SPOT is a useful tool for GWAS investigators because it allows them to test these reasonable biological hypotheses by defining specific biological priorities as a way of pursuing the ‘high hanging fruit’ in a GWAS. A GWAS often has limited statistical power and must rely on replication genotyping to establish the statistical significance of any remaining non-significant associations that appear promising. Even when GWAS experiments successfully replicate SNP associations, the totality of these variants often explain only a fraction of the genetic variance (23). We then look to the ‘high hanging fruit’ for answers—variants with smaller effect sizes that require greater power to be confirmed as a statistically significant association (23). These may require substantial further investigation such as large meta-analyses conducted by consortia with sample sizes often in the hundreds of thousands (24–26). It has also been argued that even with missing variance, one of the benefits of GWAS is, in addition to predicting individual risk, their ability to expose biological pathways that underlie human disease (2,27). Given the extremely high resource-consuming nature of these follow-up studies, a clear
