scholarly journals Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment

2021 ◽  
Author(s):  
Yongle Li ◽  
Pradeep Ruperao ◽  
Jacqueline Batley ◽  
David Edwards ◽  
William Martin ◽  
...  

1967 ◽  
Vol 59 (6) ◽  
pp. 576-577
Author(s):  
A. R. Brown ◽  
H. D. Morris


Crop Science ◽  
1965 ◽  
Vol 5 (6) ◽  
pp. 595-595 ◽  
Author(s):  
Glenn W. Burton ◽  
James C. Fortson


2020 ◽  
Vol 56 (10) ◽  
pp. 1246-1251
Author(s):  
N. Yu. Chasovskikh ◽  
A. Yu. Grechishnikova


Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1017
Author(s):  
Mohammed Bakkali ◽  
Rubén Martín-Blázquez ◽  
Mercedes Ruiz-Estévez ◽  
Manuel A. Garrido-Ramos

We sequenced the sporophyte transcriptome of Killarney fern (Vandenboschia speciosa (Willd.) G. Kunkel). In addition to being a rare endangered Macaronesian-European endemism, this species has a huge genome (10.52 Gb) as well as particular biological features and extreme ecological requirements. These characteristics, together with the systematic position of ferns among vascular plants, make it of high interest for evolutionary, conservation and functional genomics studies. The transcriptome was constructed de novo and contained 36,430 transcripts, of which 17,706 had valid BLAST hits. A total of 19,539 transcripts showed at least one of the 7362 GO terms assigned to the transcriptome, whereas 6547 transcripts showed at least one of the 1359 KEGG assigned terms. A prospective analysis of functional annotation results provided relevant insights on genes involved in important functions such as growth and development as well as physiological adaptations. In this context, a catalogue of genes involved in the genetic control of plant development, during the vegetative to reproductive transition, in stress response as well as genes coding for transcription factors is given. Altogether, this study provides a first step towards understanding the gene expression of a significant fern species and the in silico functional and comparative analyses reported here provide important data and insights for further comparative evolutionary studies in ferns and land plants in general.



2021 ◽  
Vol 245 ◽  
pp. 104421
Author(s):  
Rosiane P. Silva ◽  
Rafael Espigolan ◽  
Mariana P. Berton ◽  
Raysildo B. Lôbo ◽  
Cláudio U. Magnabosco ◽  
...  


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fatemeh Amini ◽  
Felipe Restrepo Franco ◽  
Guiping Hu ◽  
Lizhi Wang

AbstractRecent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.



2021 ◽  
Vol 41 (2) ◽  
Author(s):  
Eduardo Beche ◽  
Jason D. Gillman ◽  
Qijian Song ◽  
Randall Nelson ◽  
Tim Beissinger ◽  
...  


Sign in / Sign up

Export Citation Format

Share Document