Kerin Matthew, Marchini Jonathan
Wellcome Trust Center for Human Genetics, Oxford, OX3 7BN, UK.
Regeneron Genetics Center, Tarrytown, NY 10591, USA.
Bioinformatics. 2021 Apr 5;36(24):5632-5639. doi: 10.1093/bioinformatics/btaa1079.
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 randomized Haseman-Elston non-linear 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 more computationally efficient than LEMMA on large datasets, and produces results highly correlated with those from LEMMA when applied to simulated data and real data from the UK Biobank.
Software implementing the GPLEMMA method is available from https://jmarchini.org/gplemma/.
Supplementary data are available at Bioinformatics online.
基因-环境（GxE）相互作用是人类性状和疾病遗传结构中研究最少的方面之一。个体的环境本质上是高维度的，会随时间演变，并且测量起来可能成本高昂且耗时。英国生物银行研究中，所有50万名参与者都完成了一份详尽的基线调查问卷，这为在一个有充分效力的环境中评估许多性状和疾病的GxE遗传力提供了独特机会。
我们开发了一种随机化的哈斯曼-埃尔斯顿非线性回归方法，适用于对每个个体测量了多个环境变量的情况。该方法（GPLEMMA）同时估计线性环境得分（ES）及其GxE遗传力。我们通过模拟将该方法与用于估计GxE遗传力的全基因组回归方法（LEMMA）进行比较。我们表明，在大型数据集上，GPLEMMA在计算上比LEMMA更高效，并且在应用于模拟数据和来自英国生物银行的真实数据时，产生的结果与LEMMA的结果高度相关。
实现GPLEMMA方法的软件可从https://jmarchini.org/gplemma/获取。
补充数据可在《生物信息学》在线获取。
