Pathway-Wide Association Study Implicates Multiple Sterol Transport and Metabolism Genes in HDL Cholesterol Regulation.
Pathway-Wide Association Study Implicates Multiple Sterol Transport and Metabolism Genes in HDL Cholesterol Regulation.
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DOI：
10.3389/fgene.2011.00041
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发表时间：
2011
影响因子：
3.7
通讯作者：
Rader DJ
中科院分区：
文献类型：
--
作者：
Wang K;Edmondson AC;Li M;Gao F;Qasim AN;Devaney JM;Burnett MS;Waterworth DM;Mooser V;Grant SF;Epstein SE;Reilly MP;Hakonarson H;Rader DJ
当单标记关联检验不具备足够的能力时，基于通路的关联方法已被提出为鉴定疾病基因的有效方法。从分布的两个极端尾部取样，可以从这些方法中受益于数量性状分析。在这里，我们测试了一个小的全基因组关联研究（GWAS）在653名极高的高密度脂蛋白胆固醇（HDL-C）水平和784名低HDL-C水平的受试者的途径关联的方法。我们确定了102个与HDL-C水平相关的固醇转运和代谢途径基因，并在独立的GWAS中复制了这些相关信号。有趣的是，这些途径包括18个与先前的GWAS有关的基因，这表明真正的HDL-C基因在这些途径中高度富集。此外，先前的GWAS未检测到通路中的多个生物学相关基因座，包括先前候选基因关联研究中涉及的基因（如LEPR、APOA 2、HDLBP、SOAT 2）、导致孟德尔形式脂质紊乱的基因（如DHCR 24）和敲除小鼠中表达血脂异常表型的基因（如SOAT 1、PON 1）。我们的研究表明，从数量性状的两个极端尾部取样并检查遗传途径可能会从较小的样本中获得生物学见解，而不是在大规模GWAS中使用单标记分析通常需要的样本。我们的研究结果还表明，功能相关的基因一起工作，以调节复杂的数量性状，未来的大规模研究可能会受益于路径关联的方法，以确定新的途径调节HDL-C水平。
Pathway-based association methods have been proposed to be an effective approach in identifying disease genes, when single-marker association tests do not have sufficient power. The analysis of quantitative traits may be benefited from these approaches, by sampling from two extreme tails of the distribution. Here we tested a pathway association approach on a small genome-wide association study (GWAS) on 653 subjects with extremely high high-density lipoprotein cholesterol (HDL-C) levels and 784 subjects with low HDL-C levels. We identified 102 genes in the sterol transport and metabolism pathways that collectively associate with HDL-C levels, and replicated these association signals in an independent GWAS. Interestingly, the pathways include 18 genes implicated in previous GWAS on lipid traits, suggesting that genuine HDL-C genes are highly enriched in these pathways. Additionally, multiple biologically relevant loci in the pathways were not detected by previous GWAS, including genes implicated in previous candidate gene association studies (such as LEPR, APOA2, HDLBP, SOAT2), genes that cause Mendelian forms of lipid disorders (such as DHCR24), and genes expressing dyslipidemia phenotypes in knockout mice (such as SOAT1, PON1). Our study suggests that sampling from two extreme tails of a quantitative trait and examining genetic pathways may yield biological insights from smaller samples than are generally required using single-marker analysis in large-scale GWAS. Our results also implicate that functionally related genes work together to regulate complex quantitative traits, and that future large-scale studies may benefit from pathway-association approaches to identify novel pathways regulating HDL-C levels.