---
name: glp1r-variant-expression-screen
description: >
  Screen a personal genomic variant set (VCF, any chrom/pos/ref/alt list, or
  FASTQ/BAM/CRAM that the agent first converts to a GRCh38 VCF) for
  single-nucleotide variants in the GLP1R
  promoter whose predicted effect on GLP1R expression exceeds that of the GWAS
  hit rs10305420, using the Genomic Intelligence (GI) g0-expression model.
  Handles the two annotated variants rs10305420 and rs880067 specially, then
  scores everything else and reports exceeders with interpretation guidance.
  Exploratory research only, not medical advice.
compatible_with: [claude-for-science, codex, biomni, phylo.bio, any-LLM-agent, human]
---

# Screening personal variants for GLP1R-expression effects vs. rs10305420

This is a specification, not a program. Any capable agent (Claude for Science,
Codex, Biomni, phylo.bio, or a human) should read it and **write its own scorer**
in whatever language and environment it has. Everything needed to build and run
that scorer correctly is described below in plain language.

## Background and rationale

Su et al. (Nature 2026, "Genetic predictors of GLP1 receptor agonist weight
loss and side effects", DOI 10.1038/s41586-026-10330-z) identified rs10305420
as the index SNP for GLP-1 medication efficacy. It lies on chromosome 6 at
position 39,048,860 in the GRCh38 assembly, with reference allele C and effect
allele T. Each copy of the T allele conferred an additional 0.641 percent BMI
loss, approximately 0.76 kg of additional weight loss per allele, with a 95
percent confidence interval of minus 1.27 to minus 0.34 kg, at a p-value of
2.9 times ten to the minus tenth. The effect is additive, with no dominance.
The paper attributes it to a missense change, proline-to-leucine at the seventh
amino acid, in the GLP1R signal peptide (p.Pro7Leu). Notably, the authors tested
for colocalization with expression quantitative trait loci at the locus and
found none that colocalized with the GWAS signal, which is why they favor a
protein-coding mechanism rather than a regulatory one.

The hypothesis this skill explores is different. rs10305420 sits only 79 base
pairs downstream of the GLP1R transcription start site, inside an active
promoter element (an ENCODE candidate cis-regulatory element of the
promoter-like class, with H3K4me3 and H3K27ac marks present in pancreatic
tissue). A sequence-to-expression model (Genomic Intelligence g0-expression)
predicts that the T allele slightly raises GLP1R expression. If part of the
drug-response effect is transcriptional rather than purely coding, then other
variants in the same promoter — including rare ones that GWAS cannot detect for
lack of statistical power — might act through the same route. The purpose of
this skill is to screen a personal variant set for exactly those candidates.

Treat the whole exercise as exploratory computational biology. It is a way to
prioritize variants for further study, not a diagnostic. The Caveats section is
mandatory reading before any result is reported to a person.

## Inputs the agent should accept

Accept a VCF file of any version, using only the single-nucleotide variants that
fall inside the GLP1R window. Also accept any simple table of chromosome,
position, reference allele, and alternate allele, in tab-, comma-, or
whitespace-separated form. If the user supplies raw reads (FASTQ) or alignments
(BAM or CRAM), the agent should treat those as valid starting inputs: first align
to GRCh38 and call variants (for example with bwa or minimap2, then GATK,
bcftools, or DeepVariant) to produce a GRCh38 VCF, and then proceed with this
screen.

Coordinates must be on the GRCh38 (hg38) assembly. The agent should verify this
rather than assume it: the reference window has a known length and known end
sequences (described below), and every input variant carries a reference allele
that must match the hg38 base at that position. If the fetched reference window
has the wrong length, or if input reference bases systematically fail to match,
the most likely cause is a GRCh37 input, and the run should stop with a clear
message.

## The scorable window and the reference sequence

The GLP1R canonical transcript is ENST00000373256, on the plus strand, with its
transcription start site at chromosome 6 position 39,048,781 in GRCh38. The
Genomic Intelligence expression model requires a fixed input of 9,198 base pairs
centered on the transcription start site. For GLP1R that window spans chromosome
6 from position 39,044,182 to 39,053,379 inclusive (1-based), which is 9,198
bases and places the start site at its midpoint. Do not change this window; it
is already correct and centered for GLP1R.

The agent must obtain the reference sequence for this exact window on GRCh38. A
local indexed genome works (for example samtools faidx on an hg38 FASTA), but a
network route with no local genome also works: the Ensembl REST sequence
endpoint returns the plain-text sequence for a human GRCh38 region given the
chromosome and the start and end coordinates above. As a correctness check, the
returned sequence should be 9,198 bases long, should begin with the twenty bases
TGTCAGACTCTAAAAATCTG, and should end with the twenty bases TTTGCCATTGGCTGTGGTTA.
The base at the rs10305420 position (offset 4,678 from the window start, 0-based)
should be C. If any of those checks fail, the sequence or assembly is wrong and
the agent should not proceed.

To build an alternate-allele sequence for a variant, take the reference window,
confirm the base at offset (variant position minus 39,044,182) equals the
variant's reference allele, and substitute the alternate base at that single
position, leaving the length unchanged at 9,198.

## The Genomic Intelligence model and how to reach it

The scoring model is g0-expression from Genomic Intelligence. It takes a
9,198-base DNA sequence (alphabet A, C, G, T, N) plus a free-text description of
the biological context (a tissue or cell type, for example "endocrine pancreas
tissue"), and returns a predicted expression level as log of transcripts per
million plus one. It was trained on ENCODE RNA-seq data across many cell types.

Documentation for Genomic Intelligence is at https://docs.genomicintelligence.ai/.

There are two access routes. The first is the public MCP server at
https://mcp.genomicintelligence.ai/mcp, which speaks JSON-RPC over
Streamable-HTTP, uses an anonymous shared demo key, and exposes a
predict_expression tool that takes the sequence and the description. The agent
establishes a session by sending an initialize call and then an
initialized notification, and parses server-sent-event responses by reading the
data line of each event. The second route, if the agent holds a bearer key of
the form beginning with gi underscore, is the typed REST API at
https://api.genomicintelligence.ai, where expression prediction is a POST to the
path /v1/tasks/expression/predict with the bearer token in the Authorization
header.

The shared demo key is rate-limited to roughly 60 requests per minute with about
two concurrent in flight. The agent should pace its calls (a short sleep of
about one second between calls is sufficient), and should retry on a
429-too-many-requests response after waiting the interval named in the
Retry-After header, and retry on transient 5xx errors with a short backoff,
re-establishing the MCP session if it is dropped.

## Recommended tissue context

Use "endocrine pancreas tissue" as the default biological context, because that
is the GLP1R-relevant beta-cell setting where the active promoter marks were
found. The agent may also let the user pick another context (for example "body
of pancreas tissue" or "pancreas tissue"). The context string is passed verbatim
as the description to the model.

## The scoring procedure

First score the reference window once in the chosen context to obtain a
reference expression value. The value 0.2520 log(TPM+1) quoted here was obtained
with the description string passed to the model set to exactly "endocrine
pancreas tissue" (lower case, no punctuation, no trailing text), scoring the
9,198-base reference window described above through g0-expression via the public
MCP server. To reproduce that number, the agent must pass that exact description
string; a different phrasing of the same tissue (for example "endocrine
pancreas" or "pancreatic islet") is a different context to the model and will
generally give a slightly different reference value. The agent can and should use
its own freshly computed reference rather than this constant, but seeing a value
close to 0.2520 when the description is "endocrine pancreas tissue" is a good
sanity check that the window, the model, and the context string are all correct. Then, for each in-window single-nucleotide variant, build its
alternate-allele window, score it in the same context, and compute the effect as
the alternate value minus the reference value. Call this difference the delta.

The threshold is defined by rs10305420 itself. When rs10305420 (the variant at
chromosome 6 position 39,048,860, C to T) is scored through this same pipeline,
its delta is approximately +0.0195 log(TPM+1) in endocrine pancreas. A variant
is considered high-effect, or "exceeding the threshold", when the absolute value
of its delta is greater than 0.0195. The most robust practice is for the agent to
score rs10305420 in the same batch and use its measured delta as the live
threshold, so the comparison is internally consistent.

## The decision logic the agent must apply

Handle each in-window single-nucleotide variant according to which of three
cases it falls into, in this order.

First case, the variant is rs10305420, at chromosome 6 position 39,048,860, C to
T. Report the validated GWAS effect from the paper: an additional 0.641 percent
BMI loss, about 0.76 kg of weight loss per T allele, with a 95 percent confidence
interval of minus 1.27 to minus 0.34 kg, at p equals 2.9 times ten to the minus
tenth, acting additively, arising from the missense p.Pro7Leu change in the
signal peptide. Note explicitly that this is a continuous per-allele effect on
weight change and that the paper reports no odds ratio, because the outcome is
continuous rather than case-versus-control, so no odds ratio should be
fabricated. This variant is the one that defines the threshold. Cite DOI
10.1038/s41586-026-10330-z.

Second case, the variant is rs880067, at chromosome 6 position 39,048,581, C to
T. Explain that the model predicts this variant exceeds the threshold, with a
delta of roughly +0.10 log(TPM+1), which is about five times the rs10305420
effect, but that it is a common variant with an allele frequency near 0.19 and
that it was not reported as a GWAS hit. Make the tension explicit: a common
variant with a genuinely large expression effect would ordinarily be expected to
leave a detectable GWAS signal, and none was reported, so the in-silico
prediction is unconfirmed and its interpretation is uncertain. Do not tell the
user that this variant affects their drug response.

Third case, any other in-window single-nucleotide variant. Score it, compute its
delta, and report the variant if the absolute delta exceeds 0.0195. State the
direction: an expression-raising variant (positive delta) is the closer analogue
to the beneficial rs10305420 effect allele, whereas an expression-lowering
variant (negative delta) is hypothesized to act in the opposite direction. Where
allele-frequency information is available, from the input or from gnomAD, note
whether the variant is rare or common. Interpret exceeders as variants that the
model predicts to change GLP1R expression more than the validated GWAS hit does,
and that therefore may influence GLP-1 drug response by the same transcriptional
route: a hypothesis to investigate, not an established result.

## What to output

The agent should produce a per-variant record containing the variant identifier
(chromosome, position, reference, alternate), the predicted delta, whether it
exceeds the threshold, the direction of effect, and any annotation for the two
known variants. Indels and any variant outside the window are simply not scored;
the agent should say so rather than silently dropping them. A short human-readable
summary should lead with the two known-variant messages when present, then list
the other exceeders ordered by the magnitude of their effect, and then the
interpretation and caveats.

## Interpretation guidance to place in front of the user

A variant "exceeding the threshold" means only that the model predicts a larger
effect on GLP1R expression than rs10305420. It is a prioritization signal, not
evidence of a real effect in that individual. Direction matters: because the
rs10305420 effect allele raises predicted expression and improves weight loss,
other expression-raising variants are the closest analogues to the beneficial
GWAS allele. Rare exceeders are the scientifically interesting class, being
individually too infrequent for GWAS yet potentially real modifiers; common
exceeders that are nonetheless absent from the GWAS, such as rs880067, are a
caution flag rather than a finding.

## Caveats that must appear in any report

This is not medical advice. It is exploratory research output, and no clinical,
diagnostic, dosing, or treatment decision should be made from it; any personal
health interpretation must come from a qualified clinician and, where relevant, a
certified genetic-testing pathway, not from a model prediction.

The deltas are model predictions, not measured expression quantitative trait loci
or experimental results. The Nature paper itself found no colocalizing eQTL and
favored a coding mechanism, so this transcriptional hypothesis runs partly against
the paper's own functional evidence and would need validation by islet eQTL data,
massively parallel reporter assays, or reporter-gene experiments.

The absolute effects are small on the log(TPM+1) scale, so the rank and the
direction of an effect are more trustworthy than its magnitude; the "five times"
figure for rs880067 is relative to rs10305420's own small effect.

Only single-nucleotide variants are handled, because insertions and deletions
would change the fixed 9,198-base window length and require a separate,
length-aware analysis.

The coordinates must be GRCh38 and the window is specific to GLP1R; any other
gene needs its own transcription-start-site-centered window and its own threshold.

rs10305420 is a missense variant, so transcriptional and protein-level effects
are not mutually exclusive, and the coding change may still be the dominant
mechanism.

The g0-expression model accessed through the shared public key is a demonstration
tier intended for evaluation.