BackgroundThe theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values.MethodsSimulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated.ResultsThe gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy.ConclusionsAn animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.

BackgroundThe theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. However, there is increasing evidence that genomic selection also relies on "relationships" between individuals to accurately predict genetic values. Therefore, a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations.MethodsSimulation was used to compare the performance of estimates of breeding values based on pedigree relationships (Best Linear Unbiased Prediction, BLUP), genomic relationships (gBLUP), and based on a Bayesian variable selection model (Bayes B) to estimate breeding values under a range of different underlying models of genetic variation. The effects of different marker densities and varying animal relationships were also examined.ResultsThis study shows that genomic selection methods can predict a proportion of the additive genetic value when genetic variation is controlled by common quantitative trait loci (QTL model), rare loci (rare variant model), all loci (infinitesimal model) and a random association (a polygenic model). The Bayes B method was able to estimate breeding values more accurately than gBLUP under the QTL and rare variant models, for the alternative marker densities and reference populations. The Bayes B and gBLUP methods had similar accuracies under the infinitesimal model.ConclusionsOur results suggest that Bayes B is superior to gBLUP to estimate breeding values from genomic data. The underlying model of genetic variation greatly affects the predictive ability of genomic selection methods, and the superiority of Bayes B over gBLUP is highly dependent on the presence of large QTL effects. The use of SNP sequence data will outperform the less dense marker panels. However, the size and distribution of QTL effects and the size of reference populations still greatly influence the effectiveness of using sequence data for genomic prediction.

To determine whether impaired insulin release from perifused rat islets of vitamin D-deficient (D-def) rats is a result of vitamin D-deficiency specifically or an associated decrease in food intake, we: 1) compared insulin release from islets of vitamin D-def rats with insulin release from islets of pair fed (pf) normal rats, and 2) measured the effects of 1,25(OH)2D3 treatment on food intake and insulin secretion from islets of D-def rats. Both vitamin D-def and pf normal rat islets showed significantly diminished insulin release in comparison with normal controls but were not different from each other. When D-def rats were repleted with 1,25(OH)2D3, food intake increased and insulin secretion improved during perifusion of rat islets. When D-def rats treated with 1,25(OH)2D3 were prevented from increasing their food intake in response to 1,25(OH)2D3 by pair feeding to a group of untreated D-def rats, insulin release from islets of treated rats was not significantly different from untreated D-def rats. To separate the effects of vitamin D deficiency from hypocalcemia, a group of vitamin D-def hypocalcemic rats was compared with a group of D-def normocalcemic rats. Normocalcemia did not reverse the defect in insulin release. In studies of cellular calcium uptake, both pf and D-def rat islets took up less calcium than normal islets but calcium uptake was not different between pf and D-def rat islets. Our studies suggest that vitamin D deficiency is associated with marked impairment of biphasic insulin release and that the decrease in food intake may account for this impairment at least in part.