Understanding the Complex Interactions between Coffee, Tea Intake and Neurologically Relevant Tissues Proteins in the Development of Anxiety and Depression

Liu, L.; Cheng, Bolun; Ye, J.; Qi, Xin; Cheng, Shiqiang; Meng, Peilin; Chen, Y.; Yang, Xuena; Yao, Yucen; Zhang, None Hongshi; Zhang, Z.; Zhang, None Jindan; Li, C.; Pan, Chuyu; Wen, Yan; Jia, Yumeng; Zhang, Feng

doi:10.1007/s12603-022-1869-6

Cited by 3 publications

(1 citation statement)

References 57 publications

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“…In accordance with the conventions of prior research (Liu et al, 2022; Niu et al, 2019; Schunkert et al, 2020), we treated educational attainment as a fixed effect, considering it as a part of socioeconomic status. To assess the robustness of our findings under this condition, we conducted additional analyses, specifically excluding educational attainment as a fixed effect, for the evaluation of BMI/ALC, WHR/ALC, and HYP/WHR interactions.…”

Section: Real Data Analysismentioning

confidence: 99%

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

Jayasinghe,

Momin,

Beckmann

et al. 2024

Genetic Epidemiology

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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.

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Section: Real Data Analysismentioning

confidence: 99%