KMS Of Academy of mathematics and systems sciences, CAS
| SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Ordered Categorical Traits | |
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; Yang, Jun J.7; Liu, Zhifa2; Pui, Ching-Hon8,12; Hunger, Stephen P.9,10; Hartford, Christine M.11; Leung, Wing11,12; Zhang, Ji-Feng1
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| 2015-07-01 | |
| Source Publication | ANNALS OF HUMAN GENETICS
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| ISSN | 0003-4800 |
| Volume | 79Issue:4Pages:294-309 |
| Abstract | 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. |
| Keyword | Ordered logistic model set-valued system identification multiple thresholds genetic association study rare variants |
| DOI | 10.1111/ahg.12117 |
| Language | 英语 |
| Funding Project | 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 Research Area | Genetics & Heredity |
| WOS Subject | Genetics & Heredity |
| WOS ID | WOS:000356492000008 |
| Publisher | WILEY-BLACKWELL |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/20078 |
| Collection | 系统科学研究所 |
| Corresponding Author | Kang, Guolian |
| Affiliation | 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 |
| Recommended Citation 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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