Enhancing yield and oil content in oilseed rape hybrids: Insights from line × tester and SIIG approaches.

Background: The production of high-oil-yielding hybrid varieties is a primary objective in oilseed rape (Brassica napus L.) breeding programs. Biometric genetic experiments such as line × tester provide valuable insights into the genetic structure of traits associated with high oil yield. Methods: In this study, 21 winter hybrids of oilseed rape were evaluated, which were generated by crossing three restorers with seven CMS lines. The experiment was conducted using a line × tester experiment based on a completely randomized block design. Phenological, agronomic, yield, and oil yield components were assessed in this study. The ideal genotype selection index (SIIG) methodology was also employed to identify superior hybrids based on all studied traits simultaneously. Results: Significant differences were observed between the obtained hybrids and the check cultivars. Heritability analysis revealed that phenological traits were primarily controlled by additive effects, while agronomic and qualitative traits were mainly influenced by non-additive gene effects. Both broad-sense and narrow-sense heritability exhibited a wide range, underscoring the importance of genetic variance. Notably, the hybrids T1 × L5, T1 × L6, and T3 × L1 showed significant specific combining ability values of 394.74, 541.73, and 1236.79, respectively, making them the top specific combinations for increasing seed yield. Based on the SIIG index, hybrids T3 × L1, T1 × L5, T1 × L3, and T2 × L3 emerged as high-oil-yielding hybrids with desirable agronomic traits. Conclusions: The identified superior hybrids by line × tester and SIIG approaches hold promise for the development of high-yielding oilseed rape cultivars with desirable agronomic traits in oilseed rape breeding programs.

Dissection of quantitative trait nucleotides and candidate genes associated with agronomic and yield-related traits under drought stress in rapeseed varieties: integration of genome-wide association study and transcriptomic analysis.

Introduction: An important strategy to combat yield loss challenge is the development of varieties with increased tolerance to drought to maintain production. Improvement of crop yield under drought stress is critical to global food security. Methods: In this study, we performed multiomics analysis in a collection of 119 diverse rapeseed (Brassica napus L.) varieties to dissect the genetic control of agronomic traits in two watering regimes [well-watered (WW) and drought stress (DS)] for 3 years. In the DS treatment, irrigation continued till the 50% pod development stage, whereas in the WW condition, it was performed throughout the whole growing season. Results: The results of the genome-wide association study (GWAS) using 52,157 single-nucleotide polymorphisms (SNPs) revealed 1,281 SNPs associated with traits. Six stable SNPs showed sequence variation for flowering time between the two irrigation conditions across years. Three novel SNPs on chromosome C04 for plant weight were located within drought tolerance-related gene ABCG16, and their pleiotropically effects on seed weight per plant and seed yield were characterized. We identified the C02 peak as a novel signal for flowering time, harboring 52.77% of the associated SNPs. The 288-kbps LD decay distance analysis revealed 2,232 candidate genes (CGs) associated with traits. The CGs BIG1-D, CAND1, DRG3, PUP10, and PUP21 were involved in phytohormone signaling and pollen development with significant effects on seed number, seed weight, and grain yield in drought conditions. By integrating GWAS and RNA-seq, 215 promising CGs were associated with developmental process, reproductive processes, cell wall organization, and response to stress. GWAS and differentially expressed genes (DEGs) of leaf and seed in the yield contrasting accessions identified BIG1-D, CAND1, and DRG3 genes for yield variation. Discussion: The results of our study provide insights into the genetic control of drought tolerance and the improvement of marker-assisted selection (MAS) for breeding high-yield and drought-tolerant varieties.

BTEX in indoor air of barbershops and beauty salons: Characterization, source apportionment and health risk assessment.

BACKGROUND: Volatile organic compounds, mainly BTEX, are among the pollutants of concern in beauty salons and barbershops that threaten both staff personnel and clients' health. This study aimed to determine the concentration of BTEX in barbershops and beauty salons and assess the carcinogenic and non-carcinogenic risks based on the actual risk coefficients. Also, possible sources of BTEX were determined. METHOD: Samples were collected by passive sampling. Quantitative and qualitative measurements of BTEX compounds were performed using gas chromatography-mass spectrometry (GC-MASS). Subsequently, the health risks were assessed according to the US Environmental Protection Agency. SPSS24 software and positive matrix factorization (PMF) analysis were used for statistical analysis and source apportionment respectively. RESULTS: Toluene is the most abundant compound in beauty salons, with a maximum concentration of 219.4 (µg/m3) in beauty salons. Results indicated that the mean ELCR value estimated for benzene regarding female staff exposure (1.04 × 10-5) was higher than that for men (4.05 × 10-6). Also, ELCR values of ethylbenzene for staff exposure were 2.08 × 10-6 and 3.8 × 10-6 for men and women, respectively, and possess possible carcinogenesis risks. CONCLUSION: Use of solvents and cosmetic products, improper heating systems, and type of service are the sources that probably contribute to BTEX emissions in beauty salons. It is necessary to follow health guidelines and conduct continuous monitoring for their implementation, in addition to setting a mandated occupational regulation framework or air quality requirements, to improve the health conditions in beauty salons.

Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield.

BACKGROUND: Studying 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. RESULTS: To 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. CONCLUSION: The 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.

Phenotyping new rapeseed lines based on multiple traits: Application of GT and GYT biplot analyses.

The selection based on multiple traits enhances the crop cultivars merit to farmers. In this regard, 19 breeding lines as well as two commercial cultivars were studied using a randomized complete block design (RCBD) with three replications in three locations during the 2020-2021 growing season. In this study, to identify the association among different traits and to select the best rapeseed lines based on multiple traits, genotype × trait (GT) and genotype × yield × trait (GYT) biplot analyses were used. The results showed that using GYT biplot is more efficient than GT biplot. Based on the GYT biplot and superiority index (SI), the breeding lines G16 and G18 were considered as superior genotypes in combination with the agronomical traits, that is, 1000-seed weight, number of seeds per pod, number of pods per plant, number of lateral branches, plant height, and pod length with seed yield, which represents a genetic gain in rapeseed breeding program. Based on seed yield combination with phenological traits (early maturity), the breeding line G15 was selected as the best one. Moreover, the line G2 was defined as the superior one in combination of seed yield with pod length. The results indicated that there is a potential for simultaneous genetic improvement of the characteristics (i.e., plant height, number of seeds per pod, early maturity) in rapeseed. Generally, the graphical method of the GYT biplot represented an efficient and practical new way to recognize superior genotypes based on multiple traits in rapeseed breeding programs.

Effects of Heat Shock and 2,4-D Treatment on Morphological and Physiological Characteristics of Microspores and Microspore-Derived Doubled Haploid Plants in Brassica napus L.

BACKGROUND: Stresses such as heat shock, starvation, or osmotic is essential to lead isolated microspores towards embryogenesis. Despite the effectiveness of stresses in embryogenesis, they exert adverse effects on metabolism and growth of the regenerated plants. OBJECTIVES: The effects of heat shock and 2,4-D treatment on total protein content of treated microspores, morphological and physiological characteristics of the doubled haploid (DH) plants were assessed. MATERIALS AND METHODS: Buds containing mid- to late- uninucleate microspores were used for microspore culture. Microspores were isolated and cultured in NLN-13 medium and incubated at 30ºC for 14 days or treated with 2,4-D (35 mg.L-1) for 30 min to induce embryogenesis. Microspore-derived embryos were transferred onto B5 medium for plantlet regeneration. Ploidy level of the regenerated plantlets was determined using Partec flow cytometry. Spectrophotometric readings were carried out at 490, 663 and 645 nm to determine Chl a-b and carotenoids contents. TRIzol and cetyl-threeethyl-ammonium bromide (CTAB) were used for protein extraction from microspores and leaves. Length and width of stomata and pollen grains were also photographed using light microscope (Olympus). RESULTS: Applied stressors significantly reduced total protein content of treated microspores however, protein content and concentration of chlorophyll a and b of the DH plants were only increased by heat shock treatment when compared with the donor plant 'Hyola 420'. In contrast, carotenoids were not affected by applied stressors. Longer and wider stomata were observed by 2,4-D treatment but, the length of pollen grains was significantly decreased following heat shock and 2,4-D treatment. CONCLUSIONS: Total protein content of cultured microspores, concentration of chlorophyll a and b, length and width of stomata of microspore-derived doubled haploid plants were significantly affected by the type of inductive stresses. However, carotenoids were more stable and not affected by applied stressors.