Genome-wide association studies (GWAS) have emerged as a powerful tool to uncover the genetic basis of human common diseases, which often show a complex, polygenic and multi-factorial aetiology. These studies have revealed that 70-90% of all single nucleotide polymorphisms (SNPs) associated with common complex diseases do not occur within genes (i.e. they are non-coding), making the discovery of disease-causative genetic variants and the elucidation of the underlying pathological mechanisms far from straightforward. Based on emerging evidences suggesting that disease-associated SNPs are frequently found within cell type-specific regulatory sequences, here we present GARLIC (GWAS-based Prediction Toolkit for Connecting Diseases and Cell Types), a user-friendly, multi-purpose software with an associated database and online viewer that, using global maps of cis-regulatory elements, can aetiologically connect human diseases with relevant cell types. Additionally, GARLIC can be used to retrieve potential disease-causative genetic variants overlapping regulatory sequences of interest. Overall, GARLIC can satisfy several important needs within the field of medical genetics, thus potentially assisting in the ultimate goal of uncovering the elusive and complex genetic basis of common human disorders.
Computational Biology Databases, Genetic