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Related Concept Videos

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Related Experiment Video

Updated: Jul 4, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Evaluating sequence-to-function deep learning models for ancestry-stratified regulatory variant effect prediction

Xinyu Sun1,2, Makaela Mews3,2, Nicholas R Wheeler1,2

  • 1Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.

Biorxiv : the Preprint Server for Biology
|July 3, 2026
PubMed
Summary

Sequence-to-function (S2F) models show limited accuracy for non-coding variants across diverse populations. However, they better prioritize fine-mapped regulatory variants, especially in African American datasets, suggesting utility for variant prioritization.

Keywords:
AlphaGenomeBorzoiancestry biasfine-mappingmulti-ancestry eQTLsregulatory variant effect predictionsequence-to-function models

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Related Experiment Videos

Last Updated: Jul 4, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Sequence-to-function (S2F) deep learning models are vital for prioritizing non-coding regulatory variants.
  • Existing models and data are predominantly European-centered, limiting their applicability to diverse populations.
  • Multi-ancestry benchmarks are crucial to assess S2F score consistency across varying allele frequencies and linkage disequilibrium (LD) patterns.

Purpose of the Study:

  • To evaluate the performance of S2F models (Borzoi, AlphaGenome) across ancestrally diverse populations.
  • To determine if S2F scores consistently capture regulatory effects across African American (AA), Caribbean Hispanic (CH), and Non-Hispanic White (NHW) participants.
  • To assess the models' ability to distinguish regulatory variants using fine-mapped data.

Main Methods:

  • Evaluated Borzoi and AlphaGenome using eQTL data from the MAGENTA cohort (AA, CH, NHW).
  • Benchmarked predictions against nominal eQTLs and ancestry-stratified fine-mapped variants.
  • Utilized Spearman correlation, direction concordance, inter-model convergence, and distance-matched AUROC for evaluation.
  • Conducted sensitivity analyses for minor allele frequency (MAF) and compared functional annotation overlap.

Main Results:

  • Both models exhibited weak agreement with nominal eQTL effect sizes across ancestries.
  • Agreement and discrimination improved significantly for high-confidence fine-mapped variants.
  • The African American (AA) high-PIP variant set showed the highest discrimination (AUROC) for both models.
  • Functional annotation revealed higher overlap with chromatin accessibility/contact data for AA high-PIP variants.

Conclusions:

  • S2F models show limited predictive power for nominal eQTL effect sizes but effectively prioritize fine-mapped regulatory variants.
  • The superior discrimination in the AA cohort suggests S2F models can be valuable for variant prioritization, particularly for promoter-proximal variants.
  • Model performance is influenced by fine-mapping resolution, LD patterns, comparison variant selection, and annotation composition across ancestries.