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Association of genetic loci

The replication addressed here is of a statistical nature—to be believable, reports of disease gene localizations by means of case-control association studies need to be confirmed (replicated) by independent studies. While this principle has generally worked rather well for genetic linkage analysis applied to Mendelian traits, a troubling number of “significant” associations have been reported that could not be confirmed in later studies. This issue of Neurology contains two articles, one confirming1 and one not confirming2 a previously published article showing a strong association between a single nucleotide polymorphism (SNP) genotype and multidrug resistance in epilepsy. Here is the situation. Last year, Siddiqui et al.3 reported a strong and significant association between the CC genotype at the C3435T polymorphism in the ABCB1 gene and multidrug resistance vs multidrug responsiveness in patients with epilepsy. Sample sizes were 200 resistant and 115 responsive patients, with an associated OR (approximate relative risk) of 2.04. However, with sample sizes close to twice those in the published report and an essentially identical ascertainment scheme, Tan et al.2 in this issue of Neurology are unable to confirm the reported association. On the other hand, Zimprich et al.,1 also in this issue of the journal, with a sample size less than two thirds of that in the original report, confirm the association, although their analysis was somewhat different from those in the other two articles: they worked with a haplotype at three closely spaced SNPs (one of them being the C3435T polymorphism) and defined three instead of just two categories of multidrug resistance. How do we interpret such seemingly contradictory findings? Various authors have addressed the reported replication of—or failure to replicate—statistical gene maps.4-8 Standards for association studies have been developed by many journals including Neurology.9 There may be several reasons, possibly interrelated, for failure to replicate a study. Multiple susceptibility genes may exist, of which only a subset are required for disease. For example, there may be 10 susceptibility loci but mutations in any three of them are sufficient to lead to disease. It is then likely that a mutant allele at a specific locus may not be represented in one set of case individuals (or in only a small proportion of them) while it plays a major role in another collection of individuals. This property is particularly relevant for family data but may also apply to case-control association data. An initial search for susceptibility genes, particularly when conducted on a genome-wide scale, tends to lead to a bias in the direction of high statistical significance.7,10 One may think of this as an ascertainment bias. Closely related to it is publication bias—only positive findings are reported initially, which is why it is important that subsequent reports be published whether or not they contain positive or negative findings. Therefore, for an overall assessment of significance, initial reports are best discounted with inference being based only on subsequent replications. Multiple statistical tests can lead to a grossly exaggerated view of the significance of an initial finding if an appropriate correction for multiple testing is not carried out. For example, if 10 independent tests are carried out at the 0.05 (point-wise) significance level, each test runs a risk of 5% of coming up with a false positive result and the probability is 1 – (1 – ) 40% (the experiment-wise significance level) that at least one of the tests will furnish a false positive result. To keep the experiment-wise significance level at a low value, each individual test needs to be carried out with stricter scrutiny. The fallacy of multiple testing is often not appreciated enough by authors and reviewers and journal editors. Moreover, scientists tend not to report all the tests they carried out before finding an interesting