selection (MAS). In this study, the sets of near-isogenic lines (NILs) of Rfm in two different genetic backgrounds were hybridized with the genome-wide 60 K single-nucleotide polymorphism (SNP) chip of Brassica for screening the possible associated genomic region of Rfm. Through screening genotypes with SNP loci and sequencing the candidate loci, one 2.5 Mb physical region (covering three scaffolds) on chrA09 was identified as the candidate for the Rfm region. Then, the SSR markers for the target scaffolds were used to detect the recombination in an F2 population and narrowed the Rfm gene within the genetic distance of 0.52 cM, equivalent to a 350 kb physical segment. Moreover, the markers were tested to improve new elite restoration lines and to assess the percentage of hybrid seeds. Our results could potentially accelerate the map-based cloning of the Rfm gene to benefit rapeseed breeding.

SUBMITTER: Long W

PROVIDER: S-EPMC6711744 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Identification and application of markers closely linked to the restorer gene (Rfm) in rapeseed (Brassica napus L.).

Long Weihua W Hu Maolong M Gao Jianqin J Sun Lijie L Zhang Jiefu J Pu Huiming H

Breeding science 20190518 2

The Mutsu-Isuzu cytoplasmic male sterility (MI CMS) system is one of the three-line hybrid systems used in China. As we know, the hybrid system is tightly associated with the yield variation in F1 heterosis, while the restorer gene for the MI CMS (Rfm) has not been finely mapped for further application in marker-assisted selection (MAS). In this study, the sets of near-isogenic lines (NILs) of Rfm in two different genetic backgrounds were hybridized with the genome-wide ...[more]

PMID: 31481841

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Project description:Boron (B) is an essential micronutrient for seed plants. Information on B-efficiency mechanisms and B-efficient crop and model plant genotypes is very scarce. Studies evaluating the basis and consequences of B-deficiency and B-efficiency are limited by the facts that B occurs as a trace contaminant essentially everywhere, its bioavailability is difficult to control and soil-based B-deficiency growth systems allowing a high-throughput screening of plant populations have hitherto been lacking. The crop plant Brassica napus shows a very high sensitivity toward B-deficient conditions. To reduce B-deficiency-caused yield losses in a sustainable manner, the identification of B-efficient B. napus genotypes is indispensable. We developed a soil substrate-based cultivation system which is suitable to study plant growth in automated high-throughput phenotyping facilities under defined and repeatable soil B conditions. In a comprehensive screening, using this system with soil B concentrations below 0.1 mg B (kg soil)-1, we identified three highly B-deficiency tolerant B. napus cultivars (CR2267, CR2280, and CR2285) among a genetically diverse collection comprising 590 accessions from all over the world. The B-efficiency classification of cultivars was based on a detailed assessment of various physical and high-throughput imaging-based shoot and root growth parameters in soil substrate or in in vitro conditions, respectively. We identified cultivar-specific patterns of B-deficiency-responsive growth dynamics. Elemental analysis revealed striking differences only in B contents between contrasting genotypes when grown under B-deficient but not under standard conditions. Results indicate that B-deficiency tolerant cultivars can grow with a very limited amount of B which is clearly below previously described critical B-tissue concentration values. These results suggest a higher B utilization efficiency of CR2267, CR2280, and CR2285 which would represent a unique trait among so far identified B-efficient B. napus cultivars which are characterized by a higher B-uptake capacity. Testing various other nutrient deficiency treatments, we demonstrated that the tolerance is specific for B-deficient conditions and is not conferred by a general growth vigor at the seedling stage. The identified B-deficiency tolerant cultivars will serve as genetic and physiological "tools" to further understand the mechanisms regulating the B nutritional status in rapeseed and to develop B-efficient elite genotypes.

Project description:BackgroundStudying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY.ResultsTo achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a period of 2 years (2019-2021). Root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2) were used to evaluate the performance of the algorithms. The best performance with all fifteen measured traits as inputs was achieved by the Nu-support vector regression algorithm with quadratic polynomial kernel function (R2 = 0.860, RMSE = 0.266, MAE = 0.210). The multilayer perceptron neural network algorithm with identity activation function (MLPNN-Identity) using three traits obtained from stepwise and backward selection methods appeared to be the most efficient combination of algorithms and feature selection methods (R2 = 0.843, RMSE = 0.283, MAE = 0.224). Feature selection suggested that the set of pods per plant and days to physiological maturity along with plant height or first pod height from the ground are the most influential traits in predicting rapeseed SY.ConclusionThe results of this study showed that MLPNN-Identity along with stepwise and backward selection methods can provide a robust combination to accurately predict the SY using fewer traits and therefore help optimize and accelerate SY breeding programs of rapeseed.

Dataset Information

Identification and application of markers closely linked to the restorer gene (Rfm) in rapeseed (Brassica napus L.).

Publications

Identification and application of markers closely linked to the restorer gene (<i>Rfm</i>) in rapeseed (<i>Brassica napus</i> L.).

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