Polygenic risk scores for Alzheimer’s disease prediction and stratification
Alzheimer’s Disease (AD) is a neurodegenerative disease characterized by the accumulation of abnormal protein aggregates in the brain, i.e. amyloid (Aβ) plaques and tau tangles. AD is the most frequent cause of dementia, with over 40 million people affected worldwide. In its final phase, progressive loss of neurons impairs memory, cognition, and other functions depending on the affected brain areas. There is currently no treatment that stops or even slows down the disease progression. Mortality linked to AD therefore keeps increasing as life expectancy globally rises. AD drug development is particularly long and expensive, and the field suffers from a long history of failures. With this project we want to tackle one of the major factors explaining these clinical trial failures, namely the lack of tools and markers to enroll people in trials. The resulting heterogeneity of trial populations in terms of disease causes and progression is sufficient to mask the beneficial effect a candidate therapy might have on a subset of the trial population. Moreover, to date there is no cost-efficient, non-invasive tool to identify people with AD before they show clinical symptoms. Because AD develops silently over decades, this means that trials are performed on individuals that have had Aβ lesions in their brain for easily 20 years, and which have already suffered significant neuronal loss. Genetic background strongly determines the risk of AD, even for sporadic cases. Over 40 loci have been associated with the risk of AD through genome-wide association studies (GWAS) and meta-analyses thereof, with the strongest genetic risk factor being APOE4. Apart from these loci however, thousands of single nucleotide polymorphisms (SNPs) associated with risk of AD do not reach genome-wide significance. The concept of polygenic risk score (PRS) was developed to incorporate the contribution of thousands of such genome variations and relate it to the risk of developing AD. The challenge is now to correlate the wealth of genetic information from GWAS with disease mechanisms and to extract the relevant information to design targeted medical interventions. In this project we will define whole-genome and pathway-specific PRS to allow a much more precise stratification. This data will be supported by extensive knowledge of the clinical manifestations, associated microglia biology, and other data types like transcriptomics and serological markers.