Identification of quantitative trait loci underlying seed protein content of soybean including main, epistatic, and QTL × environment effects in different regions of Northeast China

Genome ◽  
2017 ◽  
Vol 60 (8) ◽  
pp. 649-655 ◽  
Author(s):  
Weili Teng ◽  
Wen Li ◽  
Qi Zhang ◽  
Depeng Wu ◽  
Xue Zhao ◽  
...  
Keyword(s):  
Protein Content ◽  
Phenotypic Variation ◽  
Genetic Linkage Map ◽  
Seed Protein ◽  
Soybean Protein ◽  
Recombinant Inbred Lines ◽  
Environment Interaction ◽  
Phenotypic Data ◽  
Environment Effects ◽  
Stable Qtls

The objective here was to identify QTL underlying soybean protein content (PC), and to evaluate the additive and epistatic effects of the QTLs. A mapping population, consisting of 129 recombinant inbred lines (RILs), was created by crossing ‘Dongnong 46’ and ‘L-100’. Phenotypic data of the parents and RILs were collected for 4 years in three locations of Heilongjiang Province of China. A total of 213 SSR markers were used to construct a genetic linkage map. Eight QTLs, located on seven chromosomes (Chr), were identified to be associated with PC among the 10 tested environments. Of the seven QTLs, five QTLs, qPR-2 (Satt710, on Chr9), qPR-3 (Sat_122, on Chr12), qPR-5 (Satt543, on Chr17), qPR-7 (Satt163, on Chr18), and qPR-8 (Satt614, on Chr20), were detected in six, seven, seven, six, and seven environments, respectively, implying relatively stable QTLs. qPR-3 could explain 3.33%–11.26% of the phenotypic variation across eight tested environments. qPR-5 and qPR-8 explained 3.64%–10.1% and 11.86%–18.40% of the phenotypic variation, respectively, across seven tested environments. Eight QTLs associated with PC exhibited additive and (or) additive × environment interaction effects. The results showed that environment-independent QTLs often had higher additive effects. Moreover, five epistatic pairwise QTLs were identified in the 10 environments.

Dissection of the genetic architecture for soybean seed weight across multiple environments

2017 ◽  
Vol 68 (4) ◽  
pp. 358 ◽  
Author(s):  
Weili Teng ◽  
Lei Feng ◽  
Wen Li ◽  
Depeng Wu ◽  
Xue Zhao ◽  
...  
Keyword(s):  
Phenotypic Variation ◽  
Seed Weight ◽  
Average Distance ◽  
Recombinant Inbred Lines ◽  
Molecular Genetic ◽  
Soybean Seed ◽  
Environment Interaction ◽  
Phenotypic Data ◽  
Molecular Genetic Map ◽  
Variation Explained

Seed weight (SW), measured as mass per seed, significantly affects soybean (Glycine max (L.) Merr.) yield and the quality of soybean-derived food. The objective of the present study was to identify quantitative trait loci (QTLs) and epistatic QTLs associated with SW in soybean across 129 recombinant inbred lines (RILs) derived from a cross between Dongnong 46 (100-seed weight, 20.26 g) and ‘L-100 (4.84 g). Phenotypic data were collected from this population after it was grown in nine environments. A molecular genetic map including 213 simple sequence repeat (SSR) markers was constructed, which distributed in 18 of 20 chromosomes (linkage groups). This map encompassed ~3623.39 cM, with an average distance of 17.01 cM between markers. Nine QTLs associated with SW were identified. These QTLs explained 1.07–18.43% of the observed phenotypic variation in the nine different environments, and the phenotypic variation explained by most QTLs was 5–10%. Among these nine QTLs, qSW-3 (Satt192) and qSW-5 (Satt568) explained 2.33–9.96% and 7.26–15.11% of the observed phenotypic variation across eight tested environments, respectively. QTLs qSW-8 (Satt514) and qSW-9 (Satt163) were both identified in six environments and explained 8.99–16.40% and 3.68–18.43% of the observed phenotypic variation, respectively. Nine QTLs had additive and/or additive × environment interaction effects, and the environment-independent QTLs often had higher additive effects. Moreover, nine epistatic pairwise QTLs were identified in different environments. Understanding the existence of additive and epistatic effects of SW QTLs could guide the choice of which reasonable SW QTL to manipulate and could predict the outcomes of assembling a large number of SW QTLs with marker-assisted selection of soybean varieties with desirable SW.

scholarly journals Impact of epistasis and QTL×environmental interaction on the mass filling rate during seed development of soybean

2012 ◽  
Vol 94 (2) ◽  
pp. 63-71 ◽  
Author(s):  
ZHENFENG JIANG ◽  
YINGPENG HAN ◽  
WEILI TENG ◽  
YONGGUANG LI ◽  
XUE ZHAO ◽  
...  
Keyword(s):  
Seed Development ◽  
Developmental Stages ◽  
Environmental Control ◽  
Recombinant Inbred Lines ◽  
Soybean Seed ◽  
Environment Interaction ◽  
Filling Rate ◽  
Seed Filling ◽  
Environment Effects ◽  
Initial Stage

SummarySeed filling rate of soybean has been shown to be a dynamic process in different developmental stages affected by both genotype and environment. The objective of the present study was to determine additive, epistatic and quantitative trait loci (QTLs)×environment interaction (QE) effects of the QTL underlying a seed filling rate of soybean. One hundred and forty-three recombinant inbred lines (RILs) derived from the cross of Charleston and Dongnong 594 were used with 2 years of field data (2004 and 2005). Eleven QTLs with significantly unconditional and conditional additive (a) effect and/or additive×environment interaction (ae) effect at different filling stages were identified. Of them six QTLs showed positive a effects and four QTLs had negative a effects on the seed filling rate during seed development. aa and aae effects of 12 pairs of QTLs were identified by unconditional mapping from the initial stage to the final stage. Thirteen pairs of QTLs underlying the seed filling rate with aa and aae effects were identified by conditional mapping. QTLs with aa and aae (additive×additive×environment) effects appeared to vary at different filling stages. Our results demonstrated that the mass filling rate in soybean seed were under genetic and environmental control.

scholarly journals SNP marker association for incrementing soybean seed protein content

2020 ◽  
Vol 6 ◽  
pp. 1-11
Author(s):  
Arthur Bernardeli ◽  
Aluízio Borem ◽  
Rodrigo Lorenzoni ◽  
Rafael Aguiar ◽  
Jessica Nayara Basilio Silva ◽  
...  
Keyword(s):  
Protein Content ◽  
Seed Protein ◽  
Recombinant Inbred Lines ◽  
Soybean Seed ◽  
Snp Markers ◽  
Snp Marker ◽  
Seed Protein Content ◽  
Single Marker Analysis ◽  
Soybean Breeding ◽  
Single Marker

Soybean seed protein content (SPC) has been decreasing throughout last decades and DNA marker association has shown its usefulness to improve this trait even in soybean breeding programs that focus primarily on soybean yield and seed oil content (SOC). Aiming to elucidate the association of two SNP markers (ss715630650 and ss715636852) to the SPC, a soybean population of 264 F5-derived recombinant inbred lines (RILs) from a bi-parental cross was tested in four environments. Through the single-marker analysis, the additive effect () and the portion of SPC variation due to the SNPs () for single and multi-environment data were assessed, and transgressive RILs for SPC were observed. The estimates revealed the association of both markers to SPC in most of environments. The marker ss715636852 was more frequently associated to SPC, including multi-environment data, and contributed up to  = 1.30% for overall SPC, whereas ss715630650 had significant association just in two locations, with contributions of  = 0.76% and  = 0.74% to overall SPC in Vic1 and Cap1, respectively. The RIL 84-13 was classified as an elite genotype due to its favorable alleles and high SPC means, which reached 53.78% in Cap1, and 46.33% in MET analysis. Thus, these results confirm the usefulness of the SNP marker ss715636852 in a soybean breeding program for SPC.

scholarly journals Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing

2021 ◽  
Vol 12 ◽  
Author(s):  
Daisuke Sekine ◽  
Mai Tsuda ◽  
Shiori Yabe ◽  
Takehiko Shimizu ◽  
Kayo Machita ◽  
...  
Keyword(s):  
Prediction Model ◽  
Protein Content ◽  
Quantitative Traits ◽  
Recurrent Selection ◽  
Seed Protein ◽  
Recombinant Inbred Lines ◽  
Initial Population ◽  
Seed Protein Content ◽  
Short Period ◽  
Simulation Based

Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.

scholarly journals QTL mapping for protein content in soybean cultivated in two tropical environments

2008 ◽  
Vol 43 (11) ◽  
pp. 1533-1541 ◽  
Author(s):  
Taís Cristina Bastos Soares ◽  
Pedro Ivo Vieira Good-God ◽  
Fábio Demolinari de Miranda ◽  
Janaína Bastos Soares ◽  
Ivan Schuster ◽  
...  
Keyword(s):  
Linkage Group ◽  
Protein Content ◽  
Phenotypic Variation ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Linkage Groups ◽  
Future Studies ◽  
Tropical Environments ◽  
The Mean ◽  
Qtl Effects

The objectives of this study were to detect quantitative trait loci (QTL) for protein content in soybean grown in two distinct tropical environments and to build a genetic map for protein content. One hundred eighteen soybean recombinant inbred lines (RIL), obtained from a cross between cultivars BARC 8 and Garimpo, were used. The RIL were cultivated in two distinct Brazilian tropical environments: Cascavel county, in Paraná, and Viçosa county, in Minas Gerais (24º57'S, 53º27'W and 20º45'S, 42º52'W, respectively). Sixty-six SSR primer pairs and 65 RAPD primers were polymorphic and segregated at a 1:1 proportion. Thirty poorly saturated linkage groups were obtained, with 90 markers and 41 nonlinked markers. For the lines cultivated in Cascavel, three QTL were mapped in C2, E and N linkage groups, which explained 14.37, 10.31 and 7.34% of the phenotypic variation of protein content, respectively. For the lines cultivated in Viçosa, two QTL were mapped in linkage groups G and #1, which explained 9.51 and 7.34% of the phenotypic variation of protein content. Based on the mean of the two environments, two QTL were identified: one in the linkage group E (9.90%) and other in the group L (7.11%). In order for future studies to consistently detect QTL effects of different environments, genotypes with greater stability should be used.

scholarly journals Genetic mapping high protein content QTL from soybean ‘Nanxiadou 25’ and candidate gene analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jia Wang ◽  
Lin Mao ◽  
Zhaoqiong Zeng ◽  
Xiaobo Yu ◽  
Jianqiu Lian ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Protein Content ◽  
Candidate Genes ◽  
Seed Protein ◽  
Recombinant Inbred Lines ◽  
High Protein ◽  
Quality Trait ◽  
Seed Protein Content ◽  
High Protein Content ◽  
Major Qtl

Abstract Background Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein content (SPC) is a valuable quality trait controlled by multiple genes in soybean. Results In this study, we performed quantitative trait loci (QTL) mapping, QTL-seq, and RNA sequencing (RNA-seq) to reveal the genes controlling protein content in the soybean by using the high protein content variety Nanxiadou 25. A total of 50 QTL for SPC distributed on 14 chromosomes except chromosomes 4, 12, 14, 17, 18, and 19 were identified by QTL mapping using 178 recombinant inbred lines (RILs). Among these QTL, the major QTL qSPC_20–1 and qSPC_20–2 on chromosome 20 were repeatedly detected across six tested environments, corresponding to the location of the major QTL detected using whole-genome sequencing-based QTL-seq. 329 candidate DEGs were obtained within the QTL region of qSPC_20–1 and qSPC_20–2 via gene expression profile analysis. Nine of which were associated with SPC, potentially representing candidate genes. Clone sequencing results showed that different single nucleotide polymorphisms (SNPs) and indels between high and low protein genotypes in Glyma.20G088000 and Glyma.16G066600 may be the cause of changes in this trait. Conclusions These results provide the basis for research on candidate genes and marker-assisted selection (MAS) in soybean breeding for seed protein content.

Construction of genetic linkage map and mapping of QTL for seed color in Brassica rapa

Genome ◽  
2012 ◽  
Vol 55 (12) ◽  
pp. 813-823 ◽  
Author(s):  
Berisso Kebede ◽  
Kuljit Cheema ◽  
David L. Greenshields ◽  
Changxi Li ◽  
Gopalan Selvaraj ◽  
...  
Keyword(s):  
Linkage Map ◽  
Ssr Markers ◽  
Brassica Rapa ◽  
Genetic Linkage ◽  
Genetic Linkage Map ◽  
Recombinant Inbred Lines ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Seed Color ◽  
A Genome

A genetic linkage map of Brassica rapa L. was constructed using recombinant inbred lines (RILs) derived from a cross between yellow-seeded cultivar Sampad and a yellowish brown seeded inbred line 3-0026.027. The RILs were evaluated for seed color under three conditions: field plot, greenhouse, and controlled growth chambers. Variation for seed color in the RILs ranged from yellow, like yellow sarson, to dark brown/black even though neither parent had shown brown/black colored seeds. One major QTL (SCA9-2) and one minor QTL (SCA9-1) on linkage group (LG) A9 and two minor QTL (SCA3-1, SCA5-1) on LG A3 and LG A5, respectively, were detected. These collectively explained about 67% of the total phenotypic variance. SCA9-2 mapped in the middle of LG A9, explained about 55% phenotypic variance, and consistently expressed in all environments. The second QTL on LG A9 was ∼70 cM away from SCA9-2, suggesting that independent assortment of these QTLs is possible. A digenic epistatic interaction was found between the two main effect QTL on LG A9; and the epistasis × environment interaction was nonsignificant, suggesting stability of the interaction across the environments. The QTL effect on LG A9 was validated using simple sequence repeat (SSR) markers from the two QTL regions of this LG on a B1S1 population (F1 backcrossed to Sampad followed by self-pollination) segregating for brown and yellow seed color, and on their self-pollinated progenies (B1S2). The SSR markers from the QTL region SCA9-2 showed a stronger linkage association with seed color as compared with the marker from SCA9-1. This suggests that the QTL SCA9-2 is the major determinant of seed color in the A genome of B. rapa.

scholarly journals 38 Using pooled data for single-step genomic prediction: Impact of within-pool variance and size

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 9-9
Author(s):  
Johnna L Baller ◽  
Stephen D Kachman ◽  
Larry A Kuehn ◽  
Matthew L Spangler
Keyword(s):  
Phenotypic Variation ◽  
Equal Opportunity ◽  
Cost Effective ◽  
Single Step ◽  
Pool Size ◽  
Individual Data ◽  
Phenotypic Data ◽  
Breeding Values ◽  
Mean Values ◽  
Genetic Evaluations

Abstract Economically relevant traits (ERT) are routinely collected within commercial segments of the beef industry but are rarely included in genetic evaluations because of unknown pedigrees. Individual relationships could be resurrected with genomics, which would be costly; pooling DNA and phenotypic data provides a cost-effective solution. A simulated beef cattle population consisting of 15 generations was genotyped with approximately 50k markers (841 quantitative trait loci were located across the genome) and phenotyped for a moderately heritable trait. Individuals from generation 15 were included in pools (observed genotype and phenotype were mean values of a group). Estimated breeding values (EBV) were generated from a single-step GBLUP model. The effects of pooling strategy (random and minimizing or uniformly maximizing phenotypic variation), pool size (1, 2, 10, 20, 50, 100, or no data from generation 15), and generational gaps of genotyping on EBV accuracy (correlation of EBV with true breeding values) were quantified. Greatest EBV accuracies of sires and dams were observed when no gap between genotyped parents and pooled offspring occurred. The EBV accuracies resulting from pools were greater than no data from generation 15 regardless of sire or dam genotyping. Minimizing phenotypic variation increased EBV accuracy by 8% and 9% over random pooling and uniformly maximizing phenotypic variation, respectively. Pool size of 2 was the only scenario that did not significantly decrease EBV accuracy compared to individual data when pools were formed randomly or by uniformly maximizing phenotypic variation (P > 0.05). Pool sizes of 2, 10, 20, or 50 did not generally lead to EBV accuracies that were statistically different than individual data when pools were constructed to minimize phenotypic variation (P > 0.05). Pooled genotyping to garner commercial-level phenotypes for genetic evaluations seems plausible, although differences exist depending on pool size and pool formation strategy. The USDA is an equal opportunity employer.

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