QTL analysis of soybean oil content under 17 environments

2014 ◽  
Vol 94 (2) ◽  
pp. 245-261 ◽  
Author(s):  
Zhaoming Qi ◽  
Xue Han ◽  
Meng Hou ◽  
Dawei Xin ◽  
Zhongyu Wang ◽  
...  
Keyword(s):  
Soybean Oil ◽  
Qtl Analysis ◽  
Composite Interval Mapping ◽  
Oil Content ◽  
Interval Mapping ◽  
Complex Nature ◽  
Environment Interaction ◽  
Plant Materials ◽  
Multiple Interval Mapping ◽  
Main Effects

Qi, Z., Han, X., Hou, M., Xin, D., Wang, Z., Zhu, R., Hu, Z., Jiang, H., Li, C., Liu, C., Hu, G. and Chen, Q. 2014. QTL analysis of soybean oil content under 17 environments. Can. J. Plant Sci. 94: 245–261. Soybean oil content is a key trait driver of successful soybean quality. Due to its complex nature, less stable quantitative trait loci (QTL) are known. The goal of this study was to identify important and stable QTL affecting soybean oil content using recombination inbred lines (RILs) derived from a cross between Charleston and Dongnong594. The plant materials were planted in three environments across 9 yr in China. The genetic effects were then partitioned into additive main effects (A), epistatic main effects (AA) and their environment interaction effects (AE and AAE) by using composite interval mapping, multiple interval mapping and composite interval mapping in a mixed linear model. Fifty-six QTL were identified on 15 of 20 soybean chromosomes excluding LG C1, D2, E, M and O by composite interval mapping and multiple interval mapping methods. Seven oil content QTL detected on LG A1, 1 on LG A2, 5 on LG B1, 4 on LG B2, 8 on LG C2, 11 on LG D1a, 2 on LG D1b, 4 on LG F, 5 on LG G, 2 on LG H, 1 on LG I, 1 on LG J, 1 on LG K, 2 on LG L and 2 on LG N. Eight QTL showed a good stability across 17 environments. The additive main-effect QTL contributed more phenotypic variation than the epistasis and environmental interaction. This indicated that it is feasible to improve soybean oil content by marker-assisted selection.

Genotype-by-Environment Interactions for Seed Composition Traits of Breeding Lines in The Uniform Peanut Performance Test

2008 ◽  
Vol 35 (2) ◽  
pp. 130-138 ◽  
Author(s):  
T. G. Isleib ◽  
B. L. Tillman ◽  
H. E. Pattee ◽  
T. H. Sanders ◽  
K. W. Hendrix ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Fatty Acid ◽  
Oil Content ◽  
Environmental Variation ◽  
Genetic Influence ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Main Effects ◽  
Estimation Of Variance ◽  
Restricted Maximum Likelihood Estimation

Abstract Peanut composition is influenced by several groups of factors: environmental, genetic, and their interaction. This study evaluated the relative contributions of these factors using data from the USDA-ARS quality testing program using samples from the multi-state Uniform Peanut Performance Tests (UPPT). Data were subjected to restricted maximum likelihood estimation of variance components reflecting the main effects of year, production region, location within region, genotype (cultivar or breeding line), and kernel grade (“seed size”) within genotype, and the interactions among these main effects. Genetic variation in oil content was low (9% of total variation); however, fatty acid composition of the oil was highly influenced by genotype (34–77%) with the exception of lignoceric acid (1%). Genetic influence on tocopherols was generally less than that of fatty acids. Environmental variation of tocopherols was greater than the variation attributable to genotype-by-environment interaction. The lowest genetic variation was observed in sugar content; however, environmental variation was high (68%). The magnitude of genetic influence on oil content and fatty acid concentrations suggests that these traits are amenable to improvement through breeding.

scholarly journals QTL mapping of physiological traits at the booting stage in rice under low temperature combined with nitrogen fertilization

2019 ◽  
Vol 55 (No. 4) ◽  
pp. 146-155
Author(s):  
Shu Ming Yang ◽  
Fei Fei Zhang ◽  
Su Hua Zhang ◽  
Gui Yong Li ◽  
Li Qiong Zeng ◽  
...  
Keyword(s):  
Composite Interval Mapping ◽  
Molecular Mechanisms ◽  
Cold Water ◽  
Flag Leaf ◽  
Chilling Stress ◽  
Interval Mapping ◽  
Physiological Traits ◽  
Environment Interaction ◽  
Nitrogen Application ◽  
Cold Tolerant

Further dissection of physiological molecular mechanisms is indispensable to alleviate rice yield losses resulting from cold injury. By using 105 near-isogenic lines (NILs) derived from a backcross between cv. Lijiangxintuanheigu (LTH) and cv. Towada, we detected quantitative trait loci (QTLs) for physiological traits of the rice flag leaf, based on polymorphic simple sequence repeat (SSR) markers, inclusive composite interval mapping (ICIM), mixed composite interval mapping (MCIM) approaches and phenotypic value subjected to combine with cold-water stress and three nitrogen application rates. By using ICIM, a total of 34 QTLs with additive effects (A-QTLs) were identified on chromosomes 1, 3, 4, 5, 6, 7 and 10, and the phenotypic variation (R<sup>2</sup>) explained by each QTL ranged from 8.46 to 29.14%. By using MCIM, 20 A-QTLs and 14 pairs of QTLs with epistatic × environment interaction effects (Epistatic QTLs) were detected, the contribution of environment interaction (H<sup>2</sup>AE) was 0.87 to 7.36%, while the contribution rates of E-QTL were from 0.97 to 3.58%. Fourteen A-QTLs were detected by ICIM and MCIM, which may serve as a basis for fine-mapping and candidate gene studies, and providing strategies for the development of cold-tolerant rice cultivars and nitrogen application to alleviate chilling stress.  

Inclusive composite-interval mapping reveals quantitative trait loci for plant architectural traits in sorghum (Sorghum bicolor)

2019 ◽  
Vol 70 (8) ◽  
pp. 659
Author(s):  
Huawen Zhang ◽  
Runfeng Wang ◽  
Bin Liu ◽  
Erying Chen ◽  
Yanbing Yang ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Sorghum Bicolor ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Recombinant Inbred Lines ◽  
Interval Mapping ◽  
Environment Interaction ◽  
Trait Loci ◽  
Inclusive Composite Interval Mapping ◽  
Architectural Traits

Architecture-efficient sorghum (Sorghum bicolor (L.) Moench) has erect leaves forming a compact canopy that enables highly effective utilisation of solar radiation; it is suitable for high-density planting, resulting in an elevated overall production. Development of sorghum ideotypes with optimal plant architecture requires knowledge of the genetic basis of plant architectural traits. The present study investigated seven production-related architectural traits by using 181 sorghum recombinant inbred lines (RILs) with contrasting architectural phenotypes developed from the cross Shihong 137 × L-Tian. Parents along with RILs were phenotyped for plant architectural traits for two consecutive years (2012, 2013) at two locations in the field. Analysis of variance revealed significant (P ≤ 0.05) differences among RILs for architectural traits. All traits showed medium to high broad-sense heritability estimates (0.43–0.94) and significant (P ≤ 0.05) genotype × environment effects. We employed 181 simple sequence repeat markers to identify quantitative trait loci (QTLs) and the effects of QTL × environment interaction based on the inclusive composite interval mapping algorithm. In total, 53 robust QTLs (log of odds ≥4.68) were detected for these seven traits and explained 2.11–12.11% of phenotypic variation. These QTLs had small effects of QTL × environment interaction and yet significant epistatic effects, indicating that they could stably express across environments but influence phenotypes through strong interaction with non-allelic loci. The QTLs and linked markers need to be verified through function and candidate-gene analyses. The new knowledge of the genetic regulation of architectural traits in the present study will provide a theoretical basis for the genetic improvement of architectural traits in sorghum.

scholarly journals β-composite Interval Mapping for robust QTL analysis

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0208234 ◽  
Author(s):  
Md. Mamun Monir ◽  
Mita Khatun ◽  
Md. Nurul Haque Mollah
Keyword(s):  
Qtl Analysis ◽  
Composite Interval Mapping ◽  
Interval Mapping

scholarly journals Deciphering the Genetic Architecture of Plant Height in Soybean Using Two RIL Populations Sharing a Common M8206 Parent

Plants ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 373 ◽  
Author(s):  
Yongce Cao ◽  
Shuguang Li ◽  
Guoliang Chen ◽  
Yanfeng Wang ◽  
Javaid Akhter Bhat ◽  
...  
Keyword(s):  
Plant Height ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Mixed Model ◽  
Interval Mapping ◽  
Interaction Effects ◽  
Epistatic Effect ◽  
Environment Interaction ◽  
Additive Effects ◽  
Yield And Quality

Plant height (PH) is an important agronomic trait that is closely related to soybean yield and quality. However, it is a complex quantitative trait governed by multiple genes and is influenced by environment. Unraveling the genetic mechanism involved in PH, and developing soybean cultivars with desirable PH is an imperative goal for soybean breeding. In this regard, the present study used high-density linkage maps of two related recombinant inbred line (RIL) populations viz., MT and ZM evaluated in three different environments to detect additive and epistatic effect quantitative trait loci (QTLs) as well as their interaction with environments for PH in Chinese summer planting soybean. A total of eight and 12 QTLs were detected by combining the composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) methods in MT and ZM populations, respectively. Among these QTLs, nine QTLs viz., QPH-2, qPH-6-2MT, QPH-6, qPH-9-1ZM, qPH-10-1ZM, qPH-13-1ZM, qPH-16-1MT, QPH-17 and QPH-19 were consistently identified in multiple environments or populations, hence were regarded as stable QTLs. Furthermore, Out of these QTLs, three QTLs viz., qPH-4-2ZM, qPH-15-1MT and QPH-17 were novel. In particular, QPH-17 could detect in both populations, which was also considered as a stable and major QTL in Chinese summer planting soybean. Moreover, eleven QTLs revealed significant additive effects in both populations, and out of them only six showed additive by environment interaction effects, and the environment-independent QTLs showed higher additive effects. Finally, six digenic epistatic QTLs pairs were identified and only four additive effect QTLs viz., qPH-6-2MT, qPH-19-1MT/QPH-19, qPH-5-1ZM and qPH-17-1ZM showed epistatic effects. These results indicate that environment and epistatic interaction effects have significant influence in determining genetic basis of PH in soybean. These results would not only increase our understanding of the genetic control of plant height in summer planting soybean but also provide support for implementing marker assisted selection (MAS) in developing cultivars with ideal plant height as well as gene cloning to elucidate the mechanisms of plant height.

scholarly journals Genotype-by-environment interaction for seed yield and oil content of safflower (Carthamus tinctorius L.) genotypes

Genetika ◽  
2021 ◽  
Vol 53 (1) ◽  
pp. 11-22
Author(s):  
Hasan Koç
Keyword(s):  
Seed Yield ◽  
Oil Content ◽  
Genotype By Environment Interaction ◽  
Carthamus Tinctorius ◽  
Climatic Conditions ◽  
High Yield ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Main Effects

This research was carried out to determine genotype-by-environment interaction of safflower genotypes tested from 2014 to 2017. Konya, where the research was carried out, is the location with the most irregular and the lowest precipitation in Turkey. In this research, the variance analysis over years and genotypes showed that the main effects on genotypes made by year and genotype-by-year interaction were statistically significant (p<0.01) for all characteristics examined. The climatic conditions, especially the amount and distribution of precipitation, over the years allowed genotypes to perform substantially differently in seed yield and oil content of safflower genotypes. The biplot analysis provided significant advantages in identifying the promising genotypes. The genotypes showed similar patterns of performance across the years, while the amount and distribution of precipitation showed similar patterns. The experimental results revealed that the desired genotypes in terms of both stability and high yield, such as G?kt?rk, G7, and Din?er, G5 and G9 and oil content, such as G?kt?rk, Balc?, and Linas, existed. In comparison to oil content, seed yield was more sensitive to environmental factors.

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