Legume Genomics and Genetics 2026, Vol.17, No.1, 1-13 http://cropscipublisher.com/index.php/lgg 5 Table 2 Heterozygosity metrics for genetic diversity assessment within populations using 605 SNP markers. It includes information on expected Heterozygosity (He), and observed Heterozygosity (Ho) Population He Ho SYN1359 0.0929 0.0700 P98Y11 0.1245 0.1125 BMX6160 0.0483 0.0360 97R73 0.0702 0.0477 NS7000 0.0478 0.0321 NA5909 0.0952 0.0897 3 Discussion 3.1 Phenotypic data Phenotypic variation within cultivars has been reported in soybean (Fasoula and Boema, 2005; Tokatlidis, 2015; Amaral et al., 2019; Achard et al., 2020) and other crops such as potato, wheat, cotton, and barley (Tokatlidis et al., 2008; Marand, 2019; Ninou et al., 2022). This phenotypic variability, even within cultivars, highlights the importance of understanding the genetic basis of traits to improve selection strategies. Consistent with this premise, the evaluation of late-maturing cultivars in both 2018/19 and 2019/20 revealed significant differences not only among cultivars but also within them. In 2018/19, yield differed significantly across the test, progenies, and populations, as well as between the three late maturity cultivars. In 2019/20, this pattern was reinforced, with significant differences detected for all traits within the cultivar 97R73, and at the progeny level for all evaluated traits. Ninou et al. (2022) (Ninou et al., 2022) found significant variation within improved commercial cultivars of durum wheat for grain yield and protein content. Similarly, significant intracultivar differences over two years and among three locations were observed for cotton yield. The same study also identified intracultivar variation for fiber quality traits such as length and micronaire, while fiber strength and uniformity showed no significant variation. Physiological traits like leaf carbon isotope discrimination, ash content, and potassium concentration also exhibited intracultivar variation (Tokatlidis et al., 2008). Heritability, quantifies the proportion of phenotypic variance explained by genetic differences. In the multi-environment analysis, heritability and accuracy were highest for PH. Indicating strong genetic control and suggesting that effective selection is feasible at the progeny level. In contrast, the coefficient of variation due to environmental effects (CVe) was very low for FM and DF, showing the greatest precision for this trait (Table 2). Low heritability was observed for YIELD, as expected, since it is a quantitative trait strongly influenced by environment, which is consistent with the findings of Mendonça et al. (2020). The lower heritability for YIELD (0.17) and FM (0.12) at progeny level, in the multi-environment analysis, indicates that these traits are strongly influenced by environment. Nonetheless, the detection of significant genotypic variance for both traits demonstrate the presence of exploitable genetic variability. The low coefficients of variation (3.49 for YIELD, 0.43 for FM) confirm the precision of phenotypic assessments and strengthen the reliability of the results. Soybean maturity is a complex trait controlled by the interaction of numerous genes, molecular pathways, and the cultivation environment, in the study of Zimmer et al. (2021), several QTL’s associated with maturity groups were identified across different chromosomes, including loci with effects spanning multiple maturity groups. Similarly, soybean seed yield is also a complex quantitative trait governed by multiple genes and broadly influenced by growing conditions and latitudinal adaptation (Tayade et al., 2023). The complexity of both traits explains the low heritability, as their phenotypic expression results from intricate gene-environment interactions. Besides the trait itself, the genetic structure of a population is also crucial in heritability estimation. In self-pollinated plants, we often see high heritability within a population due to homozygosity at many loci, reducing genetic variation for
RkJQdWJsaXNoZXIy MjQ4ODYzNA==