Applying AlphaGenome Variant Impact for SNV Prioritization
- 1Children's Hospital of Philadelphia
Abstract
AlphaGenome Atlas, released in September 2026, provides functional predictions for nearly all possible human single-nucleotide variants using the AlphaGenome Variant Impact (AVI) score. However, empirical thresholds relating AVI quantile rank (AVI_QUANTILE) to clinical variant classifications have not been established. We derived AVI_QUANTILE thresholds distinguishing ClinVar pathogenic, likely pathogenic, uncertain, conflicting, likely benign, and benign SNVs using receiver operating characteristic analyses. Pathogenic/likely pathogenic variants were strongly enriched at the upper end of the AVI_QUANTILE distribution, yielding a Youden-optimal ClinVar-derived cutoff of 0.9938. Optimal cutoffs were generally consistent across several major molecular consequence classes, although lower thresholds were observed for selected noncoding categories. Similarly, most well-powered gene-disease groups showed cutoffs close to the global threshold, with modest gene-specific variation. Among 24,989 variants of uncertain significance, 11,357 (45.45%) exceeded the global cutoff, identifying a substantial subset with AlphaGenome-predicted functional effects comparable to those observed among pathogenic/likely pathogenic variants and potentially warranting further evaluation. In a steroid-dependent asthma fine-mapping analysis, AVI_QUANTILE showed little correspondence with SuSiE posterior inclusion probabilities, indicating that AlphaGenome functional predictions capture information distinct from statistical fine-mapping.
Related
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- Benchmarking the AlphaGenome Variant Impact score against SpliceAI on post-release ClinVar classifications and experimental splicing assays
- Beyond a single threshold: calibration of CADD v1.7 and AlphaGenome for ACMG/AMP computational evidence across variant classes, model versions, and reference data
- Population-scale drug target and variant prioritization with Varformer
- Analysis of single nucleotide substitution in CYP21A2 gene using Alphagenome
BibTeX
@misc{qu2026applying,
title = {{Applying AlphaGenome Variant Impact for SNV Prioritization}},
author = {H.-Q. Qu and H. Hakonarson},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.26.754650},
url = {https://www.biorxiv.org/content/10.64898/2026.09.26.754650v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Applying AlphaGenome Variant Impact for SNV Prioritization
AU - H.-Q. Qu
AU - H. Hakonarson
PY - 2026
DA - 2026/09/26
PB - bioRxiv
DO - 10.64898/2026.09.26.754650
UR - https://www.biorxiv.org/content/10.64898/2026.09.26.754650v1
AB - AlphaGenome Atlas, released in September 2026, provides functional predictions for nearly all possible human single-nucleotide variants using the AlphaGenome Variant Impact (AVI) score. However, empirical thresholds relating AVI quantile rank (AVI_QUANTILE) to clinical variant classifications have not been established. We derived AVI_QUANTILE thresholds distinguishing ClinVar pathogenic, likely pathogenic, uncertain, conflicting, likely benign, and benign SNVs using receiver operating characteristic analyses. Pathogenic/likely pathogenic variants were strongly enriched at the upper end of the AVI_QUANTILE distribution, yielding a Youden-optimal ClinVar-derived cutoff of 0.9938. Optimal cutoffs were generally consistent across several major molecular consequence classes, although lower thresholds were observed for selected noncoding categories. Similarly, most well-powered gene-disease groups showed cutoffs close to the global threshold, with modest gene-specific variation. Among 24,989 variants of uncertain significance, 11,357 (45.45%) exceeded the global cutoff, identifying a substantial subset with AlphaGenome-predicted functional effects comparable to those observed among pathogenic/likely pathogenic variants and potentially warranting further evaluation. In a steroid-dependent asthma fine-mapping analysis, AVI_QUANTILE showed little correspondence with SuSiE posterior inclusion probabilities, indicating that AlphaGenome functional predictions capture information distinct from statistical fine-mapping.
ER -