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SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Ordered Categorical Traits

文献类型：期刊论文

作者Bi, Wenjian1; Kang, Guolian2; Zhao, Yanlong1; Cui, Yuehua3; Yan, Song4; Li, Yun4,5; Cheng, Cheng2; Pounds, Stanley B.2; Borowitz, Michael J.6; Relling, Mary V.7
刊名ANNALS OF HUMAN GENETICS
出版日期2015-07-01
卷号79期号:4页码:294-309
关键词Ordered logistic model set-valued system identification multiple thresholds genetic association study rare variants
ISSN号0003-4800
DOI10.1111/ahg.12117
英文摘要In genetic association studies of an ordered categorical phenotype, it is usual to either regroup multiple categories of the phenotype into two categories and then apply the logistic regression (LG), or apply ordered logistic (oLG), or ordered probit (oPRB) regression, which accounts for the ordinal nature of the phenotype. However, they may lose statistical power or may not control type I error due to their model assumption and/or instable parameter estimation algorithm when the genetic variant is rare or sample size is limited. To solve this problem, we propose a set-valued (SV) system model to identify genetic variants associated with an ordinal categorical phenotype. We couple this model with a SV system identification algorithm to identify all the key system parameters. Simulations and two real data analyses show that SV and LG accurately controlled the Type I error rate even at a significance level of 10(-6) but not oLG and oPRB in some cases. LG had significantly less power than the other three methods due to disregarding of the ordinal nature of the phenotype, and SV had similar or greater power than oLG and oPRB. We argue that SV should be employed in genetic association studies for ordered categorical phenotype.
资助项目American Lebanese and Syrian Associated Charities (ALSAC), grants from the National Natural Science Foundation of China[11171333] ; American Lebanese and Syrian Associated Charities (ALSAC), grants from the National Natural Science Foundation of China[61134013] ; National Science Foundation[DMS-1209112] ; National Institutes of Health[R01 HG006292] ; NIH[R01 GM031575]
WOS研究方向Genetics & Heredity
语种英语
WOS记录号WOS:000356492000008
出版者WILEY-BLACKWELL
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/20077]  
专题系统科学研究所
通讯作者Kang, Guolian
作者单位1.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
2.St Jude Childrens Res Hosp, Dept Biostat, Memphis, TN 38105 USA
3.Michigan State Univ, Dept Stat & Probabil, E Lansing, MI 48824 USA
4.Univ N Carolina, Dept Genet, Dept Biostat, Chapel Hill, NC 27599 USA
5.Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27599 USA
6.Johns Hopkins Med Inst, Baltimore, MD 21231 USA
7.St Jude Childrens Res Hosp, Dept Pharmaceut Sci, Memphis, TN 38105 USA
8.St Jude Childrens Res Hosp, Dept Oncol, Memphis, TN 38105 USA
9.Univ Colorado, Sch Med, Aurora, CO 80045 USA
10.Childrens Hosp Colorado, Aurora, CO 80045 USA
推荐引用方式
GB/T 7714		 Bi, Wenjian,Kang, Guolian,Zhao, Yanlong,et al. SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Ordered Categorical Traits[J]. ANNALS OF HUMAN GENETICS,2015,79(4):294-309.
APA Bi, Wenjian.,Kang, Guolian.,Zhao, Yanlong.,Cui, Yuehua.,Yan, Song.,...&Zhang, Ji-Feng.(2015).SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Ordered Categorical Traits.ANNALS OF HUMAN GENETICS,79(4),294-309.
MLA Bi, Wenjian,et al."SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Ordered Categorical Traits".ANNALS OF HUMAN GENETICS 79.4(2015):294-309.

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来源：数学与系统科学研究院

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