Gene-environment (GxE) interactions are one of the least studied aspects of the genetic architecture of human traits and diseases. The environment of an individual is inherently high dimensional, evolves through time and can be expensive and time consuming to measure. The UK Biobank study, with all 500,000 participants having undergone an extensive baseline questionnaire, represents a unique opportunity to assess GxE heritability for many traits and diseases in a well powered setting. We have developed a non-linear randomized Haseman-Elston (RHE) regression method applicable when many environmental variables have been measured on each individual. The method (GPLEMMA) simultaneously estimates a linear environmental score (ES) and its GxE heritability. We compare the method via simulation to a whole-genome regression approach (LEMMA) for estimating GxE heritability. We show that GPLEMMA is computationally efficient and produces results highly correlated with those from LEMMA when applied to simulated data and real data from the UK Biobank.

中文翻译：

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非线性随机Haseman-Elston回归估计基因环境遗传力

基因-环境（GxE）相互作用是人类特征和疾病的遗传结构研究最少的方面之一。个人的环境本质上是高维度的，会随着时间的推移而发展，并且测量起来可能既昂贵又耗时。UK Biobank研究对所有500,000名参与者进行了广泛的基线问卷调查，这是在功能强大的环境下评估许多特征和疾病的GxE遗传力的独特机会。我们已经开发了一种非线性随机Haseman-Elston（RHE）回归方法，该方法适用于在每个人身上测量了许多环境变量的情况。该方法（GPLEMMA）同时估算线性环境评分（ES）及其GxE遗传力。我们通过仿真将该方法与用于估计GxE遗传力的全基因组回归方法（LEMMA）进行了比较。我们表明，当将GPLEMMA应用于UK Biobank的模拟数据和真实数据时，其计算结果与LEMMA的结果高度相关。