scholarly journals Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding

2019 ◽  
Vol 9 (8) ◽  
pp. 2429-2439 ◽  
Author(s):  
Prabin Bajgain ◽  
Xiaofei Zhang ◽  
James A. Anderson
PLoS ONE ◽  
2019 ◽  
Vol 14 (7) ◽  
pp. e0219843 ◽  
Author(s):  
Fernanda Zatti Barreto ◽  
João Ricardo Bachega Feijó Rosa ◽  
Thiago Willian Almeida Balsalobre ◽  
Maria Marta Pastina ◽  
Renato Rodrigues Silva ◽  
...  

2019 ◽  
Author(s):  
Waltram Ravelombola ◽  
Jun Qin ◽  
Ainong Shi ◽  
Fengmin Wang ◽  
Yan Feng ◽  
...  

Abstract Background Soybean [ Glycine max (L.) Merr.] is a legume of great interest worldwide. Enhancing genetic gain for agronomic traits via molecular approaches has been long considered as the main task for soybean breeders and geneticists. The objectives of this study were to evaluate maturity, plant height, seed weight, and yield in a diverse soybean accession panel, to conduct a genome-wide association study (GWAS) for these traits and identify SNP markers associated with the four traits, and to assess genomic selection (GS) accuracy. Results A total of 250 soybean accessions were evaluated for maturity, plant height, seed weight, and yield over three years. This panel was genotyped with a total of 10,259 high quality SNPs postulated from genotyping by sequencing (GBS). GWAS was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model, and GS was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that a total of 20, 31, 37, 31, and 23 SNPs were significantly associated with the average 3-year data for maturity, plant height, seed weight, and yield, respectively; some significant SNPs were mapped into previously described loci ( E2 , E4 , and Dt1 ) affecting maturity and plant height in soybean and a new locus mapped on chromosome 20 was significantly associated with plant height; Glyma.10g228900 , Glyma.19g200800 , Glyma.09g196700 , and Glyma.09g038300 were candidate genes found in the vicinity of the top or the second best SNP for maturity, plant height, seed weight, and yield, respectively; a 11.5-Mb region of chromosome 10 was associated with both seed weight and yield; and GS accuracy was trait-, year-, and population structure-dependent. Conclusions The SNP markers identified from this study for plant height, maturity, seed weight and yield can be used to improve the four agronomic traits through marker-assisted selection (MAS) and GS in soybean breeding programs. After validation, the candidate genes can be transferred to new cultivars using SNP markers through MAS. The high GS accuracy has confirmed that the four agronomic traits can be selected in molecular breeding through GS.


PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0235089
Author(s):  
Waltram Second Ravelombola ◽  
Jun Qin ◽  
Ainong Shi ◽  
Liana Nice ◽  
Yong Bao ◽  
...  

Crop Science ◽  
2018 ◽  
Vol 58 (6) ◽  
pp. 2315-2330
Author(s):  
Shiferaw A. Gizaw ◽  
Jayfred Gaham V. Godoy ◽  
Kimberly Garland-Campbell ◽  
Arron H. Carter

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jing Su ◽  
Kai Xu ◽  
Zirong Li ◽  
Yuan Hu ◽  
Zhongli Hu ◽  
...  

AbstractRice yield per plant has a complex genetic architecture, which is mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW), and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. To understand the genetic basis of the relationship between rice yield and component traits, we investigated the four traits of two rice hybrid populations (575 + 1495 F1) in different environments and conducted meta-analyses of genome-wide association study (meta-GWAS). In total, 3589 significant loci for three components traits were detected, while only 3 loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus/gene affected component traits to further enhance yield is recommended. Mendelian randomization design is adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction effects (positive effect or negative effect). Additionally, TP (Beta = 1.865) has a greater effect on yield than KGW (Beta = 1.016) and GPP (Beta = 0.086). Five significant loci for component traits that had an indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice. Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield and provide valuable information for improving rice yield potential.


HortScience ◽  
2019 ◽  
Vol 54 (4) ◽  
pp. 629-632 ◽  
Author(s):  
Katie O’Connor ◽  
Ben Hayes ◽  
Craig Hardner ◽  
Mobashwer Alam ◽  
Bruce Topp

Current macadamia breeding programs involve a lengthy and laborious two-stage selection process: evaluation of a large number of unreplicated seedling progeny, followed by replicated trials of clonally propagated elite seedlings. Yield component traits, such as nut-in-shell weight (NW), kernel weight (KW), and kernel recovery (KR) are commercially important, are more easily measured than yield, and have a higher heritability. A genome-wide association study (GWAS) combined with marker-assisted selection offers an opportunity to reduce the time of candidate evaluation. In this study, a total of 281 progeny from 32 families, and 18 of their 29 parents have been genotyped for 7126 single nucleotide polymorphism (SNP) markers. A GWAS was performed using ASReml with 4352 SNPs. We found five SNPs significantly associated with NW, nine with KW, and one with KR. Further, three of the top 10 markers for NW and KW were shared between the two traits. Future macadamia breeding could involve prescreening of individuals for desired traits using these significantly associated markers, with only predicted elite individuals continuing to the second stage of selection, thus potentially reducing the selection process by 7 years.


2020 ◽  
Author(s):  
Jing Su ◽  
Kai Xu ◽  
Chao Wu ◽  
Zirong Li ◽  
Zhongli Hu ◽  
...  

Abstract Background: Rice yield has a complex genetic architecture, which mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW) and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. Thus, it is important that studying the genetic basis of relationship between rice yield and component traits and selecting the component traits to improve the rice production. Main text: In this study, we carried out meta-analyses of genome-wide association study (Meta-GWAS) with two populations (575 + 1495 F1) in different environments for yield and its three component traits in rice. 3589 significant loci for three components traits were detected, while only 3 significant loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus (QTL)/gene affected component traits to further enhance yield is recommended. Mendelian randomization (MR) design was adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction of effects (positive effect or negative effect). Additionally, TP (Beta=1.865) has a greater effect on yield than KGW (Beta=1.016) and GPP (Beta=0.086). Five significant loci for component traits with indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice.Conclusions: Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield, and provide valuable information for improving rice yield potential.


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