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Abstract: Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine. [Copyright &y& Elsevier]

Abstract Background The ganglioside-induced differentiation-associated protein 1 gene (GDAP1), which is involved in the Charcot-Marie-Tooth disease (CMT), the most commonly inherited peripheral neuropathy, encodes a protein anchored to the mitochondrial outer membrane. The phenotypic presentations of patients carrying GDAP1 mutations are heterogeneous, making it difficult to determine genotype-phenotype correlations, since the majority of the mutations have been found in only a few unrelated patients. Locus-specific databases (LSDB) established in the framework of the Human Variome Project provide powerful tools for the investigation of such rare diseases. Methods and Results We report the development of a publicly accessible LSDB for the GDAP1 gene. The GDAP1 LSDB has adopted the Leiden Open-source Variation Database (LOVD) software platform. This database, which now contains 57 unique variants reported in 179 cases of CMT, offers a detailed description of the molecular, clinical and electrophysiological data of the patients. The usefulness of the GDAP1 database is illustrated by the finding that GDAP1 mutations lead to primary axonal damage in CMT, with secondary demyelination in the more severe cases of the disease. Conclusion Findings of this nature should lead to a better understanding of the pathophysiology of CMT. Finally, the GDAP1 LSDB, which is part of the mitodyn.org portal of databases of genes incriminated in disorders involving mitochondrial dynamics and bioenergetics, should yield new insights into mitochondrial diseases.