遗传分析研讨会18数据中常见变异的遗传关联分析：一种狄利克雷回归方法。

Genetic association analysis for common variants in the Genetic Analysis Workshop 18 data: a Dirichlet regression approach.

作者信息

Espin-Garcia Osvaldo, Shen Xiaowei, Qiu Xin, Brhane Yonathan, Liu Geoffrey, Xu Wei

机构信息

Department of Biostatistics, Princess Margaret Cancer Centre, 610 University Ave., Toronto, ON, M5G 2M9, Canada ; Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada.

Department of Biostatistics, Princess Margaret Cancer Centre, 610 University Ave., Toronto, ON, M5G 2M9, Canada.

出版信息

BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S70. doi: 10.1186/1753-6561-8-S1-S70. eCollection 2014.

DOI:10.1186/1753-6561-8-S1-S70

PMID:25519401

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4143809/

Abstract

We propose a genetic association analysis using Dirichlet regression to analyze the Genetic Analysis Workshop 18 data. Clinical variables, arranged in a longitudinal data structure, are employed to fit a multistate transition model in which the transition probabilities are served as a response in the proposed analysis. Furthermore, a gene-based association analysis via penalized regression is implemented using the markers at a single-nucleotide polymorphism level that we previously identified via nonpenalized Dirichlet regression.

摘要

我们提出一种使用狄利克雷回归进行遗传关联分析的方法，以分析遗传分析研讨会18的数据。将按纵向数据结构排列的临床变量用于拟合多状态转换模型，在该模型中，转换概率在拟议的分析中用作响应变量。此外，我们使用之前通过非惩罚性狄利克雷回归识别出的单核苷酸多态性水平的标记，通过惩罚回归进行基于基因的关联分析。