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Efficient genomics based ‘end-to-end’ selective tree breeding framework El-Kassaby, Yousry A.; Cappa, Eduardo P.; Chen, Charles; Ratcliffe, Blaise; Porth, Ilga M.
Description
<b>Abstract</b><br/>
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>; <b>Methods</b><br />
See Readme file</p>
Item Metadata
Title |
Efficient genomics based ‘end-to-end’ selective tree breeding framework
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Creator | |
Date Issued |
2023-11-02
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Description |
<b>Abstract</b><br/>
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>; <b>Methods</b><br /> See Readme file</p> |
Subject | |
Type | |
Notes |
Dryad version number: 6</p> Version status: submitted</p> Dryad curation status: Published</p> Sharing link: https://datadryad.org/stash/share/uLaFnTRcl-BFhtUyXRQ1hHkShGwrdLpxPcfzYG3Blas</p> Storage size: 32374720</p> Visibility: public</p> |
Date Available |
2023-12-19
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Provider |
University of British Columbia Library
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License |
CC0 1.0
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DOI |
10.14288/1.0437533
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URI | |
Publisher DOI | |
Grant Funding Agency |
Natural Sciences and Engineering Research Council; Johnson’s Family Forest Biotechnology Endowment*
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Aggregated Source Repository |
Dataverse
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Item Citations and Data
Licence
CC0 1.0