两个亚洲人群中高密度脂蛋白胆固醇相关通路和基因的研究
Pathways-driven Sparse Regression Identifies Pathways and Genes Associated with High-density Lipoprotein Cholesterol in Two Asian Cohorts
摘要：
全基因组关联研究（GWAS）中分析数据的标准方法忽略了遗传标记之间的任何潜在功能关系。相反，基因通路分析使用基因组内功能结构的先验信息来识别与感兴趣的性状相关的通路。在第二步中，可以在相关途径内鉴定重要的单核苷酸多态性（SNP）或基因。大多数途径方法开始于一次测试一个SNP，因此未能利用多SNP联合建模方法中固有的潜在优势。在这里，我们描述了一个双水平，稀疏回归模型的同时识别的途径，基因和SNPs与数量性状。我们的方法考虑到了与全基因组数据联合建模途径特定的各种因素，包括遗传预测因子之间的广泛相关性，以及变体可能重叠多个途径的事实。我们使用一种重新排序策略，该策略利用有限的样本变异性来为通路、SNP和基因提供稳健的排名。我们通过模拟测试我们的方法，并使用它来执行路径驱动的SNP选择，在两个独立的GWAS亚洲成年人队列中寻找与血清高密度脂蛋白胆固醇（HDLC）水平变化相关的路径，基因和SNP。通过比较两个队列的结果，我们确定了一些候选途径，包括与心肌病相关的途径，以及T细胞受体和PPAR信号传导。突出显示的基因包括与L型钙通道、腺苷酸环化酶、整联蛋白、层粘连蛋白、MAPK信号传导和免疫功能相关的基因。
英文摘要：
Standard approaches to analysing data in genome-wide association studies (GWAS) ignore any potential functional relationships between genetic markers. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here we describe a dual-level, sparse regression model for the simultaneous identification of pathways, genes and SNPs associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways, SNPs and genes. We test our method through simulation, and use it to perform pathways-driven SNP selection in a search for pathways, genes and SNPs associated with variation in serum high-density lipoprotein cholesterol (HDLC) levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune function.
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