On the Use of P-Values in Genome Wide Disease Association Mapping

In hypothesis testing, p-value is routinely used as a measure of statistical evidence against the null hypothesis, where a smaller p-value indicates stronger evidence substantiating the alternative hypothesis. P-value is the probability of type-I error made in a hypothesis testing, namely, the chance that one falsely reject the null hypothesis when the null holds true. In a disease genome wide association study (GWAS), p-value potentially tells us how likely a putative disease associated variant is due to random chance. For a long time p-values have been taken seriously by the GWAS community as a safeguard against false positives. 

Every disease-associated mutation reported in a GWAS must reach a stringent p-value cutoff in order to survive the multiple testing corrections. This is reasonable because after testing millions of variants in the genome, some random variants ought to yield small p-values purely by chance. Despite of p-value’s theoretical justification, however, it has become increasingly evident that statistical p-values are not nearly as reliable as it was believed.