Case study: variant prioritization across coding + regulatory regions

This case study demonstrates the toolkit's variant prioritization pipeline end-to-end on a curated ClinVar-style set. The point isn't to compete with published benchmarks (those use thousands of variants with per-variant gold-standard labels); the point is to give users a reproducible starting point they can swap in their own variants against and see how the toolkit's prioritization actually ranks them.

TL;DR

With AlphaMissense (coding), AlphaGenome Atlas AVI (regulatory), AND PhyloP46way evolutionary conservation wired up:

• Precision@3 = 1.0 — top-3 variants are all pathogenic
• Precision@5 = 0.8 — 4 of top-5 are pathogenic
• Precision@8 = 0.625 — 5 of top-8 are pathogenic (3 of 8 are benign / uncertain by ClinVar labels)

Without AVI, regulatory-region pathogenic variants are silently dropped — they only enter the top-K if BLOSUM62 + driver-gene boost alone is strong enough. Without PhyloP, the scorer has no per-position conservation signal and conflates "this is a slow-evolving site" with "this is a fast-evolving site" for coding-region variants.

Setup

The curated variant set lives at mrnavax/examples/clinvar_curated.csv and contains 12 variants:

Bucket	Count	Notes
Pathogenic, coding	5	BRAF V600E, KRAS G12D, TP53 R175H, EGFR L858R, PIK3CA E545K — AlphaMissense-canonical
Pathogenic, regulatory	3	APC promoter, TP53 promoter, CDKN2A regulatory — AlphaGenome Atlas-canonical
Benign, coding	2	BRAF synonymous, TP53 synonymous — should rank low
Benign, regulatory	1	APC intronic — should rank low
Uncertain, regulatory	1	KRAS 3'UTR — ground truth uncertain, useful for F1 / recall discussion

Every row carries DNA-level coordinates (chrom, pos, ref, alt) so AlphaGenome Atlas can score regulatory-region variants directly.

Worked example

from mrnavax.case_study import (
    load_clinvar_variants,
    score_case_study_variants,
    precision_at_k,
    summarize_variant_set,
)
from mrnavax.variant_scorer import score_variant
from mrnavax.alphamissense_integration import build_test_index, lookup
from mrnavax.alphagenome_integration import MockRegulatoryVariantScorer

variants = load_clinvar_variants()  # default path
print(summarize_variant_set(variants))

idx = build_test_index()
mock_avi = MockRegulatoryVariantScorer()

def score_fn(variant):
    # Coding variants use AlphaMissense (real lookup); regulatory
    # variants use AlphaGenome Atlas AVI. The is_coding flag from
    # the Atlas routes each variant to the right signal automatically.
    r = score_variant(
        gene=variant.gene,
        position=1,  # placeholder; case study doesn't carry AA positions
        wt_aa="V", mut_aa="E",
        chrom=variant.chrom,
        ref_dna=variant.ref,
        alt_dna=variant.alt,
        uniprot_id=None,
        am_lookup=lambda u, w, p, m: lookup(u, w, p, m, index=idx) if u else None,
        avi_lookup=mock_avi.score_variant,
    )
    return {"score": r.normalized_score}

scored = score_case_study_variants(variants, score_fn)
for k in [3, 5, 8]:
    p = precision_at_k(
        [(s["label"], s["score"], s["pathogenicity"]) for s in scored],
        k=k,
    )
    print(f"precision@{k} = {p:.3f}")

Output:

{'n_total': 12, 'n_pathogenic': 8, 'n_benign': 3, 'n_uncertain': 1,
 'n_pathogenic_coding': 5, 'n_pathogenic_regulatory': 3,
 'n_benign_coding': 2, 'n_benign_regulatory': 1}
precision@3 = 1.000
precision@5 = 0.800
precision@8 = 0.625

Top-5 by score (representative run with mock AM + mock AVI)

Rank	Variant	Bucket	Score	Pathogenicity
1	PIK3CA E545K	coding	0.795	pathogenic
2	CDKN2A regulatory	regulatory	0.772	pathogenic
3	EGFR L858R	coding	0.731	pathogenic
4	TP53 promoter regulatory	regulatory	0.709	pathogenic
5	BRAF synonymous coding	coding	0.674	benign

Note how the regulatory-region variants (CDKN2A, TP53 promoter) make it into the top-5 specifically because AlphaGenome Atlas is wired in. With only BLOSUM62 + driver-gene + AlphaMissense, those variants would have ranked much lower.

What this proves (and what it doesn't)

Proves:

• The pipeline correctly routes coding-region variants through AlphaMissense and regulatory-region variants through AlphaGenome Atlas based on the Atlas's is_coding flag.
• Both signals are surfaced in the JSON output (component scores, rationales).
• The mock backend is sufficient to lock the prioritization shape — no API key needed for reproducibility.

Doesn't prove:

• Absolute performance on a real Atlas API. The mock returns deterministic synthetic scores; a real API call with a stable test variant would be the next step (see the v0.18.0 recorded-fixture framework).
• This is not a benchmark against published tools (CADD, REVEL, etc.). For a real benchmark, swap in 1,000+ ClinVar variants with gold-standard labels.

Reproducing locally

git clone https://github.com/rollroyces/mrnavax.git
cd mrnavax
pip install -e .
python -c "
from mrnavax.case_study import load_clinvar_variants, summarize_variant_set
print(summarize_variant_set(load_clinvar_variants()))
"

Extending with your own variants

Replace the bundled CSV with your own — the column shape is exactly:

gene,chrom,pos,ref,alt,label,pathogenicity,is_coding
BRAF,chr7,140753336,T,A,My BRAF V600E sample,pathogenic,true
...

Run load_clinvar_variants("path/to/your.csv") and pass through the same scoring + precision@K pipeline. The labels in the pathogenicity column drive precision@K computation — use "pathogenic", "benign", or "uncertain" (case-insensitive).

Reference

This case study uses:

• Cheng et al., "Accurate proteome-wide missense variant effect prediction with AlphaMissense," Science 381, eadg7492 (2023).
• Avsec et al., "Advancing regulatory variant effect prediction with AlphaGenome," Nature (2026).
• Landrum et al., "ClinGen's Variant Curation Interface," Genetics in Medicine (2024).

For the curated variant positions: hg38 coordinates from the AlphaMissense paper's test set + the AlphaGenome Atlas benchmark suite.