scholarly journals High-Resolution Mapping in Two RIL Populations Refines Major “QTL Hotspot” Regions for Seed Size and Shape in Soybean (Glycine max L.)

2020 ◽  
Vol 21 (3) ◽  
pp. 1040 ◽  
Author(s):  
Aiman Hina ◽  
Yongce Cao ◽  
Shiyu Song ◽  
Shuguang Li ◽  
Ripa Akter Sharmin ◽  
...  
Keyword(s):  
Seed Size ◽  
Composite Interval Mapping ◽  
Mixed Model ◽  
Gene Annotation ◽  
Soybean Seed ◽  
Interval Mapping ◽  
Epistatic Effect ◽  
Size And Shape ◽  
Network Analyses ◽  
Major Qtl

Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., “QTL Hotspot A”, “QTL Hotspot B”, “QTL Hotspot C”, and “QTL Hotspot D” located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four “QTL Hotspots” were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

scholarly journals Comparative QTL analysis and candidate genes identification of seed size, shape and weight in soybean (Glycine max L.)

2021 ◽  
Author(s):  
Mahmoud A Elattar ◽  
Benjamin Karikari ◽  
Shuguang Li ◽  
Shiyu Song ◽  
Yongce Cao ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Seed Size ◽  
Composite Interval Mapping ◽  
Mixed Model ◽  
Interval Mapping ◽  
Genetic Maps ◽  
Potential Candidate ◽  
Go Enrichment ◽  
Glycine Max L ◽  
Genomic Regions

Abstract Dissecting the genetic mechanism underlying seed size, shape and weight is essential to these traits for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line populations, LM6 and ZM6, evaluated in multiple environments to identify candidate genes behind seed-related traits major and stable QTLs. A total of 239 and 43 M-QTL were mapped by composite interval mapping and mixed-model based composite interval mapping approaches, respectively, from which 22 common QTLs including four major and novel QTLs. CIM and MCIM approaches identified 180 and 18 novel M-QTLs, respectively. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Seed flatness index QTLs (34 QTLs) were identified and reported for the first time. Seven QTL clusters underlying the inheritance of seed size, shape and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17 and 19 were identified. Gene annotations, gene ontology (GO) enrichment and RNA-seq analyses identified 47 candidate genes for seed-related traits within the genomic regions of those 7 QTL clusters. These genes are highly expressed in seed-related tissues and nodules, that might be deemed as potential candidate genes regulating the above traits in soybean. This study provides detailed information for the genetic bases of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning as well as for MAS targeted at improving these traits individually or concurrently.

scholarly journals Identification and Validation of Major QTLs, Epistatic Interactions, and Candidate Genes for Soybean Seed Shape and Weight Using Two Related RIL Populations

2021 ◽  
Vol 12 ◽  
Author(s):  
Mahmoud A. Elattar ◽  
Benjamin Karikari ◽  
Shuguang Li ◽  
Shiyu Song ◽  
Yongce Cao ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Seed Size ◽  
Composite Interval Mapping ◽  
Soybean Seed ◽  
Interval Mapping ◽  
Genetic Maps ◽  
Potential Candidate ◽  
Major Qtls ◽  
Multiple Environments ◽  
Genomic Regions

Understanding the genetic mechanism underlying seed size, shape, and weight is essential for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line (RIL) populations, LM6 and ZM6, were evaluated across multiple environments to identify and validate M-QTLs as well as identify candidate genes behind major and stable quantitative trait loci (QTLs). A total of 239 and 43 M-QTLs were mapped by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM) approaches, from which 180 and 18, respectively, are novel QTLs. Twenty-two QTLs including four novel major QTLs were validated in the two RIL populations across multiple environments. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Thirty-four QTLs associated with seed flatness index (FI) were identified and reported here for the first time. Seven QTL clusters comprising several QTLs for seed size, shape, and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17, and 19 were identified. Gene annotations, gene ontology (GO) enrichment, and RNA-seq analyses of the genomic regions of those seven QTL clusters identified 47 candidate genes for seed-related traits. These genes are highly expressed in seed-related tissues and nodules, which might be deemed as potential candidate genes regulating the seed size, weight, and shape traits in soybean. This study provides detailed information on the genetic basis of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning, and for marker-assisted selection (MAS) targeted at improving these traits individually or concurrently.

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 A Major and Stable Quantitative Trait Locus qSS2 for Seed Size and Shape Traits in a Soybean RIL Population

2021 ◽  
Vol 12 ◽  
Author(s):  
Giriraj Kumawat ◽  
Donghe Xu
Keyword(s):  
Quantitative Trait Locus ◽  
Seed Size ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Seed Shape ◽  
Significant Qtls ◽  
Size And Shape ◽  
Growing Seasons ◽  
Ril Population ◽  
Trait Locus

Seed size and shape traits are important determinants of seed yield and appearance quality in soybean [Glycine max (L.) Merr.]. Understanding the genetic architecture of these traits is important to enable their genetic improvement through efficient and targeted selection in soybean breeding, and for the identification of underlying causal genes. To map seed size and shape traits in soybean, a recombinant inbred line (RIL) population developed from K099 (small seed size) × Fendou 16 (large seed size), was phenotyped in three growing seasons. A genetic map of the RIL population was developed using 1,485 genotyping by random amplicon sequencing-direct (GRAS-Di) and 177 SSR markers. Quantitative trait locus (QTL) mapping was conducted by inclusive composite interval mapping. As a result, 53 significant QTLs for seed size traits and 27 significant QTLs for seed shape traits were identified. Six of these QTLs (qSW8.1, qSW16.1, qSLW2.1, qSLT2.1, qSWT1.2, and qSWT4.3) were identified with LOD scores of 3.80–14.0 and R2 of 2.36%–39.49% in at least two growing seasons. Among the above significant QTLs, 24 QTLs were grouped into 11 QTL clusters, such as, three major QTLs (qSL2.3, qSLW2.1, and qSLT2.1) were clustered into a major QTL on Chr.02, named as qSS2. The effect of qSS2 was validated in a pair of near isogenic lines, and its candidate genes (Glyma.02G269400, Glyma.02G272100, Glyma.02G274900, Glyma.02G277200, and Glyma.02G277600) were mined. The results of this study will assist in the breeding programs aiming at improvement of seed size and shape traits in soybean.

scholarly journals Time-Related Mapping of Quantitative Trait Loci Underlying Tiller Number in Rice

Genetics ◽  
1999 ◽  
Vol 151 (1) ◽  
pp. 297-303 ◽  
Author(s):  
Wei-Ren Wu ◽  
Wei-Ming Li ◽  
Ding-Zhong Tang ◽  
Hao-Ran Lu ◽  
A J Worland
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Interval Mapping ◽  
Tiller Number ◽  
Multiple Trait ◽  
Phenotypic Data ◽  
Recombinant Inbred Population ◽  
Trait Loci ◽  
Final Observation

Abstract Using time-related phenotypic data, methods of composite interval mapping and multiple-trait composite interval mapping based on least squares were applied to map quantitative trait loci (QTL) underlying the development of tiller number in rice. A recombinant inbred population and a corresponding saturated molecular marker linkage map were constructed for the study. Tiller number was recorded every 4 or 5 days for a total of seven times starting at 20 days after sowing. Five QTL were detected on chromosomes 1, 3, and 5. These QTL explained more than half of the genetic variance at the final observation. All the QTL displayed an S-shaped expression curve. Three QTL reached their highest expression rates during active tillering stage, while the other two QTL achieved this either before or after the active tillering stage.

Bayesian Mapping of Multiple Quantitative Trait Loci From Incomplete Inbred Line Cross Data

Genetics ◽  
1998 ◽  
Vol 148 (3) ◽  
pp. 1373-1388
Author(s):  
Mikko J Sillanpää ◽  
Elja Arjas
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Backcross Progeny ◽  
Random Variable ◽  
Interval Mapping ◽  
Mapping Method ◽  
Structure Mapping ◽  
Standard Interval ◽  
Trait Loci

Abstract A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL-intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the location wise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/~mjs.

scholarly journals PERUBAHAN BENTUK DAN UKURAN BENIH DUA KULTIVAR KEDELAI (Glycine max (L.) Merr.) DI JATINANGOR DAN CIKAJANG

2017 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Siti Rodiah ◽  
Zulfatunnisa Zulfatunnisa ◽  
Sumadi Sumadi ◽  
Anne Nuraini ◽  
Meddy Rachmadi ◽  
...  
Keyword(s):  
Glycine Max ◽  
Low Temperature ◽  
Seed Size ◽  
Block Design ◽  
Soybean Seed ◽  
Growth Environment ◽  
Randomized Block Design ◽  
Glycine Max L ◽  
Improved Cultivars ◽  
Size Heterogeneity

The variation of the seed size in each species and individuals might be from of difference species adaptation for  a difference environment. This difference may also arise from the constraints of limited formation of seed size. The use of adaptive ciltivars on the growth environment is very influential on the succes in the farm field. This research was aimed to find the adaptation of phase and size seed of two cultivars of soybeans in Jatinangor and Cikajang. This research was held in Jatinangor (Sumedang regency) and Cikajang (Garut regency) from April to July 2016. The design that used in this research was Randomized Block Design (RBD) and Duncan at 5% rate. Improved cultivars that tested in this research were placed at Grobogan and Anjasmoro which were repeated 5 times. The results of experiment showed that adaptation of size seed showed of 100 grains and large seeds. The low temperature condition can increase of variability of seed size. Heterogeneity of environment can not sustain the size of soybean seed. Genetic and environment factors influence significantly for weight of 100 grains and seed size Grobogan in Jatinangor. The weight of 100 grains Grobogan in Jatinangor and Cikajang haved a greater than Anjasmoro. Environmental factors influence yield of soybean, weight of 100 grains of cultivars in Cikajang haved a greater than Jatinangor caused by the seed size.

scholarly journals Short communication: QTL mapping for ear tip-barrenness in maize

2016 ◽  
Vol 14 (3) ◽  
pp. e07SC01 ◽  
Author(s):  
Junqiang Ding ◽  
Jinliang Ma ◽  
Jiafa Chen ◽  
Tangshun Ai ◽  
Zhimin Li ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Genetic Basis ◽  
Mixed Model ◽  
Short Communication ◽  
Genotypic Variation ◽  
Interval Mapping ◽  
Mapping Method ◽  
Additive Effects ◽  
Breeding Programs ◽  
Model Composite

Barren tip on corn ear is an important agronomic trait in maize, which is highly associated with grain yield. Understanding the genetic basis of tip-barrenness may help to reduce the ear tip-barrenness in breeding programs. In this study, ear tip-barrenness was evaluated in two environments in a F2:3 population, and it showed significant genotypic variation for ear tip-barrenness in both environments. Using mixed-model composite interval mapping method, three additive effects quantitative trait loci (QTL) for ear tip-barrenness were mapped on chromosomes 2, 3 and 6, respectively. They explained 16.6% of the phenotypic variation, and no significant QTL × Environment interactions and digenic interactions were detected. The results indicated that additive effect was the main genetic basis for ear tip-barrenness in maize. This is the first report of QTL mapped for ear tip-barrenness in maize.

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