Authors and Affiliations Samar S. M. Elsheikh1, Emile R. Chimusa1, Nicola J. Mulder1, Alessandro Crimi2,3

1 Computational Biology Group, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town

2 University Hospital of Zürich, Switzerland

3 African Institute for Mathematical Sciences, Ghana


Content Background: Alzheimer’s Disease (AD) is a chronic neurodegenerative and progressive disease that destroys memory and other mental functions. Genetic factors are believed to form > 75% of the disease risk. Genome-Wide Association Studies (GWASs) are known to have identified numerous Single Nucleotide Polymorphisms (SNPs) that inference complex diseases risk. A connectome is a mathematical representation of the brain connections as a network; global network metrics allow representing the topology and efficiency of a network with single value depending on which feature of the network is under study.

Aim: Our rationale is that looking at longitudinal changes in those metrics can be informative and be related to genetic information. In particular, we aim at assessing the associations between loci along the genome and neurodegeneration of Alzheimer’s brain network.

Methods: We quantified the latter by measuring the longitudinal change in connectomes after 1 year. We used a dataset from Alzheimer’s Disease Neuroimaging Initiative

Results: We show the outcomes of conducting a quantitative GWAS, i.e. with the dependent variable measured as quantitative values, in order to investigate if any correlations exist between global brain metrics changes and SNPs.

Conclusions: Our results proposed a simple way to correlate neurodegeneration to individual SNPs and highlighted significant associations.

Abstract structure include : Aim, Background, Methods, Results, Conclusions
Abstract guidelines ü Abstracts must not exceed 300 words, excluding the title, authors and affiliations.

ü Do not include figures and tables.

ü  Do not include references


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