Six decades of soybean breeding in Ontario, Canada: a tradition of innovation

Yoosefzadeh-Najafabadi, Mohsen; Rajcan, Istvan

doi:10.1139/cjps-2022-0183

Cited by 7 publications

(6 citation statements)

References 174 publications

(216 reference statements)

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“…The genetic improvement of soybean cultivars, as well as advancements in farming technology and agronomic practices, have significantly contributed to this substantial increase (Specht et al, 1999;Rowntree et al, 2013;Koester et al, 2014;Rincker et al, 2014;Canella Vieira and Chen, 2021). A typical soybean breeding pipeline consists of several years of multi-environment field trials to select and advance high-yielding breeding lines (Canella Vieira and Chen, 2021;Yoosefzadeh-Najafabadi and Rajcan, 2022). The availability of high-dimensional genomic data (Song et al, 2013; and the advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains as well as higher testing efficiency in soybean breeding programs (Jarquin et al, 2014a;Jarquin et al, 2014b;Persa et al, 2020;Widener et al, 2021;Canella Vieira et al, 2022).…”

Section: )mentioning

confidence: 99%

Improving predictive ability in sparse testing designs in soybean populations

Persa,

Canella Vieira,

Rios

et al. 2023

Front. Genet.

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The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments.

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Section: )mentioning

confidence: 99%

Improving predictive ability in sparse testing designs in soybean populations

Persa,

Canella Vieira,

Rios

et al. 2023

Front. Genet.

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“…Developing soybean cultivars with high oil and protein concentrations has always been one of the major goals of soybean breeding programs [ 3 ]. However, these two traits are quantitative traits that are controlled by many minor and major genes and are highly affected by environments [ 4 , 5 ]. Previous studies verified the strong negative correlation between soybean oil and protein and recommended identifying quantitative trait loci (QTL) that might inversely affect those traits [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

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

Yoosefzadeh-Najafabadi

Torabi

Tulpan

et al. 2023

Plants

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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.

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“…Bigdata in plant breeding is the use of high-throughput technologies, such as omics, to collect and analyze large volumes of data on plant characteristics and their interactions with the environment [ 24 ]. This data is used to identify and improve desirable traits, such as plant yield.…”

Section: What Is Bigdata In Plant Breeding?mentioning

confidence: 99%

“…In addition, bigdata in plant breeding can uncover new insights into the biology of plants, such as genetic markers associated with specific traits, the yield of a particular crop under different growing conditions, or the genes that can be used to develop new varieties with specific characteristics [ 24 ]. The growth of bigdata has enabled plant breeders to study interaction effects in more detail using online resources.…”

Section: What Is Bigdata In Plant Breeding?mentioning

confidence: 99%

Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs

Yoosefzadeh-Najafabadi

Hesami

Eskandari

2023

Genes

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In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.

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Six decades of soybean breeding in Ontario, Canada: a tradition of innovation

Cited by 7 publications

References 174 publications

Improving predictive ability in sparse testing designs in soybean populations

Improving predictive ability in sparse testing designs in soybean populations

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

Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs

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