A Latent Variable Model of Segregation Analysis for Ordinal Traits
有序性状分离分析的潜变量模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要针对遗传流行病学的有序性状与家族传递，并非经济离散选择、结构估计或网络干扰研究。
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有序性状分离分析的潜变量模型
- 研究问题
- 如何对有序疾病性状开展遗传分离分析，在保留严重程度等级信息的同时，描述家族聚集、遗传传递及环境因素的作用？
- 方法与思路
- 作者将二元性状的潜变量模型扩展到有序性状，以协变量纳入环境因素，使用 EM 算法最大化及 peeling 算法提高计算效率。
- 材料陈述的贡献
- 摘要称，模型配有渐近推断理论和模拟；三级酗酒性状应用支持家族聚集，并提示此前二元性状分析未揭示的主基因成分。
- 阅读关注点
- 应核对潜变量与等级划分的关系、遗传传递机制、环境交互项的实现，以及似然计算、渐近理论和酗酒应用的证据边界。
- 阅读边界
- 仅据所给 OpenAlex 索引摘要，稿件版本未注明；摘要未说明遗传模型识别及样本细节，主基因成分属于模型分析提示，不能视为独立验证。
原文摘要
Many health conditions, including cancer and psychiatric disorders, are believed to have a complex genetic basis, and genes and environmental factors are likely to interact in the presence and severity of these conditions. Assessing familial aggregation and inheritability of disease is a classic topic of genetic epidemiology, commonly referred to as segregation analysis. Although today it is routine to conduct such analyses for quantitative and dichotomous traits, methods and software that accommodate ordinal traits do not exist. Tothis end, we propose a latent variable model by extending the work of Zhang and Merikangas, who examined binary traits. The advantage of this latent variable model lies in its flexibility to include environmental factors (usually represented by covariates) and its potential to allow gene–environment interactions. The model building uses the EM algorithm for maximization and a peeling algorithm for computational efficiency. We provide asymptotic theory for statistical inference, and conduct simulation studies to confirm that the asymptotic theory is adequate in practical applications. We also apply our model to examine the familial transmission of alcoholism, which is categorized into three ordinal levels: normal control, alcohol abuse, and alcohol dependence. Our analysis not only confirms that alcoholism is familial, but also suggests that the transmission may have a major gene component not revealed by previous analyses using dichotomous traits.