Genomic Selection and Genome-Wide Association Study in Populus trichocarpa and Pinus taeda
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Forest tree breeding methods rank among the most efficient ways to increase productivity and quality of forests. With the advent of high-throughput genotyping technology, genome-enabled breeding has started to gain importance and may overcome some weaknesses of traditional tree breeding. Genomic Selection (GS), which involves using genome-wide markers to predict breeding values of individuals in a population, has been proposed for animal and plant breeding programs. GS enables very accurate selection decisions through estimation of genomic estimated breeding values (GEBVs). While the goal of GS is to predict phenotype from genotype, it does not identify the underlying genes that have important roles in a trait. Genome-Wide Association Studies (GWAS) approaches are therefore complementary to GS, enabling identification of these genes, which may be useful for marker-assisted selection in some traits. In this study, we first estimated heritability for several adaptive traits (cold hardiness, dbh, bud flush, height, and bud set) in a population of Populus trichocarpa and for height, diameter, and stem straightness in Pinus taeda. GEBVs accuracies were estimated using a ridge regression–best linear unbiased prediction (rrBLUP) model, and these accuracies were compared with estimated heritabilities. GWAS was also performed for the both imputed and non–imputed data of P. taeda population using TASSEL (Trait Analysis by aSSociation Evolution and Linkage) software, as well as rrBLUP and FFBSKAT (Fast Family-Based Sequence Kernel Association Test) packages in R. Heritabilities ranged from 0.34 to 0.56 for P. trichocarpa and 0.14 to 0.37 for P.taeda. GWAS identified 3244 associations for dbh, 4077 associations for stem straightness, and 5280 SNPs for height (p≤0.05) in TASSEL using the reduced model (marker data only), whereas 2729, 3272 and 3531 associations were found with the full model where we also included population structure as a covariate. FFBSKAT showed a similar number of SNP associations (2989, 3046 and 3058). There was an inflation of SNP associations (~20k) found in rrBLUP, which suggests population structure was not effectively controlled. The GEBVs accuracies ranged from 0.09 and 0.22 for P.trichocarpa and 0.09 to 0.23 for P.taeda using rrBLUP method. Testing the effect of repetation on the accuracy of GEBV for poplar showed that there was no significant difference between the number of cycles. Also, there was no significant difference the accuracy of GEBVs in pine between two different imputation methods, the marker mean value and Beagle software.
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