DOI: 10.5061/dryad.7h44j101d
title: Efficient genomics based ‘end-to-end’ selective tree breeding framework

description: 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.
description: See Readme file
description: This El-Kassaby_README.md file was generated on 2023-10-22 by Yousry A.
 El-Kassaby Revised: 2023-12-15 GENERAL INFORMATION 1. Title of Dataset:
 Efficient genomics-based ‘end-to-end’ selective tree breeding framework 2.
 Author Information Name: Yousry A. El-Kassaby Institution: Department of
 Forest and Conservation Sciences | Faculty of Forestry | The University of
 British Columbia Address: 4603-2424 Main Mall | Vancouver, BC Canada V6T
 1Z4 Email: y.el-kassaby@ubc.ca 3. Date of data collection (single date,
 range, approximate date): See Materials and Methods section in the
 manuscript. 4. Geographic location of data collection : See Materials and Methods
 section in the manuscript. 5. Information about funding sources that
 supported the collection of the data: NSERC Discovery Grant to Yousry A.
 El-Kassaby. SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on
 the data: - 2. Links to publications that cite or use the data: - 3. Links
 to other publicly accessible locations of the data: - 4.
 Links/relationships to ancillary data sets: - 5. Was data derived from
 another source? Yes A. If yes, list source(s): Dryad Digital Repository:
 http://dx.doi.org/10.5061/dryad.m4vh4 and
 http://doi.org/10.5061/dryad.8kb37. 6. Recommended citation for this
 dataset: - DATA & FILE OVERVIEW 1. File List: Phenotype and SNPs.xlsx:
 Contains information on tree phenotype and the SNP markers used in this
 study. SNP_effects.txt: Provides SNP effects corresponding to each trait.
 SNPs selected by GWAS.txt: Lists 5,628 SNP IDs selected based on GWAS
 absolute effects, associated with the three studied traits. Genomic-based
 relationship matrices.R: R-script used for calculating various genomic
 relationship matrices in different study scenarios. Single-trait
 multi-site ABLUP and GBLUP models.R: R-script employed for fitting the
 single-trait multi-site ABLUP, GBLUP-ALL, and GBLUP-ADE models.
 Multi-trait multi-site ABLUP and GBLUP models.R: R-script utilized for
 fitting the multi-trait multi-site ABLUP, GBLUP-ALL, and GBLUP-GWAS
 models. 2. Relationship between files, if important: - 3. Additional
 related data collected that was not included in the current data package:
 Raw marker data, and adjusted-phenotypes. 4. Are there multiple versions
 of the dataset? no A. If yes, name of file(s) that was updated: i. Why was
 the file updated? ii. When was the file updated? METHODOLOGICAL
 INFORMATION 1. Description of methods used for collection/generation of
 data: See the Methods section in the manuscript. 2. Methods for processing
 the data: - 3. Instrument- or software-specific information
 needed to interpret the data: - 4. Standards and calibration information, if appropriate: - 5.
 Environmental/experimental conditions: - 6. Describe any quality-assurance
 procedures performed on the data: - 7. People involved with sample
 collection, processing, analysis and/or submission: Yousry A. El-Kassaby,
 Eduardo P. Cappa, and Blaise Ratcliffe. DATA-SPECIFIC INFORMATION FOR:
 Phenotype and SNPs.xlsx 1. Number of variables: 8,775 2. Number of
 cases/rows: 1,101 3. Variable List: ID_TREE: tree ID MUM: mum ID DAD: dad
 ID SITE: Site name REP: Replication design effect for each site HT40:
 total height measured in meters DBH40: diameter at breast height (1.3 m,
 DBH) measured in centimeters AvgXray: X-ray densitometry to determine the
 wood density measured in g·cm−3 TP244 - TP1478967: marker ID 4. Missing
 data codes: NA 5. Specialized formats or other abbreviations used: -
 SNP_effects.txt 1. Number of variables: 4 2. Number of cases/rows: 8,767
 3. Variable List: snp: SNP marker ID HT40: SNP marker effects for total
 height DBH40: SNP marker effects diameter at breast height (1.3 m, DBH)
 AvgXray: SNP marker effects X-ray densitometry 4. Missing data codes: NA
 5. Specialized formats or other abbreviations used: - SNPs selected by
 GWAS.txt 1. Number of variables: 1 2. Number of cases/rows: 5,628 3.
 Variable List: snp: SNP marker ID 4. Missing data codes: NA 5. Specialized
 formats or other abbreviations used: - Genomic-based relationship
 matrices.R R-script code with 159 lines. Single-trait multi-site ABLUP and
 GBLUP models.R R-script code with 436 lines. Multi-trait multi-site ABLUP
 and GBLUP models.R R-script code with 269 lines.