KMS Of Academy of mathematics and systems sciences, CAS
| Gene-level association analysis of ordinal traits with functional ordinal logistic regressions | |
| Chiu, Chi-Yang1,2; Wang, Shuqi3; Zhang, Bingsong3; Luo, Yutong3; Simpson, Claire4; Zhang, Wei5; Wilson, Alexander F.2; Bailey-Wilson, Joan E.2; Agron, Elvira6; Chew, Emily Y.6; Zhang, Jun7; Xiong, Momiao8; Fan, Ruzong2,3 | |
| 2022-04-19 | |
| Source Publication | GENETIC EPIDEMIOLOGY
 |
| ISSN | 0741-0395 |
| Pages | 22 |
| Abstract | 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. |
| Keyword | association mapping complex disease functional data analysis ordinal traits rare variants sequence data |
| DOI | 10.1002/gepi.22451 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | US National Science Foundation[DMS-1915904] |
| WOS Research Area | Genetics & Heredity ; Mathematical & Computational Biology |
| WOS Subject | Genetics & Heredity ; Mathematical & Computational Biology |
| WOS ID | WOS:000783534000001 |
| Publisher | WILEY |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/60332 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Fan, Ruzong |
| Affiliation | 1.Univ Tennessee, Ctr Hlth Sci, Div Biostat, Dept Prevent Med, Memphis, TN 38163 USA 2.NHGRI, Computat & Stat Genom Branch, NIH, Baltimore, MD USA 3.Georgetown Univ, Med Ctr, Dept Biostat Bioinformat & Biomath, Washington, DC 20007 USA 4.Univ Tennessee, Ctr Hlth Sci, Dept Genet Genom & Informat, Memphis, TN 38163 USA 5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 6.NEI, NIH, Bethesda, MD 20892 USA 7.Univ Maryland Eastern Shore, Dept Comp Sci & Engn Technol, Princess Anne, MD USA 8.Univ Texas Houston, Human Genet Ctr, Houston, TX USA |
| Recommended Citation 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. |
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