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Association Tests for a Censored Quantitative Trait and Candidate Genes in Structured Populations with Multilevel Genetic Relatedness

Authors

  • Meijuan Li,

    Corresponding author
    1. Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. SE, Minneapolis, Minnesota 55455, U.S.A.
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  • Cavan Reilly,

    1. Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. SE, Minneapolis, Minnesota 55455, U.S.A.
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  • Tim Hanson

    1. Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. SE, Minneapolis, Minnesota 55455, U.S.A.
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email:meijuan.li@fda.hhs.gov

Current address: Division of Biostatistics, CDRH/FDA, 10903 New Hampshire Avenue, Silver Spring, Maryland 20993, U.S.A.

Abstract

Summary 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 Arabidopsisthaliana line flowering time data sets demonstrated the advantage of the new proposals over existing approaches.

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