# Bioinformatics Strategies for Genome-Wide Association Studies

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2021 · $391,879

## Abstract

One promise of precision medicine for Alzheimer’s disease is to edit a patient’s DNA and/or administer
therapeutics targeting etiologic molecules that prevent or reverse the disease process using a tailored design.
All of this happens at the level of the individual and requires precision knowledge of that patient’s biology. In
stark contrast, much of the knowledge we possess about genomic risk factors comes from statistical measures
of association in subjects ascertained with and without Alzheimer’s. The conceptual and practical disconnect
between the populations we study and the individuals we want to treat is a major source of confusion about
how to move forward in an era driven by genome technology. The primary goal of this proposal is to develop
novel informatics methodology and software to facilitate precision medicine for Alzheimer’s by connecting
population and individual genomic phenomena. We propose here a Virtual Genomic Medicine (VGMed)
workbench where clinicians can carry out thought experiments about the treatment of individual Alzheimer’s
patients using models of disease risk derived from population-level studies. This will be accomplished by first
developing a novel Genomics-guided Automated Machine Learning (GAML) algorithm for deriving risk models
from real data that is accessible to Alzheimer’s clinicians (AIM 1). We will then develop a novel simulation
approach that is able to generate artificial Alzheimer’s data that preserves the distribution of genetic effects
observed in the real data while maintaining other characteristics such as genotype frequencies (AIM 2). This
will generate open data allowing anyone to perform virtual interventions on Alzheimer’s patients derived from a
population-level risk distribution. The workbench will allow editing of individual genotypes and simulate the
administration of drugs by editing machine learning parameters in the simulation model (AIM 3). The change in
risk and Alzheimer’s disease status for the specific patient will be tracked in real time. Finally, we provide a
feature in the workbench that will allow the Alzheimer’s clinician to generate specific hypotheses about
individual genetic variants that can then be validated using integrated Alzheimer’s knowledge sources that
include databases such as PubMed and ClinVar thus giving the user immediate feedback (AIM 4). All methods
and software will be provided as open-source to the Alzheimer’s disease research community (AIM 5).

## Key facts

- **NIH application ID:** 10284977
- **Project number:** 3R01LM010098-12S1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Folkert Wouter Asselbergs
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $391,879
- **Award type:** 3
- **Project period:** 2009-09-30 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10284977

## Citation

> US National Institutes of Health, RePORTER application 10284977, Bioinformatics Strategies for Genome-Wide Association Studies (3R01LM010098-12S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10284977. Licensed CC0.

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