Unveiling the Mysteries of Non-Mendelian Heredity in Plant Breeding

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.