Multigenerational prediction of genetic values using genome-enabled prediction

The identification of elite individuals is a critical component of most breeding programs. However, the achievement of this goal is limited by the high cost of phenotyping and experimental research. A significant benefit of genomic selection (GS) to plant breeding is the identification of elite individuals without the need for phenotyping. This study aimed to propose different calibration strategies using combinations between generations from different genetic backgrounds to improve the reliability of GS and to investigate the effects of LD in different types of mating systems: outcrossing (A(n)) self-pollination (S(n)) and hybridization (H(n)). For this purpose, we simulated a genome with 10 linkage groups. In each group, two QTL were simulated. Subsequently, an F(2) population was created, followed by four generations of inbreeding (S(1) to S(4,) H(1) to H (4,) A(1), to A(4,)). Quantitative traits were simulated in three scenarios considering three degrees of dominance (d/a = 0, 0.5 and 1) and two broad sense heritabilities (h(2) = 0.30 and 0.70), totaling six genetic architectures. To evaluate prediction reliability, a model (RR-BLUP) was trained in one generation and used to predict the following generations of mating systems. For example, the marker effects estimated in the F(2) population were used to estimate the expected genomic breeding value (GEBV) in populations S(1) through A(4.) The squared correlation between the GEBV and the true genetic value were used to measure the reliability of the predictions. Independently of the population used to estimate the marker effect, reliability showed the lowest values in the scenario where d = 1. For any scenario, the use of the multigenerational prediction methodology improved the reliability of GS.