Mitigating type 1 error inflation and power loss in GxE PRS: Genotype–environment interaction in polygenic risk score models

IF 1.7 4区 医学 Q3 GENETICS & HEREDITY
Dovini Jayasinghe, Md. Moksedul Momin, Kerri Beckmann, Elina Hyppönen, Beben Benyamin, S. Hong Lee
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Abstract

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype–environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype–environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.

Abstract Image

缓解 GxE PRS 中的 1 型错误膨胀和功率损失:多基因风险评分模型中基因型与环境的相互作用。
多基因风险评分(PRS)模型的使用改变了遗传学领域,它可以根据个体的遗传特征预测复杂的性状和疾病。然而,基因型-环境交互作用(GxE)对多基因风险评分模型的性能和适用性的影响仍然是一个有待探索的重要方面。目前,现有的基因型-环境交互作用多基因风险评分(GxE PRS)模型经常被不恰当地使用,这可能会导致1型错误率升高,结果大打折扣。在本研究中,我们提出了新的 GxE PRS 模型,该模型联合了加性遗传效应和交互遗传效应,同时还包括一个额外的二次项,用于非遗传协变量,从而增强了模型对模型错误规范的稳健性。通过大量模拟,我们证明我们提出的模型在控制 1 类错误率和提高统计能力方面优于现有模型。此外,我们将提出的模型应用于真实数据,并报告了显著的 GxE 效果。具体来说,我们强调了我们的模型对定量特征和二元特征的影响。在数量性状方面,我们发现了酒精摄入频率对体重指数遗传效应的 GxE 调节作用。在二元性状方面,我们发现了腰臀比对高血压遗传效应的 GxE 调节作用。这些发现强调了采用稳健模型的重要性,该模型可有效控制 1 类错误率,从而防止出现虚假的 GxE 信号。为了便于实施我们的方法,我们开发了一个名为 GxEprs 的创新 R 软件包,专门用于检测和估计 GxE 效应。总之,我们的研究强调了准确 GxE 建模的重要性及其对遗传风险预测的影响,同时提供了一种实用工具来支持该领域的进一步研究。
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来源期刊
Genetic Epidemiology
Genetic Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
4.40
自引率
9.50%
发文量
49
审稿时长
6-12 weeks
期刊介绍: Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed on the relative contribution of genetic and environmental factors to human disease as revealed by genetic, epidemiological, and biologic investigations. Genetic Epidemiology primarily publishes papers in statistical genetics, a research field that is primarily concerned with development of statistical, bioinformatical, and computational models for analyzing genetic data. Incorporation of underlying biology and population genetics into conceptual models is favored. The Journal seeks original articles comprising either applied research or innovative statistical, mathematical, computational, or genomic methodologies that advance studies in genetic epidemiology. Other types of reports are encouraged, such as letters to the editor, topic reviews, and perspectives from other fields of research that will likely enrich the field of genetic epidemiology.
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