Gene-environment interactions are at the heart of why many complex traits are not fully heritable, and why prediction of disease incidence and individual response to environmental changes based on genetics has been underwhelming in utility. Understanding these interactions is the primary limiting factor for the application of personalized medicine, but current methods are not well suited for dealing with complex traits that pose both a dimensionality and sparse data problem to unsupervised analysis methods. Genteract has developed a proprietary analytical technique that allows for detection and interpretation of GxEs regarding specific pairs of a single phenotype with a single environmental factor; these methods allow us to develop a platform that can be used to predict how individuals will respond to changes in their environment based on their genetics. To validate the methods we performed two types of testing: cross-validation against a dataset of clinical study results, and application of the methods in a simulated dataset. These tests enable a greater understanding of the methods' utility, statistical power and predictive capabilities.

中文翻译：

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仿真，敏感性分析和交叉验证，证明了Genteract GxE发现方法的实用性

基因与环境之间的相互作用是为什么许多复杂性状不能完全遗传的原因，以及为什么人们对基于遗传学的疾病发病率预测和对环境变化的个体反应的预测一直难以理解。理解这些相互作用是应用个性化医学的主要限制因素，但是当前的方法不适用于处理复杂特征，这些复杂特征给无监督分析方法带来了维数和稀疏数据问题。Genteract已经开发了一种专有的分析技术，该技术可以检测和解释有关具有单个环境因素的单个表型的特定对的GxE。这些方法使我们能够开发一个平台，该平台可用于预测个人如何根据其遗传学对环境变化做出反应。为了验证方法，我们执行了两种类型的测试：针对临床研究结果的数据集进行交叉验证，以及将方法应用于模拟数据集。这些测试使您可以更好地了解这些方法的效用，统计能力和预测能力。