國家衛生研究院 NHRI:Item 3990099045/11430
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 12145/12927 (94%)
Visitors : 914980      Online Users : 1411
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/11430


    Title: Polygenic approaches to detect gene-environment interactions when external information is unavailable
    Authors: Lin, WY;Huang, CC;Liu, YL;Tsai, SJ;Kuo, PH
    Contributors: Center for Neuropsychiatric Research
    Abstract: The exploration of 'gene-environment interactions' (G x E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G x E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G x E signals and test the significance of G x E by a polygenic test. We here explore a powerful polygenic approach for G x E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP x E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene x alcohol and gene x smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G x E when external information is unavailable.
    Date: 2019-11
    Relation: Briefings in Bioinformatics. 2019 Nov;20(6):2236-2252.
    Link to: http://dx.doi.org/10.1093/bib/bby086
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1467-5463&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000509720200021
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85064229480
    Appears in Collections:[Yu-Li Liu] Periodical Articles

    Files in This Item:

    File Description SizeFormat
    PUB30219835.pdf5576KbAdobe PDF426View/Open


    All items in NHRI are protected by copyright, with all rights reserved.

    Related Items in TAIR

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback