Association tests for a censored quantitative trait and candidate genes in structured populations with multilevel genetic relatedness

Meijuan Li, Cavan S Reilly, Tim Hanson

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)925-933
Number of pages9
JournalBiometrics
Volume66
Issue number3
DOIs
StatePublished - Sep 2010

Keywords

  • Association mapping
  • Gaussian conditional autoregressive (CAR)
  • Kinship coefficient
  • Mixture of Polya trees (MPT)
  • Population structure
  • Principal component analysis (PCA)
  • Time-to-event data

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