For over a decade, genome-wide association studies (GWAS) have contributed to the identification of reproducible genomic regions associated with an impressive number of common traits, including breast cancer (1,2), ovarian cancer (3), coronary artery disease (4), type 2 diabetes (5), osteoarthritis (6) and systemic lupus erythematosus (7). Further, Nelson et al. estimate that the inclusion of genetic associations in the drug discovery process could double the success rate of targets in clinical development (8). The GWAS landscape has evolved over the last ten years with an increase in publications employing complex methodologies, such as trait pleiotropy (9,10), interaction studies (11,12), Mendelian randomization (13) and large meta-analysis (14,15) and this has led to the discovery of new loci. Figure 1 provides an example for coronary artery disease before and after 2017. Many of these new methods require re-analysis of summary statistics results (SS) from GWAS. Full P-value SS are defined as the aggregate P-values and association data for every variant analysed in an independent GWAS.
