Bias toward the Null Hypothesis in Model-Free Linkage Analysis Is Highly Dependent on the Test Statistic Used

Abstract

Recently, it has been suggested that traditional nonparametric multipoint-linkage procedures can show a "bias" toward the null hypothesis of no effect when there is incomplete information about allele sharing at genotyped marker loci (or at positions in between marker loci). Here, I investigate the extent of this bias for a variety of test statistics commonly used in qualitative- ("affecteds only") and quantitative-trait linkage analysis. Through simulation and analytical derivation, I show that many of the test statistics available in standard linkage analysis packages (such as Genehunter, Merlin, and Allegro) are, in fact, not affected by this bias problem. A few test statistics--most notably the nonparametric linkage statistic and, to a lesser extent, the Aspex-MLS and Haseman-Elston statistics--are affected by the bias. Variance-components procedures, although unbiased, can show inflation or deflation of the test statistic attributable to the inclusion of pairs with incomplete identity-by-descent information. Results obtained--for instance, in genome scans--using these methods might therefore be worth revisiting to see if greater power can be obtained by use of an alternative statistic or by eliminating or downweighting uninformative relative pairs.