Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies.
Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies.
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DOI：
10.1186/s12863-017-0586-3
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发表时间：
2017-12-16
期刊：
影响因子：
2.9
通讯作者：
Schwender H
中科院分区：
文献类型：
--
作者：
Hüls A;Krämer U;Carlsten C;Schikowski T;Ickstadt K;Schwender H
加权遗传风险评分（GRS）被定义为单核苷酸多态性（snp）风险等位基因的加权总和，在检测基因-环境（GxE）相互作用方面具有统计学上的强大作用。要分配权重，黄金标准是使用来自独立研究的外部权重。然而，适当的外部权重并不总是可用的。在这种情况下，在存在显性边际遗传效应的情况下，我们在之前的研究中表明，具有边际遗传效应内部权重的GRS（“GRS-边际-内部”）是单一SNP方法或使用未加权GRS的强大而可靠的替代方法。然而，这种方法可能不适用于检测显性相互作用，即表现出强于边际遗传效应的相互作用。在本文中，我们提出了一种加权方法（“GRS-交互-训练”），其中部分数据用于估计交互项的权重，其余数据用于确定GRS。我们对GxE相互作用的检测进行了模拟研究，其中我们评估了功率、I型误差和符号错配。我们将这种新的加权方法与GRS-边际-内部方法和GRS与外部权重进行了比较。我们的模拟研究表明，在没有外部权重和主要相互作用的情况下，grs -相互作用训练方法达到了最高的功率。如果边际遗传效应占主导地位，则采用grs -边际-内部方法。此外，GRS-交互训练方法检测交互的能力仅略低于带有外部权重的GRS方法。在交通、哮喘和遗传学（TAG）研究（N = 4465个观察值）的实际数据应用中，grs -交互训练方法的功能得到了证实。当没有适当的外部权重时，我们建议使用研究群体本身的内部权重来构建GxE相互作用研究的加权GRS。如果选择SNPs是因为假设有很强的边际遗传效应，则应使用grs -边际-内部。如果选择snp是因为它们对介导环境效应的生物机制的集体影响（优势相互作用假设），则应应用grs -相互作用训练。本文的在线版本（10.1186/s12863-017-0586-3）包含补充材料，授权用户可使用。
Weighted genetic risk scores (GRS), defined as weighted sums of risk alleles of single nucleotide polymorphisms (SNPs), are statistically powerful for detection gene-environment (GxE) interactions. To assign weights, the gold standard is to use external weights from an independent study. However, appropriate external weights are not always available. In such situations and in the presence of predominant marginal genetic effects, we have shown in a previous study that GRS with internal weights from marginal genetic effects (“GRS-marginal-internal”) are a powerful and reliable alternative to single SNP approaches or the use of unweighted GRS. However, this approach might not be appropriate for detecting predominant interactions, i.e. interactions showing an effect stronger than the marginal genetic effect. In this paper, we present a weighting approach for such predominant interactions (“GRS-interaction-training”) in which parts of the data are used to estimate the weights from the interaction terms and the remaining data are used to determine the GRS. We conducted a simulation study for the detection of GxE interactions in which we evaluated power, type I error and sign-misspecification. We compared this new weighting approach to the GRS-marginal-internal approach and to GRS with external weights. Our simulation study showed that in the absence of external weights and with predominant interaction effects, the highest power was reached with the GRS-interaction-training approach. If marginal genetic effects were predominant, the GRS-marginal-internal approach was more appropriate. Furthermore, the power to detect interactions reached by the GRS-interaction-training approach was only slightly lower than the power achieved by GRS with external weights. The power of the GRS-interaction-training approach was confirmed in a real data application to the Traffic, Asthma and Genetics (TAG) Study (N = 4465 observations). When appropriate external weights are unavailable, we recommend to use internal weights from the study population itself to construct weighted GRS for GxE interaction studies. If the SNPs were chosen because a strong marginal genetic effect was hypothesized, GRS-marginal-internal should be used. If the SNPs were chosen because of their collective impact on the biological mechanisms mediating the environmental effect (hypothesis of predominant interactions) GRS-interaction-training should be applied. The online version of this article (10.1186/s12863-017-0586-3) contains supplementary material, which is available to authorized users.
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影响因子：
3.8
作者：
Lee MK;Hong Y;Kim SY;Kim WJ;London SJ
通讯作者：
London SJ
影响因子：
2.1
作者：
Dudbridge F
通讯作者：
Dudbridge F
影响因子：
2.9
作者：
Hüls A;Ickstadt K;Schikowski T;Krämer U
通讯作者：
Krämer U
影响因子：
4.9
作者：
Kelly, FJ
通讯作者：
Kelly, FJ
DOI：
10.1136/bmj.g1610
发表时间：
2014-03-19
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Qi Q;Chu AY;Kang JH;Huang J;Rose LM;Jensen MK;Liang L;Curhan GC;Pasquale LR;Wiggs JL;De Vivo I;Chan AT;Choi HK;Tamimi RM;Ridker PM;Hunter DJ;Willett WC;Rimm EB;Chasman DI;Hu FB;Qi L
通讯作者：
Qi L