Genomic prediction in family bulks using different traits and cross-validations in pine

Abstract

Genomic prediction integrates statistical, genomic and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family 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, 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 genomic prediction in these situations.

Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks

“…In this way, there was a lack of representation of the genetic background of that cluster in the training set. This result reinforces the importance of the composition of the training population, which needs to represent the target population (validation set) closely, as shown for other studies (de Bem Oliveira et al., 2020; Rios et al., 2021; Xu et al., 2020).…”

“…However, its wide implementation in most crops is still hindered by investment costs, the analytical skills required, and the complexity of production systems (Hickey et al., 2017). In this study, a nondormant alfalfa reference population was phenotyped and genotyped in family bulks to facilitate the GS logistic, operational implementation, and reduction of costs (Rios et al., 2021) because alfalfa breeding is also performed in bulks. Using the TES approach, we obtained 114,945 SNPs that were used to perform population genetics analyses and to build GS models following three prediction schemes.…”

“…Nonetheless, alfalfa is often bred by evaluating full-or half-sib families and subsequently intercrossing selected genotypes to produce populations to be commercialized as synthetic cultivars (Annicchiarico & Pecetti, 2021). The application of GS to alfalfa breeding programs could be facilitated and made less costly by genotyping families as a bulk (i.e., by pooling multiple individuals within each family) to obtain allele frequency marker data rather than individual genotyping calls (Cericola et al, 2018;Fè et al, 2015Fè et al, , 2016Guo et al, 2018;Rios et al, 2021). Genome-wide family prediction can be used in crops bred as family bulks because this approach uses family pools as the basis for measuring genotypes and phenotypes (Ashraf et al, 2016;Fè et al, 2015Fè et al, , 2016Rios et al, 2021).…”

“…The application of GS to alfalfa breeding programs could be facilitated and made less costly by genotyping families as a bulk (i.e., by pooling multiple individuals within each family) to obtain allele frequency marker data rather than individual genotyping calls (Cericola et al, 2018;Fè et al, 2015Fè et al, , 2016Guo et al, 2018;Rios et al, 2021). Genome-wide family prediction can be used in crops bred as family bulks because this approach uses family pools as the basis for measuring genotypes and phenotypes (Ashraf et al, 2016;Fè et al, 2015Fè et al, , 2016Rios et al, 2021). Several GS pipelines were developed to predict the phenotypic performance of genotypes under specific environmental conditions (Costa-Neto et al, 2021;Lorenzana & Bernardo, 2009).…”

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inUrochloa ruziziensis

“…This practice makes the development of genome-wide family prediction (GWFP) approaches highly advantageous. By considering family groups as the measurement unit, there is a reduction in genotyping efforts, as well as the costs associated with developing GP models (Zou et al, 2016; Rios et al, 2021; Murad Leite Andrade et al, 2022). Furthermore, the implementation of GWFP can improve the predictive ability of selection, increasing the rate of genetic gains for complex traits, as demonstrated in studies on loblolly pine and alfalfa (Rios et al, 2021; Murad Leite Andrade et al, 2022).…”