We have entered the big data paradigm. Now that whole genome sequence data is available on a population scale basis, a fundamental issue is: what can be done with sequence data that cannot be achieved with former datasets? This question has not a closed response, partly due to the fact that information contained in sequence data is highly repetitive (e.g., linkage disequilibrium) and also noisy (e.g., missing data due to shallow coverage). We argue that using accurate biology informed decisions can make a big difference in the prediction of genetic merit when sequence is available. Here we review the main kinds of external biological information and some approaches to combine these disparate sources. Despite the richness or resources available, however, two main difficulties lie ahead: (i) an improved understanding of the phenotype's biology to make the right the choice among the plethora of datasets available, and (ii) how this information is weighed and incorporated into selection decisions.