Integrated Identification of Disease-Gene Links and their Utility in Next-Generation Sequencing Interpretation

The study of human diseases is at the core of present-day biological research. It is an interdisciplinary effort encompassing genomics, bioinformatics, systems biology, and systems medicine. Currently, many efforts are being made to elucidate the genetic underpinnings of human diseases. A consequence thereof is that many different sources use different nomenclatures, definitions, and classifications. Furthermore, the identification of gene-disease links, in addition to being challenging in its own right, is also affected by this lack of convention. We addressed both of these issues when creating MalaCards (www.malacards.org), an integrated and unified database of human diseases and their annotations, which capitalizes on information from the GeneCards database (www.genecards.org) [1-2]. GeneCards has annotations relevant to various characteristics of genes, which can be used as a discovery platform for identifying gene-disease links [3-4]. At the heart of MalaCards is a consolidated gene-disease matrix based on nine sources, some manually curated and others text-mined. A scoring algorithm prioritizes the list of disease-associated genes based on the strength of the evidence from each source. Figure 1 shows the frequencies of gene-disease links across the GeneCards gene categories.

The gene-disease matrix can be used in the interpretation of Next Generation Sequencing (NGS) data, whereby identified filtered variant-harboring genes are associated with a patient's disease keywords. VarElect (varelect.genecards.org) [5], the GeneCards suite's NGS interpretation tool, leverages MalaCards and GeneCards to infer direct and/or indirect keyword-gene links. Our tools can thus facilitate biomedical research of both basic-scientific and clinical relevance.