Haplotype analysis of a major and stable QTL underlying soybean (Glycine max) seed oil content reveals footprint of artificial selection

Long, Yan; Di, Rui; Wu, Chengjun; Liu, Qian; Wei, Yunxie; Hou, Wei; Zhao, Qingsong; Liu, Bingqiang; Yang, Chen; Song, Qijian; Zhang, Mengchen

doi:10.1007/s11032-019-0951-1

Cited by 6 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, QTLs underlying seed oil content were previously identified by GWAS and QTL research, which including the mapping region of ST1 (Yan et al ., 2019 ; Zhang et al ., 2018a , 2018b ). Coincidently, seeds harvested from ST1 transgenic lines had higher oil content than those from control non‐transgenic Kefeng 1 seeds (Figure 3f ), which accumulated more triglycerides than non‐transgenic seeds in Arabidopsis (Figure S6 f), suggesting that ST1 promotes oil accumulation in seeds.…”

Section: Resultsmentioning

confidence: 99%

Identification of ST1 reveals a selection involving hitchhiking of seed morphology and oil content during soybean domestication

Zhang

et al. 2022

Plant Biotechnology Journal

View full text Add to dashboard Cite

Seed morphology and quality of cultivated soybean (Glycine max) have changed dramatically during domestication from their wild relatives, but their relationship to selection is poorly understood. Here, we describe a semi-dominant locus, ST1 (Seed Thickness 1), affecting seed thickness and encoding a UDP-D-glucuronate 4-epimerase, which catalyses UDP-galacturonic acid production and promotes pectin biosynthesis. Interestingly, this morphological change concurrently boosted seed oil content, which, along with up-regulation of glycolysis biosynthesis modulated by ST1, enabled soybean to become a staple oil crop. Strikingly, ST1 and an inversion controlling seed coat colour formed part of a single selective sweep. Structural variation analysis of the region surrounding ST1 shows that the critical mutation in ST1 existed in earlier wild relatives of soybean and the region containing ST1 subsequently underwent an inversion, which was followed by successive selection for both traits through hitchhiking during selection for seed coat colour. Together, these results provide direct evidence that simultaneously variation for seed morphology and quality occurred earlier than variation for seed coat colour during soybean domestication. The identification of ST1 thus sheds light on a crucial phase of human empirical selection in soybeans and provides evidence that our ancestors improved soybean based on taste.

show abstract

Section: Resultsmentioning

confidence: 99%

Identification of ST1 reveals a selection involving hitchhiking of seed morphology and oil content during soybean domestication

Zhang

et al. 2022

Plant Biotechnology Journal

View full text Add to dashboard Cite

show abstract

“…To study the effect of genetic similarity between the training population and prediction population on the prediction efficiency, population structure was determined using STRUCTURE 2.3.4 ( Pritchard et al., 2000 ). A total of 7,244 SNPs with linkage disequilibrium (LD) less than 0.50 to adjacent loci were selected using the program PLINK (version 1.07) ( Purcell et al., 2007 ; Yan et al., 2019 ) and used to perform the structure analysis. The number of subsets (k) ranged from 2 to 6, while the burn-in time and iterations for each run were both set to 100,000.…”

Section: Methodsmentioning

confidence: 99%

Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content

Sun

Guo²,

Li³

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

IntroductionGenomic selection (GS) is a potential breeding approach for soybean improvement.MethodsIn this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN.ResultsOur results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log10(P) greater than 1 were included.DiscussionThis information will help improve GS efficiency and facilitate the application of GS.

show abstract

“…To facilitate breeding, effort has been made to develop markers linked to a genomic region conferring specific agronomic traits. Examples of these markers concern mainly traits including disease resistance [18,19], abiotic stress tolerance [20,21], maturity [22][23][24][25][26][27][28][29], plant architecture [30,31], and nutritional values [32,33]. For instance, loci governing maturity are important for soybean acreage expansion along the latitude.…”

Section: Introductionmentioning

confidence: 99%

Development of a Set of Polymorphic DNA Markers for Soybean (Glycine max L.) Applications

Li,

Wang,

Sze

et al. 2023

Agronomy

View full text Add to dashboard Cite

Soybean (Glycine max L.) is gaining in importance due to its many uses, including as a food crop and a source of industrial products, among others. Increasing efforts are made to accelerate soybean research and develop new soybean varieties to meet global demands. Soybean research, breeding, identification, and variety protection all rely on precise genomic information. While DNA markers are invaluable tools for these purposes, the older generations, especially those developed before the advent of genome sequencing, lack precision and specificity. Thankfully, advancements in genome sequencing technologies have generated vast amounts of sequence data over the past decade, allowing precise and high-resolution analyses. However, making sense of the genomic information requires a certain level of professional training and computational power, which are not universally available to researchers. To address this, we generated a set of PCR-based DNA markers out of the existing genomic data from 228 popular soybean varieties that offer precise, unambiguous genomic information and can be easily adapted in various applications. A standard operating procedure (SOP) was also designed for these markers and validated on diverse soybean varieties to ensure their reproducibility. This user-friendly universal panel of DNA markers, along with the SOP, will facilitate soybean research and breeding programs through simple applications.

show abstract

Haplotype analysis of a major and stable QTL underlying soybean (Glycine max) seed oil content reveals footprint of artificial selection

Cited by 6 publications

References 32 publications

Identification of ST1 reveals a selection involving hitchhiking of seed morphology and oil content during soybean domestication

Identification of ST1 reveals a selection involving hitchhiking of seed morphology and oil content during soybean domestication

Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content

Development of a Set of Polymorphic DNA Markers for Soybean (Glycine max L.) Applications

Contact Info

Product

Resources

About