Genomic prediction for crossbred performance using metafounders.

Future genomic evaluation models to be used routinely in breeding programs for pigs and poultry need to be able to optimally use information of crossbred (CB) animals to predict breeding values for CB performance of purebred (PB) selection candidates. Important challenges in the commonly used single-step genomic best linear unbiased prediction (ssGBLUP) model are the definition of relationships between the different line compositions and the definition of the base generation per line. The use of metafounders (MFs) in ssGBLUP has been proposed to overcome these issues. When relationships between lines are known to be different from 0, the use of MFs generalizes the concept of genetic groups relying on the genotype data. Our objective was to investigate the effect of using MFs in genomic prediction for CB performance on estimated variance components, and accuracy and bias of GEBV. This was studied using stochastic simulation to generate data representing a three-way crossbreeding scheme in pigs, with the parental lines being either closely related or unrelated. Results show that using MFs, the variance components should be scaled appropriately, especially when basing them on estimates obtained with, for example a pedigree-based model. The accuracies of GEBV that were obtained using MFs were similar to accuracies without using MFs, regardless whether the lines involved in the CB were closely related or unrelated. The use of MFs resulted in a model that had similar or somewhat better convergence properties compared to other models. We recommend the use of MFs in ssGBLUP for genomic evaluations in crossbreeding schemes.

Response to genomic selection: the Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting.

BACKGROUND: Over the last ten years, genomic selection has developed enormously. Simulations and results on real data suggest that breeding values can be predicted with high accuracy using genetic markers alone. However, to reach high accuracies, large reference populations are needed. In many livestock populations or even species, such populations cannot be established when traits are difficult or expensive to record, or when the population size is small. The value of genomic selection is then questionable. METHODS: In this study, we compare traditional breeding schemes based on own performance or progeny information to genomic selection schemes, for which the number of phenotypic records is limiting. Deterministic simulations were performed using selection index theory. Our focus was on the equilibrium response obtained after a few generations of selection. Therefore, we first investigated the magnitude of the Bulmer effect with genomic selection. RESULTS: Results showed that the reduction in response due to the Bulmer effect is the same for genomic selection as for selection based on traditional BLUP estimated breeding values, and is independent of the accuracy of selection. The reduction in response with genomic selection is greater than with selection based directly on phenotypes without the use of pedigree information, such as mass selection. To maximize the accuracy of genomic estimated breeding values when the number of phenotypic records is limiting, the same individuals should be phenotyped and genotyped, rather than genotyping parents and phenotyping their progeny. When the generation interval cannot be reduced with genomic selection, large reference populations are required to obtain a similar response to that with selection based on BLUP estimated breeding values based on own performance or progeny information. However, when a genomic selection scheme has a moderate decrease in generation interval, relatively small reference population sizes are needed to obtain a similar response to that with selection on traditional BLUP estimated breeding values. CONCLUSIONS: When the trait of interest cannot be recorded on the selection candidate, genomic selection schemes are very attractive even when the number of phenotypic records is limited, because traditional breeding requires progeny testing schemes with long generation intervals in those cases.

Intrinsic aerobic capacity sets a divide for aging and longevity.

RATIONALE: Low aerobic exercise capacity is a powerful predictor of premature morbidity and mortality for healthy adults as well as those with cardiovascular disease. For aged populations, poor performance on treadmill or extended walking tests indicates closer proximity to future health declines. Together, these findings suggest a fundamental connection between aerobic capacity and longevity. OBJECTIVES: Through artificial selective breeding, we developed an animal model system to prospectively test the association between aerobic exercise capacity and survivability (aerobic hypothesis). METHODS AND RESULTS: Laboratory rats of widely diverse genetic backgrounds (N:NIH stock) were selectively bred for low or high intrinsic (inborn) treadmill running capacity. Cohorts of male and female rats from generations 14, 15, and 17 of selection were followed for survivability and assessed for age-related declines in cardiovascular fitness including maximal oxygen uptake (VO(2max)), myocardial function, endurance performance, and change in body mass. Median lifespan for low exercise capacity rats was 28% to 45% shorter than high capacity rats (hazard ratio, 0.06; P<0.001). VO(2max), measured across adulthood was a reliable predictor of lifespan (P<0.001). During progression from adult to old age, left ventricular myocardial and cardiomyocyte morphology, contractility, and intracellular Ca(2+) handling in both systole and diastole, as well as mean blood pressure, were more compromised in rats bred for low aerobic capacity. Physical activity levels, energy expenditure (Vo(2)), and lean body mass were all better sustained with age in rats bred for high aerobic capacity. CONCLUSIONS: These data obtained from a contrasting heterogeneous model system provide strong evidence that genetic segregation for aerobic exercise capacity can be linked with longevity and are useful for deeper mechanistic exploration of aging.