Genomic selection : the option for new robustness traits ?

20 Genomic selection is rapidly becoming the state-of-the-art genetic selection 21 methodology in dairy cattle breeding schemes around the world. The objective of this 22 paper was to explore possibilities to apply genomic selection for traits related to dairy 23 cow robustness. Deterministic simulations indicate that replacing progeny testing with 24 genomic selection may favour genetic response for production traits at the expense 25 of robustness traits, due to a disproportional change in accuracies obtained across 26 trait groups. Nevertheless, several options are available to improve accuracy of 27 genomic selection for robustness traits. Moreover, genomic selection opens up the 28 opportunity to begin selection for new traits using specialised reference populations 29 of limited size where phenotyping large populations of animals are currently 30 prohibitive. Reference populations for such traits may be nucleus-type herds, 31 research herds, or pooled data from (international) research experiments or research 32 herds. The RobustMilk project has set an example for the latter approach, by 33 collating international data for progesterone based traits, feed intake and energy 34 balance related traits. Reference population design, both in terms of relatedness of 35 the animals and variability in phenotypic performance, is important to optimise 36 accuracy of genomic selection. Use of indicator traits, combined with multi-trait 37 genomic prediction models, can further contribute to improved accuracy of genomic 38 prediction for robustness traits. Experience to-date indicates that for newly recorded 39 robustness traits that are negatively correlated with the main breeding goal, cow 40 reference populations of ≥10,000 are required when genotyping is based on medium41 or high-density SNP arrays. Further genotyping advances (e.g. sequencing) 42 combined with post-genomics technologies will enhance the opportunities for 43 (genomic) selection to improve cow robustness. 44