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Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants.

Gaetano Manzo1, Kathryn Borkowski1,2, Ivan Ovcharenko1

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Convolutional Neural Network (CNN) models excel at predicting genetic variant effects on enhancer activity. Hybrid CNN-transformer models are best for identifying causal single-nucleotide polymorphisms (SNPs) within linkage disequilibrium blocks.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Genome-wide association studies (GWAS) identify noncoding variants linked to diseases.
  • Distinguishing causal variants from associations is challenging.
  • Deep learning models predict regulatory effects of genetic variants.

Purpose of the Study:

  • Evaluate state-of-the-art deep learning models for predicting genetic variant effects on enhancer activity.
  • Compare performance across different model architectures and datasets.
  • Identify optimal models for variant effect prediction.

Main Methods:

  • Assessed nine datasets from MPRA, raQTL, and eQTL experiments.
  • Evaluated 54,859 single-nucleotide polymorphisms (SNPs) across four human cell lines.
  • Compared Convolutional Neural Network (CNN), transformer, and hybrid models.

Main Results:

  • CNN models (TREDNet, SEI) consistently outperformed others in predicting SNP regulatory impact.
  • Hybrid CNN-transformer models (Borzoi) showed superior performance in identifying causal SNPs within linkage disequilibrium blocks.
  • Fine-tuning improved transformer models but did not surpass CNN or hybrid models.

Conclusions:

  • CNNs are highly effective for predicting the regulatory impact of genetic variants on enhancers.
  • Hybrid models offer advantages for pinpointing causal variants in complex genomic regions.
  • Standardized benchmarks are needed for robust deep learning model evaluation in genomics.