Genomes can inform us on the amount of genetic load, i.e., the genetic variants that negatively affect survival and tend to accumulate during population’s contractions when closely related individuals breed. Estimating genetic load is not trivial as it requires knowledge on individual survival, which might be difficult to obtain in wild, elusive populations. Bioinformatics approaches can help inferring the effect of mutations and identifying those variants that are benefic, neutral, or harmful even in absence of direct functional evidence. We have reviewed how genetic load has been interpreted and measured in wild animals using bioinformatics methods and whole genomes so far (Bertorelle et al. Genetic load: genomic estimates and applications in non-model animals, Nature Reviews Genetics, (2022) doi.org/10.1038/s41576-022-00448-x). Additionally, we are developing standardized bioinformatics protocols to explore genetic load and its consequences in the five ENDEMIXIT species. Forward simulations, where we used in-silico models to potential future conservation actions including reintroductions from larger populations, indicated that these potential source populations might introduce further deleterious mutations in the small group and lead to the loss of some of the unique features of the Marsican bear (docile behavior, vegetarian diet), while it could be more beneficial to increase the small population size.