Job Description

Bioinformatics for Neuroscience Intern Overview

Envision spending your summer working with energetic colleagues and inspirational leaders, all while gaining world-class experience in one of the most dynamic organizations in the pharmaceutical industry. This is a reality for AbbVie Interns.

The Bioinformatics for Neuroscience intern will work with the Neuroscience Bioinformatics team of the Genomics Research Center (GRC) at AbbVie. The GRC is a multidisciplinary center of excellence with expertise in genomic technologies and their application, human genetics, genomic medicine, computational analysis, data integration, genome biology, and application of functional genomics. 

Several recent single-cell eQTL studies (AbbVie internal and public) have revealed cell type specific variants that are linked to Alzheimer's Disease (AD). The Neuroscience Discovery team prioritized colocalized genes to identify potential drug targets in microglia, the innate immune cells of the brain that undergo inflammation response in AD. However, genetic regulation of gene expression under GWAS loci is often complex, often extending to multiple casual variants or presenting under specific disease context. Many of these implicated variants are non-coding variants and linking them to potential target genes requires using other assays. For this purpose, we can leverage on our high coverage microglia eQTL study and use publicly available chromatin conformation data from microglia to find associations between regions with shortlisted variants and promoters of AD relevant protein coding genes.

The internship project will focus on target identification for Alzheimer’s Disease through a deep dive into brain cell type specific eQTL information. This project will involve: 

Collecting datasets: microglia relevant variants from our internal eQTL study and microglia promoter-capture Hi-C from manuscript 

Analysis: identify variants that are eQTLs, extending to disease specific or microglia subtype specific, and perform colocalization analysis under the assumption of more than one casual variant

Analysis: filter variants that overlap with peaks in Hi-C and identify risk genes associated with the promoters 

Filtering: shortlist relevant genes by using information from other single-nuclei RNAseq and consortium studies 

Pitch: share results with Neuroscience stakeholders to do a deep dive on the prioritized targets and perform knock-out experiments in microglia to understand the biology and pathways involved