J. Qu

Burden of rare coding variants reveals genetic heterogeneity between obese and non-obese asthma patients in the African American population

Joseph T. Glessner, J. Glessner, H. Hakonarson, 2022, Respiratory Research.

Machine Learning for Interpretation of DNA Variants of Maturity-Onset Diabetes of the Young Genes Based on ACMG Criteria

Joseph T. Glessner, H. Hakonarson, H. Qu, 2020, medRxiv.

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters

Joseph T. Glessner, J. Glessner, H. Hakonarson, 2020, medRxiv.

Association of DLL1 with type 1 diabetes in patients characterized by low polygenic risk score.

Joseph T. Glessner, J. Glessner, H. Hakonarson, 2020, Metabolism: clinical and experimental.

Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients

J. Glessner, H. Hakonarson, H. Qu, 2022, Molecular Psychiatry.

Metabolomic profiling of samples from pediatric patients with asthma unveils deficient nutrients in African Americans

John J. Connolly, Joseph T. Glessner, J. Glessner, 2022, iScience.

Corrigendum

Shau-ku Huang, Yichuan Liu, Hao Chen, 2021, FEBS letters.

Combined application of genetic and polygenic risk scores for type 1 diabetes risk prediction

John J. Connolly, Joseph T. Glessner, J. Glessner, 2021, Diabetes, obesity & metabolism.