Abstract

All major neurodegenerative diseases are characterized by substantial heritability and while recent large-scale genetic efforts have identified variants associated with disease, these often lie in non-coding, regulatory regions and cannot be linked to any functional outcomes [11]. Recent work has repeatedly highlighted that the genetic risk for Alzheimer’s disease acts primarily via microglia - the resident macrophages of the central nervous system [14]. However, most of these variants do not directly affect protein function, instead they are suspected of influencing gene expression by altering genomic regulatory elements.

One of the main challenges in genome biology is the understanding of highly cell-type specific gene regulatory mechanisms. In the context of diseases, research is typically targeted towards cell-types relevant to disease phenotype, or which are presumed to have a causal downstream effect. Genetic variants which are associated with the function or activity of regulatory elements (known as quantitative trait loci or QTLs) will exert their effects in a cell-type specific manner [5]. Mapping out molecular and regulatory QTLs comprehensively in disease-relevant cell-types would enable us to interpret functional outcomes of genetic variants on gene expression and regulation.

While we are focused on understanding its impact on dementias, the impact of improving regulatory genomic predictions will affect research on all biological traits for which good genetic traits are available (approximately 2000 diseases/phenotypes). Specifically in the context of Alzheimers, it would enable us to test for genome-wide associations with particular regulatory factors (e.g. transcription factors) which are directionally involved in disease. If we become able to detect such an enrichment, then we would be well positioned to start looking into drug target development.

Citation information

Data Study Group team. (2022, July 5). Data Study Group Final Report: UK Dementia Research Institute and DEMON Network. Zenodo. https://doi.org/10.5281/zenodo.6799048

Additional information

PI: Mike Phuycharoen