# Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease > **NIH NIH U01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2022 · $116,998 ## Abstract Abstract There is still a lack of knowledge on the key genetic factors associated with post acute syndrome for COVID-19 patients, especially those related to neurological complications. In this study, we will utilize both the genetic and clinical data available through the All of Us researchers platform to study the genetic association with COVID-19 complications. In order to more accurately phenotype the patients based on their clinical trajectory mostly recorded in their electronic health records, we will utilize a pretrained deep learning model trained on more than four million patients from the N3C cohort. The pretrained model will be further fine-tuned on the All of US data, and will be used to phenotype the patients with genetic data. Further GWAS study will be performed to correlate between the deep learning based phenotype and the genetic information. Successful completion of this project will bring new insights to guide COVID-19 patients treatment to better prevent or manage further complications. ## Key facts - **NIH application ID:** 10660742 - **Project number:** 3U01AG070112-02S1 - **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON - **Principal Investigator:** Laila Rasmy Gindy Bekhet - **Activity code:** U01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $116,998 - **Award type:** 3 - **Project period:** 2021-07-01 → 2026-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10660742 ## Citation > US National Institutes of Health, RePORTER application 10660742, Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (3U01AG070112-02S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10660742. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*