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Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies
Li, Zhengbang1,2; Zhang, Wei3; Pan, Dongdong4; Li, Qizhai3
2016-05-18
Source PublicationSCIENTIFIC REPORTS
ISSN2045-2322
Volume6Pages:10
AbstractPrincipal component analysis (PCA) is a useful tool to identify important linear combination of correlated variables in multivariate analysis and has been applied to detect association between genetic variants and human complex diseases of interest. How to choose adequate number of principal components (PCs) to represent the original system in an optimal way is a key issue for PCA. Note that the traditional PCA, only using a few top PCs while discarding the other PCs, might significantly lose power in genetic association studies if all the PCs contain non-ignorable signals. In order to make full use of information from all PCs, Aschard and his colleagues have proposed a multi-step combined PCs method (named mCPC) recently, which performs well especially when several traits are highly correlated. However, the power superiority of mCPC has just been illustrated by simulation, while the theoretical power performance of mCPC has not been studied yet. In this work, we attempt to investigate theoretical properties of mCPC and further propose a novel and efficient strategy to combine PCs. Extensive simulation results confirm that the proposed method is more robust than existing procedures. A real data application to detect the association between gene TRAF1-C5 and rheumatoid arthritis further shows good performance of the proposed procedure.
DOI10.1038/srep26243
Language英语
Funding ProjectNational Nature Science Foundation of China[11401240] ; National Nature Science Foundation of China[11471135] ; CCNU from the colleges' basic research of MOE[CCNU15A05038] ; CCNU from the colleges' basic research of MOE[CCNU15ZD011] ; National Natural Science Foundation of China[11301465] ; National Natural Science Foundation of China[61134013] ; National Natural Science Foundation of China[11371353] ; Youth Program of Applied Basic Research Programs of Yunnan Province[2013FD001] ; Young and Middle-aged Key Teachers Training Program of Yunnan University[XT412003] ; Breakthrough Project of Strategic Priority Program of the Chinese Academy of Sciences[XDB13040600]
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000375996000001
PublisherNATURE PUBLISHING GROUP
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/22758
Collection系统科学研究所
Affiliation1.Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China
2.Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
4.Yunnan Univ, Dept Stat, Kunming 650091, Peoples R China
Recommended Citation
GB/T 7714
Li, Zhengbang,Zhang, Wei,Pan, Dongdong,et al. Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies[J]. SCIENTIFIC REPORTS,2016,6:10.
APA Li, Zhengbang,Zhang, Wei,Pan, Dongdong,&Li, Qizhai.(2016).Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies.SCIENTIFIC REPORTS,6,10.
MLA Li, Zhengbang,et al."Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies".SCIENTIFIC REPORTS 6(2016):10.
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