Identification and validation of number of pod ‐ and seed ‐ related traits <scp>QTL</scp>s in soybean

Li, Candong; Zou, Jianan; Jiang, Hongwei; Yu, Jingquan; Huang, Shi-Yu; Wang, Xinyu; Liu, Chunyan; Tai, Guo; Zhu, Rong; Wu, Xiaoxia; Hu, Zhenbang; Wang, Zhixin; Zheng, Wei; Zhao, Haiming; Wang, Nannan; ChunGuang, Cheng; Xin, Dawei; Chen, Qingshan; Qi, Zhaoming

doi:10.1111/pbr.12635

Cited by 11 publications

(9 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Either way, for these QTL the alleles that favour pod number originated from the parent 'Sigalia' (Figure d and e). Their position is physically close to a QTL reported by Li et al () for pod number obtained from on‐field experiments (33.43–39.41 Mb). They postulated the gene Glyma.13G223300 (WM82.a2.v1) as a candidate underlying the QTL, which is located less than 600 kb downstream of our pod number QTL.…”

Section: Discussionsupporting

confidence: 88%

Cold stress tolerance of soybeans during flowering: QTL mapping and efficient selection strategies under controlled conditions

et al. 2019

View full text Add to dashboard Cite

Breeding soybeans for higher latitudes requires cultivars with an increased chilling stress tolerance, especially when flowering occurs. Phenotyping in climate chambers to select for this trait is labour‐intensive and requires an optimal allocation of resources due to limited space. We screened a diversity panel of 35 early maturity cultivars and a biparental population of 103 RILs for their cold stress tolerance at flowering stage. Pod number under control and stress conditions is highly heritable and showed only a weak correlation between the two treatments. Based on different testing scenarios, we could show that testing more genotypes with less replicates yields much higher responses to selection and hence should be pursued in such climate‐controlled experiments. We identified quantitative trait loci (QTL) for pod number under both conditions (chromosomes 7 and 13) and a cold tolerance‐specific QTL (chromosome 11). Furthermore, we performed genomic predictions using different test set scenarios and prediction models, showing that genomic prediction is a promising tool to select for cold stress tolerance, particularly if known QTL can be used as fixed effects in the model.

show abstract

Section: Discussionsupporting

confidence: 88%

Cold stress tolerance of soybeans during flowering: QTL mapping and efficient selection strategies under controlled conditions

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In PP, most of the genes detected by SVR-mediated GWAS are associated with auxin influx carrier or auxin response factors (ARFs), gibberellin synthesis, and response to brassinosteroid (Lin et al , 2020; Yin et al , 2018). Song et al (2020) and Li et al (2018a) also reported that some genes related to PP were associated with embryo development, stamen development, ovule development, cytokinin biosynthesis, and response gibberellin that we also identified in our study. Soybean seed yield significantly depends on seed number and seed size (Liu et al , 2010; Rotundo et al , 2009).…”

Section: Discussionsupporting

confidence: 80%

Machine Learning based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and its Components

Yoosefzadeh-Najafabadi

Torabi

Torkamaneh

et al. 2021

Preprint

View full text Add to dashboard Cite

Genome-wide association study (GWAS) is currently one of the important approaches for discovering quantitative trait loci (QTL) associated with traits of interest. However, insufficient statistical power is the limiting factor in current conventional GWAS methods for characterizing quantitative traits, especially in narrow genetic bases plants such as soybean. In this study, we evaluated the potential use of machine learning (ML) algorithms such as support vector machine (SVR) and random forest (RF) in GWAS, compared with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying QTL associated with soybean yield components. In this study, important soybean yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity were assessed using 227 soybean genotypes evaluated across four environments. Our results indicated SVR-mediated GWAS outperformed RF, MLM and FarmCPU in discovering the most relevant QTL associated with the traits, supported by the functional annotation of candidate gene analyses. This study for the first time demonstrated the potential benefit of using sophisticated mathematical approaches such as ML algorithms in GWAS for identifying QTL suitable for genomic-based breeding programs.

show abstract

“…Soybeans are an important food crop all around the world, and are also a major source of oil and protein ( Singh et al, 2010 ). In recent years, many scholars have worked on yield components, such as the number of grains and pods per plant in order to improve soybean yield ( Li et al, 2018 , 2019 ; Du et al, 2019 ; Song et al, 2020 ). However, the attempts made so far have not fundamentally improved yields ( Zhang et al, 2012 ), so it is essential to find the main factors affecting soybean yields in order to increase them.…”

Section: Introductionmentioning

confidence: 99%

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

The soybean flower and the pod drop are important factors in soybean yield, and the use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study of the soybean flower and pod drop rate (PDR). This paper compared a variety of deep learning algorithms for identifying and counting soybean flowers and pods, and found that the Faster R-CNN model had the best performance. Furthermore, the Faster R-CNN model was further improved and optimized based on the characteristics of soybean flowers and pods. The accuracy of the final model for identifying flowers and pods was increased to 94.36 and 91%, respectively. Afterward, a fusion model for soybean flower and pod recognition and counting was proposed based on the Faster R-CNN model, where the coefficient of determinationR2 between counts of soybean flowers and pods by the fusion model and manual counts reached 0.965 and 0.98, respectively. The above results show that the fusion model is a robust recognition and counting algorithm that can reduce labor intensity and improve efficiency. Its application will greatly facilitate the study of the variable patterns of soybean flowers and pods during the reproductive period. Finally, based on the fusion model, we explored the variable patterns of soybean flowers and pods during the reproductive period, the spatial distribution patterns of soybean flowers and pods, and soybean flower and pod drop patterns.

show abstract

Identification and validation of number of pod ‐ and seed ‐ related traits QTLs in soybean

Cited by 11 publications

References 44 publications

Cold stress tolerance of soybeans during flowering: QTL mapping and efficient selection strategies under controlled conditions

Cold stress tolerance of soybeans during flowering: QTL mapping and efficient selection strategies under controlled conditions

Machine Learning based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and its Components

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

Contact Info

Product

Resources

About