Suppr超能文献

一种用于数量性状基因-基因相互作用多因素降维分析的简单且计算高效的方法。

A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits.

作者信息

Gui Jiang, Moore Jason H, Williams Scott M, Andrews Peter, Hillege Hans L, van der Harst Pim, Navis Gerjan, Van Gilst Wiek H, Asselbergs Folkert W, Gilbert-Diamond Diane

机构信息

Institute for Quantitative Biomedical Sciences, Geisel School of Medicine, Lebanon, New Hampshire, United States of America ; Section of Biostatistics and Epidemiology, Departments of Community and Family Medicine, Geisel School of Medicine, Lebanon, New Hampshire, United States of America.

出版信息

PLoS One. 2013 Jun 21;8(6):e66545. doi: 10.1371/journal.pone.0066545. Print 2013.

Abstract

We present an extension of the two-class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR's constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to identify the empirical distribution of QMDR's testing score. We then applied QMDR to genetic data from the ongoing prospective Prevention of Renal and Vascular End-Stage Disease (PREVEND) study.

摘要

我们提出了一种两类多因素降维(MDR)算法的扩展方法,该方法能够在数量性状的背景下检测上位性单核苷酸多态性(SNP)-SNP相互作用并对其进行特征描述。所提出的定量MDR(QMDR)方法通过修改MDR的构造性归纳算法以使用T检验来处理连续数据。QMDR用T检验统计量取代平衡准确度指标作为分数,以确定最佳相互作用模型。我们通过模拟确定了QMDR检验分数的经验分布。然后,我们将QMDR应用于正在进行的前瞻性预防终末期肾病和血管疾病(PREVEND)研究的遗传数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8b1/3689797/2370d165f68a/pone.0066545.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验