Gene-level association analysis of ordinal traits with functional ordinal logistic regressions

doi:10.1002/gepi.22451

	Gene-level association analysis of ordinal traits with functional ordinal logistic regressions
	Chiu, Chi-Yang 7,8; Wang, Shuqi 6; Zhang, Bingsong 6; Luo, Yutong 6; Simpson, Claire 5; Zhang, Wei 4; Wilson, Alexander F.7; Bailey-Wilson, Joan E.7; Agron, Elvira 3; Chew, Emily Y.3
刊名	GENETIC EPIDEMIOLOGY
	2022-04-19
页码	22
关键词	association mapping complex disease functional data analysis ordinal traits rare variants sequence data
ISSN号	0741-0395
DOI	10.1002/gepi.22451
英文摘要	In this paper, we develop functional ordinal logistic regression (FOLR) models to perform gene-based analysis of ordinal traits. In the proposed FOLR 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 FOLR models are built upon functional data analysis which can be revised to analyze the ordinal traits and high dimension genetic data. The proposed methods are capable of dealing with dense genotype data which is usually encountered in analyzing the next-generation sequencing 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. Simulation studies show that the likelihood ratio test statistics of the FOLR models control type I errors well and have good power performance. The proposed methods achieve the goals of analyzing ordinal traits directly, reducing high dimensionality of dense genetic variants, being computationally manageable, facilitating model convergence, properly controlling type I errors, and maintaining high power levels. The FOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes are found to strongly associate with four ordinal traits.
资助项目	US National Science Foundation[DMS-1915904]
WOS研究方向	Genetics & Heredity ; Mathematical & Computational Biology
语种	英语
出版者	WILEY
WOS记录号	WOS:000783534000001
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/60331]
专题	中国科学院数学与系统科学研究院
通讯作者	Fan, Ruzong
作者单位	1.Univ Texas Houston, Human Genet Ctr, Houston, TX USA 2.Univ Maryland Eastern Shore, Dept Comp Sci & Engn Technol, Princess Anne, MD USA 3.NEI, NIH, Bethesda, MD 20892 USA 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 5.Univ Tennessee, Ctr Hlth Sci, Dept Genet Genom & Informat, Memphis, TN 38163 USA 6.Georgetown Univ, Med Ctr, Dept Biostat Bioinformat & Biomath, Washington, DC 20007 USA 7.NHGRI, Computat & Stat Genom Branch, NIH, Baltimore, MD USA 8.Univ Tennessee, Ctr Hlth Sci, Div Biostat, Dept Prevent Med, Memphis, TN 38163 USA
推荐引用方式 GB/T 7714	Chiu, Chi-Yang,Wang, Shuqi,Zhang, Bingsong,et al. Gene-level association analysis of ordinal traits with functional ordinal logistic regressions[J]. GENETIC EPIDEMIOLOGY,2022:22.
APA	Chiu, Chi-Yang.,Wang, Shuqi.,Zhang, Bingsong.,Luo, Yutong.,Simpson, Claire.,...&Fan, Ruzong.(2022).Gene-level association analysis of ordinal traits with functional ordinal logistic regressions.GENETIC EPIDEMIOLOGY,22.
MLA	Chiu, Chi-Yang,et al."Gene-level association analysis of ordinal traits with functional ordinal logistic regressions".GENETIC EPIDEMIOLOGY (2022):22.