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

doi:10.1111/ahg.12117

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	SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Ordered Categorical Traits
	Bi, Wenjian 1; Kang, Guolian 2; Zhao, Yanlong1 ; Cui, Yuehua 3; Yan, Song 4; Li, Yun 4,5; Cheng, Cheng 2; Pounds, Stanley B.2; Borowitz, Michael J.6; Relling, Mary V.7; Yang, Jun J.7; Liu, Zhifa 2; Pui, Ching-Hon 8,12; Hunger, Stephen P.9,10; Hartford, Christine M.11; Leung, Wing 11,12; Zhang, Ji-Feng1
	2015-07-01
发表期刊	ANNALS OF HUMAN GENETICS
ISSN	0003-4800
卷号	79 期号:4 页码:294-309
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.
关键词	Ordered logistic model set-valued system identification multiple thresholds genetic association study rare variants
DOI	10.1111/ahg.12117
语种	英语
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
WOS类目	Genetics & Heredity
WOS记录号	WOS:000356492000008
出版者	WILEY-BLACKWELL
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/20078
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 11.St Jude Childrens Res Hosp, Dept Bone Marrow Transplantat & Cellular Therapy, Memphis, TN 38105 USA 12.Univ Tennessee, Ctr Hlth Sci, Dept Pediat, Memphis, TN 38163 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.