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.

Mapping QTLs for 100-seed weight in an interspecific soybean cross of Williams 82 (Glycine max) and PI 366121 (Glycine soja)

2017 ◽  
Vol 68 (2) ◽  
pp. 148 ◽  
Author(s):  
Krishnanand P. Kulkarni ◽  
Sovetgul Asekova ◽  
Dong-Ho Lee ◽  
Kristin Bilyeu ◽  
Jong Tae Song ◽  
...  
Keyword(s):  
Seed Weight ◽  
Glycine Soja ◽  
Recombinant Inbred Lines ◽  
Molecular Genetic ◽  
Interval Mapping ◽  
Major Influence ◽  
Environment Interaction ◽  
Molecular Genetic Basis ◽  
The Individual ◽  
100 Seed Weight

Seed weight can be an important component for soybean quality and yield. The objective of the present study was to identify quantitative trait loci (QTLs) for 100-seed weight by using 169 recombinant inbred lines (RILs) derived from the cross Williams 82 × PI 366121. The parental lines and RILs were grown for four consecutive years (2012–15) in the field. The seeds were harvested after maturity, dried and used to measure 100-seed weight. Analysis of variance indicated significant differences among the RILs for 100-seed weight. The environment had significant effect on seed-weight expression as indicated by the genotype × environment interaction. QTL analysis employing inclusive composite interval mapping of additive QTLs implemented in QTL IciMapping (Version 4.1) identified nine QTLs (LOD >3) on chromosomes 1, 2, 6, 8, 13, 14, 17 and 20. The individual QTLs explained phenotypic variation in the range 6.1–12.4%. The QTLs were detected in one or two environments, indicating major influence of the growing environment on seed-weight expression. Four QTLs identified in this study, qSW-02_1, qSW-06_1, qSW-13_1 and qSW-14_1, were found to be new QTLs. The findings of the study may be helpful to reveal the molecular genetic basis of the seed-weight trait in soybean.

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.

Identification of quantitative trait loci underlying lutein content in soybean seeds across multiple environments

2017 ◽  
Vol 155 (8) ◽  
pp. 1263-1271 ◽  
Author(s):  
W. L. TENG ◽  
W. J. FENG ◽  
J. Y. ZHANG ◽  
N. XIA ◽  
J. GUO ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Average Distance ◽  
Recombinant Inbred Lines ◽  
Chromosome 9 ◽  
Soybean Seeds ◽  
Trait Loci ◽  
Breeding Programmes ◽  
Lutein Content

SUMMARYLutein benefits human health significantly, including that of the eyes, skin and heart. Therefore, increasing lutein content in soybean seeds is an important objective for breeding programmes. However, no information about soybean lutein-related quantitative trait loci (QTL) has been reported, as of 2016. The aim of the present study was to identify QTLs underlying the lutein content in soybean seeds. A population including 129 recombinant inbred lines was developed from the cross between ‘Dongnong46’ (lutein 13·10 µg/g) and ‘L-100’ (lutein 23·96 µg/g), which significantly differed in seed lutein contents. This population was grown in ten environments including Harbin in 2012, 2013, 2014 and 2015; Hulan in 2013, 2014 and 2015; and Acheng in 2013, 2014 and 2015. A total of 213 simple sequence repeat markers were used to construct the genetic linkage map, which covered approximately 3623·39 cM, with an average distance of 17·01 cM between markers. In the present study, eight QTLs associated with lutein content were found initially, which could explain 1·01–19·66% of the observed phenotypic variation in ten different tested environments. The phenotypic contribution of qLU-1 (located near BARC-Satt588 on chromosome 9 (Chr 9; linkage group (LG) K)) was >10% across seven tested environments, while qLU-2 (located near Satt192 of Chr 12 (LG H)) and qLU-3 (located near Satt353 of Chr12 (LGH)) could explain 5–10% of the observed phenotypic variation in more than seven environments, respectively. qLU-5, qLU-6, qLU-7 and qLU-8 could be detected in more than four environments. These eight QTLs were novel, and have considerable potential value for marker-assistant selection of higher lutein content in soybean lines.

scholarly journals Recombinant inbred lines derived from cultivars of pea for understanding the genetic basis of variation in breeders' traits

2018 ◽  
Vol 16 (5) ◽  
pp. 424-436 ◽  
Author(s):  
Carol Moreau ◽  
Maggie Knox ◽  
Lynda Turner ◽  
Tracey Rayner ◽  
Jane Thomas ◽  
...  
Keyword(s):  
Recombinant Inbred ◽  
Seed Weight ◽  
Genetic Basis ◽  
Recombinant Inbred Lines ◽  
Field Trials ◽  
Genetic Maps ◽  
Genotypic Differences ◽  
Phenotypic Data ◽  
Growing Seasons ◽  
Randomized Field Trials

AbstractIn order to gain an understanding of the genetic basis of traits of interest to breeders, the pea varieties Brutus, Enigma and Kahuna were selected, based on measures of their phenotypic and genotypic differences, for the construction of recombinant inbred populations. Reciprocal crosses were carried out for each of the three pairs, and over 200 F2 seeds from each cross advanced to F13. Bulked F7 seeds were used to generate F8–F11 bulks, which were grown in triplicated plots within randomized field trials and used to collect phenotypic data, including seed weight and yield traits, over a number of growing seasons. Genetic maps were constructed from the F6 generation to support the analysis of qualitative and quantitative traits and have led to the identification of four major genetic loci involved in seed weight determination and at least one major locus responsible for variation in yield. Three of the seed weight loci, at least one of which has not been described previously, were associated with the marrowfat seed phenotype. For some of the loci identified, candidate genes have been identified. The F13 single seed descent lines are available as a germplasm resource for the legume and pulse crop communities.

scholarly journals Impact of epistasis and QTL×environment interaction on the accumulation of seed mass of soybean (Glycine max L. Merr.)

2008 ◽  
Vol 90 (6) ◽  
pp. 481-491 ◽  
Author(s):  
YINGPENG HAN ◽  
WEILI TENG ◽  
DESHENG SUN ◽  
YUPING DU ◽  
LIJUAN QIU ◽  
...  
Keyword(s):  
Seed Development ◽  
Seed Weight ◽  
Developmental Stages ◽  
Environmental Control ◽  
Recombinant Inbred Lines ◽  
Seed Mass ◽  
Environment Interaction ◽  
Glycine Max L ◽  
Trait Locus ◽  
100 Seed Weight

SummaryThe accumulation of seed mass in soybean is affected by both genotype and environment. The aim of the present study was to measure additive, epistatic and quantitative trait locus (QTL)×environment (QE) interaction effects of QTLs on the development of 100-seed weight in a population of 143 F5 derived recombinant inbred lines (RILs) developed from the cross between the soybean cultivars ‘Charleston’ and ‘Dong Nong 594’. Broad-sense heritability of 100-seed weight from 30 days (30D) to 80D stages was 0·58, 0·52, 0·62, 0·60, 0·66 and 0·57, respectively. A total of 17 QTLs with conditional additive (a) effect and/or conditional additive×environment interaction (ae) effect at specific stages were identified in ten linkage groups by conditional mapping. Of them, only 4 QTLs had significant a effect or ae effect at different stages of seed development. Among QTLs with significant a effect, five acted positively and six acted negatively on seed development. A total of 35 epistatic pairwise QTLs of 100-seed weight were identified by conditional mapping at different developmental stages. Five pairs of QTL showed the additive×additive epistatic (aa) effect and 16 QTLs showed the aa×environment interaction (aae) effect at the different developmental stages. QTLs with aa effect as well with their environmental interaction effect appeared to vary at different developmental stages. Overall, the results indicated that 100-seed weight in soybean is under developmental, genetic and environmental control.

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.

Identification of quantitative trait loci underlying seed shape in soybean across multiple environments

2017 ◽  
Vol 156 (1) ◽  
pp. 3-12 ◽  
Author(s):  
W. L. Teng ◽  
M. N. Sui ◽  
W. Li ◽  
D. P. Wu ◽  
X. Zhao ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Average Distance ◽  
Recombinant Inbred Lines ◽  
Soybean Seeds ◽  
Seed Shape ◽  
Phenotypic Data ◽  
Trait Loci ◽  
Component Traits ◽  
Seed Width

AbstractSeed shape (SS) affects the yield and appearance of soybean seeds significantly. However, little detailed information has been reported about the quantitative trait loci (QTL) affecting SS, especially SS components such as seed length (SL), seed width (SW) and seed thickness (ST), and their mutual ratios of length-to-weight (SLW), length-to-thickness (SLT) and weight-to-thickness (SWT). The aim of the present study was to identify QTL underlying SS components using 129 recombinant inbred lines derived from a cross between Dongnong46 and L-100. Phenotypic data were collected from this population after it was grown across nine environments. A total of 213 simple sequence repeat markers were used to construct the genetic linkage map, which covered approximately 3623·39 cM, with an average distance of 17·01 cM between markers. Five QTL were identified as being associated with SL, five with SW, three with ST, four with SLW, two with SLT and three with SWT. These QTL could explain 1·46–22·16% of the phenotypic variation in SS component traits. Three QTL were identified in more than six tested environments three for SL, two for SW, one for ST, two for SLW and one for SLT. These QTL have great potential value for marker-assistant selection of SS in soybean seeds.

scholarly journals Genetic mapping of leaf-related traits in autotetraploid alfalfa (Medicago sativa L.)

2019 ◽  
Vol 39 (10-11) ◽  
Author(s):  
Fei He ◽  
Junmei Kang ◽  
Fan Zhang ◽  
Ruicai Long ◽  
Long-Xi Yu ◽  
...  
Keyword(s):  
Phenotypic Variation ◽  
Great Influence ◽  
Leaf Size ◽  
Leaf Traits ◽  
Leaf Length ◽  
High Yield ◽  
Phenotypic Data ◽  
New Varieties ◽  
Paternal Parent ◽  
Variation Explained

Abstract Understanding the genetic architecture of leaf-related traits is important for improving alfalfa yield. Leaf size has a great influence on the protein content and yield for alfalfa. In this study, a low-yielding precocious alfalfa individual (paternal parent) and a high-yielding late-maturing alfalfa individual (maternal parent) were used to build a hybrid F1 population of 149 individuals. The linkage map was constructed using simple sequence repeat and single nucleotide polymorphism markers, and quantitative trait loci (QTL) for leaf length, leaf width, and leaf area were mapped using 3 years phenotypic data. We identified a total of 60 QTLs associated with leaf size. These QTLs were located on chromosomes 1 to 8, and the percent of phenotypic variation explained by QTL ranged from 2.97% to 18.78%. There were 13 QTLs explain more than 10% of phenotypic variation, most of which represent novel loci controlling leaf traits that have not been found in previous studies. The nearest markers of QTL may be used in marker-assisted selection and breeding alfalfa new varieties with high yield.

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

2017 ◽  
Vol 68 (7) ◽  
pp. 625 ◽  
Author(s):  
Weili Teng ◽  
Binbin Zhang ◽  
Qi Zhang ◽  
Wen Li ◽  
Depeng Wu ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Oil Content ◽  
Recombinant Inbred Lines ◽  
Soybean Seed ◽  
Environment Interaction ◽  
Additive Effects ◽  
Linkage Groups ◽  
Soybean Breeding ◽  
Trait Loci

Oil content is a primary trait in soybean and determines the quality of soy food, feed and oil product. Increasing oil content is a major objective of soybean breeding. The aims of the present study were to identify quantitative trait loci (QTLs) and epistatic QTLs associated with oil content in soybean seed by using 129 recombinant inbred lines derived from a cross between cultivar Dongnong 46 (oil content 22.53%) and the semi-wild line L-100 (oil content 17.33%). Phenotypic data were collected from 10 tested environments including Harbin in the years 2012–15, Hulan in 2013–15 and Acheng in 2013–15. A genetic linkage map including 213 simple sequence repeat markers in 18 chromosomes (or linkage groups) was constructed, covering ~3623.39 cM. Seven QTLs, located on five chromosomes (or linkage groups), were identified to be associated with oil content, explaining 2.24–17.54% of the phenotypic variation in multi-environments. Among these identified QTLs, five (qOIL-2, qOIL-4, qOIL-5, qOIL-6 and qOIL-7) were detected in more than five environments. Seven QTLs had additive and/or additive × environment interaction effects. QTLs with higher additive effects were more stable in multi-environments than those with lower additive effects. Moreover, five epistatic, pairwise QTLs were identified in different environments. The findings with respect to genetic architecture for oil content could be valuable for marker-assisted selection in soybean breeding programs for high oil content.

scholarly journals Fine Mapping of Qph9, A Major Quantitative Trait Locus, Responsible for Plant Height In Foxtail Millet [Setaria Italica (L.) P. Beauv.]

2021 ◽  
Author(s):  
Xiaofen Du ◽  
Zhilan Wang ◽  
Kangni Han ◽  
Shichao Lian ◽  
Yuxin Li ◽  
...  
Keyword(s):  
Plant Height ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Marker Assisted Selection ◽  
Major Quantitative Trait Locus ◽  
Molecular Breeding ◽  
Average Distance ◽  
Foxtail Millet ◽  
Trait Locus ◽  
Variation Explained

Abstract Plant height is vital for crop yield by influencing plant architecture and resistance to lodging. Although lots of quantitative trait loci (QTLs) controlling plant height had been mapped in foxtail millet, their contributions to phenotypic variation were generally small and mapping regions were relatively large, indicating the difficult application in molecular breeding using marker-assisted selection. In the present paper, a total of 23 QTLs involving in 15 traits were identified via a high-density Bin map containing 3024 Bin markers with an average distance of 0.48 cM from an F2 population. Among them, qPH9 with a large phenotypic variation explained (51.6%) related to plant height, was one of the major QTLs. Furthermore, qPH9 was repeatedly detected in multi-environments under field conditions using two new F2 population from the same F1 plant, and was narrowed down to a smaller interval of 281 kb using 1024 recessive individuals of F2 population. Finally, we found that there was an extremely significant correlation between marker MRI1016 and plant height, and speculated that Seita.9G088900 and Seita.9G089700 could be key candidates of qPH9. This study laid an important foundation for the cloning of qPH9 and molecular breeding of dwarf varieties via marker-assisted selection.

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