Evaluation of experimental designs in durum wheat trials

Katsileros, Anastasios; Koukouvinos, Christos

doi:10.1515/bile-2015-0010

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Each phenotypic plot consisted of five rows, which were 4.2 m long each, and the seeding rate was 50–57 seeds per m 2 . Nearest-neighbor analysis (NNA), as one of the well-known error control methods [ 96 , 97 , 98 ], was used to reduce the spatial variation and increase the accuracy of measured phenotypic data in each phenotypic plot.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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A Review on Comparison of Complete and Incomplete Block Designs

Sewenet¹

2019

JBAH

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The study of strategies for efficient plans for the collection of data, which lead to proper estimates of parameters relevant to the researcher's objective is known as experimental design. Field-based agronomic and genetic research is a decision-based process and many of decisions are required to decide the type of design used to conduct a field experiment, collect, analyze data and interpret the results. Many researchers are locked into particular dimensions that are partly determined by the size or capacity of planting or harvesting equipment, available resources, the size and shape of fields available for research, and colleagues' perceptions, or perhaps even peer pressure. A properly designed experiment for a particular research objective is the basis of all successful experiments. Experimental designs are classified in to complete block and incomplete block designs based on the treatment numbers found in a block. In case of complete block designs, all treatments are found in a block while some treatments found in incomplete block designs. Some of complete block designs are, completely randomized design (CRD), completely randomized block design (RCBD, Latin square (LSD), split plot design (SPD) whereas incomplete block designs are lattice design (LD) and Augmented designs (AD) are most known. Most of the time complete block design is widely used by researchers more than the balanced incomplete block designs because the missing data are computed before analysis. Each research designs had their own level of precision and conditions. Therefore, no single design is best all over designs. Due to this, the present review highlights the basic comparison concepts of complete and incomplete experimental designs and the focus of selection criteria.

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Evaluation of experimental designs in durum wheat trials

Cited by 2 publications

References 13 publications

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

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

A Review on Comparison of Complete and Incomplete Block Designs

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