Why DNA Variant Effect Prediction Matters
The human genome contains roughly three billion base pairs, and any two individuals differ at about four to five million positions. Most of these variants are harmless, but a small fraction alter gene regulation in ways that drive disease, influence drug response, or shape phenotypic traits. The challenge is figuring out which variants matter — and how.
Traditional approaches to variant interpretation rely on population frequency, conservation scores, and proximity to known genes. These methods miss the vast majority of non-coding variants, which make up over 90% of GWAS hits. AlphaGenome changes this by predicting the functional consequences of any variant directly from DNA sequence, including variants deep in regulatory regions that conventional tools ignore.
What AlphaGenome Does
AlphaGenome is a genomic foundation model from Google DeepMind that takes a DNA sequence centered on a variant of interest and predicts how that variant alters gene regulation. Rather than producing a single pathogenicity score, it outputs a rich set of molecular predictions:
- Gene expression effects — predicted change in transcript levels (log2 fold-change) across tissues
- Splicing changes — whether the variant creates or disrupts splice donor/acceptor sites or branchpoints
- Chromatin accessibility — predicted impact on open chromatin state (DNase-seq / ATAC-seq signal)
- Transcription factor binding — which TF binding sites are gained or lost at the variant position
This multi-output approach is what distinguishes AlphaGenome from simpler variant scoring tools. Instead of asking “is this variant pathogenic?” it answers “what does this variant do to the regulatory landscape?”
How AlphaGenome Works
AlphaGenome follows the genomic foundation model paradigm: a deep neural network trained on large-scale functional genomics data learns to predict regulatory readouts directly from DNA sequence. The model ingests a long stretch of genomic sequence — typically hundreds of kilobases surrounding the variant — and processes it through a transformer-based architecture that captures both local motif grammar and long-range regulatory interactions.
To score a variant, the model runs inference on both the reference and alternate allele sequences, then computes the difference in predicted outputs. This delta captures the variant's effect on each regulatory readout. Multi-task training across expression, splicing, chromatin, and TF binding forces the model to learn shared representations of gene regulation, improving accuracy on each individual task.
AlphaGenome vs Enformer and Other Predictors
Several models predict variant effects from DNA sequence, each with different strengths:
- Enformer — The predecessor genomic model from DeepMind. Predicts gene expression and chromatin marks but with a smaller context window and lower accuracy on variant-level benchmarks than AlphaGenome.
- Sei — A sequence-level model from the Troyanskaya lab that classifies variants into regulatory categories. Good for annotation, less quantitative for expression effect sizes.
- SpliceAI — Specialized for splice-altering variants. Excellent at splicing prediction but does not model expression, chromatin, or TF binding.
- CADD / REVEL — Aggregate scoring methods that combine conservation, functional annotations, and model predictions. Useful for ranking but offer limited mechanistic insight.
AlphaGenome's advantage is its unified, multi-task framework: one model covers expression, splicing, chromatin, and TF binding simultaneously, producing mechanistic hypotheses rather than opaque scores.
Using AlphaGenome via the SciRouter API
SciRouter's AlphaGenome endpoint lets you submit variants and receive the full set of regulatory predictions without managing GPU infrastructure or model weights. Here is a working example:
import requests
API_KEY = "sk-sci-your-api-key"
BASE = "https://api.scirouter.ai/v1"
response = requests.post(
f"{BASE}/genomics/alphagenome",
headers={"Authorization": f"Bearer {API_KEY}"},
json={
"variants": [
{
"chromosome": "chr17",
"position": 41245466,
"ref": "A",
"alt": "G",
"gene": "BRCA1"
}
]
}
)
results = response.json()
for v in results["predictions"]:
expr = v["expression_effect"]
print(f"Gene: {v['variant']['gene']}")
print(f" Expression: {expr['direction']} "
f"(log2FC={expr['log2_fold_change']:.2f})")
print(f" Splicing impact: {v['splicing']['impact']}")
print(f" Severity: {v['overall_assessment']['severity']}")For scoring large variant sets — such as all variants from a GWAS locus — you can submit batches of up to 50 variants per request. Combine AlphaGenome results with the variant effect prediction endpoint for a complementary pathogenicity score.
Applications
GWAS Interpretation
Genome-wide association studies identify statistical associations between variants and traits but rarely pinpoint the causal variant or its mechanism. AlphaGenome can score every variant in a GWAS locus to identify which one directly disrupts a regulatory element, in which tissue, and through which mechanism (expression change, splicing alteration, or TF binding disruption).
Cancer Genomics
Tumor sequencing produces thousands of somatic variants per sample. AlphaGenome helps prioritize non-coding variants that may drive oncogene activation or tumor suppressor silencing through regulatory disruption — variants that conventional coding-focused pipelines overlook.
Pharmacogenomics
Regulatory variants can alter the expression of drug-metabolizing enzymes, transporters, and drug targets. AlphaGenome predictions can flag variants that change CYP enzyme expression or alter receptor levels, informing dosing decisions and adverse event risk stratification.
Getting Started
AlphaGenome is available through SciRouter's Genomics API. Each prediction uses 5 credits and runs on GPU-accelerated infrastructure — no local setup required. For variant pathogenicity scoring without the full regulatory profile, the Variant Effect Prediction endpoint provides a complementary classification-based approach.
Ready to interpret your variants? Sign up for a free SciRouter API key and start predicting regulatory effects today.