Correcting away the hidden heritability

In the age of high-density genome-wide association (GWAS) data, correcting for multiple comparisons is a substantial issue for genetic epidemiological studies. However, the current manuscript review process generally requires both stringent correction and independent replication. The result of this stringency is that studies that are published suffer from inflated Type 2 error rates (false negatives), thereby removing many likely real signals from follow-up. Elimination of these alleles, if they are truly associated, from further study will slow research progress in studies of complex disease. We argue that this method of correction is overly conservative, especially in an age when high-density follow-up experiments are possible and reasonably inexpensive.