Unlocking genome-based prediction and selection in conifers: the key role of within-family prediction accuracy

Context Genomic selection is a promising approach for forest tree breeding. However, its advantage in terms of prediction accuracy over conventional pedigree-based methods is unclear and within-family accuracy is rarely assessed. Aims We used an pedigree-based model (ABLUP) with corrected pedigree data as a baseline reference for assessing the prediction accuracy of genome-based model (GBLUP) at the global and within-family levels in maritime pine ( Pinus pinaster Ait). Methods We sampled 39 full-sib families, each comprising 10 to 40 individuals, to constitute an experimental population of 833 individuals. A stochastic simulation model was also developed to explore other scenarios of heritability, training set size and tagging density. Results Prediction accuracies with GBLUP and ABLUP were similar and accuracy with GBLUP within-family was on average zero with large variation between families. Simulations revealed that the number of individuals in the training set was the principal factor limiting GBLUP accuracy in our study and likely in many forest tree breeding programmes. Accurate within-family prediction is possible if 40-65 individuals per full-sib family are included in the genomic training set, from a total of 1600-2000 individuals in the training set. Conclusion Such conditions lead to a significant advantage of GBLUP over ABLUP in terms of prediction accuracy and more clearly justify the switch to genome-based prediction and selection in forest trees. ### Competing Interest Statement The authors have declared no competing interest. * ABLUP : pedigree-based best linear unbiased prediction BLUP : best linear unbiased prediction CV : cross-validation DEV : stem deviation to verticality DNA : deoxyribonucleic acid EBV : pedigree-based estimated breeding value GBLUP : genome-based best linear unbiased prediction GEBV : genome-based estimated breeding values GS : genomic selection HT : height LD : linkage disequilibrium nTset : training set size nSNP : number of SNP OCS : optimum contribution selection POPr : trees sampled for this study POPs : simulated version of POPr QTL : quantitative trait loci SNP : single nucleotide polymorphism Tset : training set Vset : validation set