# Integrating Alzheimer's disease GWAS with proteomic and metabolomic QTL data

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2024 · $464,922

## Abstract

Summary
 In response to PA-17-088, “Secondary Analyses of Existing Cohorts, Data Sets and Stored Biospecimens
to Address Clinical Aging Research Questions (R01)”, we propose integrating existing GWAS summary data of
Alzheimer's disease (AD) with existing proteomic and metabolomic quantitative trait locus (pQTL/mQTL) data to
identify proteins and metabolites putatively causal to AD. The overarching goal is to both boost statistical power
and enhance interpretability for causal inference in the post-GWAS era by leveraging many published large-scale
GWAS summary association datasets and omic data. In an emerging and increasingly influential approach called
transcriptome-wide association studies (TWAS), by integrating GWAS summary data with gene expression (or
eQTL) data, one aims to improve over the current practice of GWAS to not only increase statistical power to
identify more genetic variants associated with GWAS traits, but also link the (non-coding) genetic variants to
their target genes, thus gaining insights into the genetic basis of common diseases and complex traits. In practice,
however, TWAS may fail to identify true causal genes while giving false positives due to the violation of its modeling
assumptions (e.g. due to LD or horizontal pleiotropy of SNPs). We first propose three new methods to check
possible violations of modeling assumptions in TWAS, then propose two more robust and powerful approaches
that improve over the standard TWAS. Next, we extend TWAS to xWAS to integrate GWAS with proteomic and
metabolomic traits (i.e. pQTL and mQTL), to identify (putatively) causal proteins and metabolites, analogous to
detecting causal genes/transcripts in TWAS. We apply the new (and existing) methods to integrate large-scale
GWAS summary data of AD and atrial fibrillation (AF) with pQTL and mQTL to identify putatively causal proteins
and metabolites for AD and AF respectively, and to investigate whether AF is causal to AD, thus not only advancing
our understanding of the etiology of AD and AF, but also possibly offering modifiable targets for interventions on
the two devastating diseases. Finally, we will develop and disseminate publicly available software implementing
the proposed analysis methods, e.g. as R packages, to facilitate the wide use by the scientific community.

## Key facts

- **NIH application ID:** 10875729
- **Project number:** 4R01AG067924-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Wei Pan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $464,922
- **Award type:** 4N
- **Project period:** 2020-09-15 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10875729

## Citation

> US National Institutes of Health, RePORTER application 10875729, Integrating Alzheimer's disease GWAS with proteomic and metabolomic QTL data (4R01AG067924-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10875729. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*