Motivated by genetic association studies of pleiotropy, we propose a Bayesian latent variable approach to jointly study multiple outcomes. The models studied here can incorporate both continuous and binary responses, and can account for serial and cluster correlations. We consider Bayesian estimation for the model parameters, and we develop a novel MCMC algorithm that builds upon hierarchical centering and parameter expansion techniques to efficiently sample from the posterior distribution. We evaluate the proposed method via extensive simulations and demonstrate its utility with an application to an association study of various complication outcomes related to Type 1 diabetes. This article has supplementary material online.

中文翻译：

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遗传多向性数据贝叶斯潜变量建模的参数扩展算法

受多效性遗传关联研究的启发，我们提出了一种贝叶斯潜在变量方法来联合研究多种结果。这里研究的模型可以包含连续和二元响应，并且可以解释序列和集群相关性。我们考虑模型参数的贝叶斯估计，并且我们开发了一种新的 MCMC 算法，该算法建立在分层居中和参数扩展技术的基础上，以有效地从后验分布中采样。我们通过广泛的模拟评估了所提出的方法，并通过应用于与 1 型糖尿病相关的各种并发症结果的关联研究来证明其实用性。这篇文章在网上有补充材料。