As the global burden of mental illness is estimated to become a severe issue in the near future, it demands the development of more effective treatments. Most psychiatric diseases are moderately to highly heritable and believed to involve many genes. Development of new treatment options demands more knowledge on the molecular basis of psychiatric diseases. Towards this end, we propose to develop new statistical methods with improved sensitivity and accuracy to identify disease-related genes specialized for psychiatric diseases. The qualitative psychiatric diagnoses such as case-control often suffer from high rates of misdiagnosis and oversimplify the disease phenotypes. Our proposed method utilizes endophenotypes, the quantitative traits hypothesized to underlie disease syndromes, to better characterize the heterogeneous phenotypes of psychiatric diseases. We employ the structural equation modeling using the liability-index model to link multiple genetically regulated expressions (GReX) from PrediXcan and the manifest variables including endophenotypes and case-control status. The proposed method can be considered as a general method for multivariate regression, which is particularly helpful for psychiatric diseases. We derive penalized retrospective likelihood estimators to deal with the typical small sample size issue. Simulation results demonstrate the advantages of the proposed method and the real data analysis of Alzheimer's disease illustrates the practical utility of the techniques. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. This article is protected by copyright. All rights reserved.

中文翻译：

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一种惩罚结构方程建模方法，用于解释阿尔茨海默病 predixcan 基因调控表达的变量选择的次要表型

由于估计在不久的将来精神疾病的全球负担将成为一个严重的问题，因此需要开发更有效的治疗方法。大多数精神疾病具有中度至高度遗传性，并被认为与许多基因有关。开发新的治疗方案需要更多关于精神疾病分子基础的知识。为此，我们建议开发具有更高灵敏度和准确性的新统计方法，以识别专用于精神疾病的疾病相关基因。诸如病例控制之类的定性精神病学诊断经常遭受高误诊率并且过度简化疾病表型。我们提出的方法利用内表型，假设是疾病综合征的基础的数量特征，更好地表征精神疾病的异质表型。我们采用使用责任指数模型的结构方程建模将来自 PrediXcan 的多个基因调控表达 (GReX) 与包括内表型和病例控制状态在内的显性变量联系起来。所提出的方法可以被认为是多元回归的通用方法，这对精神疾病特别有帮助。我们推导出惩罚性回顾性似然估计量来处理典型的小样本量问题。仿真结果证明了所提出方法的优势，阿尔茨海默病的真实数据分析说明了该技术的实际实用性。用于准备本文的数据来自阿尔茨海默病神经影像学倡议 (ADNI) 数据库。本文受版权保护。版权所有。