Scholarlink

<p>In this paper, we develop bivariate stochastic functional linear models (BSFLM) for gene-based association analysis of quantitative traits with high-dimensional sequencing genetic data in longitudinal studies. In longitudinal studies, the traits of different subjects are assumed to be independent for population data. The two traits of one subject, however, are correlated with each other. In addition, the multiple measurements of one trait of a subject are also correlated. To build valid models to analyze bivariate quantitative traits with sequencing genetic data in longitudinal studies, a variance-covariance structure is constructed to describe (a) variation accounting for the correlation between the two traits and (b) variation accounting for the correlation within multiple measurements of a trait on the same subject. Functional data analysis techniques are utilized to reduce high dimensionality of sequence data and draw useful genetic information. Spline models are used to approximate temporal mean functions and genetic effect functions. By intensive simulation studies, it is shown that the proposed BSFLM control type I errors well and have good power levels. We test and refine the models and related software using real datasets of multi-ethnic study of atherosclerosis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Bivariate Stochastic Functional Linear Models for Gene-based Association Analysis of Quantitative Traits in Longitudinal Studies

  • 期刊论文

  • 发表日期：

    2026年6月29日

查看全文
  • Shuqi Wang,
  • Yutong Luo,
  • Chi-Yang Chiu,
  • Garret Gathrie,
  • Gang Xu,
  • Amei Amei,
  • Jun Zhang,
  • Zuoheng Wang,
  • Hong-Bin Fang,
  • Alexander F. Wilson,
  • Joan E. Bailey-Wilson,
  • Ruzong Fan

摘要

In this paper, we develop bivariate stochastic functional linear models (BSFLM) for gene-based association analysis of quantitative traits with high-dimensional sequencing genetic data in longitudinal studies. In longitudinal studies, the traits of different subjects are assumed to be independent for population data. The two traits of one subject, however, are correlated with each other. In addition, the multiple measurements of one trait of a subject are also correlated. To build valid models to analyze bivariate quantitative traits with sequencing genetic data in longitudinal studies, a variance-covariance structure is constructed to describe (a) variation accounting for the correlation between the two traits and (b) variation accounting for the correlation within multiple measurements of a trait on the same subject. Functional data analysis techniques are utilized to reduce high dimensionality of sequence data and draw useful genetic information. Spline models are used to approximate temporal mean functions and genetic effect functions. By intensive simulation studies, it is shown that the proposed BSFLM control type I errors well and have good power levels. We test and refine the models and related software using real datasets of multi-ethnic study of atherosclerosis.