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

• Applicants: Professor Michael Nothnagel, Ph.D.; Professor Alfredo Ramirez, Ph.D.
• Subject Area: Biological Psychiatry ; Human Cognitive and Systems Neuroscience ; Molecular Biology and Physiology of Neurons and Glial Cells
• Term: from 2019 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 429106243

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.

• DFG Programme: Research Grants