Vanderbilt Genetics Institute

A machine learning method based on neural networks outperformed a mutational scanning model at identifying disease-causing mutations in an Alzheimer’s disease protein, suggesting the method could be useful for facilitating therapeutic design.

Polygenic risk scores — scores that reflect the influence of common genetic variants — could be used to predict the likelihood of developing chronic overlapping pain conditions and guide biomarker and targeted prevention efforts.

Computational genetics tools have implicated inflammatory pathways in exfoliation syndrome, the most common cause of secondary glaucoma, which can result in blindness.

A multidisciplinary team led by Vanderbilt University Medical Center investigator Alexander Bick, MD, PhD, has been awarded a $2 million, four-year grant to study inflammation at the single-cell level in the rare disease RUNX1-FPD.

Vanderbilt researchers have developed a framework for the analysis of multiancestry, large-scale genomic studies across multiple biobanks.

Including polygenic risk scores for blood pressure may improve predictive models to identify people at risk for treatment-resistant hypertension.

Vanderbilt researchers used a novel analytical approach to identify a network of genes associated with Type 2 diabetes, including 31 genes that had not previously been associated with the disease.