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首页> 外文会议>Intelligent data engineering and automated learning-IDEAL 2011 >Novel Data Mining Approaches for Detecting Quantitative Trait Loci of Bone Mineral Density in Genome-Wide Linkage Analysis
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Novel Data Mining Approaches for Detecting Quantitative Trait Loci of Bone Mineral Density in Genome-Wide Linkage Analysis

机译：全基因组连锁分析中检测骨矿物质密度特征位点的新数据挖掘方法

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摘要

Haseman-Elston (H-E) regression is a commonly used conventional approach for detecting quantitative trait loci (QTLs), which regulate the quantitative phenotype based on the Identical-By-Descent (IBD) information between twins in Genome-wide scan. However, this approach only considers genetic effect at individual loci, but not any interaction between genes. A Pair-Wise H-E regression (PWH-E) and a Feature Screening Approach (FSA) are proposed in this paper to take gene-gene interaction into account when detecting QTLs. After testing these approaches with several series of simulation studies, they are applied to a real-world bone mineral density (BMD) dataset, and find three site specific sets of potential QTLs. Further comparison analyses show that our results not only corroborate the 14 findings from previous published studies, but also suggest 22 new QTLs of BMD.
机译：Haseman-Elston（H-E）回归是一种用于检测数量性状基因座（QTL）的常用常规方法，该基因组基于全基因组扫描中双胞胎之间的同生后代（IBD）信息来调节数量表型。但是，这种方法仅考虑单个基因座的遗传效应，而不考虑基因之间的任何相互作用。为了检测QTL时考虑到基因与基因之间的相互作用，本文提出了基于配对的H-E回归（PWH-E）和特征筛选方法（FSA）。在通过一系列模拟研究测试了这些方法之后，将它们应用于真实世界的骨矿物质密度（BMD）数据集，并找到三个特定于站点的潜在QTL集。进一步的比较分析表明，我们的结果不仅证实了先前发表的研究的14个发现，而且还提出了22个新的BMD QTL。

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