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Single-step genomic evaluations have the advantage of simultaneously combining all pedigree, phenotypic, and genotypic information available. However, systems with a large number of genotyped animals have some computational challenges. In many genomic breeding programs, genomic predictions of young animals should become available for selection decisions in the shortest time possible, which requires either a very effective estimation or an approximation with negligible loss in accuracy. We investigated different procedures for predicting breeding values of young genotyped animals without setting up the full single-step system augmented for the additional genotypes. Methods were based on transmitting the information from single-step breeding values of genotyped animals that took part in the previous full run to young animals, either through genomic relationships or through a marker-based model. The different procedures were tested on real data from the April 2017 run of the German-Austrian official genomic evaluation for Fleckvieh. The data set included 62,559 genotyped animals and was used to run single-step evaluations for 23 conformation traits. A further data set comprising 1,768 young animals was used for interim prediction and we called it the validation set. The reference values for validation were the predicted breeding values of the young animals from a full single-step run containing the genotypes of all 64,327 animals. Correlations between the approximated predictions and those from the full single-step run also containing genotypes from young animals averaged 0.9932 for the best method (from 0.990 to 0.995 across traits). In conclusion, prediction of single-step breeding values for young animals can be well approximated using systems of size equal to the number of markers., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

We investigated the effects on body composition in triathletes at the Triple Iron Triathlon Austria in 2006, where athletes had to perform 11.6 km swimming, 540 km cycling, and 126.6 km running within 58 h. In 16 male triathletes, body mass (BM), skinfold thicknesses, and circumferences of extremities were measured before and after the competition in order to calculate body mass index (BMI), percent body fat (%BF), fat mass (FM), and skeletal muscle mass (SM). Body mass, BMI, %BF, FM, and SM decreased statistically significantly (p < 0.01). The loss of BM is associated neither with the decrease of %BF (p > 0.05, r(2)= 0.00) nor with the decrease of SM (p> 0.05, r(2)= 0.06). There is no association between total race time and the loss of BM (r(2) = 0.06), %BF (r(2) = 0.10), and SM (r2 = 0.11). No significant correlation (p > 0.05, r(2)= 0.43) was found between the initial SM and loss of SM.