A tobit variance-component method for linkage analysis of censored trait data.
Variance-component (VC) methods are flexible and powerful procedures for the mapping of genes that influence quantitative traits. However, traditional VC methods make the critical assumption that the quantitative-trait data within a family either follow or can be transformed to follow a multivariate normal distribution. Violation of the multivariate normality assumption can occur if trait data are censored at some threshold value. Trait censoring can arise in a variety of ways, including assay limitation or confounding due to medication. Valid linkage analyses of censored data require the development of a modified VC method that directly models the censoring event. Here, we present such a model, which we call the "tobit VC method." Using simulation studies, we compare and contrast the performance of the traditional and tobit VC methods for linkage analysis of censored trait data. For the simulation settings that we considered, our results suggest that (1) analyses of censored data by using the traditional VC method lead to severe bias in parameter estimates and a modest increase in false-positive linkage findings, (2) analyses with the tobit VC method lead to unbiased parameter estimates and type I error rates that reflect nominal levels, and (3) the tobit VC method has a modest increase in linkage power as compared with the traditional VC method. We also apply the tobit VC method to censored data from the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics study and provide two examples in which the tobit VC method yields noticeably different results as compared with the traditional method.
Epstein
Michael P
MP
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA. mepstein@genetics.emory.edu
Lin
Xihong
X
Boehnke
Michael
M
D000704:Analysis of Variance; D002874:Chromosome Mapping / Q000379:methods; D002877:Chromosomes, Human; D008040:Genetic Linkage; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D040641:Quantitative Trait Loci