Genetic Association Studies in Complex Disease: Disentangling Additional Predisposing Loci from Associated Neutral Loci Using a Constrained ‐ Permutation Approach

Perils and pitfalls of permutation tests for distinguishing the effects of neighbouring polymorphisms

In a small region several marker loci may be associated with a trait, either because they directly influence the trait or because they are in linkage disequilibrium (LD) with a causal variant. Having established a potentially causal effect at a primary variant, we may ask if any other variants in the region appear to further contribute to the trait, indicating that the additional variant is either causal or is in LD with another causal locus. Methods of approaching this problem using case-parent trio data include the stepwise conditional logistic regression approach described by Cordell and Clayton ([2002] Am. J. Hum. Genet. 70:124-141), and a constrained-permutation method recently proposed by Spijker et al. ([2005] Ann. Hum. Genet. 69:90-101). Through simulation we demonstrate that the procedure described by Spijker et al. [2005], as well as unconditional logistic regression with "affected family-based controls" (AFBACs), can lead to inflated type 1 errors in situations when haplotypes are not inferable for all trios, whereas the conditional logistic regression approach gives correct significance levels. We propose an alternative to the permutation method of Spijker et al. [2005], which does not rely on haplotyping, and results in correct type 1 errors and potentially high power when assumptions of random mating, Hardy-Weinberg Equilibrium, and multiplicative effects of disease alleles are satisfied.