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 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.

Identification of quantitative trait loci associated with soybean seed protein content using two populations derived from crosses between Glycine max and Glycine soja

2014 ◽  
Vol 12 (S1) ◽  
pp. S104-S108 ◽  
Author(s):  
Long Yan ◽  
Li-Li Xing ◽  
Chun-Yan Yang ◽  
Ru-Zhen Chang ◽  
Meng-Chen Zhang ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Protein Content ◽  
Quantitative Trait ◽  
Seed Protein ◽  
Glycine Soja ◽  
Soybean Seed ◽  
Transgressive Segregation ◽  
Seed Protein Content ◽  
Trait Loci ◽  
Two Populations

Seed protein content is one of the most important traits controlled by quantitative trait loci (QTLs) in soybean. In this study, a Glycine soja accession (ZYD2738) was crossed with two elite cultivars Jidou 12 and Jidou 9 separately and subsequently the resulting F2:3 populations were used to identify QTLs associated with seed protein content. Protein contents in either population appeared to have a normal distribution with transgressive segregation. A total of five QTLs associated with high protein content were identified and mapped to chromosomes 2, 6, 13, 18 and 20, respectively. Of these QTLs, three (qPRO_2_1, qPRO_13_1 and qPRO_20_1) were identified in the same region in both the populations, whereas the other two (qPRO_6_1 and qPRO_18_1) were mapped in two different regions. qPRO_2_1 appears to be a novel protein QTL. qPRO_6_1, qPRO_18_1 and qPRO_20_1 had additive effects on seed protein content, while qPRO_13_1 had an over-dominant effect on seed protein content. These QTLs and their linked markers could serve as effective tools for marker-assisted selection to increase seed protein content.

scholarly journals Modeling the Competitive Effect of Euphorbia dracunculoides and Astragalus sp. in Zero Input Rainfed Chickpea

2018 ◽  
Vol 36 (0) ◽  
Author(s):  
R.M. IKRAM ◽  
A. TANVEER ◽  
R. MAQBOOL ◽  
M.A. NADEEN
Keyword(s):  
Protein Content ◽  
Critical Period ◽  
Field Experiments ◽  
Seed Protein ◽  
Yield Loss ◽  
Seed Protein Content ◽  
Yield Losses ◽  
Competition Period ◽  
Crop Competition ◽  
Weed Removal

ABSTRACT: Brown chickpea (Cicer arietinum L.) is one of the two chickpea types grown in Pakistan and other countries. The critical period for weed removal in a rainfed chickpea system is an important consideration in devising weed management strategies. Field experiments were conducted in the winter season of 2011 and 2012 to determine the extent of yield loss with different periods of weed crop competition. Seven weed crop competition periods (0, 45, 60, 75, 90, 105 and 160 days after sowing - DAS) were used to identify the critical period for weed removal in rainfed chickpea. Experimental plots were naturally infested with Euphorbia dracunculoides and Astragalus sp. in both years. Individual, composite density and dry weights of E. dracunculoides and Astragalussp. increased significantly with an increase in the competition period. However, yield and yield-contributing traits of chickpea significantly decreased with an increase in the competition period. Chickpea seed yield loss was 11-53% in different weed crop competition periods. Euphorbia dracunculoides and Astragalus sp. removed 39.9 and 36.9 kg ha-1 of N, 9.61 and 7.27 kg ha-1 of P and 38.3 and 36.9 kg ha-1 of K, respectively. Season long weed competition (160 days after sowing) resulted in 19.5% seed protein content compared with 24.5% seed protein content in weed-free chickpea. A Logistic equation was fitted to yield data in response to increasing periods of weed crop competition. The critical timing of weed removal at 5 and 10% acceptable yield losses were 26 and 39 DAS, respectively. The observed critical period suggests that in rainfed chickpea, a carefully timed weed removal could prevent grain yield losses.

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