Wang Shuqi, Chiu Chi-Yang, Wilson Alexander F, Bailey-Wilson Joan E, Agron Elvira, Chew Emily Y, Ahn Jaeil, Xiong Momiao, Fan Ruzong
Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA.
Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA.
Genet Epidemiol. 2023 Sep;47(6):409-431. doi: 10.1002/gepi.22524. Epub 2023 Apr 26.
In genetic studies, many phenotypes have multiple naturally ordered discrete values. The phenotypes can be correlated with each other. If multiple correlated ordinal traits are analyzed simultaneously, the power of analysis may increase significantly while the false positives can be controlled well. In this study, we propose bivariate functional ordinal linear regression (BFOLR) models using latent regressions with cumulative logit link or probit link to perform a gene-based analysis for bivariate ordinal traits and sequencing data. In the proposed BFOLR models, genetic variant data are viewed as stochastic functions of physical positions, and the genetic effects are treated as a function of physical positions. The BFOLR models take the correlation of the two ordinal traits into account via latent variables. The BFOLR models are built upon functional data analysis which can be revised to analyze the bivariate ordinal traits and high-dimension genetic data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Extensive simulation studies show that the likelihood ratio tests of the BFOLR models control type I errors well and have good power performance. The BFOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes, CFH and ARMS2, are found to strongly associate with eye drusen size, drusen area, age-related macular degeneration (AMD) categories, and AMD severity scale.
在基因研究中，许多表型具有多个自然有序的离散值。这些表型可能相互关联。如果同时分析多个相关的有序性状，分析效能可能会显著提高，同时假阳性可以得到很好的控制。在本研究中，我们提出了双变量函数有序线性回归（BFOLR）模型，使用具有累积logit链接或probit链接的潜在回归来对双变量有序性状和测序数据进行基于基因的分析。在所提出的BFOLR模型中，基因变异数据被视为物理位置的随机函数，而基因效应被视为物理位置的函数。BFOLR模型通过潜在变量考虑两个有序性状的相关性。BFOLR模型基于函数数据分析构建，可以进行修订以分析双变量有序性状和高维基因数据。这些方法具有灵活性，可以分析三种类型的基因数据：（1）仅稀有变异，（2）仅常见变异，以及（3）稀有和常见变异的组合。广泛的模拟研究表明，BFOLR模型的似然比检验能很好地控制I型错误，并且具有良好的效能表现。BFOLR模型被应用于分析年龄相关性眼病研究数据，其中发现两个基因CFH和ARMS2与眼玻璃膜疣大小、玻璃膜疣面积、年龄相关性黄斑变性（AMD）类别以及AMD严重程度量表密切相关。
