  Drug development is a very costly and lengthy process, while repositioned or
repurposed drugs could be brought into clinical practice within a shorter
time-frame and at a much reduced cost. The past decade has observed a massive
growth in the amount of data from genome-wide association studies (GWAS). The
rich information contained in GWAS data has great potential to guide drug
discovery or repositioning. Here we provide an overview of different
computational approaches which employ GWAS data to guide drug repositioning.
These methods include selection of top candidate genes from GWAS as drug
targets, deducing drug candidates based on drug-drug and disease-disease
similarity, searching for reversed expression profiles between drugs and
diseases, pathway-based methods as well as repositioning based on analysis of
biological networks. Each method is illustrated with examples, and their
respective strengths and limitations are discussed. Finally we discussed
several areas for future research.
