Bias toward the null hypothesis in model-free linkage analysis is highly dependent on the test statistic used.
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.
Cordell
Heather J
HJ
Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom. heather.cordell@cimr.cam.ac.uk
D000483:Alleles; D003198:Computer Simulation; D008040:Genetic Linkage; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D019655:Quantitative Trait, Heritable; D035781:Siblings; D018709:Statistics, Nonparametric