Genetic Insights from the ADSP into Drug Discovery In Alzheimer’s Disease

Abstract

AbstractBackgroundA major goal of the NAPA plan initiated in 2013 is the development of novel drugs for the treatment and prevention of Alzheimer’s disease (AD). It is widely recognized that drug targets with genetic support are more likely to successfully make it through the drug development pipeline than those without genetic support. The target may be a genetic risk factor for AD or within a genetically regulated network implicated by an AD genetic risk factor. Thus, one of the primary goals of the Alzheimer’s disease sequencing project (ADSP) is to identify and validate putative novel drug targets and to work with the community to advance testing of these candidates.MethodsSince 2013 the ADSP has performed genome wide association studies of both common and rare variants to identify novel AD risk genes in diverse human populations.ResultsGenomic analyses have demonstrated that these AD risk alleles are enriched with regulatory regions, in microglial enhancers and the PU.1 regulatory network. The ADSP has also made a major effort in including different ancestries into AD research and utilized the differences in AD risk between ancestries for major genetic risk factors (like ApoE) to identified potential therapeutic targets. A key challenge for common variants associated with AD is to link them to specific genes, and to understand the subsequent biological processes that lead to AD. This is a major focus of the Functional Genetics Consortium within the ADSP. Rare coding variants have directly implicated specific genes, which also provide support for the central role of microglial function in AD risk, e.g. Trem2, ABI3, PLCg2. The genetics of endophenotypes (e.g. sTrem2) and of protein quantitative trait loci offer alternative approaches to link genetics to biology.ConclusionsTogether, common and rare variants identify myeloid cell efferocytosis as a key disease risk hub. An alternative approach to drug discovery is the use of in silico approaches to match disease relevant signatures to existing drugs to enable drug repurposing. To date this approach has focused on transcriptomic signatures and implicates several biological pathways including the integrated stress response.