國家衛生研究院 NHRI:Item 3990099045/11409
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/11409


    Title: General retrospective mega-analysis framework for rare variant association tests
    Authors: Chien, LC;Chiu, YF
    Contributors: Institute of Population Health Sciences
    Abstract: Here, we describe a retrospective mega-analysis framework for gene- or region-based multimarker rare variant association tests. Our proposed mega-analysis association tests allow investigators to combine longitudinal and cross-sectional family- and/or population-based studies. This framework can be applied to a continuous, categorical, or survival trait. In addition to autosomal variants, the tests can be applied to conduct mega-analyses on X-chromosome variants. Tests were built on study-specific region- or gene-level quasiscore statistics and, therefore, do not require estimates of effects of individual rare variants. We used the generalized estimating equation approach to account for complex multiple correlation structures between family members, repeated measurements, and genetic markers. While accounting for multilevel correlations and heterogeneity across studies, the test statistics were computationally efficient and feasible for large-scale sequencing studies. The retrospective aspect of association tests helps alleviate bias due to phenotype-related sampling and type I errors due to misspecification of phenotypic distribution. We evaluated our developed mega-analysis methods through comprehensive simulations with varying sample sizes, covariates, population stratification structures, and study designs across multiple studies. To illustrate application of the proposed framework, we conducted a mega-association analysis combining a longitudinal family study and a cross-sectional case–control study from Genetic Analysis Workshop 19.
    Date: 2018-10
    Relation: Genetic Epidemiology. 2018 Oct;42(7):621-635.
    Link to: http://dx.doi.org/10.1002/gepi.22147
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0741-0395&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000447523100003
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85052916988
    Appears in Collections:[Yen-Feng Chiu] Periodical Articles

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