Association tests for a censored quantitative trait and candidate genes in structured populations with multilevel genetic relatedness.
Several statistical methods for detecting associations between quantitative traits and candidate genes in structured populations have been developed for fully observed phenotypes. However, many experiments are concerned with failure-time phenotypes, which are usually subject to censoring. In this article, we propose statistical methods for detecting associations between a censored quantitative trait and candidate genes in structured populations with complex multiple levels of genetic relatedness among sampled individuals. The proposed methods correct for continuous population stratification using both population structure variables as covariates and the frailty terms attributable to kinship. The relationship between the time-at-onset data and genotypic scores at a candidate marker is modeled via a parametric Weibull frailty accelerated failure time (AFT) model as well as a semiparametric frailty AFT model, where the baseline survival function is flexibly modeled as a mixture of Polya trees centered around a family of Weibull distributions. For both parametric and semiparametric models, the frailties are modeled via an intrinsic Gaussian conditional autoregressive prior distribution with the kinship matrix being the adjacency matrix connecting subjects. Simulation studies and applications to the Arabidopsis thaliana line flowering time data sets demonstrated the advantage of the new proposals over existing approaches.
Li
Meijuan
M
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, USA. meijuan.li@fda.hhs.gov
Reilly
Cavan
C
Hanson
Tim
T
D017360:Arabidopsis / Q000502:physiology; D003198:Computer Simulation; D035264:Flowers / Q000254:growth & development; D056726:Genetic Association Studies; D015233:Models, Statistical; D016011:Normal Distribution; D010641:Phenotype; D011153:Population; D040641:Quantitative Trait Loci; D013997:Time Factors