One of the advantages of studying variants with clear biological, although possibly not phenotypically causal, consequences, such as missense SNPs as compared to SNPs not in or near genes, is the potential for conducting functional studies such as knockouts in animal models (21). SPOT mimics these study designs by allowing researchers to specify the particular biological hypotheses they wish to test so that SNPs related to these hypotheses receive additional priority when resources are limited. As has been submitted elsewhere in the literature (1,5,22), this strategy is reasonable with the stipulation that biological priorities should be established a priori in order to avoid post hoc arguments for biological plausibility, because given a gene it is not difficult to use Internet databases to mine connections between that gene and a phenotype. As we described in (4), the GIN prioritization method is well suited for establishing a specific, quantitative a priori plan for implementing biological hypotheses into the post-GWAS prioritization process, and this method is now implemented in SPOT.
