The joint modeling of brain imaging information and genetic data is a promising research avenue to highlight the functional role of genes in determining the pathophysiological mechanisms of Alzheimer’s disease (AD). However, since genome-wide association (GWA) studies are essentially limited to the exploration of statistical correlations between genetic variants and phenotype, the validation and interpretation of the findings are usually nontrivial and prone to false positives. To address this issue, in this work, we investigate the functional genetic mechanisms underlying brain atrophy in AD by studying the involvement of candidate variants in known genetic regulatory functions. This approach, here termed functional prioritization, aims at testing the sets of gene variants identified by high-dimensional multivariate statistical modeling with respect to known biological processes to introduce a biology-driven validation scheme. When applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, the functional prioritization allowed for identifying a link between tribbles pseudokinase 3 (TRIB3) and the stereotypical pattern of gray matter loss in AD, which was confirmed in an independent validation sample, and that provides evidence about the relation between this gene and known mechanisms of neurodegeneration.

大脑成像信息和遗传数据的联合建模是一个有前途的研究途径，可以突出基因在确定阿尔茨海默病（AD）的病理生理机制中的功能。但是，由于全基因组关联（GWA）研究基本上仅限于探索遗传变异与表型之间的统计相关性，因此对发现的确认和解释通常并不容易，而且容易出现假阳性。为了解决这个问题，在这项工作中，我们通过研究候选变体在已知遗传调控功能中的参与，研究了AD脑萎缩的潜在遗传机制。这种方法，这里称为功能优先级，目的是针对已知的生物学过程，测试通过高维多元统计建模确定的基因变异集，以引入生物学驱动的验证方案。当应用于阿尔茨海默氏病神经影像学倡议（ADNI）队列时，功能优先级划分可以识别三点假激酶3（TRIB3）和AD中灰质损失的定型模式，这在一个独立的验证样本中得到了证实，这为该基因与已知的神经退行性机制之间的关系提供了证据。