Unveiling the Mysteries of Non-Mendelian Heredity in Plant Breeding

Assessment of potential candidate genes for partial resistance to Sclerotinia stem rot caused by Sclerotinia sclerotiorum using real-time quantitative PCR

Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum (Lib) de Bary (S. sclerotiorum), is one of the most important diseases that causes significant soybean [Glycine max (L.) Merr.] seed yield and quality losses in Canada and globally. Initiation of plant defense mechanisms is crucial for establishing partial resistance to the pathogenic fungus. To understand plant response to S. sclerotiorum, we conducted a temporal (1, 3, and 5 days post-inoculation [DPI]) assessment of gene expression changes in the stem of soybean genotypes with contrasting phenotypic response. We focused on four genes that have been previously reported as associated with SSR partial resistance and are known to be involved in defense-related functions such as cell wall modification, signaling, response to wounding, and response to fungus. The results showed a higher and earlier expression of the genes in partially resistant cultivars compared to the susceptible. Expression of some genes increased up to 11- (Glyma.02G059700) to 16-fold (Glyma.09G232100) by 3 DPI in the partially resistant cultivar, OAC Drayton, while the genes were generally downregulated in the susceptible cultivar, OAC Shire, at the same DPI. This study improves our understanding of expression patterns of genes involved in plant defense against fungal pathogens in soybean. More importantly, the knowledge of genes that are essential in defense against S. sclerotiorum can be used to fine-map the quantitative trait loci for SSR resistance and facilitate accelerated breeding of SSR-resistant cultivars through gene-based marker-assisted selection.

Machine Learning-Aided Optimization of In Vitro Tetraploid Induction in Cannabis

Polyploidy, characterized by an increase in the number of whole sets of chromosomes in an organism, offers a promising avenue for cannabis improvement. Polyploid cannabis plants often exhibit altered morphological, physiological, and biochemical characteristics with a number of potential benefits compared to their diploid counterparts. The optimization of polyploidy induction, such as the level of antimitotic agents and exposure duration, is essential for successful polyploidization to maximize survival and tetraploid rates while minimizing the number of chimeric mixoploids. In this study, three classification-based machine learning algorithms-probabilistic neural network (PNN), support vector classification (SVC), and k-nearest neighbors (KNNs)-were used to model ploidy levels based on oryzalin concentration and exposure time. The results indicated that PNN outperformed both KNNs and SVC. Subsequently, PNN was combined with a genetic algorithm (GA) to optimize oryzalin concentration and exposure time to maximize tetraploid induction rates. The PNN-GA results predicted that the optimal conditions were a concentration of 32.98 µM of oryzalin for 17.92 h. A validation study testing these conditions confirmed the accuracy of the PNN-GA model, resulting in 93.75% tetraploid induction, with the remaining 6.25% identified as mixoploids. Additionally, the evaluation of morphological traits showed that tetraploid plants were more vigorous and had larger leaf sizes compared to diploid or mixoploid plants in vitro.

Machine learning-enhanced multi-trait genomic prediction for optimizing cannabinoid profiles in cannabis

Cannabis sativa L., known for its medicinal and psychoactive properties, has recently experienced rapid market expansion but remains understudied in terms of its fundamental biology due to historical prohibitions. This pioneering study implements GS and ML to optimize cannabinoid profiles in cannabis breeding. We analyzed a representative population of drug-type cannabis accessions, quantifying major cannabinoids and utilizing high-density genotyping with 250K SNPs for GS. Our evaluations of various models-including ML algorithms, statistical methods, and Bayesian approaches-highlighted Random Forest's superior predictive accuracy for single and multi-trait genomic predictions, particularly for THC, CBD, and their precursors. Multi-trait analyses elucidated complex genetic interdependencies and identified key loci crucial to cannabinoid biosynthesis. These results demonstrate the efficacy of integrating GS and ML in developing cannabis varieties with tailored cannabinoid profiles.

Clarifying Mendelian vs non-Mendelian inheritance

Gregor Mendel developed the principles of segregation and independent assortment in the mid-1800s based on his detailed analysis of several traits in pea plants. Those principles, now called Mendel's laws, in fact, explain the behavior of genes and alleles during meiosis and are now understood to underlie "Mendelian inheritance" of a wide range of traits and diseases across organisms. When asked to give examples of inheritance that do NOT follow Mendel's laws, in other words, examples of non-Mendelian inheritance, students sometimes list incomplete dominance, codominance, multiple alleles, sex-linked traits, and multigene traits and cite as their sources the Khan Academy, Wikipedia, and other online sites. Against this background, the goals of this Perspective are to (1) explain to students, healthcare workers, and other stakeholders why the examples above, in fact, display Mendelian inheritance, as they obey Mendel's laws of segregation and independent assortment, even though they do not produce classic Mendelian phenotypic ratios and (2) urge individuals with an intimate knowledge of genetic principles to monitor the accuracy of learning resources and work with us and those resources to correct information that is misleading.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.