188宝金博页面版

  • 图案背景
  • 纯色背景
视图
标记
批注
批注本地保存成功，开通会员云端永久保存 去开通
taipingcrh

上传于：2020-10-08

粉丝量：2

本人985学校毕业后一直从事证券类工作，在金融、证券领域有深厚的认识


  • 相关
  • 目录
  • 笔记
  • 书签

188宝金博页面版:更多相关文档

  • Development of a SNP-based CAPS assay for the

    星级： 7 页

  • sniphunter a snp-based search engine

    星级： 8 页

  • snp-based method for the genetic identification of ramin

    星级： 9 页

  • Conditions for the validity of SNP-based heritability estimation

    星级： 24 页

  • Simple SNP-based minimal marker genotyping for

    星级： 12 页

  • Conditions for the validity of SNP-based heritability estimation

    星级： 12 页

  • Highly effective SNP-based association mapping and management of

    星级： 38 页

  • An SNP-based second-generation genetic map of

    星级： 15 页

  • A dense SNP-based linkage map for Atlantic salmon (

    星级： 10 页

  • A SNP-based phylogenetic analysis of Corynebacterium diphtheriae in Malaysia

    星级： 8 页

  • Development of high-throughput SNP-based genotyping in

    星级： 18 页

  • multiblup improved snp-based prediction for complex traits

    星级： 9 页

  • RESEARCH ARTICLE Open Access Construction of a SNP-based…

    星级： 14 页

  • multiblup improved snp-based prediction for complex traits

    星级： 9 页

  • A dense SNP-based genetic linkage map of grapevine

    星级： 44 页

暂无目录

点击鼠标右键菜单,创建目录

    暂无笔记

    选择文本，点击鼠标右键菜单，添加笔记

    暂无书签

    在左侧文档中，点击鼠标右键，添加书签

    188宝金博页面版: SNP-based pathway enrichment analysis for genome-wide association studies(基于SNP的途径富集分析用于全基因组关联研究)

    下载积分： 2000

    内容提示： METHODOLOGY ARTICLE Open AccessSNP-based pathway enrichment analysis forgenome-wide association studiesLingjie Weng 1 , Fabio Macciardi 2 , Aravind Subramanian 3 , Guia Guffanti 2 , Steven G Potkin 2* , Zhaoxia Yu 4* andXiaohui Xie 1,5*AbstractBackground: Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) todiscover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotidepolymorphisms (SNPs), have been identified in ...

    文档格式：PDF | 页数：9 | 浏览次数：32 | 上传日期：2020-10-08 02:27:57 | 文档星级：
    METHODOLOGY ARTICLE Open AccessSNP-based pathway enrichment analysis forgenome-wide association studiesLingjie Weng 1 , Fabio Macciardi 2 , Aravind Subramanian 3 , Guia Guffanti 2 , Steven G Potkin 2* , Zhaoxia Yu 4* andXiaohui Xie 1,5*AbstractBackground: Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) todiscover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotidepolymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, andpsychiatric diseases. A common theme arising from these studies is that the genetic variations discovered byGWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategiesand statistical approaches are needed to address this lack of explanation. One such approach is the pathwayanalysis, which considers the genetic variations underlying a biological pathway, rather than separately as in thetraditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of associationover multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the mostsignificant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. Thisapproach leads to preferential identification of genes with a greater number of SNPs.Results: We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of thefollowing two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify allrepresentative (potentially more than one) SNPs of each gene, calculating the average number of representativeSNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing ifthe set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one fromEuropean-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways withnominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AAdata set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 ofthese pathways overlap with those found in the EA sample. We have implemented our method in a JAVA softwarepackage, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available athttp://cbcl.ics.uci.edu/SSEA.Conclusions: The SNP-based pathway enrichment method described here offers a new alternative approach foranalysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identifystatistically significant pathways, and importantly, pathways that can be replicated in large genetically distinctsamples.* Correspondence: sgpotkin@uci.edu; zhaoxia@ics.uci.edu; xhx@ics.uci.edu1 Department of Computer Science, University of California, Irvine, CA, USA2 Department of Psychiatry & Human Behaviour, University of California,Irvine, CA, USAFull list of author information is available at the end of the articleWeng et al. BMC Bioinformatics 2011, 12:99http://www.biomedcentral.com/1471-2105/12/99© 2011 Weng et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.

    阅读了该文档的用户还阅读了这些文档

    188宝金博页面版:关于我们

    188宝金博页面版:帮助中心

    188宝金博页面版:关注我们

    • 新浪微博

    关注188宝金博页面版公众号

    188宝金博页面版
    阅读
    APP
    阅读
    返回
    顶部
    188宝金博页面版官网登录在线平台入口（2026已更新）—江苏协昌电子科技股份有限公司