Genomic Prediction in Family Bulks Using Different Traits and Cross-Validations in Pine

Abstract

Genomic prediction (GP) integrates statistical, genomic and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in biology, breeding scheme, propagation method, and unit of selection, no universal GP approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family bulk is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5-20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values at the individual (GEBV) and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family bulks, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of GP in these situations.