AD subtypes

Alzheimer’s disease (AD) is a highly heterogeneous disease with diverse clinical manifestations and rate of progression. AD subtypes identification will improve our understanding of the underlying disease mechanisms, enable us to predict disease trajectory and develop new disease-modifying treatments.
Objective. To identify distinct AD subtypes from WGS data of AD individuals
Analysis plan. We will use 3000 WGS data derived from the ADSP Discovery Case-Control Based Extension Study. We will use the available SNVs and INDELS and infer structural variants (SVs) with our in-house multi-caller pipelines. Rare variants will be retained for further analysis. We will then split the dataset in training and tests set, and use the identified set of genetic variants (i.e. SNVs, INDELS and SVs) as input to a deep neural network (an autoencoder architecture) to learn an unsupervised latent representation of the data. AD subtypes will be identified within this reduced space and characterized using, demographics and clinical data. We will then contrast each subtype with the control groups to identify subtype relevant variants (i.e. putative subtype biomarkers), which will be used as input features to a gradient boosted tree model, to generate a subtype predictive model and subtype specific features.
Planned collaboration. Each member of the team will devote effort in specific areas of investigation, nevertheless, all the team members will discuss, through regular meeting, individual progress and potential challenges. In particular, Dr Coppola (Research Scientist, Department of Pathology, Yale University, USA), together with Dr Dean Palejev (Associate Professor, GATE Institute, Sofia University, Bulgaria) will be involved in the deep learning model generation and validation, and subtype identification; Dr Fredrik Johansson (Assistant Professor, Department of Computer Science & Engineering, Chalmers University of Technology. Sweden), will work on the supervised machine learning model; Dr Alexander Schliep, Associate Professor, Department of Computer Science & Engineering, University of Gothenburg, Sweden), will work on the SVs inference.

Alzheimer’s disease (AD) is a highly heterogeneous disease with diverse clinical manifestations and rate of progression. The heterogeneity of AD has complicated both clinical trial design and outcomes, and thus the need for better models of AD, and/or better strategies for selection of participants into specific clinical trials is evident. The identification of more homogeneous disease subgroups (i.e. AD subtypes) will improve our understanding of the underlying disease mechanisms, enable us to predict disease trajectory and develop new disease-modifying treatments. We will use a comprehensive set of genetic variants in combination with deep learning algorithms to identify AD subtypes. Subtypes will be characterized using clinical and demographic data. Finally, variants specific to each cluster will be identified and used to train a predictive machine-learning model to classify new individuals.