# Cognitive Computing of Alzheimer's Disease Genes and Risk

> **NIH NIH U01** · BAYLOR COLLEGE OF MEDICINE · 2022 · $799,998

## Abstract

Cognitive Computing of Alzheimer’s Disease Genes and Risk
The molecular basis and genetic architecture of dementia remain a puzzle. As no drug yet prevents, delays, or
reverses it, aging populations potentially face a tidal threat of incipient and socially disruptive Alzheimer’s
Disease (AD) cases. Genome-wide association studies (GWAS) have linked over 100 loci with AD and explain
much of population attributable risk, but only a fraction of heritability. This heritability gap means it remains
difficult to design and assess which surveillance, screening, preventive, and stratification programs are effective.
In turn, this hinders therapeutic trials. The challenge in translating genetic variants into patient classifications is
twofold. First, AD is polygenic, so relevant disease driving mutations are spread thin across a multitude of
different genes and patients. Second, current interpretations of the deleterious effects of mutations lack
accuracy, so the impactful few cannot be distinguished from the benign multitude in any given subject. These
problems compound and fog the statistical genetics of AD risk and morbidity with poor signal to noise ratio. The
crux of our solution is to add a massive amount of new information, exploit it efficiently through computation,
then perform rigorous multi-pronged experimental validation. We start from the hypothesis that AD arises through
mutational perturbations that affect functional pathways beyond the built-in evolutionary tolerances. New
algorithms compute these excessive mutational forces and place them in integrative machine learning
frameworks to sort between AD patients and controls, and which can also reflect functional interactions among
proteins or genes. Innovations include a mathematical model of evolution based on calculus; ensemble machine
learning over human genome variations; and harmonic analysis of mutational perturbations in functional
networks. The outcome will, for the first time, integrate genomic variations relevant to AD in the context of all
relevant evolutionary history and all known functional interactions. In practice, this will increase power and
resolution, enable gender-specific analysis and AD stratification of men and women, and identify new and
experimentally validated AD genes. To carry out this program, AIM 1 will fuse a novel mathematical analysis of
evolution with machine learning and network wavelet theory. This will yield complementary integrative
approaches to identify genes and mutations that sort AD vs healthy subjects based on the abnormal mutational
burden of rare gene variants in sequenced cohorts. AIM 2 will focus similar tools on patients and controls with
known paradoxical phenotypes that run counter to their APOEɛ2/4 status. The results will identify modifier genes
that drive AD in APOEɛ2 carriers or that protect APOEɛ4 carriers from AD. AIM 3 will provide direct experimental
validation, leveraging high-throughput, robot-assisted genetic modifier screening i...

## Key facts

- **NIH application ID:** 10436879
- **Project number:** 5U01AG068214-02
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** OLIVIER LICHTARGE
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $799,998
- **Award type:** 5
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10436879

## Citation

> US National Institutes of Health, RePORTER application 10436879, Cognitive Computing of Alzheimer's Disease Genes and Risk (5U01AG068214-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10436879. Licensed CC0.

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