Novel statistical and bioinformatic methods to identify genetic factors involved in cognitive decline and rate of disease progression in pre-dementia stages of Alzheimer's disease

多因素疾病，如阿尔茨海默病（AD），通常在临床诊断前几年就开始了。在临床前阶段调节疾病进展提供了延迟临床阶段开始的机会。因此，研究的重点是确定参与疾病进展的因素和途径，预计将对多因素疾病的护理成本和预防政策产生重大影响。在大多数多因素疾病中，遗传因素占其归因风险的重要部分。因此，可能大多数调节疾病进展的病理生理途径将由遗传决定因素驱动或包括遗传决定因素。不幸的是，关于疾病进展的基因研究目前还处于起步阶段。因此，本提案的主要目标是开发创新和稳健的统计方法来分析遗传学在表型进展中的作用。为此，我们将开发鲁棒且计算可行的线性混合模型（LMM）。少数可用的纵向数据遗传方法是基于LMM的，因为这些统计模型提供了几个优点，包括管理缺失数据，重复测量的整合，固定效应和随机效应的结合。然而，它们是计算时间消耗，有时，仅限于纵向表型的线性轨迹。为了解决这些问题，我们将使用计算速度更快的LMM开发改进的LMM，作为条件LMM。我们的目标是：a)开发纵向表型的方形轨迹建模方法，b)开发一种将疾病发病的年龄特异性风险视为随机效应的方法，以及c)开发一种模型来寻找驱动纵向表型的生物学途径。为了在真实数据上测试这些模型，我们访问了欧洲最大和全面的痴呆症前期AD纵向数据集，即轻度认知障碍（MCI）。对于所有MCI病例，在阿尔茨海默病联盟EADB中生成了全基因组基因型数据，其中包括9,000个MCI样本。除了认知表型外，MCI病例还具有脑脊液和成像数据的额外生物标志物数据，为我们的建议提供了一个独特的机会，将我们的研究扩展到疾病进展之外的假设。最后，我们将实现一种方法来生成甲基化调控的鲁棒遗传估计器。在此，甲基化被认为是遗传研究中鉴定的易感性变异功能相关性的分子介质。总之，我们的建议将为遗传研究提供重要的工具来分析纵向表型。将这些方法应用于真实的MCI遗传数据将有助于识别调节AD疾病进展的新遗传因素，以及它们驱动所观察到的遗传关联的潜在分子机制。

Multifactorial diseases, such as Alzheimer’s disease (AD), normally starts years before clinical diagnose is made. Modulating disease progression at preclinical stages offers the opportunity to delay the beginning of the clinical stage. Thus, research focused on identifying factors and pathways involved in disease progression is expected to have major impact on care cost and prevention policies of multifactorial diseases. In most multifactorial diseases, genetic factors account for an important part of their attributable risk. It is therefore likely that most of the pathophysiological pathways modulating disease progression will be driven by or include genetic determinants. Unfortunately, genetic research on disease progression is currently in its infancy. Consequently, the main objective of this proposal is to develop innovative and robust statistical methods to analyse the role of genetics on phenotypes progression over time. To this end, we will develop robust and computationally feasible linear mixed models (LMM). The few available genetic approaches on longitudinal data are based on LMM because these statistical models offer several advantages including management of missing data, integration of repeated measurements, combination of fixed and random effects. However, they are computationally time consuming and, sometime, limited only to linear trajectories of longitudinal phenotypes. To tackle these problems, we will develop improved LMMs using computationally faster LMMs, as the conditional LMM. We aim to: a) develop methods modelling square trajectories of longitudinal phenotypes, b) develop an approach considering age-specific risk on disease onset as a random effect, and c) develop a model to search for biological pathways driving longitudinal phenotypes. To test these models on real data, we have access to the European largest and comprehensive longitudinal dataset of pre-dementia AD, i.e. mild cognitive impairment (MCI). For all MCI cases, genome-wide genotype data has been generated within the Alzheimer’s disease consortium EADB, and comprises 9,000 samples of MCI. In addition to cognitive phenotypes, MCI cases have additional biomarker data on cerebrospinal fluid and imaging data providing our proposal with a unique opportunity to expand our research to hypotheses beyond disease progression. Finally, we will implement a method to generate robust genetic estimators of methylation regulation. Herein, methylation has been proposed as a molecular mediator for the functional relevance of susceptibility variants identified in genetic studies. In conclusion, our proposal will provide genetic research with important tools to analyse longitudinal phenotypes. Application of these methods to real MCI genetic data will lead to identification of novel genetic factors modulating disease progression in AD, as well as their potential molecular mechanism driving the observed genetic association.