On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women's Genome Health Study.
On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women's Genome Health Study.
复制标题
DOI：
10.1371/journal.pgen.1000981
复制
发表时间：
2010-06-17
期刊：
影响因子：
4.5
通讯作者：
Chasman DI
中科院分区：
文献类型：
--
作者：
Paré G;Cook NR;Ridker PM;Chasman DI
对数量性状平均值的遗传效应进行测试是一种非常成功的策略。然而，迄今为止，大多数研究还没有探讨数量性状方差的遗传效应作为遗传变异的相关后果。在这份报告中，我们证明，在合理的情况下的遗传相互作用，一个数量性状的方差预计不同的三种可能的基因型的双等位基因SNP。利用这一观察与Levene的方差等性检验，我们提出了一种新的方法来优先考虑SNP的后续基因-基因和基因-环境测试。该方法具有有利的特征，即对于要优先化的SNP，不需要知道或测量相互作用协变量。使用模拟，我们表明，这种方法在一定条件下增加了穷举搜索的功率。我们进一步调查每个基因型方差的效用，通过检查妇女基因组健康研究的数据。使用该数据集，我们确定了LEPR SNP rs 12753193和体重指数之间在预测C反应蛋白水平方面的新的相互作用，ICAM 1 SNP rs 1799969和吸烟之间在预测可溶性ICAM-1水平方面的相互作用，以及PNPLA 3 SNP rs738409和体重指数之间在预测可溶性ICAM-1水平方面的相互作用。这些结果证明了我们的方法的实用性，并为肥胖，吸烟和炎症之间的关系提供了新的遗传见解。寻找基因-基因和基因-环境相互作用是遗传学的一个重大挑战。在这份报告中，我们提出了一种新的方法来帮助检测这些相互作用。这种方法的工作原理是，首先识别出更有可能参与遗传相互作用的遗传变异子集，然后测试这些变异的相互作用效应。使用这种方法，我们能够识别三种以前未知的遗传相互作用。第一种相互作用涉及体脂的测量和LEPR基因的遗传变异，用于预测炎症标志物C反应蛋白浓度。第二种相互作用涉及相同的体脂测量和PNPLA 3基因的遗传变体，用于预测ICAM-1水平，ICAM-1也是炎症的标志物。这些结果是重要的，因为LEPR和PNPLA 3都与身体脂肪增加的生物反应有关，并且已知炎症本身在肥胖中增加，并被认为有助于其不良健康影响。最后，在ICAM-1水平的预测中，确定了ICAM-1基因的遗传变异和吸烟之间的第三种相互作用。ICAM 1基因编码ICAM-1本身，吸烟是ICAM-1浓度的重要决定因素。
Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene–gene and gene–environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation. Finding gene–gene and gene–environment interactions is a major challenge in genetics. In this report, we propose a novel method to help detect these interactions. This method works by first identifying a subset of genetic variants more likely to be involved in genetic interactions and then testing these variants for interaction effects. Using this method, we were able to identify three previously unknown genetic interactions. The first interaction involves a measure of body fat and a genetic variant of the LEPR gene in the prediction of C-reactive protein concentration, a marker of inflammation. The second interaction involves the same measure of body fat and a genetic variant of the PNPLA3 gene in the prediction of ICAM-1 levels, also a marker of inflammation. These results are significant because both LEPR and PNPLA3 are linked to the biological response to increased body fat, and inflammation itself is known to be increased in obesity and is thought to contribute to its adverse health effect. Finally, a third interaction was identified between a genetic variant of the ICAM1 gene and smoking in the prediction of ICAM-1 levels. The ICAM1 gene encodes ICAM-1 itself and smoking is known to be an important determinant of ICAM-1 concentrations.
登录
查看更多内容
影响因子：
4.5
作者：
Evans, David M.;Marchini, Jonathan;Morris, Andrew P.;Cardon, Lon R.
通讯作者：
Cardon, Lon R.
影响因子：
7.7
作者：
Song, Yiqing;Manson, JoAnn E.;Liu, Simin
通讯作者：
Liu, Simin
影响因子：
3
作者：
Ritchie MD;White BC;Parker JS;Hahn LW;Moore JH
通讯作者：
Moore JH
影响因子：
5.2
作者：
Ponthieux, A;Lambert, D;Visvikis, S
通讯作者：
Visvikis, S
影响因子：
7
作者：
Nelson, MR;Kardia, SLR;Sing, CF
通讯作者：
Sing, CF