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

新的统计和生物信息学方法可识别与阿尔茨海默病痴呆前期认知能力下降和疾病进展速度相关的遗传因素

• 批准号: 429106243
• 负责人: Professor Dr. Michael Nothnagel, Ph.D.
• 金额: --
• 依托单位: Statistical Genetics and Bioinformatics Group
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/429106243?language=en
• 关键词: Novel; statistical; bioinformatic; methods; identify

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.

多因素疾病，如阿尔茨海默病（AD），通常在临床诊断前几年开始。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，很可能大多数调节疾病进展的病理生理学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仍处于起步阶段。因此，该提案的主要目标是开发创新和强大的统计方法，以分析遗传学对表型随时间推移的作用。为此，我们将开发强大的和计算可行的线性混合模型（LMM）。纵向数据的几个可用的遗传方法是基于LMM，因为这些统计模型提供了几个优点，包括缺失数据的管理，重复测量的整合，固定和随机效应的组合。然而，它们在计算上是耗时的，并且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来开发改进的LMM。我们的目标是：a）开发模拟纵向表型的方形轨迹的方法，B）开发将疾病发作的年龄特异性风险视为随机效应的方法，以及c）开发搜索驱动纵向表型的生物学途径的模型。为了在真实的数据上测试这些模型，我们可以访问欧洲最大和最全面的痴呆前AD纵向数据集，即轻度认知障碍（MCI）。对于所有MCI病例，阿尔茨海默病联盟EADB已生成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病例还具有关于脑脊液和成像数据的额外生物标志物数据，为我们的提案提供了一个独特的机会，将我们的研究扩展到疾病进展以外的假设。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。在此，甲基化已被提出作为遗传研究中鉴定的易感性变体的功能相关性的分子介体。总之，我们的建议将提供遗传研究的重要工具，分析纵向表型。将这些方法应用于真实的MCI遗传数据将导致识别调节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。