# Fine-Mapping Genome-Wide Associated Loci using Multi-omics Data to Identify Mechanisms Affecting Serious Mental Illness

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $792,393

## Abstract

Genome-wide association studies have been key for identifying genetic variation associated
with psychiatric disorders. Whenever these GWAS are based on large sample sizes, however,
they implicate a plethora of single nucleotide polymorphisms (SNPs) in risk. This polygenicity
presents challenges for mapping risk variation onto the biological mechanisms that predispose
individuals to illness. Many studies have integrated genomic and transcriptomic variation with the
goal of colocalizing the GWAS SNP associations and cis transcriptional patterns determined by
expression quantitative trait loci (eQTLs), as well as other QTLs. In some instances, these studies
highlight one or more genes whose transcriptomic variation is driven largely by variation in specific
risk SNPs. For a substantial fraction of the risk loci, however, colocalization is inconsistent across
studies or no effect on transcription is observed. These missing links between genetic risk variation
and biological variation could be due to many factors, including cell-type specificity, developmental
patterns, or missing -omics characterizations. Notably, bulk tissue and even single cell mRNA
levels are imperfect predictors of the cellular levels of the proteins they code for. We hypothesize
that a substantial portion of these missing links is due to our limited knowledge of how proteomic
variation relates to genetic variation in the human brain. SNPs can regulate the proteome via
mechanisms that “skip” transcript levels and protein levels are tightly regulated by posttranslational
modifications (PTMs) that are not readily predictable from the transcriptome.
 We propose to characterize transcriptomic and proteomic variation in human post-mortem
brain, specifically protein expression (Aim 1); PTMs (Aim 2); map genetic variation onto
transcriptomic (eQTLs) and proteome and PTM variation (pQTLs and PTMQTLs) and evaluate their
interrelationships (Aim 3); and then perform colocalization analysis to inform the biological
pathways by which genetic variation confers risk to psychiatric disorders (Aim 4). In our preliminary
proteogenomic experiments, we combined proteomics with SNP genotyping to identify pQTLs.
We discovered that a substantial fraction of pQTLs bypass the transcriptome (~50%), in line with
another recent human brain pQTL study and our hypothesis.
 Our aims are consistent with goals from RFA-MH-21-100: (1) develop novel proteomic
and other omics resources; (2) use them to map how genetic risk variation influences
omics features in neural tissue and cell types; and (3) provide a high confidence set of
causal variants, genes, and isoforms that likely contribute to disease risk, enhancing our
insights into proximate disease mechanisms.

## Key facts

- **NIH application ID:** 10115941
- **Project number:** 1R01MH125235-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** BERNIE DEVLIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $792,393
- **Award type:** 1
- **Project period:** 2021-01-01 → 2024-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10115941

## Citation

> US National Institutes of Health, RePORTER application 10115941, Fine-Mapping Genome-Wide Associated Loci using Multi-omics Data to Identify Mechanisms Affecting Serious Mental Illness (1R01MH125235-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10115941. Licensed CC0.

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