Reference number : GC-2017-Q1-01485, amendment number : , agreement type : G, recipient legal name : El-Kassaby, Yousry (The University of British Columbia), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The availability of genomic marker (SNPs) for non-model organisms with complex and large genome such as conifers, made it possible to infuse genomics in forest quantitative genetics. Traditional evaluation of breeding populations relied on the expected pedigree-based relationship ( A -matrix) for estimating the genetic parameters. With SNPs, the genomic-based relationship ( G -matrix) between individuals can be estimated and used as a substitute to the A -matrix in the individual-tree mixed model to predict breeding values by the Best Linear Unbiased Prediction (BLUP). Recent plant and animal genetic evaluations have demonstrated the superiority of G -matrix for its ability to account for the Mendelian segregation and hidden pedigree as well as historical pedigree that cannot be estimated with the A -matrix. Fitting the G -matrix in the mixed model equations is known as the genomic BLUP (GBLUP) which has proven to be effective in tree breeding; however, the tree breeding populations are exceedingly large, with 1000s of progenies from 100s parents, planted over multiple sites. Thus, GBLUP implementation is hampered by genotyping costs (many individuals) and logistical issues (sampling across sites). Recently, a novel approach combines both the A - and G -matrices in a single-step was proposed, where the traditional A -matrix is concurrently utilized with the G -matrix, thus forming a blended relationship H -matrix combines the A -matrix of many non-genotyped individuals and G -matrix of a subset of genotyped individuals. The H -matrix can be seen as a projection of genetic merit from genotyped to non-genotyped individuals using pedigree relationships. Thus, additional information is generated in the blended approach as the A -matrix acts like bridges across individuals and parents, thus ultimately facilitating better information utilization during the BLUP analysis, resulting in the generation of more reliable and accurate genetic parameters. The H -matrix is common in animal breeding as genotype x environment interaction (GxE) is minimal, a situation does not apply to tree testing as GxE is common. Since the implementation of the H -matrix requires genotyped (subset) and non-genotyped (the entire progeny test), then several questions must be answered, including; 1) what is the number of SNPs needed for constructing the G -matrix?, 2) what is the balance between genotyped and non-genotyped?, 3) can genotyping be limited to specific sites?, and 4) what is the impact of GxE on the estimated genetic parameters? Here, I plan to implement the H -matrix on the British Columbia's white spruce 40 year-old testing program which is ideal for providing reliable growth and wood attributes., program purpose french : The availability of genomic marker (SNPs) for non-model organisms with complex and large genome such as conifers, made it possible to infuse genomics in forest quantitative genetics. Traditional evaluation of breeding populations relied on the expected pedigree-based relationship ( A -matrix) for estimating the genetic parameters. With SNPs, the genomic-based relationship ( G -matrix) between individuals can be estimated and used as a substitute to the A -matrix in the individual-tree mixed model to predict breeding values by the Best Linear Unbiased Prediction (BLUP). Recent plant and animal genetic evaluations have demonstrated the superiority of G -matrix for its ability to account for the Mendelian segregation and hidden pedigree as well as historical pedigree that cannot be estimated with the A -matrix. Fitting the G -matrix in the mixed model equations is known as the genomic BLUP (GBLUP) which has proven to be effective in tree breeding; however, the tree breeding populations are exceedingly large, with 1000s of progenies from 100s parents, planted over multiple sites. Thus, GBLUP implementation is hampered by genotyping costs (many individuals) and logistical issues (sampling across sites). Recently, a novel approach combines both the A - and G -matrices in a single-step was proposed, where the traditional A -matrix is concurrently utilized with the G -matrix, thus forming a blended relationship H -matrix combines the A -matrix of many non-genotyped individuals and G -matrix of a subset of genotyped individuals. The H -matrix can be seen as a projection of genetic merit from genotyped to non-genotyped individuals using pedigree relationships. Thus, additional information is generated in the blended approach as the A -matrix acts like bridges across individuals and parents, thus ultimately facilitating better information utilization during the BLUP analysis, resulting in the generation of more reliable and accurate genetic parameters. The H -matrix is common in animal breeding as genotype x environment interaction (GxE) is minimal, a situation does not apply to tree testing as GxE is common. Since the implementation of the H -matrix requires genotyped (subset) and non-genotyped (the entire progeny test), then several questions must be answered, including; 1) what is the number of SNPs needed for constructing the G -matrix?, 2) what is the balance between genotyped and non-genotyped?, 3) can genotyping be limited to specific sites?, and 4) what is the impact of GxE on the estimated genetic parameters? Here, I plan to implement the H -matrix on the British Columbia's white spruce 40 year-old testing program which is ideal for providing reliable growth and wood attributes., agreement title english : Single-step genomic evaluation in forest genetics, agreement title french : Single-step genomic evaluation in forest genetics, agreement number : RGPIN, agreement value : 125000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada