English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 57970/91504 (63%)
造访人次 : 13691017      在线人数 : 47
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/117012


    题名: Identifying rare and common disease associated variants in genomic data using Parkinson's disease as a model
    作者: YC, Lin;Hsieh, Ai-Ru;CL, Hsiao;SJ, Wu;HM, Wang;IB, Lian;SJ, Fann Cathy
    关键词: Genomic data;Parkinson's disease;Rare variant association
    日期: 2014-08-30
    上传时间: 2019-09-17 12:11:38 (UTC+8)
    摘要: Background
    Genome-wide association studies have been successful in identifying common genetic variants for human diseases. However, much of the heritable variation associated with diseases such as Parkinson's disease remains unknown suggesting that many more risk loci are yet to be identified. Rare variants have become important in disease association studies for explaining missing heritability. Methods for detecting this type of association require prior knowledge on candidate genes and combining variants within the region. These methods may suffer from power loss in situations with many neutral variants or causal variants with opposite effects.

    Results
    We propose a method capable of scanning genetic variants to identify the region most likely harbouring disease gene with rare and/or common causal variants. Our method assigns a score at each individual variant based on our scoring system. It uses aggregate scores to identify the region with disease association. We evaluate performance by simulation based on 1000 Genomes sequencing data and compare with three commonly used methods. We use a Parkinson's disease case–control dataset as a model to demonstrate the application of our method.

    Our method has better power than CMC and WSS and similar power to SKAT-O with well-controlled type I error under simulation based on 1000 Genomes sequencing data. In real data analysis, we confirm the association of α-synuclein gene (SNCA) with Parkinson's disease (p = 0.005). We further identify association with hyaluronan synthase 2 (HAS2, p = 0.028) and kringle containing transmembrane protein 1 (KREMEN1, p = 0.006). KREMEN1 is associated with Wnt signalling pathway which has been shown to play an important role for neurodegeneration in Parkinson's disease.

    Conclusions
    Our method is time efficient and less sensitive to inclusion of neutral variants and direction effect of causal variants. It can narrow down a genomic region or a chromosome to a disease associated region. Using Parkinson's disease as a model, our method not only confirms association for a known gene but also identifies two genes previously found by other studies. In spite of many existing methods, we conclude that our method serves as an efficient alternative for exploring genomic data containing both rare and common variants.
    關聯: Journal of Biomedical Science 21, p.88
    DOI: 10.1186/s12929-014-0088-9
    显示于类别:[統計學系暨研究所] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    Identifying rare and common disease associated variants in genomic data using Parkinson's disease as a model.pdf705KbAdobe PDF0检视/开启
    index.html0KbHTML23检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈