The impact of genetic relationship information on genome-assisted breeding values.

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Abstract

The success of genomic selection depends on the potential to predict genome-assisted breeding values (GEBVs) with high accuracy over several generations without additional phenotyping after estimating marker effects. Results from both simulations and practical applications have to be evaluated for this potential, which requires linkage disequilibrium (LD) between markers and QTL. This study shows that markers can capture genetic relationships among genotyped animals, thereby affecting accuracies of GEBVs. Strategies to validate the accuracy of GEBVs due to LD are given. Simulations were used to show that accuracies of GEBVs obtained by fixed regression-least squares (FR-LS), random regression-best linear unbiased prediction (RR-BLUP), and Bayes-B are nonzero even without LD. When LD was present, accuracies decrease rapidly in generations after estimation due to the decay of genetic relationships. However, there is a persistent accuracy due to LD, which can be estimated by modeling the decay of genetic relationships and the decay of LD. The impact of genetic relationships was greatest for RR-BLUP. The accuracy of GEBVs can result entirely from genetic relationships captured by markers, and to validate the potential of genomic selection, several generations have to be analyzed to estimate the accuracy due to LD. The method of choice was Bayes-B; FR-LS should be investigated further, whereas RR-BLUP cannot be recommended.

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Effect of total allelic relationship on accuracy of evaluation and response to selection.

A Nejati-Javaremi, Kyle J. Gibson, Gordon C. S. Smith (1997)

Total allelic relationship (TA) as a possible alternative to the pedigree-derived additive genetic relationship (RA) is defined. The TA measures the actual allelic identity between individuals for loci segregating for the trait concerned. Its value was studied by simulation in populations of different family structure, different numbers of loci, different numbers of alleles per locus, and different heritability levels. The alternative types of relationship matrices were used in mixed model equations to derive best linear unbiased prediction estimates (EBV) of breeding values (BV). Accuracies of evaluations were calculated as correlations of EBV with true breeding values. In populations with random selection and mating, EBVTA derived using TA had higher accuracies than EBVRA derived using RA. In populations with selection, EBVTA was more accurate and resulted in higher responses than selection on EBVRA. We conclude that not accounting for variation in average measures of relationship and identity in state can be important sources of variance of prediction error, and taking account of them increases the accuracy of selection.