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In genome-wide association studies, failure to remove variation due to population structure results in spurious associations. In contrast, for predic-tions of future phenotypes or estimated breeding values from dense SNP data, exploiting population structure arising from relatedness can actually increase the accu-racy of prediction in some cases, for example, when the selection candidates are offspring of the reference popu-lation where the prediction equation was derived. In populations with large effective population size or with multiple breeds and strains, it has not been demonstrated whether and when accounting for or removing variation due to population structure will affect the accuracy of genomic prediction. Our aim in this study was to deter-mine whether accounting for population structure would increase the accuracy of genomic predictions, both with-in and across breeds. First, we have attempted to decom-pose the accuracy of genomic prediction into contribu-tions from population structure or linkage disequilib-rium (LD) between markers and QTL using a diverse multi-breed sheep (Ovis aries) data set, genotyped for 48,640 SNP. We demonstrate that SNP from a single chromosome can achieve up to 86% of the accuracy for genomic predictions using all SNP. This result suggests that most of the prediction accuracy is due to population structure, because a single chromosome is expected to capture relationships but is unlikely to contain all QTL. We then explored principal component analysis (PCA) as an approach to disentangle the respective contribu-tions of population structure and LD between SNP and QTL to the accuracy of genomic predictions. Results showed that fitting an increasing number of principle components (PC; as covariates) decreased within breed accuracy until a lower plateau was reached. We specu-late that this plateau is a measure of the accuracy due to LD. In conclusion, a large proportion of the accuracy for genomic predictions in our data was due to varia-tion associated with population structure. Surprisingly, accounting for this structure generally decreased the accuracy of across breed genomic predictions. [ABSTRACT FROM AUTHOR]

Summary: With the new opportunities from DNA technology, multitier breeding schemes have the potential to become more effective and more integrated. Integrated breeding schemes can also be better adapted to account for potential genotype by environment interactions (G × E) between tiers. In this case, phenotypic and genotype information from lower tiers becomes more valuable as it involves measurement of traits that directly represent the breeding objective. The objective of this study was to compare scenarios that represented different selection strategies and their economic effectiveness in fine‐wool commercial sheep operations that exploit multitier breeding structures. Genomic selection (GS) applied in the multiplier and the commercial tier presented the largest additional revenue among all scenarios, as it resulted in the largest amount of genetic progress. The largest benefits from GS were outweighed by the genotyping costs, which made DNA parentage the most feasible strategy for the multiplier tier, resulting in the highest cumulative net present value (CNPV). The benefits of phenotypes and genotype information from the commercial environment were larger in the presence of G × E between the nucleus and the commercial tier. The CNPV was larger with a 50% reduction in genotyping costs, which increased the returns of GS scenarios by 2.7‐fold on average. Higher selection intensity when selecting multiplier rams also resulted in larger benefits. In this case, returns for the breeding scheme were 3.5‐fold higher when 33% of multiplier males were selected based on commercial information, compared to scenarios selecting 50% of the available multiplier rams. The benefits of collecting commercial phenotypes and genotypes were long term, which means that return on investment often took more than 10 years to be achieved, and were largely dependent on two‐stage selection to reduce cost while maintaining selection efficiency and on the cost of a genotype test. [ABSTRACT FROM AUTHOR]