# High Throughput Functional Dissection Of Adiposity GWAS Loci Using Model Systems

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2020 · $672,588

## Abstract

We believe that the field of human obesity genetics has stalled. Massive Genome-Wide Association studies of
hundreds of thousands of subjects succeeded in identifying over a thousand novel “statistical loci” that
unquestionably tag increased obesity risk variants. Because these loci are almost all in regions that were not
on anyone’s candidate gene list, the promise has been that these novel loci point to new biology that could lead
to new therapies and prevention strategies. But exactly what these loci do and how they do it continues to
remain a mystery for almost all loci. Functional scientists have faced challenges following up on these findings,
because of a “perfect storm” of difficulties. LD patterns in the genome complicate efforts to fine map and
statistically dissect driver from passenger variants, even after deep sequencing of the regions in multiple
ethnicities, so the exact causal variants are rarely known. Even if they were known, the effect sizes of these
variants are each clinically trivial despite their overwhelming statistical significance. Worst of all, it is estimated
that over 90% appear to be tagging non-coding regions. We believe a huge rate-limiting step to exploiting these
new discoveries continues to be moving from “statistical loci” to the gene(s) through which they are acting. Many
researchers use a default annotation of the “nearest gene” to the statistical locus as a starting point, but if our
published experiments (Baranski et al., 2018) are generalizable, this may be wrong about half the time.
Regulome annotation in reference databases is emerging as a critical resource (e.g. ENCODE and GTEX), but
it is still early days. The non-coding genome is huge, and much of the annotation is either lacking, insufficient,
or not specific enough to allow definitive mapping from locus to gene with these resources alone. Large cohort
studies and consortia such as TOPMed have begun conducting various Omics scans in the same individuals in
which locus discoveries were made, thus providing the potential to shed light on the underlying mechanisms
behind the statistical loci, and in particular, suggest which genes, might be critical. But almost all of these large
human efforts are of practical necessity limited to whole blood and tissues of convenience, and much less has
been possible in the presumed tissues of action. By contrast, the most important tissues for obesity, like the
brain, can be accessed and manipulated in model systems to shed light on mechanisms. Likely every such
locus will have a different biological explanation, but pursuing functional experiments for each locus one at a
time is challenging, time consuming and expensive. What is needed is a high throughput strategy, that will
interrogate many loci simultaneously. We propose to use our successful, published high throughput Drosophila
system to efficiently screen for many fat storage genes among the set of human candidates that have fly
orthologs (recognizing that thi...

## Key facts

- **NIH application ID:** 9971771
- **Project number:** 1R01DK122031-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Thomas John Baranski
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $672,588
- **Award type:** 1
- **Project period:** 2020-05-15 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9971771

## Citation

> US National Institutes of Health, RePORTER application 9971771, High Throughput Functional Dissection Of Adiposity GWAS Loci Using Model Systems (1R01DK122031-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9971771. Licensed CC0.

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