Xi Duo, Zhang Minjianan, Shang Muheng, Du Lei, Han Junwei
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Bioinformatics. 2024 Dec 26;41(1). doi: 10.1093/bioinformatics/btae728.
Alzheimer's disease (AD) typically progresses gradually for ages rather than suddenly. Thus, staging AD progression in different phases could aid in accurate diagnosis and treatment. In addition, identifying genetic variations that influence AD is critical to understanding the pathogenesis. However, staging the disease progression and identifying genetic variations is usually handled separately.
To address this limitation, we propose a novel sparse multi-stage multi-task mixed-effects collaborative longitudinal regression method (MSColoR). Our method jointly models long disease progression as a multi-stage procedure and identifies genetic risk factors underpinning this complex trajectory. Specifically, MSColoR models multi-stage disease progression using longitudinal neuroimaging-derived phenotypes and associates the fitted disease trajectories with genetic variations at each stage. Furthermore, we collaboratively leverage summary statistics from large genome-wide association studies to improve the powers. Finally, an efficient optimization algorithm is introduced to solve MSColoR. We evaluate our method using both synthetic and real longitudinal neuroimaging and genetic data. Both results demonstrate that MSColoR can reduce modeling errors while identifying more accurate and significant genetic variations compared to other longitudinal methods. Consequently, MSColoR holds great potential as a computational technique for longitudinal brain imaging genetics and AD studies.
The code is publicly available at https://github.com/dulei323/MSColoR.
阿尔茨海默病（AD）通常是随年龄逐渐发展，而非突然发病。因此，对AD进展进行不同阶段的分期有助于准确诊断和治疗。此外，识别影响AD的基因变异对于理解其发病机制至关重要。然而，疾病进展分期和基因变异识别通常是分开处理的。
为解决这一局限性，我们提出了一种新颖的稀疏多阶段多任务混合效应协作纵向回归方法（MSColoR）。我们的方法将长期疾病进展作为一个多阶段过程进行联合建模，并识别支撑这一复杂轨迹的遗传风险因素。具体而言，MSColoR使用纵向神经影像学衍生的表型对多阶段疾病进展进行建模，并将拟合的疾病轨迹与每个阶段的基因变异相关联。此外，我们协作利用来自大型全基因组关联研究的汇总统计数据来提高效能。最后，引入了一种高效的优化算法来求解MSColoR。我们使用合成和真实的纵向神经影像学及基因数据对我们的方法进行评估。两个结果均表明，与其他纵向方法相比，MSColoR在识别更准确和显著的基因变异的同时能够减少建模误差。因此，MSColoR作为一种用于纵向脑成像遗传学和AD研究的计算技术具有巨大潜力。
