Graduate School of Education, Stanford University and Center for Population Health Sciences, Stanford Medicine, Stanford, USA.
Graduate School of Education, Stanford University, Stanford, USA.
Behav Genet. 2022 Jan;52(1):56-64. doi: 10.1007/s10519-021-10090-8. Epub 2021 Dec 2.
Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.
基因型-环境交互作用（GxE）研究探究了对风险因素或干预措施的反应的异质性。用于估计 GxE 的流行方法检查个体遗传和环境测量之间的乘法交互作用。然而，风险因素和干预措施可能会调节流行病学结果的总方差，而该结果本身代表了许多其他病因成分的聚合。我们扩展了传统的 GxE 模型，以直接模拟遗传和环境对结果离散度的调节。我们推导出一个检验统计量[公式：见文本]，用于推断在个体遗传和环境测量之间识别出的相互作用是否代表了遗传或环境测量对表型总方差的更一般模式的调节。我们通过广泛的模拟验证了我们的方法，并应用它来研究健康与退休研究（N = 11586）中的多基因评分与体重指数（BMI）的出生年份之间的基因型-出生年份相互作用，以及英国生物库中的个体遗传变异（N = 380605）。我们发现，BMI 的全基因组多基因评分在出生年份的穿透性变化部分代表了 BMI 变异在代际间扩展的更一般模式。在晚出生的个体中发现与 BMI 更强相关的三个个体变异体，也与任何特定出生年份内 BMI 本身的变异性幅度相关，这表明它们可能赋予 BMI 对出生年份之外的一系列未测量因素的普遍敏感性，而这些因素无法通过出生年份来捕捉。我们引入了一个扩展的 GxE 回归模型，该模型明确地模拟了遗传和环境对所研究结果离散度的调节。这种方法可以确定识别出的 GxE 相互作用是否特定于测量的预测因子，或者代表遗传和环境测量对结果总方差的更一般模式的调节。
