Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression.
Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression.
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DOI：
10.1186/s12863-017-0519-1
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发表时间：
2017-06-12
期刊：
影响因子：
2.9
通讯作者：
Krämer U
中科院分区：
文献类型：
--
作者：
Hüls A;Ickstadt K;Schikowski T;Krämer U
对于基因-环境（GxE）相互作用的分析，通常使用单核苷酸多态性（SNP）来表征遗传易感性，这种方法大多缺乏效力且重现性差。一个有希望的方法来克服这个问题可能是使用加权遗传风险评分（GRS），它被定义为基因变异的风险等位基因的加权和。金标准是使用已发表的荟萃分析的外部权重。在这项研究中，我们使用的内部权重从边际遗传效应的SNP估计的多元弹性网络回归，从而提供了一种方法，可以使用，如果没有外部权重。我们进行了一项模拟研究，用于检测GxE相互作用，并在6个风险SNP和越来越多的高度相关（高达210）和噪声SNP（高达840）的情况下，比较了单个SNP分析与Bonferroni校正的功效和I型错误，以及相应的分析与未加权和我们的加权GRS方法。与常见的单一SNP方法相比，应用加权GRS极大地增加了功效（例如，分别为94.2%和35.4%，以检测弱相互作用，对于6个不相关的风险SNP，OR = 1.04，并且n = 700，具有良好控制的I型错误）。此外，加权的GRS优于未加权的GRS，特别是在对表型没有任何影响的SNP的存在下（例如，当将20个噪声SNP添加到6个风险SNP时，分别为90.1%对43.9%）。在SALIA队列（n = 402）肺部炎症的真实的数据应用中证实了加权GRS的这种优于。然而，在具有大量噪声SNP（> 200对6个风险SNP）的情况下，需要更大的样本量以避免增加的I型错误，而即使在小样本（例如n = 400）中也可以处理大量相关SNP。总之，加权GRS的权重从边际遗传效应的SNPs估计的多元弹性网络回归被证明是一个强大的工具，以检测基因-环境相互作用的情况下，高连锁不平衡和噪音。本文的在线版本（doi：10.1186/s12863 - 017 - 0519 - 1）包含补充材料，可供授权用户使用。
For the analysis of gene-environment (GxE) interactions commonly single nucleotide polymorphisms (SNPs) are used to characterize genetic susceptibility, an approach that mostly lacks power and has poor reproducibility. One promising approach to overcome this problem might be the use of weighted genetic risk scores (GRS), which are defined as weighted sums of risk alleles of gene variants. The gold-standard is to use external weights from published meta-analyses. In this study, we used internal weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression and thereby provided a method that can be used if there are no external weights available. We conducted a simulation study for the detection of GxE interactions and compared power and type I error of single SNPs analyses with Bonferroni correction and corresponding analysis with unweighted and our weighted GRS approach in scenarios with six risk SNPs and an increasing number of highly correlated (up to 210) and noise SNPs (up to 840). Applying weighted GRS increased the power enormously in comparison to the common single SNPs approach (e.g. 94.2% vs. 35.4%, respectively, to detect a weak interaction with an OR ≈ 1.04 for six uncorrelated risk SNPs and n = 700 with a well-controlled type I error). Furthermore, weighted GRS outperformed the unweighted GRS, in particular in the presence of SNPs without any effect on the phenotype (e.g. 90.1% vs. 43.9%, respectively, when 20 noise SNPs were added to the six risk SNPs). This outperforming of the weighted GRS was confirmed in a real data application on lung inflammation in the SALIA cohort (n = 402). However, in scenarios with a high number of noise SNPs (>200 vs. 6 risk SNPs), larger sample sizes are needed to avoid an increased type I error, whereas a high number of correlated SNPs can be handled even in small samples (e.g. n = 400). In conclusion, weighted GRS with weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression were shown to be a powerful tool to detect gene-environment interactions in scenarios of high Linkage disequilibrium and noise. The online version of this article (doi:10.1186/s12863-017-0519-1) contains supplementary material, which is available to authorized users.