Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information

Abstract

BackgroundNon-key traits (NKTs) in radiata pine (Pinus radiata D. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding.ResultsNine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55–0.75 when using the documented pedigree and 0.61–0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37–115% and 13–77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively.ConclusionIt was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.

Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

“…GBS marker information has been successfully used for parentage reconstruction in Scots pine [ 34 ], as well as to perform genomic predictions studies in livestock [ 35 , 36 ], maize [ 37 ], wheat [ 33 ], soybean [ 38 ], Picea glauca (Moench) Voss × Picea engelmannii Parry ex Engelm. [ 39 ] and radiata pine [ 40 ]. Therefore, GBS is an attractive technology that can be used to perform GS and genome-wide association studies (GWAS) for Scots pine [ 41 , 42 ].…”

Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate

“…Trait data must be in Linkage Disequilibrium-LD or genetic auto-correlation (e.g., Kelleher et al, 2012), with the molecular markers or with the samples' genetic co-ancestry. GP utility has been demonstrated ( Table 1) in model forest tree species such as Eucalyptus Suontama et al, 2019), and conifers as Pinus Li et al, 2019) and Douglas-fir (Thistlethwaite et al, 2017(Thistlethwaite et al, , 2019b, but also in non-model perennial crops such as coffee (Sousa et al, 2018), rubber (Cros et al, 2019;Souza et al, 2019) and oil palm (Cros et al, 2015). GP may even fit epigenetics (Roudbar et al, 2020), as well as multi-trait genomic models as was recently confirmed in Norway spruce for growth, wood quality and weevil resistance traits (Lenz et al, 2020).…”

“…Yet, the power of population genomics must be further extended to comprehend neutral and adaptive processes in noncommercial species of ecological value in order to advance not just productivity, but also climate adaptation, forest health and conservation (Isabel et al, 2020). In this sense, GP is starting to permeate novel non-key traits other than growth and wood density, but still of interest for breeding, such as branching, stem straightness and external resin bleeding (Li et al, 2019). GP is also predicting adaptive trait variation for abiotic (Eckert et al, 2010) and biotic (Westbrook et al, 2020) stresses.…”

“…More recently, major developments in the field of predictive breeding (i.e., GP) promise to speed up selection from natural sources, as well as within the breeding cycle, by shortening the generation intervals and increasing the selection accuracy prior field trials. We have already identified and discussed major improvements in this line, such as multitrait GP models (Lenz et al, 2020), coupled with integrative selection scores (Burdon and KlápšTě, 2019) on novel nonkey (Li et al, 2019) and ecological-worth (Holliday et al, 2017;Isabel et al, 2020) traits. These innovations can capture multi-scale trait-environment relationships (Bruelheide et al, 2018) in non-model tree species (Mayol et al, 2020).…”

“…Yet, this information has limitedly been utilized in tree improvement and conservation (Flanagan et al, 2018), despite genetic gains (Figure 1) and optimized management are urgently required due to environmental issues (Scherer et al, 2020). Besides, breeding woody perennials is primarily bottlenecked by their outcrossing reproductive systems, prolonged juvenile phases (Grattapaglia et al, 2018), large genome sizes lacking elimination mechanisms of long-terminal transposons (Nystedt et al, 2013), and an excessive focus on productivity (Burdon and KlápšTě, 2019) that omits adaptive traits ( Table 1; Li et al, 2019). Thus, here we discuss ways to side step these limitations by arguing how predictive genomics can increase selection accuracy and shorten generation intervals (Grattapaglia et al, 2018), assist the detection of exotic variants from tree germplasm (Migicovsky and Myles, 2017), and disclose the genomic potential of adaptation to different climates (Lind et al, 2018).…”