Integrating sequence-to-omics predictions and personal multi-omics for rare variant prioritization
Abstract
Sequence-to-omics (S2O) predictions and molecular outliers provide complementary evidence to prioritize functional rare variants and facilitate rare disease diagnoses. However, current approaches do not integrate both lines of evidence. We present Watershed-DL, a framework that integrates S2O predictions of variant effects from DNA and protein sequence with multi-omic outliers to identify large-effect rare variants. We applied Watershed-DL to 1000 Genomes samples with RNA-seq (n=1,030) to prioritize rare variants underlying expression, splicing, and allele-specific expression outliers, and, using a separate peak-level model, to samples with ATAC-seq (n=174) for chromatin accessibility outliers. Compared to a model without S2O predictions, Watershed-DL increased the number of prioritized variants by 61.4%, and S2O features were weighted highly in the model across all molecular signals. Prioritized variants were over 60-fold enriched for ClinVar pathogenic classifications. Matching prioritized variants to known pathogenic variants in the same gene with a concordant molecular effect identified 331 additional candidate recessive disease alleles. Applying this framework to the GREGoR rare disease cohort with RNA-seq (n=677) prioritized previously reported causal variants across 10 solved cases and identified 451 candidate variants in unsolved cases, including a diagnostic variant in PPP1R12A and a novel candidate disease gene, ATP5PF.
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BibTeX
@misc{lake2026integrating,
title = {{Integrating sequence-to-omics predictions and personal multi-omics for rare variant prioritization}},
author = {Julie Lake and Esther Robb and Victoria Rosa and Taylor M. Arriaga and Rebekah Barrick and Seth I. Berger and Benjamin D. Heavner and Charles Hadley King and Arthur Ko and Jonathan LoTempio and Daniel Nachun and Genomics Research to Elucidate the Genetics of Rare Diseases consortium and Anshul Kundaje and Devon E. Bonner and Emmanuèle C. Délot and Eric Vilain and Matthew T. Wheeler and Stephen B. Montgomery},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.24.26366361},
url = {https://www.medrxiv.org/content/10.64898/2026.10.24.26366361v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Integrating sequence-to-omics predictions and personal multi-omics for rare variant prioritization
AU - Julie Lake
AU - Esther Robb
AU - Victoria Rosa
AU - Taylor M. Arriaga
AU - Rebekah Barrick
AU - Seth I. Berger
AU - Benjamin D. Heavner
AU - Charles Hadley King
AU - Arthur Ko
AU - Jonathan LoTempio
AU - Daniel Nachun
AU - Genomics Research to Elucidate the Genetics of Rare Diseases consortium
AU - Anshul Kundaje
AU - Devon E. Bonner
AU - Emmanuèle C. Délot
AU - Eric Vilain
AU - Matthew T. Wheeler
AU - Stephen B. Montgomery
PY - 2026
DA - 2026/10/24
PB - medRxiv
DO - 10.64898/2026.10.24.26366361
UR - https://www.medrxiv.org/content/10.64898/2026.10.24.26366361v1
AB - Sequence-to-omics (S2O) predictions and molecular outliers provide complementary evidence to prioritize functional rare variants and facilitate rare disease diagnoses. However, current approaches do not integrate both lines of evidence. We present Watershed-DL, a framework that integrates S2O predictions of variant effects from DNA and protein sequence with multi-omic outliers to identify large-effect rare variants. We applied Watershed-DL to 1000 Genomes samples with RNA-seq (n=1,030) to prioritize rare variants underlying expression, splicing, and allele-specific expression outliers, and, using a separate peak-level model, to samples with ATAC-seq (n=174) for chromatin accessibility outliers. Compared to a model without S2O predictions, Watershed-DL increased the number of prioritized variants by 61.4%, and S2O features were weighted highly in the model across all molecular signals. Prioritized variants were over 60-fold enriched for ClinVar pathogenic classifications. Matching prioritized variants to known pathogenic variants in the same gene with a concordant molecular effect identified 331 additional candidate recessive disease alleles. Applying this framework to the GREGoR rare disease cohort with RNA-seq (n=677) prioritized previously reported causal variants across 10 solved cases and identified 451 candidate variants in unsolved cases, including a diagnostic variant in PPP1R12A and a novel candidate disease gene, ATP5PF.
ER -