STEPS: an efficient prospective likelihood approach to genetic association analyses of secondary traits in extreme phenotype sequencing.
It has been well acknowledged that methods for secondary trait (ST) association analyses under a case-control design (ST$_{\text{CC}}$) should carefully consider the sampling process to avoid biased risk estimates. A similar situation also exists in the extreme phenotype sequencing (EPS) designs, wh...
| Publicado en: | Biostatistics Vol. 21; no. 1; pp. 33 - 50 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141218522&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141218522 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Jan2020 vid: 21 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 141218522 141218522 NLM30007308 141218522 10.1093/biostatistics/kxy030 NLM30007308 141218522 ppf: 33 ppct: 17 formats: tig: atl: STEPS: an efficient prospective likelihood approach to genetic association analyses of secondary traits in extreme phenotype sequencing. aug: au: Bi, Wenjian Li, Yun Smeltzer, Matthew P Gao, Guimin Zhao, Shengli Kang, Guolian affil: Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA sug: subj: Models, Theoretical Genetic Techniques Methods Human Computer Simulation Probability Phenotype Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source ab: It has been well acknowledged that methods for secondary trait (ST) association analyses under a case-control design (ST$_{\text{CC}}$) should carefully consider the sampling process to avoid biased risk estimates. A similar situation also exists in the extreme phenotype sequencing (EPS) designs, which is to select subjects with extreme values of continuous primary phenotype for sequencing. EPS designs are commonly used in modern epidemiological and clinical studies such as the well-known National Heart, Lung, and Blood Institute Exome Sequencing Project. Although naïve generalized regression or ST$_{\text{CC}}$ method could be applied, their validity is questionable due to difference in statistical designs. Herein, we propose a general prospective likelihood framework to perform association testing for binary and continuous STs under EPS designs (STEPS), which can also incorporate covariates and interaction terms. We provide a computationally efficient and robust algorithm to obtain the maximum likelihood estimates. We also present two empirical mathematical formulas for power/sample size calculations to facilitate planning of binary/continuous STs association analyses under EPS designs. Extensive simulations and application to a genome-wide association study of benign ethnic neutropenia under an EPS design demonstrate the superiority of STEPS over all its alternatives above. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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