Genes don't work in isolation, and your gene lists shouldn't either.
Researchers will often have a list of known genes to look for when prioritizing variants based on the patient's clinical diagnosis. but what should you do if no candidate variants can be found solely based on your gene list? There are many approaches, but one option is to expand the gene list based on known gene interactions and pathways. We rely on the highly curated database called BioGRID to expand the gene list to include the network of genes known to interact either directly or through protein-to-protein interactions. To use this new feature, there is a new checkbox called 'Gene interactions' which users can tick to expand their gene-based search in this way.
We've recently changed our authentication to use the Australian Access Federation. The main benefit is that users from other universities across Australia can use their own institutional credentials to access our database, without having to create a new username/password. However, we still have support for generic username and passwords to support our friends overseas. The other benefit of making this switch is to provide a better user experience in terms of data integration from our ecosystem of databases via single-sign on. We can seamlessly pull information from various places to provide an aggregated view of data. Australian users should use the [Login via AAF] option, while others should use [Basic Login].
In addition to Clinvar and Snpedia, we've recently added GWAS Catalog to the health reports based on the the rsNumbers for a patient. GWAS is particularly useful in a research context by comparing variant frequencies in the affected population against a control (healthy) population using statistical analysis to establish a hypothetical link between variants and disease traits. In the health report under GWAS, we've added the following columns: disease traits, studies, risk allele, initial sample size, replication sample size, p-value and risk allele frequency. In GWAS it has been shown that false positives are not uncommon (false association between variant and disease) due to uncontrolled biases and so it's important to take into consideration whether any replicate studies were done to give more confidence to the hypothesized association.