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[56]
and phenotype data is crucial to improving genetic healthcare . Data sharing is necessary to describe the
key features of the phenotype of those with RDs, establish the association between genetic RDs and the
[56]
causative genes, classify genomic variants, and improve standards used in variant classification . Data
[56]
sharing is compatible with the imperative of protecting privacy in healthcare . Currently, there are online
data-sharing resources such as Matchmaker Exchange, open collaboration between different platforms
(including (GeneMatcher, PhenomeCentral, DECIPHER, and others) to facilitate the matching of cases
with similar phenotypic and genotypic profiles (matchmaking) through standardized application
[57]
programming interfaces and procedural conventions . With the advent of multi-omics approaches, there
is an increasing need to share data on tools and methods for data integration and interpretation. Regarding
the discovery of new genes associated with disease, the analysis of genomic population data allows the
evaluation of the strength of natural selection to identify genes and genomic regions that are constrained for
variation compared to the expected mutation rates. This information reveals which genes are most
intolerant to loss-of-function or missense variants . The predicted loss-of-function intolerance (pLI) score
[58]
and the lower observed/expected upper-bound fraction (LOEUF) score can be used to identify candidate
haploinsufficient disease genes [7,58,59] . Another alternative strategy for the discovery of new genes is the
analysis of the phenotypic effects of gene disruption using model organisms when the gene of interest is
evolutionarily conserved . Four related projects that use animal models are the Monarch Initiative, the
[7]
Mouse Genome Database, the Knockout Mouse Project, and the International Mouse Phenotyping
Consortium . However, it should be noted that animal models often fail to recapitulate human disease
[7]
phenotypes. Other options are the possibility of deriving human-induced pluripotent stem cells (hiPSCs)
from patient cells (e.g., fibroblasts) or generating pathogenic variants in wild-type hiPSCs using editing
approaches (e.g., CRISPR/Cas9 technologies), which could be coupled with the generation of 3D organoids.

CONCLUSION
We are currently witnessing how genetics and genomics of URDs are one of the fields in which precision
medicine and translational research are opening new paths and opportunities in etiological diagnosis.
Advances in genetic and genomic testing have markedly improved the rate and time to diagnosis of patients
with URDs. However, it is important to note that all techniques have limitations. Novel multi-omics
techniques are rapidly advancing toward clinical practice, and in silico studies and functional analyses allow
us to validate the significance of the findings.

Increasing access to exome and genome sequencing technologies and biological validation, gaining insight
into the interpretation of multi-omics datasets, and fostering data sharing would reduce the long diagnostic
odyssey and diagnostic gap.

DECLARATIONS
Authors’ contributions
Conception or design of the work, drafting the article, critical revision of the article, final approval of the
version to be published: Casas-Alba D, Pijuan J
Conception or design of the work, critical revision of the article, final approval of the version to be
published: Hoenicka J, Vilanova-Adell A, Vega-Hanna L, Palau F

Availability of data and materials
Not applicable.