MENDELIAN RANDOMIZATION FOR UNBIAS BIOMARKER DISCOVERY FOR AD AND OTHER COMPLEX TRAITS

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Abstract Alzheimer’s disease (AD) is the most common form of dementia but has no effective prevention or treatment. The brain proteome, ascertained in cerebrospinal fluid (CSF) have proven to be a rich source of information, reflecting Aβ plaques deposition, neurofibrillary tangles, neuronal injury and inflammation, and have helped the enrollment of participants in clinical trials. Still, additional non-invasive and cost-effective biomarkers are critical for the early detection, disease intervention and monitoring. Novel biomarkers may also help identify and characterize the additional aspects of AD, such as rate of progression and age at onset. Although genetic studies have focused on the identification of variants associated with risk through genome-wide association studies (GWAS) and whole genome and exome sequencing projects, the genetic factors modulating these additional aspects of AD are less investigated. The current proposal focuses on these understudied aspects of disease etiology, namely the role of common and rare genetic variation on quantitative diagnostic and prognostic endophenotypes of Alzheimer’s disease (AD). We will develop a unique resource – the proteome ascertained in a plasma, CSF and brain for a large number of samples – that will allow us to leverage unbiased approaches to reveal novel biomarkers and endophenotypes associated with AD and complex traits. We will utilize Mendelian Randomization to identify causal proteins involved in AD and extend our studies to other complex traits. We will use GWAS, exome-chip, whole-exome and whole-genome sequences to identify single variants, genes and pathways associated with plasma, cerebrospinal fluid (CSF) and brain protein levels of AD biomarkers.