scholarly journals Environmental and Genetic Regulation of Plant Height in Soybean

2020 ◽  
Author(s):  
Qing Yang ◽  
Gaoming Lin ◽  
Huiyong Lv ◽  
Cunhu Wang ◽  
Yongqing Yang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Properties ◽  
Plant Height ◽  
Meteorological Factors ◽  
Pearson Correlation ◽  
Genetic Regulation ◽  
Soybean Plant ◽  
Environment Interaction ◽  
Shoot Architecture ◽  
Soybean Genotypes

Abstract Shoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce. Here, 308 representative soybean lines from a core collection and 168 F9:11 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH, SNN and AIL, respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80%~26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD, AMaT, pH, and AN had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties. Overall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

scholarly journals Environmental and Genetic Regulation of Plant Height in Soybean

2020 ◽  
Author(s):  
Qing Yang ◽  
Gaoming Lin ◽  
Huiyong Lv ◽  
Cunhu Wang ◽  
Yongqing Yang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Properties ◽  
Plant Height ◽  
Meteorological Factors ◽  
Day Length ◽  
Maximum Temperature ◽  
Soybean Plant ◽  
Environment Interaction ◽  
Shoot Architecture ◽  
Average Maximum

Abstract Background: Shoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce.Results: In this study, 308 representative soybean lines from a core collection and 168 F9 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction (G × E) effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs (quantitative trait locus) for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH (shoot height), SNN (stem node number) and AIL (average internode length), respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80% - 26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19-1, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA (principal component analysis) revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD (average day-length), AMaT (average maximum temperature), pH, and AN (available nitrogen) had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties.Conclusions: Overall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

scholarly journals Environmental and Genetic Regulation of Plant Height in Soybean

2021 ◽  
Author(s):  
Qing Yang ◽  
Gaoming Lin ◽  
Huiyong Lv ◽  
Cunhu Wang ◽  
Yongqing Yang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Properties ◽  
Plant Height ◽  
Meteorological Factors ◽  
Day Length ◽  
Maximum Temperature ◽  
Soybean Plant ◽  
Environment Interaction ◽  
Shoot Architecture ◽  
Average Maximum

Abstract Background:Shoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce.Results:In this study, 308 representative soybean lines from a core collection and 168 F9 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction (G × E) effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs (quantitative trait locus) for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH (shoot height), SNN (stem node number) and AIL (average internode length), respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80% - 26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19-1, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA (principal component analysis) revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD (average day-length), AMaT (average maximum temperature), pH, and AN (available nitrogen) had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties.Conclusions:Overall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

scholarly journals Environmental and Genetic Regulation of Plant Height in Soybean

2021 ◽  
Author(s):  
Qing Yang ◽  
Gaoming Lin ◽  
Huiyong Lv ◽  
Cunhu Wang ◽  
Yongqing Yang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Properties ◽  
Plant Height ◽  
Meteorological Factors ◽  
Day Length ◽  
Maximum Temperature ◽  
Soybean Plant ◽  
Environment Interaction ◽  
Shoot Architecture ◽  
Average Maximum

Abstract Background:Shoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce.Results:In this study, 308 representative soybean lines from a core collection and 168 F9 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction (G × E) effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs (quantitative trait locus) for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH (shoot height), SNN (stem node number) and AIL (average internode length), respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80% - 26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19-1, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA (principal component analysis) revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD (average day-length), AMaT (average maximum temperature), pH, and AN (available nitrogen) had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties.Conclusions:Overall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

scholarly journals Environmental and genetic regulation of plant height in soybean

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qing Yang ◽  
Gaoming Lin ◽  
Huiyong Lv ◽  
Cunhu Wang ◽  
Yongqing Yang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Properties ◽  
Plant Height ◽  
Meteorological Factors ◽  
Day Length ◽  
Maximum Temperature ◽  
Soybean Plant ◽  
Environment Interaction ◽  
Shoot Architecture ◽  
Average Maximum

Abstract Background Shoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce. Results In this study, 308 representative soybean lines from a core collection and 168 F9 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction (G × E) effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs (quantitative trait locus) for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH (shoot height), SNN (stem node number) and AIL (average internode length), respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80–26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19–1, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA (principal component analysis) revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD (average day-length), AMaT (average maximum temperature), pH, and AN (available nitrogen) had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties. Conclusions Overall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

scholarly journals Effect of Soil Geomechanical Properties and Geo-Environmental Factors on Landslide Predisposition at Mount Oku, Cameroon

2020 ◽  
Vol 17 (18) ◽  
pp. 6795
Author(s):  
Wamba Danny Love Djukem ◽  
Anika Braun ◽  
Armand Sylvain Ludovic Wouatong ◽  
Christian Guedjeo ◽  
Katrin Dohmen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Properties ◽  
Environmental Factor ◽  
Pearson Correlation ◽  
Google Earth ◽  
Information Value ◽  
High Water Content ◽  
Prediction Ability ◽  
Geomechanical Properties ◽  
Model Combining

In this work, we explored a novel approach to integrate both geo-environmental and soil geomechanical parameters in a landslide susceptibility model. A total of 179 shallow to deep landslides were identified using Google Earth images and field observations. Moreover, soil geomechanical properties of 11 representative soil samples were analyzed. The relationship between soil properties was evaluated using the Pearson correlation coefficient and geotechnical diagrams. Membership values were assigned to each soil property class, using the fuzzy membership method. The information value method allowed computing the weight value of geo-environmental factor classes. From the soil geomechanical membership values and the geo-environmental factor weights, three landslide predisposition models were produced, two separate models and one combined model. The results of the soil testing allowed classifying the soils in the study area as highly plastic clays, with high water content, swelling, and shrinkage potential. Some geo-environmental factor classes revealed their landslide prediction ability by displaying high weight values. While the model with only soil properties tended to underrate unstable and stable areas, the model combining soil properties and geo-environmental factors allowed a more precise identification of stability conditions. The geo-environmental factors model and the model combining geo-environmental factors and soil properties displayed predictive powers of 80 and 93%, respectively. It can be concluded that the spatial analysis of soil geomechanical properties can play a major role in the detection of landslide prone areas, which is of great interest for site selection and planning with respect to sustainable development at Mount Oku.

scholarly journals Mathematical modelling of soybean var. anjasmoro plant growth

2021 ◽  
Vol 2072 (1) ◽  
pp. 012009
Author(s):  
P Aditiawati ◽  
S Viridi ◽  
S Palupi ◽  
R Rostiani ◽  
M D Samosir ◽  
...  
Keyword(s):  
Plant Height ◽  
Correlation Coefficient ◽  
Sampling Method ◽  
Pearson Correlation ◽  
Soybean Plant ◽  
Pearson Correlation Coefficient ◽  
Destructive Sampling ◽  
Plant Cultivation ◽  
Cultivation Process ◽  
Number Of Leaves

Abstract Thorough understanding of interactions between all factors involved in soybean plant cultivation process is needed to increase the yield. This study is aimed to determine which interaction has the highest correlation according to the Pearson correlation coefficient, to define a certain model for said interaction, and to confirm the identicality of all samples that were observed using destructive sampling method. After being tested using Pearson correlation coefficient, the highest r value is 0,81 which is between plant height and number of leaves, proving they are highly related. Both parameters were simulated in several different ways until it was found that number of leaves variable is best described as function of plant height variable, the equation is y1 = 0,85x1 + c1 where y1 represents number of leaves and x1 represents plant height. Identicality between all plants is not confirmed, thus destructive sampling method is not recommended to be applied for similar studies.

scholarly journals Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hae-Un Jung ◽  
Won Jun Lee ◽  
Tae-Woong Ha ◽  
Ji-One Kang ◽  
Jihye Kim ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Bayes Factor ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interaction Analysis ◽  
Carbohydrate Intake ◽  
Calorie Intake ◽  
Environment Interaction

AbstractMultiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.

scholarly journals Genetic regulation of shoot architecture in cucumber

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiaofeng Liu ◽  
Jiacai Chen ◽  
Xiaolan Zhang
Keyword(s):  
Leaf Shape ◽  
Economic Value ◽  
Genetic Regulation ◽  
Strong Impact ◽  
Crop Species ◽  
Visual Appearance ◽  
Regulatory Pathways ◽  
Architectural Features ◽  
Shoot Architecture ◽  
Plant Form

AbstractCucumber (Cucumis sativus L.) is an important vegetable crop species with great economic value. Shoot architecture determines the visual appearance of plants and has a strong impact on crop management and yield. Unlike most model plant species, cucumber undergoes vegetative growth and reproductive growth simultaneously, in which leaves are produced from the shoot apical meristem and flowers are generated from leaf axils, during the majority of its life, a feature representative of the Cucurbitaceae family. Despite substantial advances achieved in understanding the regulation of plant form in Arabidopsis thaliana, rice, and maize, our understanding of the mechanisms controlling shoot architecture in Cucurbitaceae crop species is still limited. In this review, we focus on recent progress on elucidating the genetic regulatory pathways underlying the determinant/indeterminant growth habit, leaf shape, branch outgrowth, tendril identity, and vine length determination in cucumber. We also discuss the potential of applying biotechnology tools and resources for the generation of ideal plant types with desired architectural features to improve cucumber productivity and cultivation efficiency.

scholarly journals Gene–gene and gene-environment interactions on cord blood total IgE in Chinese Han children

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Li Hua ◽  
Quanhua Liu ◽  
Jing Li ◽  
Xianbo Zuo ◽  
Qian Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Cord Blood ◽  
Synergistic Effects ◽  
Gene Interaction ◽  
Independent Effect ◽  
Environment Interaction ◽  
Nucleotide Polymorphisms ◽  
Chinese Han ◽  
Gene Environment Interaction ◽  
Gene Environment

Abstract Background IL13, IL4, IL4RA, FCER1B and ADRB2 are susceptible genes of asthma and atopy. Our previous study has found gene–gene interactions on asthma between these genes in Chinese Han children. Whether the interactions begin in fetal stage, and whether these genes interact with prenatal environment to enhance cord blood IgE (CBIgE) levels and then cause subsequent allergic diseases have yet to be determined. This study aimed to determine whether there are gene–gene and gene-environment interactions on CBIgE elevation among the aforementioned five genes and prenatal environmental factors in Chinese Han population. Methods 989 cord blood samples from a Chinese birth cohort were genotyped for nine single-nucleotide polymorphisms (SNPs) in the five genes, and measured for CBIgE levels. Prenatal environmental factors were collected using a questionnaire. Gene–gene and gene-environment interactions were analyzed with generalized multifactor dimensionality methods. Results A four-way gene–gene interaction model (IL13 rs20541, IL13 rs1800925, IL4 rs2243250 and ADRB2 rs1042713) was regarded as the optimal one for CBIgE elevation (testing balanced accuracy = 0.5805, P = 9.03 × 10–4). Among the four SNPs, only IL13 rs20541 was identified to have an independent effect on elevated CBIgE (odds ratio (OR) = 1.36, P = 3.57 × 10–3), while the other three had small but synergistic effects. Carriers of IL13 rs20541 TT, IL13 rs1800925 CT/TT, IL4 rs2243250 TT and ADRB2 rs1042713 AA were estimated to be at more than fourfold higher risk for CBIgE elevation (OR = 4.14, P = 2.69 × 10–2). Gene-environment interaction on elevated CBIgE was found between IL4 rs2243250 and maternal atopy (OR = 1.41, P = 2.65 × 10–2). Conclusions Gene–gene interaction between IL13 rs20541, IL13 rs1800925, IL4 rs2243250 and ADRB2 rs1042713, and gene-environment interaction between IL4 rs2243250 and maternal atopy begin in prenatal stage to augment IgE production in Chinese Han children.

Soybean Sensitivity to Florpyrauxifen-benzyl during Reproductive Growth and the Impact on Subsequent Progeny

2017 ◽  
Vol 32 (2) ◽  
pp. 135-140 ◽  
Author(s):  
M. Ryan Miller ◽  
Jason K. Norsworthy
Keyword(s):  
Plant Height ◽  
Field Studies ◽  
Yield Reduction ◽  
Growth Stages ◽  
Soybean Plant ◽  
Reproductive Growth ◽  
Similar Reduction ◽  
Structural Class ◽  
Soybean Plants ◽  
The Impact

AbstractTo address recent concerns related to auxin herbicide drift onto soybean, a study was developed to understand the susceptibility of the reproductive stage of soybean to a new auxin herbicide compared with dicamba. Florpyrauxifen-benzyl is under development as the second herbicide in a new structural class of synthetic auxins, the arylpicolinates. Field studies were conducted to (1) evaluate and compare reproductive soybean injury and yield following applications of florpyrauxifen-benzyl or dicamba across various concentrations and reproductive growth stages and (2) determine whether low-rate applications of florpyrauxifen-benzyl or dicamba to soybean in reproductive stages would have similar effect on the progeny of the affected plants. Soybean were treated with 0, 1/20, or 1/160, of the 1X rate of florpyrauxifen-benzyl (30 g ai ha−1) or dicamba (560 g ae ha−1) at R1, R2, R3, R4, or R5 growth stage. Soybean plant height and yield was reduced from 1/20X dicamba across all reproductive stages. High drift rates (1/20X) of florpyrauxifen-benzyl also reduced soybean plant height >25% and yield across R1 to R4 stages. Germination, stand, plant height, and yield of the offspring of soybean plants treated with dicamba and florpyrauxifen-benzyl were significantly affected. Dicamba applied at a rate of 1/20X at R4 and R5 resulted in 20% and 35% yield reduction for the offspring, respectively. A similar reduction occurred from florpyrauxifen-benzyl applied at R4 and R5 at the 1/20X rate, resulting in 15% to 24% yield reduction for the offspring, respectively. Based on these findings, it is suggested that growers use caution when applying these herbicides in the vicinity of reproductive soybean.

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