scholarly journals DArTseq-based analysis of genomic relationships among species of tribe Triticeae

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Offiong U. Edet ◽  
Yasir S. A. Gorafi ◽  
Shuhei Nasuda ◽  
Hisashi Tsujimoto
Keyword(s):  
Genomic Relationships ◽  
Tribe Triticeae

scholarly journals Genotyping Strategies Using ddRAD Sequencing in Farmed Arctic Charr (Salvelinus alpinus)

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 899
Author(s):  
Fotis Pappas ◽  
Christos Palaiokostas
Keyword(s):  
Genetic Diversity ◽  
Salvelinus Alpinus ◽  
Arctic Charr ◽  
Cost Effective ◽  
Limiting Factors ◽  
Phenotypic Traits ◽  
Production Volume ◽  
High Coverage ◽  
Genomic Relationships ◽  
Low Coverage

Incorporation of genomic technologies into fish breeding programs is a modern reality, promising substantial advances regarding the accuracy of selection, monitoring the genetic diversity and pedigree record verification. Single nucleotide polymorphism (SNP) arrays are the most commonly used genomic tool, but the investments required make them unsustainable for emerging species, such as Arctic charr (Salvelinus alpinus), where production volume is low. The requirement to genotype a large number of animals for breeding practices necessitates cost effective genotyping approaches. In the current study, we used double digest restriction site-associated DNA (ddRAD) sequencing of either high or low coverage to genotype Arctic charr from the Swedish national breeding program and performed analytical procedures to assess their utility in a range of tasks. SNPs were identified and used for deciphering the genetic structure of the studied population, estimating genomic relationships and implementing an association study for growth-related traits. Missing information and underestimation of heterozygosity in the low coverage set were limiting factors in genetic diversity and genomic relationship analyses, where high coverage performed notably better. On the other hand, the high coverage dataset proved to be valuable when it comes to identifying loci that are associated with phenotypic traits of interest. In general, both genotyping strategies offer sustainable alternatives to hybridization-based genotyping platforms and show potential for applications in aquaculture selective breeding.

PhylogeneticRelationships in the Tribe Triticeae

2018 ◽  
pp. 149-164
Author(s):  
Ponaka V. Reddy ◽  
Khairy M. Solimán
Keyword(s):  
Tribe Triticeae

scholarly journals SPInDel Analysis of the Non-Coding Regions of cpDNA as a More Useful Tool for the Identification of Rye (Poaceae: Secale) Species

2020 ◽  
Vol 21 (24) ◽  
pp. 9421
Author(s):  
Lidia Skuza ◽  
Ewa Filip ◽  
Izabela Szućko ◽  
Jan Bocianowski
Keyword(s):  
Mitochondrial Dna ◽  
Molecular Markers ◽  
Related Species ◽  
Economic Importance ◽  
Closely Related Species ◽  
Coding Sequences ◽  
Coding Regions ◽  
Genomic Regions ◽  
Tribe Triticeae

Secale is a small but very diverse genus from the tribe Triticeae (family Poaceae), which includes annual, perennial, self-pollinating and open-pollinating, cultivated, weedy and wild species of various phenotypes. Despite its high economic importance, classification of this genus, comprising 3–8 species, is inconsistent. This has resulted in significantly reduced progress in the breeding of rye which could be enriched with functional traits derived from wild rye species. Our previous research has suggested the utility of non-coding sequences of chloroplast and mitochondrial DNA in studies on closely related species of the genus Secale. Here we applied the SPInDel (Species Identification by Insertions/Deletions) approach, which targets hypervariable genomic regions containing multiple insertions/deletions (indels) and exhibiting extensive length variability. We analysed a total of 140 and 210 non-coding sequences from cpDNA and mtDNA, respectively. The resulting data highlight regions which may represent useful molecular markers with respect to closely related species of the genus Secale, however, we found the chloroplast genome to be more informative. These molecular markers include non-coding regions of chloroplast DNA: atpB-rbcL and trnT-trnL and non-coding regions of mitochondrial DNA: nad1B-nad1C and rrn5/rrn18. Our results demonstrate the utility of the SPInDel concept for the characterisation of Secale species.

scholarly journals Genomic predictions for enteric methane production are improved by metabolome and microbiome data in sheep (Ovis aries)

2020 ◽  
Vol 98 (10) ◽  
Author(s):  
Elizabeth M Ross ◽  
Ben J Hayes ◽  
David Tucker ◽  
Jude Bond ◽  
Stuart E Denman ◽  
...  
Keyword(s):  
Methane Production ◽  
Prediction Accuracy ◽  
Live Weight ◽  
Ovis Aries ◽  
Prediction Algorithm ◽  
Genomic Relationship ◽  
Time Points ◽  
Genomic Relationships ◽  
Genomic Predictions ◽  
Time Point

Abstract Methane production from rumen methanogenesis contributes approximately 71% of greenhouse gas emissions from the agricultural sector. This study has performed genomic predictions for methane production from 99 sheep across 3 yr using a residual methane phenotype that is log methane yield corrected for live weight, rumen volume, and feed intake. Using genomic relationships, the prediction accuracies (as determined by the correlation between predicted and observed residual methane production) ranged from 0.058 to 0.220 depending on the time point being predicted. The best linear unbiased prediction algorithm was then applied to relationships between animals that were built on the rumen metabolome and microbiome. Prediction accuracies for the metabolome-based relationships for the two available time points were 0.254 and 0.132; the prediction accuracy for the first microbiome time point was 0.142. The second microbiome time point could not successfully predict residual methane production. When the metabolomic relationships were added to the genomic relationships, the accuracy of predictions increased to 0.274 (from 0.201 when only the genomic relationship was used) and 0.158 (from 0.081 when only the genomic relationship was used) for the two time points, respectively. When the microbiome relationships from the first time point were added to the genomic relationships, the maximum prediction accuracy increased to 0.247 (from 0.216 when only the genomic relationship was used), which was achieved by giving the genomic relationships 10 times more weighting than the microbiome relationships. These accuracies were higher than the genomic, metabolomic, and microbiome relationship matrixes achieved alone when identical sets of animals were used.

The γ-gliadin-like γ-prolamin genes in the tribe Triticeae

2014 ◽  
Vol 93 (1) ◽  
pp. 35-41 ◽  
Author(s):  
PENG-FEI QI ◽  
CHENG-XING LE ◽  
ZHAO WANG ◽  
YU-BIN LIU ◽  
QING CHEN ◽  
...  
Keyword(s):  
Tribe Triticeae
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