Genetic analysis of Alzheimer’s disease risk factors

At Alector we are focused on developing antibody-based therapies for cancer and neurodegenerative disorders such as Alzheimer's disease. Our main therapeutic hypotheses are: that the immune system plays a critical role in neurodegenerative diseases, and that redirecting aberrant immune cell activity in the brain could improve healthy function. We thus are very interested in untangling the role of microglia in Alzheimer’s disease, and understanding the underlying biological pathways.

Large GWAS studies of Alzheimer's disease (AD) uncovered a number of loci that are associated with the disease, however the mechanism of their involvement in the pathology is largely unknown. To better understand the role of various AD associated SNPs, we are looking for large datasets with both genotype and transcriptomics data in various cell types, and the Microglia Genomic Atlas study (MiGA) is an excellent resource of such data for microglia.

With this data in hand we plan to perform the following analysis:
We will query for a linear relationship between AD risk factors (risk allele loads) and mRNA levels to identify transcriptional signatures associated with each SNP. This analysis will be conducted using plink and R, correcting for covariates, such as gender, age, and population structure. We will follow with functional annotation using gene set enrichment analysis to further characterize impact of risk factors.
In addition, we are performing similar analysis of various other cell types (monocytes, macrophages, neurons). By doing comparative analysis we are looking to identify cell type specific mechanisms that might be involved in the disease pathology.

Overall, mining data from MiGA and other datasets will help us better understand the mechanism of action of risk factors of AD, and aid Alector with biomarker selection strategy, as well as antibody screening.

Understanding the role of myeloid immune cells in neurodegenerative disorders and cancer is central to Alector. Large datasets of samples from patients with Alzheimer's disease and healthy controls are an invaluable resource for scientists striving to understand the biological mechanisms leading to disease and find ways to cure it. The Microglia Genomic Atlas study is one of the rare resources of a large number of microglia samples with both gene expression and genetic variation data. By performing statistical analysis of this dataset in combination with data from other cell types we will gain better understanding into mechanisms of action of Alzheimer's disease’s risk factors, and help Alector with developing treatment for patients.