Automated Author Profile

El-Kassaby, Yousry A.

0000-0002-4887-8977

Current S-Index

5.1

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

1.3

Average Dataset Index per dataset

Total Datasets

4

Total datasets for this author

Average FAIR Score

77.9%

Average FAIR Score per dataset

Total Citations

2

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Efficient genomics based ‘end-to-end’ selective tree breeding framework (Version: 2.0)

<b>Abstract</b><br/><p>Since their initiation in the 1950s, worldwide selective tree breeding programs followed the recurrent selection scheme of repeated cycles of selection, breeding (mating), and testing phases and essentially remained unchanged to accelerate this process or address environmental contingences and concerns. Here, we introduce an “end-to-end” selective tree breeding framework that: 1) leverages strategically preselected GWAS-based sequence data capturing trait architecture information, 2) generates unprecedented resolution of genealogical relationships among tested individuals, and 3) leads to the elimination of the breeding phase through the utilization of readily available wind-pollinated (OP) families. Individuals’ breeding values generated from multi-trait multi-site analysis were also used in an optimum contribution selection protocol to effectively manage genetic gain/co-ancestry trade-offs and traits’ correlated response to selection. The proof-of-concept study involved a 40-year-old spruce OP testing population growing on three sites in British Columbia, Canada, clearly demonstrating our method's superiority in capturing most of the available genetic gains in a substantially reduced timeline relative to the traditional approach. The proposed framework is expected to increase the efficiency of existing selective breeding programs, accelerate the start of new programs for ecologically and environmentally important tree species, and address climate-change caused biotic and abiotic stress concerns more effectively.</p>

Authors

  • El-Kassaby, Yousry A. ;
  • Cappa, Eduardo P. ;
  • Chen, Charles ;
  • Ratcliffe, Blaise ;
  • Porth, Ilga M.
1 Citation0 Mentions88% FAIR2.3 Dataset Index
10.5683/sp3/x6pcgxDecember 2023

Efficient genomics based ‘end-to-end’ selective tree breeding framework (Version: 6)

Since their initiation in the 1950s, worldwide selective tree breeding programs followed the recurrent selection scheme of repeated cycles of selection, breeding (mating), and testing phases and essentially remained unchanged to accelerate this process or address environmental contingences and concerns. Here, we introduce an “end-to-end” selective tree breeding framework that: 1) leverages strategically preselected GWAS-based sequence data capturing trait architecture information, 2) generates unprecedented resolution of genealogical relationships among tested individuals, and 3) leads to the elimination of the breeding phase through the utilization of readily available wind-pollinated (OP) families. Individuals’ breeding values generated from multi-trait multi-site analysis were also used in an optimum contribution selection protocol to effectively manage genetic gain/co-ancestry trade-offs and traits’ correlated response to selection. The proof-of-concept study involved a 40-year-old spruce OP testing population growing on three sites in British Columbia, Canada, clearly demonstrating our method's superiority in capturing most of the available genetic gains in a substantially reduced timeline relative to the traditional approach. The proposed framework is expected to increase the efficiency of existing selective breeding programs, accelerate the start of new programs for ecologically and environmentally important tree species, and address climate-change caused biotic and abiotic stress concerns more effectively.

Authors

  • El-Kassaby, Yousry A. ;
  • Cappa, Eduardo P. ;
  • Chen, Charles ;
  • Ratcliffe, Blaise ;
  • Porth, Ilga M.
1 Citation0 Mentions69% FAIR1.2 Dataset Index
10.5061/dryad.7h44j101dOctober 2023

Douglas-fir exomic SNP file

No description available

Authors

  • Thistlethwaite, Frances R. ;
  • Ratcliffe, Blaise ;
  • Klápště, Jaroslav ;
  • Porth, Ilga ;
  • Chen, Charles ;
  • Stoehr, Michael U. ;
  • El-Kassaby, Yousry A.
0 Citations0 Mentions77% FAIR0.8 Dataset Index
10.5061/dryad.vk048/1January 2017

Douglas-fir phenotypes

No description available

Authors

  • Thistlethwaite, Frances R. ;
  • Ratcliffe, Blaise ;
  • Klápště, Jaroslav ;
  • Porth, Ilga ;
  • Chen, Charles ;
  • Stoehr, Michael U. ;
  • El-Kassaby, Yousry A.
0 Citations0 Mentions77% FAIR0.8 Dataset Index
10.5061/dryad.vk048/2January 2017