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Competing risk modeling and testing for X-chromosome genetic association

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  • Hao, Meiling
  • Zhao, Xingqiu
  • Xu, Wei

Abstract

The complexity of X-chromosome inactivation arouses the X-linked genetic association being overlooked in most of the genetic studies, especially for genetic association analysis on time to event outcomes. To fill this gap, we propose novel methods to analyze the X-linked genetic association for competing risk failure time data based on a subdistribution hazard function. Specifically, we consider two mechanisms for a single genetic variant on X-chromosome: (1) all the subjects in a population undergo the same inactivation process; (2) the subjects randomly undergo different inactivation processes. According to the assumptions, one of the proposed methods can be used to infer the unknown biological process under scenario (1), while another method can be used to estimate the proportion of a certain biological process in the population under scenario (2). Both of the two methods can infer the direction of skewness for skewed X-chromosome inactivation and derive asymptotically unbiased estimates of the model parameters. The asymptotic distributions for the parameter estimates and constructed score tests with nuisance parameters only presented under the alternative hypothesis are illustrated under both assumptions. Finite sample performance of these novel methods is examined via extensive simulation studies. An application is illustrated with implementation on a cancer genetic study with competing risk outcomes.

Suggested Citation

  • Hao, Meiling & Zhao, Xingqiu & Xu, Wei, 2020. "Competing risk modeling and testing for X-chromosome genetic association," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:csdana:v:151:y:2020:i:c:s0167947320300980
    DOI: 10.1016/j.csda.2020.107007
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    References listed on IDEAS

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