Jeremy Schwartzentruber

Former PhD Student at the Sanger Institute

Alumni

This person is a member of Sanger Institute Alumni.

I am interested in advancing our understanding of how human genetic variation leads to differences between people in disease risk and other traits. One of my major projects is to integrate multiple types of data - epigenetic, gene expression, conservation, etc. - to better predict which genetic variants affect gene regulation.

After working for a few years as a software developer, I wound my way through further studies in biology, chemistry, and biophysics before finally finding a real home in genomics research. I spent 3 years using exome sequencing to discover more than 40 novel genes associated with rare diseases, as well as somatic mutations involved in pediatric brain tumours. However, I found myself drawn to a more fundamental question – how are genes regulated in more subtle ways to give rise to the differences we see between people, either in health or common disease?

Genetic associations for common diseases and traits can be approximately localised with genome-wide association studies, and most are located in non-protein-coding genomic regions. However, because of linkage disequilibrium (blocks of genetic variants that tend to be inherited together), it is often impossible to precisely localize the one genetic difference that is actually having an effect. This is where additional genomic data can help us — epigenetic data such as histone modifications, open chromatin assays (DNase-seq and ATAC-seq), cross-species conservation, and computation predictions based on DNA sequence. I use statistical models to integrate these data to more precisely localise, or “fine-map”, these causal genetic variants. Because epigenetic data is specific to a given cell type, this also gives us the opportunity to identify the cell types likely to be involved in specific diseases, and has the potential to highlight molecular mechanisms.

I am broadly interested in learning and developing statistical methods to integrate genomic “big data” to provide insights into the molecular mechanisms of gene regulation and its association with disease.

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