Prediction of LDL cholesterol response to statin using transcriptomic and genetic variation.
Prediction of LDL cholesterol response to statin using transcriptomic and genetic variation.
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DOI：
10.1186/s13059-014-0460-9
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发表时间：
2014-09-30
期刊：
影响因子：
12.3
通讯作者：
Krauss RM
中科院分区：
文献类型：
--
作者：
Kim K;Bolotin E;Theusch E;Huang H;Medina MW;Krauss RM
他汀类药物被广泛用于降低低密度脂蛋白胆固醇（LDLC）水平和心血管疾病的风险。然而，他汀类药物诱导的低密度脂蛋白降低的幅度存在显著的个体间差异。迄今为止，对单个DNA序列变异的分析只解释了这种变异的一小部分。本研究旨在评估转录组学分析是否可以用于确定他汀类药物疗效个体间差异的额外遗传贡献。利用来自372名胆固醇和药物遗传学临床试验参与者的永生化淋巴母细胞样细胞系的表达阵列数据，我们确定了100个区分高和低他汀类药物应答的特征基因。这些特征基因的径向基支持向量机预测模型解释了他汀类药物介导的LDLC变化的12.3%的方差。添加与标记基因（eqtl）表达水平相关的snp或先前在全基因组关联研究中报道的与他汀类药物反应相关的snp，导致组合模型预测15.0%的方差。值得注意的是，一个单独的标记基因相关的eqtl模型解释了高达17.2%的胆固醇和药物遗传学人群的单独子集的尾部方差。此外，我们使用支持向量机分类模型，对最极端的15%的高、低响应者进行了高精度的分类。这些结果表明，转录组学信息可以解释LDLC对他汀类药物治疗反应的很大一部分差异，并表明这可能为识别影响胆固醇代谢的新途径提供框架。本文的在线版本（doi:10.1186/s13059-014-0460-9）包含补充材料，可供授权用户使用。
Statins are widely prescribed for lowering LDL-cholesterol (LDLC) levels and risk of cardiovascular disease. There is, however, substantial inter-individual variation in the magnitude of statin-induced LDLC reduction. To date, analysis of individual DNA sequence variants has explained only a small proportion of this variability. The present study was aimed at assessing whether transcriptomic analyses could be used to identify additional genetic contributions to inter-individual differences in statin efficacy. Using expression array data from immortalized lymphoblastoid cell lines derived from 372 participants of the Cholesterol and Pharmacogenetics clinical trial, we identify 100 signature genes differentiating high versus low statin responders. A radial-basis support vector machine prediction model of these signature genes explains 12.3% of the variance in statin-mediated LDLC change. Addition of SNPs either associated with expression levels of the signature genes (eQTLs) or previously reported to be associated with statin response in genome-wide association studies results in a combined model that predicts 15.0% of the variance. Notably, a model of the signature gene associated eQTLs alone explains up to 17.2% of the variance in the tails of a separate subset of the Cholesterol and Pharmacogenetics population. Furthermore, using a support vector machine classification model, we classify the most extreme 15% of high and low responders with high accuracy. These results demonstrate that transcriptomic information can explain a substantial proportion of the variance in LDLC response to statin treatment, and suggest that this may provide a framework for identifying novel pathways that influence cholesterol metabolism. The online version of this article (doi:10.1186/s13059-014-0460-9) contains supplementary material, which is available to authorized users.
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