Mendelian Randomization (MR) is a causal inference approach that uses genetic variation to test cause-and-effect relationships in human disease. This project will focus on identifying proteins that contribute to insulin resistance, and related cardiometabolic conditions using MR.

A variety of rotation projects are available including:

- Analysis of massively parallel reporter assays to identify regulatory variants

- Application of machine learning models to predict the function of non-coding genetic variants

- Analysis of single-cell gene expression and chromatin datasets

- Analysis of “Superb-seq” data consisting of single cell readouts of CRISPR edits and transcriptomes.

The Li lab has generated multiple single cell datasets of normal, perturbed, and regenerative hematopoiesis that serve as a launching pad for exploration of novel bioinformatic approaches to reveal biology relevant to understanding and treating blood disorders. Rotation projects leveraging existing high-value datasets are available for prospective graduate students.