Identification of quantitative trait loci associated with seed quality traits between Canadian and Ukrainian mega-environments using genome-wide association study

Hong, Huilin; Yoosefzadeh-Najafabadi, Mohsen; Torkamaneh, Davoud; Rajcan, Istvan

doi:10.1007/s00122-022-04134-8

Cited by 5 publications

(3 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the quality traits, previous studies verified the strong negative correlation between soybean oil and protein [ 23 , 24 ], and that point was also confirmed in the present study. There was an extremely significant negative correlation between OA and LA.…”

Section: Resultssupporting

confidence: 91%

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (Glycine max L.)

Gao,

Ma,

et al. 2024

IJMS

View full text Add to dashboard Cite

Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean.

show abstract

Section: Resultssupporting

confidence: 91%

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (Glycine max L.)

Gao,

Ma,

et al. 2024

IJMS

View full text Add to dashboard Cite

show abstract

“…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 ]. Therefore, a deep understanding of the genetic structure of soybean oil and protein concentration would be pivotal in designing efficient molecular breeding approaches [ 8 , 9 ].…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…3) GWAS can achieve higher mapping resolution due to high-density molecular markers and diverse historical recombination events ( Wang et al., 2020a ; Li et al., 2022b ). Over the past decade, dozens of GWAS have been performed to identify QTLs or quantitative trait nucleotides (QTNs) involving seed size, lipid accumulation, and protein level in soybean ( Hwang et al., 2014 ; Zhou et al., 2015 ; Zhang et al., 2016a ; Yan et al., 2017 ; Zhang et al., 2018b ; Lee et al., 2019 ; Zhao et al., 2019 ; He et al., 2021 ; Zhang et al., 2021 ; Hong et al., 2022 ). Based on this approach, GmOLEO1 ( Zhang et al., 2019b ), GmPDAT ( Liu et al., 2020a ), GmSWEET10a (also known as GmSWEET39 ) ( Miao et al., 2020 ; Wang et al., 2020b ), and GmST05 ( Duan et al., 2022 ) have been identified and confirmed to relate to these seed traits, suggesting this way is more effective.…”

Section: Genetic Mapping Associated With Seed Size Oil and Protein Co...mentioning

confidence: 99%

Genetic regulatory networks of soybean seed size, oil and protein contents

Duan

Li²,

Wang³

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

As a leading oilseed crop that supplies plant oil and protein for daily human life, increasing yield and improving nutritional quality (high oil or protein) are the top two fundamental goals of soybean breeding. Seed size is one of the most critical factors determining soybean yield. Seed size, oil and protein contents are complex quantitative traits governed by genetic and environmental factors during seed development. The composition and quantity of seed storage reserves directly affect seed size. In general, oil and protein make up almost 60% of the total storage of soybean seed. Therefore, soybean’s seed size, oil, or protein content are highly correlated agronomical traits. Increasing seed size helps increase soybean yield and probably improves seed quality. Similarly, rising oil and protein contents improves the soybean’s nutritional quality and will likely increase soybean yield. Due to the importance of these three seed traits in soybean breeding, extensive studies have been conducted on their underlying quantitative trait locus (QTLs) or genes and the dissection of their molecular regulatory pathways. This review summarized the progress in functional genome controlling soybean seed size, oil and protein contents in recent decades, and presented the challenges and prospects for developing high-yield soybean cultivars with high oil or protein content. In the end, we hope this review will be helpful to the improvement of soybean yield and quality in the future breeding process.

show abstract

Identification of quantitative trait loci associated with seed quality traits between Canadian and Ukrainian mega-environments using genome-wide association study

Cited by 5 publications

References 83 publications

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (Glycine max L.)

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (Glycine max L.)

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

Genetic regulatory networks of soybean seed size, oil and protein contents

Contact Info

Product

Resources

About