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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 (e.g., 10-8) 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. It has not been uncommon for a GWAS to identify some very significant associations that later turn out to be false positives. The current routine is therefore to require every reported genetic variant for a disease to be replicated at least once, which is a much more reliable criterion against false positives.....read more
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