Towards prediction and therapeutic targets of Alzheimer's disease through directed analysis of genetic risk loci.

Alzheimer's disease (AD) is the predominant form of dementia and one of the principal challenges in modern medicine. In 2015, an estimated 46.8 million people suffer from dementia with a global cost of US$ 818 billion. Due to our worldwide aging society, the number of patients is projected to double every 20 years, posing a tremendous burden on patients, society and economy. Finding a cure is paramount to avoid this calamity, but AD clinical trials to date have met with limited success. Focusing treatment on patients with overt symptomatology, which already endured decades of neurodegenerative decay, and an underestimation of the multifactorial etiology of AD, are thought to be the main reasons for this struggle. Several paradigm shifts may improve the clinical trial success rate, including: (1) Expanding our knowledge of the AD pathomechanisms to identify novel drug targets, (2) developing better biomarkers for early detection of the disease, and (3) treating patients according to the affected pathomechanism and finding means to determine the underlying etiology. The aim of this PhD project is to contribute to this transformation: (1) through analysis of common, rare and structural variants in AD susceptibility loci, identified through genome-wide association studies (GWAS). GWAS only provide information on associated SNPs that are in linkage disequilibrium with functional variants and it is therefore often unknown which genes and/or mechanisms are involved in the disease process. These loci will be elucidated with novel "third-generation" Oxford Nanopore sequencing technology, and promising variants will be validated and genotyped on our entire Belgian study population of more than 3000 individuals with the use of targeted resequencing on an Illumina sequencing platform. This innovative mix of next-generation sequencing will be fast, inexpensive, and enables unprecedented studying of (structural) variants in the entire loci. When feasible within the workplan, the downstream mechanisms of identified variants can be studied with the use of our in-house RNA-seq dataset and biobank, and through collaborations. As a result, novel insights in AD pathomechanisms and potential targets are provided, which in long-term can lead to a cure. (2) The previously identified variants will be included in genetic risk profiles, which will improve the existing risk scores that are based on indirectly associated GWAS SNPs. In addition, novel weighting approaches will be used and epistasis between variants and genes will be assessed. These profiles will be evaluated on prodromal cohorts, which allow further association with molecular and clinical (endo)phenotypes. (3) In addition to genetic risk scores, which represent the overall risk of developing AD, we will generate network-based genetic profiles based on biological pathways to distinguish individuals based on their underlying disease etiology. Through alignment with electronic health records and post-hoc analysis of clinical trials, their utility will be assessed in prediction of susceptibility to treatment. This PhD project has the following deliverables: (1) novel insights into the genes and pathways causing AD and (2) genetic profiles to serve as biomarkers for (subforms of) AD, along with a genotyping assay for immediate application in pre-selection of clinical trial participants. In the long term, this project can contribute to the discovery of a treatment for AD and the developed profiles may be used in personalized medicine.

Keywords:GENETIC EPIDEMIOLOGY, ALZHEIMER DEMENTIA

Disciplines:Genetics, Systems biology, Molecular and cell biology, Neurosciences, Public health care, Public health sciences, Public health services, Biological and physiological psychology, Cognitive science and intelligent systems, Developmental psychology and ageing