Genomic prediction with multiple biparental families

For genomic prediction within biparental families using multiple biparental families, combined training sets comprising full-sibs from the same family and half-sib families are recommended to reach high and robust prediction accuracy, whereas inclusion of unrelated families is risky and can have negative effects. In recycling breeding, where elite inbreds are recombined to generate new source material, genomic and phenotypic information from lines of numerous biparental families (BPFs) is commonly available for genomic prediction (GP). For each BPF with a large number of candidates in the prediction set (PS), the training set (TS) can be composed of lines from the same full-sib family or multiple related and unrelated families to increase the TS size. GP was applied to BPFs generated in silico and from two published experiments to evaluate the prediction accuracy ($$\rho$$) of different TS compositions. We compared $$\rho$$ for individual pairs of BPFs using as TS either full-sib, half-sib, or unrelated BPFs. While full-sibs yielded highly positive $$\rho$$ and half-sibs also mostly positive $$\rho$$ values, unrelated families had often negative $$\rho$$, and including these families in a combined TS reduced $$\rho$$. By simulations, we demonstrated that optimized TS compositions exist, yielding 5–10% higher $$\rho$$ than the TS including all available BPFs. However, identification of poorly predictive families and finding the optimal TS composition with various quantitative-genetic parameters estimated from available data was not successful. Therefore, we suggest omitting unrelated families and combining in the TS full-sib and few half-sib families produced by specific mating designs, with a medium number (~ 50) of genotypes per family. This helps in balancing high $$\rho$$ in GP with a sufficient effective population size of the entire breeding program for securing high short- and long-term selection progress.