Tian Yuan, Felsky Daniel, Gronsbell Jessica, Park Jun Young
Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada.
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
medRxiv. 2025 Mar 3:2025.02.27.25322897. doi: 10.1101/2025.02.27.25322897.
The UK Biobank study has produced thousands of brain imaging-driven phenotypes (IDPs) collected from more than 40,000 genotyped individuals so far, facilitating the investigation of genetic and imaging biomarkers for brain disorders. Motivated by efforts in genetics to integrate gene expression levels with genome-wide association studies (GWASs), recent methods in imaging genetics adopted an instrumental variable (IV) approach to identify causal IDPs for brain disorders. However, several methodological challenges arise with existing methods in achieving causality in imaging genetics, including horizontal pleiotropy and high dimensionality of candidate IVs. In this work, we propose testing the causality of each brain modality (i.e., structural, functional, and diffusion MRI) for each gene as a useful alternative, which offers an enhanced understanding of the roles of genetic variants and imaging features on behavior by controlling for the pleiotropic effects of IDPs from other imaging modalities. We demonstrate the utility of the proposed method by using Alzheimer's GWAS data from the UK Biobank and the International Genomics of Alzheimer's Project (IGAP) study. Our method is implemented using summary statistics, which is available on GitHub.
英国生物银行研究迄今已从4万多名基因分型个体中产生了数千种由脑成像驱动的表型（影像诊断学特征），这为研究脑部疾病的遗传和成像生物标志物提供了便利。受遗传学领域将基因表达水平与全基因组关联研究（GWAS）相结合的努力的推动，影像遗传学领域的最新方法采用了一种工具变量（IV）方法来识别脑部疾病的因果影像诊断学特征。然而，现有方法在实现影像遗传学中的因果关系方面出现了几个方法学上的挑战，包括水平多效性和候选工具变量的高维度性。在这项工作中，我们提议将测试每个基因在每种脑成像模式（即结构、功能和扩散磁共振成像）中的因果关系作为一种有用的替代方法，通过控制来自其他成像模式的影像诊断学特征的多效性效应，能增强对基因变异和成像特征在行为上所起作用的理解。我们通过使用来自英国生物银行的阿尔茨海默病GWAS数据以及国际阿尔茨海默病基因组计划（IGAP）研究来证明所提出方法的实用性。我们的方法是使用汇总统计数据实现的，该代码可在GitHub上获取。
