Genetic association analysis—identifying polymorphisms in the human genome that are correlated with altered risk of disease—is a powerful method for discovering disease mechanisms. These polymorphisms can indicate what goes wrong at the cellular level in the disease process, knowledge that is critically important for developing better diagnostics and therapies.
The Type 2 Diabetes Knowledge Portal offers a wealth of pre-calculated information on genetic associations between variants and type 2 diabetes (T2D) or other related traits. These results are computed using broadly defined groups of samples: either an entire sample set from a project, or ancestry-specific cohorts. This approach, while it generates very valuable results, masks effects that could only be detected in even more narrowly defined groups: for example, individuals within a certain range of age, body mass index, or cholesterol level.
Until now, analysis of such fine-grained subsets of individual-level data has only been possible for expert geneticists with access to protected data. But our new Genetic Association Interactive Tool (GAIT) offers everyone an unprecedented amount of access to individual-level data along with an easy-to-use interface for analyzing genetic associations using custom subsets of samples and variants.
Two versions of GAIT are available in the Portal. One, on Variant pages (see an example) computes association statistics for the single variant featured on that page. The other, accessible on Gene pages (see an example) powers an interactive burden test that considers the collection of variants in or near a gene, or a selected subset of those variants.
|Where to find GAIT on Gene pages (left) and Variant pages (right)|
The GAIT interface offers incredible flexibility for designing custom analyses. In the interactive burden test, you can filter variants by their predicted effects, or pick and choose individual variants to include. When creating sample sets for either single-variant association analysis or a gene burden test, you can specify a gender, set ranges for the values for multiple phenotypes, and choose principal components or phenotypes to use as covariates. And all these parameters may be set differently for different ethnic groups.
|The GAIT interface displays phenotype values within the sample set and allows you to filter samples by multiple criteria|
Once you set parameters of your choice, GAIT computes associations on the fly, based on individual-level data. To protect patient confidentiality, GAIT will not display results from sample sets consisting of fewer than 100 individuals.
To help you get familiar with this versatile tool, we’ve created a User Guide (download PDF) that summarizes all the details of the interface. Please give GAIT a try and let us know what you think!