# Multiomics data integration methods to discover putative causal variants, genes and patient heterogeneity for Alzheimers disease

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2024 · $446,886

## Abstract

PROJECT SUMMARY
Despite the success of genome-wide association studies (GWAS) in identifying over 70 susceptibility loci for
Late-onset (LO) Alzheimer’s disease (AD), AD related disease and endophenotypes, it remains challenging to
pinpoint 1) which are truly causal AD variants; 2) the molecular processes that cause AD; and 3) how AD patients
are pathogenically different from each other. Emerging resources for the study of AD genetics, including
sequence, functional genomics and epigenomic data, provide unparalleled opportunity to investigate these
questions at different molecular levels. We propose a multiomics data integration project to characterizes AD
risk for both genetic variants and individual patients, by developing and applying a series of novel computational
approaches using Bayesian hierarchical modeling, variable selection and multivariate analysis, for analyses of
a wide range of existing and novel AD multiomics data. These methods are designed to integrate many genetic
factors — single nucleotide variants, brain tissue molecular traits such as gene expression, alternative splicing,
alternative polyadenylation, methylation, histone acylation and proteomics, and various functional annotations
for coding and non-coding regions — into a coherent framework for discovery of causal AD variants and genes,
and understand patient heterogeneity. Our goals are to 1) combine genetic association evidence from population
and family-based studies of diverse ancestry backgrounds; 2) incorporate functional information to infer putative
causal genetic variants; 3) identify novel molecular traits and QTLs for alternative polyadenylation and
differentially methylated regions in brain tissues; 4) dissect AD association signals using multiple molecular traits
across a comprehensive collection of brain tissues and relevant cell types; and 5) characterize AD patients’ risk
profiles using causal effects at different molecular levels across brain tissues. Our methods and bioinformatics
analyses will be engineered into a high-quality toolbox to also facilitate multiomics studies of other complex
diseases. We will develop fine-mapping methods for family and multi-ancestry data, integrated with thousands
of genomic functional annotations, to identify putative causal variants from whole-genome sequences. We will
develop a new method to generate alternative polyadenylation from RNA-seq data in brain tissues of AD patients
and controls, and fine-map its QTL. We will develop and apply new approaches to fine-map differentially
methylated regions in brains, to colocalize QTLs for dozens of molecular traits with AD, and to identify novel
gene-level associations using predicted molecular traits. Causal effects estimated at variants and gene levels
will be integrated to identify new AD gene-sets and pathways, and to characterize risk profiles for AD patients.
Causal variants and genes discovered from our project will provide insight for development of therapeutic drugs
...

## Key facts

- **NIH application ID:** 10771305
- **Project number:** 5R01AG076901-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Gao Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $446,886
- **Award type:** 5
- **Project period:** 2023-02-01 → 2027-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10771305

## Citation

> US National Institutes of Health, RePORTER application 10771305, Multiomics data integration methods to discover putative causal variants, genes and patient heterogeneity for Alzheimers disease (5R01AG076901-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10771305. Licensed CC0.

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