Molecular Plant Breeding 2024, Vol.15, No.6, 371-378 http://genbreedpublisher.com/index.php/mpb 373 2023). Whole-genome sequencing offers a more comprehensive approach, capturing a broader spectrum of genetic variation, but at a higher cost (Khan et al., 2019; Priyanatha et al., 2022). 2.4 Phenotypic trait data collection Accurate phenotypic data collection is crucial for the success of GWAS. In soybean, yield and yield-related traits such as seed yield, plant height, seed weight, and pod shattering are commonly measured. These traits are often evaluated across multiple environments to account for genotype-by-environment interactions, which can significantly influence trait expression (Ayalew et al., 2022; Izquierdo et al., 2023). High heritability of traits like seed yield and shattering resistance ensures reliable association mapping (Bhat et al., 2022). 2.5 Challenges in GWAS GWAS faces several challenges, including population structure, marker density, and environmental effects. Population structure can lead to spurious associations if not properly accounted for, necessitating the use of models like MLM (Yoosefzadeh-Najafabadi et al., 2021; Rani et al., 2023). Marker density is another critical factor; insufficient marker coverage can result in missing important QTL. High-density SNP arrays and whole-genome sequencing help mitigate this issue (Khan et al., 2019; Priyanatha et al., 2022). Environmental effects and genotype-by-environment interactions add another layer of complexity, requiring multi-environment trials to ensure robust QTL detection (Ayalew et al., 2022; Izquierdo et al., 2023). By addressing these challenges and leveraging advanced statistical models and genotyping techniques, GWAS continues to be a powerful tool in identifying yield-related QTLs in soybean, ultimately aiding in the development of improved cultivars. 3 Yield-Related Traits and Their Importance in Soybean 3.1 Key yield-related traits identified in previous studies Several key yield-related traits have been identified in soybean through various studies. These traits include seed yield per plant (SYP), number of pods per plant, number of seeds per plant, and 100-seed weight (HSW) (Bhat et al., 2022). Additionally, traits such as seed weight, shattering resistance, days to maturity, and plant height have also been highlighted as significant contributors to yield (Ayalew et al., 2022). The identification of these traits is crucial as they directly influence the productivity and overall yield of soybean crops. 3.2 Genetic factors influencing soybean yield The genetic basis of soybean yield has been extensively studied using genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping. For instance, a study identified 19 single-nucleotide polymorphisms (SNPs) significantly associated with seed yield, with stable QTLs detected on chromosomes 9 and 17 (Ayalew et al., 2022). Another study revealed 57 SNPs associated with four yield-related traits across multiple environments, identifying six consistent SNPs that were considered stable QTL regions (Bhat et al., 2022). Furthermore, machine learning algorithms have been employed to enhance the discovery of relevant QTLs, demonstrating the potential of sophisticated mathematical approaches in genomic studies (Yoosefzadeh-Najafabadi et al., 2021; Zhang, 2024). These genetic factors are pivotal for marker-assisted selection and breeding programs aimed at improving soybean yield. 3.3 Influence of environment on yield and QTL expression Environmental factors play a significant role in the expression of yield-related traits and QTLs in soybean. Studies have shown that genotype by environment (GxE) interactions can significantly impact yield stability and the expression of QTLs (Happ et al., 2021). For example, a study identified QTLs for seed yield in both irrigated and rain-fed environments, highlighting the differential responses of yield under varying irrigation conditions (Lee et al., 2021). Another study used a new GWAS model to identify QTL-by-environment interactions (QEIs) for tocopherol content in soybean seed, demonstrating that environmental interactions are often overlooked in traditional GWAS models (Yu et al., 2022). These findings underscore the importance of considering environmental factors in breeding programs to develop high-yielding and stable soybean cultivars. By understanding the key yield-related traits, the genetic factors influencing these traits, and the impact of environmental interactions, researchers can develop more effective strategies for improving soybean yield through targeted breeding and genetic modification (Hina et al., 2020; Zhang et al., 2020).
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