Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease

NIH RePORTER · NIH · U01 · $116,998 · view on reporter.nih.gov ↗

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
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
Principal Investigator
Laila Rasmy Gindy Bekhet
Activity code
U01
Funding institute
NIH
Fiscal year
2022
Award amount
$116,998
Award type
3
Project period
2021-07-01 → 2026-06-30