The SNP/gene transcript properties used by our algorithm are currently limited to a sophisticated prediction provided by the PolyPhen (9–10) algorithm on the impact of an amino acid substitution, and those that can be observed directly from DNA and RNA sequence such as coding regions, untranslated regions, missense and nonsense amino acid substitutions and frameshifts. Given the amount of experimental human genomic data available, this is a relatively limited amount of information. However, when integrating biological information into a GWAS, even with this relatively limited set, there are several aspects to consider. First, a SNP may be associated with many genes, whether it be in one gene and near another, or in the intersection of multiple genes or perhaps in a gene with several known transcripts due to alternative splicing and having different functional consequences of the SNP on each transcript. SPOT considers all known SNP/gene transcript associations, and selects the one with the highest priority to ensure no biologically promising association signal is missed. Furthermore, when genes overlap, SPOT will take into account specific genes prioritized by the user.
