Identification and validation of quantitative trait loci for seed yield, oil and protein contents in two recombinant inbred line populations of soybean

Wang, Xianzhi; Jiang, Guang‐Zhen; Green, Marci; Scott, L. Ridgway; Song, Qijian; Hyten, David L.; Cregan, Perry B.

doi:10.1007/s00438-014-0865-x

Cited by 88 publications

(77 citation statements)

References 41 publications

(131 reference statements)

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“…However, there were some major loci detected in different environments using one method in our study. In linkage mapping, qOil-10-1 and qOil-14-1 were detected in four environments, had adjacent or physically overlapping QTLs, as reported by previous studies, and might actually be the same QTL (Tajuddin et al, 2003; Wang et al, 2014). In the GWAS, the significant SNPs on Chr.…”

Section: Discussionsupporting

confidence: 77%

Identification of Major Quantitative Trait Loci for Seed Oil Content in Soybeans by Combining Linkage and Genome-Wide Association Mapping

Cao

Wang

et al. 2017

Front. Plant Sci.

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Soybean oil is the most widely produced vegetable oil in the world and its content in soybean seed is an important quality trait in breeding programs. More than 100 quantitative trait loci (QTLs) for soybean oil content have been identified. However, most of them are genotype specific and/or environment sensitive. Here, we used both a linkage and association mapping methodology to dissect the genetic basis of seed oil content of Chinese soybean cultivars in various environments in the Jiang-Huai River Valley. One recombinant inbred line (RIL) population (NJMN-RIL), with 104 lines developed from a cross between M8108 and NN1138-2, was planted in five environments to investigate phenotypic data, and a new genetic map with 2,062 specific-locus amplified fragment markers was constructed to map oil content QTLs. A derived F2 population between MN-5 (a line of NJMN-RIL) and NN1138-2 was also developed to confirm one major QTL. A soybean breeding germplasm population (279 lines) was established to perform a genome-wide association study (GWAS) using 59,845 high-quality single nucleotide polymorphism markers. In the NJMN-RIL population, 8 QTLs were found that explained a range of phenotypic variance from 6.3 to 26.3% in certain planting environments. Among them, qOil-5-1, qOil-10-1, and qOil-14-1 were detected in different environments, and qOil-5-1 was further confirmed using the secondary F2 population. Three loci located on chromosomes 5 and 20 were detected in a 2-year long GWAS, and one locus that overlapped with qOil-5-1 was found repeatedly and treated as the same locus. qOil-5-1 was further localized to a linkage disequilibrium block region of approximately 440 kb. These results will not only increase our understanding of the genetic control of seed oil content in soybean, but will also be helpful in marker-assisted selection for breeding high seed oil content soybean and gene cloning to elucidate the mechanisms of seed oil content.

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Section: Discussionsupporting

confidence: 77%

Identification of Major Quantitative Trait Loci for Seed Oil Content in Soybeans by Combining Linkage and Genome-Wide Association Mapping

Cao

Wang

et al. 2017

Front. Plant Sci.

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“…Meta-analyses of amino acid composition and fatty acid composition were carried out in two groups, respectively. Soybean seed protein and oil contents have a well-established negative correlation [10,45,46] and seed protein versus Cys concentration and seed protein versus Met concentration have also been reported as displaying positive or negative correlations depending on the study [5,11,38,42,47,48]. Therefore, meta-analyses of QTL for these traits known to co-vary were also carried out in predetermined combinations of protein and oil (protein+oil) and protein, Cys and Met (protein+Cys+Met).…”

Section: Resultsmentioning

confidence: 99%

“…Meta-QTLs mPO9-1~4, mPO14-1~3 and mPO15-1~5 were synthesized from 17, 16 and 21 projected QTLs, respectively. As the projected QTL contributing to these meta-QTL were from each from multiple studies [10,11,13,26,27,29,30,31,33,34,57,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75], these QTL with LOD scores below 2.0 have been identified in multiple environments and genetic backgrounds, indicating that they are, indeed, reliable QTL. Thus, meta-analysis can be applied to identify robust QTL from “suggestive” QTL.…”

Section: Discussionmentioning

confidence: 99%

“…Through these studies, hundreds of loci contributing to the protein and oil contents and compositions of seed have been identified, as well as few molecular markers which have been successfully applied in marker-assisted breeding [5,10,11,12,13]. Of the more than 700 QTLs identified from over 60 studies mapping protein and oil seed contents, only 57 QTLs have been confirmed (available online: ).…”

Section: Introductionmentioning

confidence: 99%

“…Contents of soybean seed protein [24] and oil [25] have also been previously studied by meta-analysis with 107 and 121 QTLs, respectively. However, in recent years numerous additional QTL mapping studies for these two traits have rapidly accumulated [10,11,12,13,26,27,28,29,30,31,32,33,34,35,36], as well as, QTL studies on the amino acid and fatty acid compositions of soybean seed protein and oil [37,38,39,40,41,42,43]. Thus, with a nearly three-fold increase in the number of QTL available, a new meta-analysis for seed protein and oil contents which includes recent QTLs for seed protein and oil content as well as seed amino acid and fatty acids profiles is timely.…”

Section: Introductionmentioning

confidence: 99%

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Meta-Analyses of QTLs Associated with Protein and Oil Contents and Compositions in Soybean [Glycine max (L.) Merr.] Seed

Van

McHale

2017

IJMS

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Soybean [Glycine max (L.) Merr.] is a valuable and nutritious crop in part due to the high protein meal and vegetable oil produced from its seed. Soybean producers desire cultivars with both elevated seed protein and oil concentrations as well as specific amino acid and fatty acid profiles. Numerous studies have identified quantitative trait loci (QTLs) associated with seed composition traits, but validation of these QTLs has rarely been carried out. In this study, we have collected information, including genetic location and additive effects, on each QTL for seed contents of protein and oil, as well as amino acid and fatty acid compositions from over 80 studies. Using BioMercator V. 4.2, a meta-QTL analysis was performed with genetic information comprised of 175 QTLs for protein, 205 QTLs for oil, 156 QTLs for amino acids, and 113 QTLs for fatty acids. A total of 55 meta-QTL for seed composition were detected on 6 out of 20 chromosomes. Meta-QTL possessed narrower confidence intervals than the original QTL and candidate genes were identified within each meta-QTL. These candidate genes elucidate potential natural genetic variation in genes contributing to protein and oil biosynthesis and accumulation, providing meaningful information to further soybean breeding programs.

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Accuracy of genomic prediction for seed oil concentration in high‐oleic soybean populations using a low‐density marker panel

2021

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Insoybean [Glycine max (L.) Merr.], seed oil concentration is a complex quantitative trait, and genomic selection (GS) has been shown to be a valuable tool for performing selection on such traits. The objectives of this study were to evaluate multiple GS models for seed oil concentration using a low-density marker panel in four biparental soybean populations and to assess predictive ability of the models using six unique training populations (TPs). Individuals were grown as BC 1 F 4 :F 5 progeny rows in 2014. Genomic estimated breeding values (GEBVs) were calculated for each genotype within a population using genomic best linear unbiased predictor (GBLUP), BayesA, and BayesB models in a biparental specific context. In 2015, 60 individuals from each population were randomly selected and grown at six locations with two replications each to generate a "true" phenotypic value for each genotype. Prediction accuracies for each estimation set were generated by correlating the GEBVs with the "true" phenotypic value. Across all populations, the GS prediction accuracy was greatest using GBLUP; however, no GS prediction model showed a significant advantage in accuracy over the phenotypic values. Generally, TPs consisting of more individuals had higher prediction accuracies; however, variations were observed across populations and models. The results show that GS in a biparental context and with low marker densities can be a valuable tool for breeders focused on making gains for oil concentration; however, consideration must be given as to how to apply these methods to each situation.

show abstract

Identification and validation of quantitative trait loci for seed yield, oil and protein contents in two recombinant inbred line populations of soybean

Cited by 88 publications

References 41 publications

Identification of Major Quantitative Trait Loci for Seed Oil Content in Soybeans by Combining Linkage and Genome-Wide Association Mapping

Identification of Major Quantitative Trait Loci for Seed Oil Content in Soybeans by Combining Linkage and Genome-Wide Association Mapping

Meta-Analyses of QTLs Associated with Protein and Oil Contents and Compositions in Soybean [Glycine max (L.) Merr.] Seed

Accuracy of genomic prediction for seed oil concentration in high‐oleic soybean populations using a low‐density marker panel

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