Biology subjects

Elsheikh, S. S. M.

Publications and source records attributed to Elsheikh, S. S. M..

3 recordsLinked to original sources

bioRxiv2020

BiGen: Integrative Clinical and Brain-Imaging Genetics Analysis Using Structural Equation Model

The identification of genetic variants associated with complex brain diseases has evolved in the past decades. Studies in the field have taken different approaches and study designs including genome-wide association studies. Neuroimaging and connectomics have also improved our understanding of the structural connectivity of the human brain and produced reliable measurements. Combining both neuroimaging and genetic characteristics significantly contributes to understand their complex relationship in affecting behaviour and cognition. Throughout this thesis we proposed analysis pipeline to study the association between imaging and genetics of two different types of brain disease, which is, Alzheimers disease and glioblastoma. We observe the need for a unified model to study the complex interplay between genetic, environmental and clinical, neuroimaging and phenotype features. In this chapter, we developed BiGen, a mathematical model to measure the inter-correlation structure through the integration of genetic, environmental, neuroimaging and disease measurements. We utilised the structural equation model and used a path construct of latent variables to study the hidden association between genes and brain-related diseases, mediated by connectivity characteristics. We applied BiGen to simulated data and to a dataset from the Alzheimers Disease Neuroimaging Initiative.

bioinformatics↗

bioRxiv2019

Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer’s Disease

Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the genetic variations on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimers disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. Here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimers genetic risk factors in the estimation of regional brain connectivity alterations.

bioinformatics↗

bioRxiv2018

Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer’s Disease

Variations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimers disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration. In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimers Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimers disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimers disease.

bioinformatics↗