A transcontinental research effort led by scientists from Vanderbilt University Medical Center and the University of Michigan has upended some long-standing assumptions about mutations — how often they occur, what causes them and what they do.

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.

An international coalition of biomedical researchers co-led by Vanderbilt's Alexander Bick, MD, PhD, has determined a new way to measure the growth rate of precancerous clones of blood stem cells that one day could help doctors lower their patients’ risk of blood cancer.

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

A Vanderbilt study found that prostate cancer polygenic risk score has limited utility for enhancing prostate cancer screening.

Vanderbilt researchers have developed a new method to analyze mutations in blood stem cells that can trigger explosive, clonal expansions of abnormal cells.