The above recommendations for variant discovery are simple but important; our key points are annotated in Table 4. Traditional candidate gene studies, where genes are selected a priori based on animal models or non-genetic neurobiological findings in humans, are mired in replication difficulties and have not provided clear and robust associations. Genome-wide arrays are now more affordable than individually genotyping many candidate genes and should be preferred in any new genotyping effort. Genome-wide arrays allow for standard ancestry corrections and can be imputed, which makes genome-wide meta-analysis easy. Power to detect novel associations is small because effects are small, so researchers should consider this at the outset and plan to build the largest sample possible and, in perhaps many cases, realize they will be unable to conduct an adequately powered study. Using GREML to study the genetic architecture of endophenotypes and their genetic relationship to clinical phenotypes also requires large samples. If the goal is to discover and understand the biological pathways by which a gene affects a phenotype, then a Bonferroni correction that controls family-wise error is essential to control the proliferation of low-confidence findings.
