title: Theoretical and empirical power of regression and maximum-likelihood methods to map quantitative trait loci in general pedigrees.
abstract: Both theoretical calculations and simulation studies have been used to compare and contrast the statistical power of methods for mapping quantitative trait loci (QTLs) in simple and complex pedigrees. A widely used approach in such studies is to derive or simulate the expected mean test statistic under the alternative hypothesis of a segregating QTL and to equate a larger mean test statistic with larger power. In the present study, we show that, even when the test statistic under the null hypothesis of no linkage follows a known asymptotic distribution (the standard being chi(2)), it cannot be assumed that the distribution under the alternative hypothesis is noncentral chi(2). Hence, mean test statistics cannot be used to indicate power differences, and a comparison between methods that are based on simulated average test statistics may lead to the wrong conclusion. We illustrate this important finding, through simulations and analytical derivations, for a recently proposed new regression method for the analysis of general pedigrees to map quantitative trait loci. We show that this regression method is not necessarily more powerful nor computationally more efficient than a maximum-likelihood variance-component approach. We advocate the use of empirical power to compare trait-mapping methods.
authors.affiliation: School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Xijiang
authors.initials: X
authors.lastname: Yu
authors.forename: Sara A
authors.initials: SA
authors.lastname: Knott
authors.forename: Peter M
authors.initials: PM
authors.lastname: Visscher
mesh_terms: D002874:Chromosome Mapping; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D009679:Nuclear Family; D010375:Pedigree; D040641:Quantitative Trait Loci; D012044:Regression Analysis; D018433:Twin Studies as Topic