Explore the vast range of titles available.

Genomic prediction (GP) has shown to be a valuable tool for genetic improvement in breeding programs but requires large training populations in order to build robust models. This is difficult to obtain for newly established breeding programs. Here, we aimed to overcome this challenge by combining datasets from 4 different barley breeding programs, utilizing up to 12 years of data to increase prediction accuracy in a more recently established 6-rowed winter (6RW) barley breeding program. By allowing data to accumulate in a breeding program as the years progress, we investigated when GP accuracy in 6RW benefitted from external populations. To do this, we focused on several parameters: training population size, choice of model for multipopulation GP (univariate versus multivariate), the key trait under investigation (grain yield, plant height, or rust resistance), and genetic distance between populations. We found that in the early stages of a breeding program, prediction of the 6RW population could benefit from inclusion of an external population, but the advantage depended on the specific population and trait under investigation. However, when data from all 4 years were available, multipopulation GP generally performed similarly to within-population GP. Additionally, when comparing multivariate and univariate models for multipopulation GP, the multivariate model often performed significantly worse, despite strong genetic correlations between the populations involved. This was especially the case when data were sparse and the model required estimation of numerous parameters from a small number of observations. Altogether, our results suggest that multipopulation GP is beneficial only in the very early stages of new breeding programs, emphasizing its relevance for newly established breeding programs or new breeding goals, especially for related populations.

Genome-wide association study (GWAS) is a powerful tool for identifying marker-trait associations that can accelerate breeding progress. Yet, its power is typically constrained in newly established breeding programs where large phenotypic and genotypic datasets have not yet accumulated. Expanding the dataset by inclusion of data from well-established breeding programs with many years of phenotyping and genotyping can potentially address this problem. In this study we performed single- and multi-population GWAS on heading date and lodging in four barley breeding populations with varying combinations of row-type and growth habit. Focusing on a recently established 6-rowed winter (6RW) barley population, single-population GWAS hardly resulted in any significant associations. Nevertheless, the combination of the 6RW target population with other populations in multi-population GWAS detected four and five robust candidate quantitative trait loci for heading date and lodging, respectively. Of these, three remained undetected when analysing the combined populations individually. Further, multi-population GWAS detected markers capturing a larger proportion of genetic variance in 6RW. For multi-population GWAS, we compared the findings of a univariate model (MP1) with a multivariate model (MP2). While both models surpassed single-population GWAS in power, MP2 offered a significant advantage by having more realistic assumptions while pointing towards robust marker-trait associations across populations. Additionally, comparisons of GWAS findings for MP2 and single-population GWAS allowed identification of population-specific loci. In conclusion, our study presents a promising approach to kick-start genomics-based breeding in newly established breeding populations.