Environmental and Genetic Regulation of Plant Height in Soybean
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.
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Additional file 1: Table S1 Putative QTLs detected for plant height traits by MapQTL 6.0 and QTL IciMaping 4.1 using 168 F9 soybean RILs under different environments. (XLSX 18.8 KB)
Posted 12 Jan, 2021
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On 28 Dec, 2020
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On 13 Jul, 2020
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Received 13 Mar, 2020
On 28 Feb, 2020
On 27 Feb, 2020
Received 19 Feb, 2020
Received 11 Feb, 2020
On 31 Jan, 2020
On 27 Jan, 2020
Invitations sent on 24 Jan, 2020
On 27 Dec, 2019
On 26 Dec, 2019
On 26 Dec, 2019
On 23 Dec, 2019
Environmental and Genetic Regulation of Plant Height in Soybean
Posted 12 Jan, 2021
On 31 Dec, 2020
On 28 Dec, 2020
On 18 Dec, 2020
On 13 Dec, 2020
On 13 Dec, 2020
On 13 Dec, 2020
On 17 Nov, 2020
Received 23 Oct, 2020
On 01 Oct, 2020
Received 04 Aug, 2020
Invitations sent on 13 Jul, 2020
On 13 Jul, 2020
On 09 Jun, 2020
On 08 Jun, 2020
On 08 Jun, 2020
On 27 Apr, 2020
Received 13 Mar, 2020
Received 13 Mar, 2020
On 28 Feb, 2020
On 27 Feb, 2020
Received 19 Feb, 2020
Received 11 Feb, 2020
On 31 Jan, 2020
On 27 Jan, 2020
Invitations sent on 24 Jan, 2020
On 27 Dec, 2019
On 26 Dec, 2019
On 26 Dec, 2019
On 23 Dec, 2019
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5