Kelemen Martin, Xu Yu, Jiang Tao, Zhao Jing Hua, Anderson Carl A, Wallace Chris, Butterworth Adam, Inouye Michael
British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
Nat Commun. 2025 Jun 2;16(1):5122. doi: 10.1038/s41467-025-60056-1.
Polygenic scores, which estimate an individual's genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. Neural-network based deep-learning has emerged as a method of intense interest to model complex, nonlinear phenomena, which may be adapted to exploit gene-gene and gene-environment interactions to potentially improve polygenic scores. We fit neural-network models to both simulated and 28 real traits in the UK Biobank. To infer the amount of nonlinearity present in a phenotype, we also present a framework using neural-networks, which controls for the potential confounding effect of linkage disequilibrium. Although we found evidence for small amounts of nonlinear effects, neural-network models were outperformed by linear regression models for both genetic-only and genetic+environmental input scenarios. In this work, we find that the usefulness of neural-networks for generating polygenic scores may currently be limited and confounded by joint tagging effects due to linkage disequilibrium.
多基因分数用于估计个体患某种疾病或具有某种特征的遗传倾向，它有可能成为基因组医疗保健的一部分。基于神经网络的深度学习已成为一种备受关注的方法，用于对复杂的非线性现象进行建模，这种方法可加以调整，以利用基因-基因和基因-环境相互作用来潜在地提高多基因分数。我们将神经网络模型应用于英国生物银行中的模拟性状和28种真实性状。为了推断表型中存在的非线性程度，我们还提出了一个使用神经网络的框架，该框架可控制连锁不平衡的潜在混杂效应。尽管我们发现了少量非线性效应的证据，但在仅遗传和遗传+环境输入场景中，线性回归模型的表现均优于神经网络模型。在这项工作中，我们发现神经网络在生成多基因分数方面的实用性目前可能受到限制，并且会因连锁不平衡导致的联合标签效应而产生混淆。
