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首页> 外文会议>2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops >Compairing quantitative trait analysis to qualitative trait analysis for complex traits disease: A genome wide association study for hyperlipidemia
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Compairing quantitative trait analysis to qualitative trait analysis for complex traits disease: A genome wide association study for hyperlipidemia

机译：复杂性状疾病定量特征分析与定性特征分析的比较：高脂血症的全基因组关联研究

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Current standard genome-wide association studies (GWAS) have relied on the simple analysis by focusing on the association between single genetic factor and one single common complex trait. However, since most common complex traits are associated with multiple genetic factors and their epistasis, this simple analysis is not powerful enough to detect multiple genetic factors. Furthermore, in many GWAS, one binary trait is commonly used and it is usually a summary trait derived from several quantitative traits. For example, a binary trait representing hyperlipidemia status is defined by combining four quantitative traits: Total cholesterol (Tchl), High density lipoprotein (HDL) cholesterol, Low density lipoprotein (LDL), and cholesterol and Triglycerides (TG). More information can be extracted from these quantitative traits than from one summary binary trait. However, not many methods have been proposed to account for the multiple traits simultaneously. In this study, we propose the following simple stepwise strategy to increase the power of detecting multiple genetic factors jointly for the multiple traits: (1) prescreening, (2) joint identification of putative SNPs based on elastic-net (EN) variable selection, and (3) collapsing. Joint identification of multiple genetic factors would be more powerful and provide better prediction on complex traits. We illustrated our approach with a large scale genome-wide dataset from a Korean population and identified the genetic factors associated with lipid-related traits.
机译：当前的标准全基因组关联研究（GWAS）依靠简单的分析，重点是单一遗传因子和一个单一的常见复杂性状之间的关联。但是，由于最常见的复杂性状与多种遗传因素及其上位性相关，因此这种简单的分析方法不足以检测多种遗传因素。此外，在许多GWAS中，通常使用一种二元性状，它通常是衍生自几种定量性状的概括性状。例如，通过组合四个定量特征来定义代表高脂血症状态的二进制特征：总胆固醇（Tchl），高密度脂蛋白（HDL）胆固醇，低密度脂蛋白（LDL）以及胆固醇和甘油三酸酯（TG）。从这些定量性状中提取的信息比从一个概括性二元性状中提取的信息更多。但是，没有提出太多方法来同时考虑多种性状。在这项研究中，我们提出以下简单的分步策略，以提高针对多种性状共同检测多种遗传因素的能力：（1）预筛选，（2）基于弹性网（EN）变量选择的推定SNP联合鉴定， （3）崩溃。多种遗传因素的联合鉴定将更加有力，并为复杂性状提供更好的预测。我们用来自韩国人群的大规模全基因组数据集说明了我们的方法，并确定了与脂质相关性状相关的遗传因素。

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