In the era of Next-Generation Sequencing (NGS) applied to human congenital diseases, developmental biology meets with transcriptome-wide analyses and predictive bioinformatics to address the following issues: a) identify novel disease-genes, b) clarify functions of detected mutations and/or polymorphisms, c) identify relevant networks and core regulations from ‘omic’ data. With the appropriate animal models, sets of high-throughput data can be intersected with relevant datasets, and used to predict/prioritize disease-genes. The same data can also be used to draw the blueprint of the core cellular and molecular processes altered in specific conditions. Finally, simple models such as Danio rerio or Caenorhabditis elegans can be used to functionally examine the effect of identified mutated genes in developmental processes. Kallmann syndrome (KS) is a paradigm of a genetically heterogeneous and complex set of conditions, in which many genes have been found mutated in patients’ DNA, but in the majority of cases the disease-gene is unknown. We illustrate a general workflow that combines coding and non-coding RNA data from animal models to infer relevant pathways and predict novel disease-genes. The choice of the appropriate animal model, the generation of coherent sets of high-throughput data and ad hoc bioinformatic (meta)analyses are effective in revealing novel relationships between groups of genes and pathways, and work towards innovative hypotheses such as the involvement of microRNA (miR) misregulation. Predictions can rapidly be verified via resequencing of patients’ genomes, in progress in several laboratories. Conversely, NGS data need parallel developmental models and profiling data to acquire functional relevance. We anticipate that similar approaches will become routine along with the evaluation of high-throughput genome-sequencing data from patients’ DNAs.