Circumventing multiple testing: A multilocus Monte Carlo approach to testing for association

Advances in marker technology have made a dense marker map a reality. If ea ch marker is considered separately and separate tests for association with a disease gene are performed, then multiple testing becomes an issue. A com mon solution uses a Bonferroni correction to account for multiple tests per formed. However, with dense marker maps, neighboring markers are tightly li nked and may have associated alleles; thus tests at nearby marker loci may not be independent. When alleles at different marker loci are associated, t he Bonferroni correction may lead to a conservative test, and hence a power loss. As an alternative, for tests of association that use family data, we propose a Monte Carlo procedure that provides a global assessment of signi ficance. We examine the case of tightly linked markers with varying amounts of association between them. Using computer simulations, we study a family -based test for association (the transmission/disequilibrium test), and com pare its power when either the Bonferroni or Monte Carlo procedure is used to determine significance. Our results show that when the alleles at differ ent marker loci are not associated, using either procedure results in tests with similar power. However, when alleles at linked markers are associated , the test using the Monte Carlo procedure is more powerful than the test u sing the Bonferroni procedure. This proposed Monte Carlo procedure can be a pplied whenever it is suspected that markers examined have high amounts of association, or as a general approach to ensure appropriate significance le vels and optimal power. Genet. Epidemiol. 19:18-29, 2000. (C) 2000 Wiley-Li ss, Inc.