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Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding

Xiang-He Meng1,2,3, Hong-Mei Xiao1, Hong-Wen Deng1,2,3

  • 1Centers of System Biology, Data Information and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan 410008, China.

Bioinformatics (Oxford, England)
|November 16, 2020
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Summary

This study introduces an AI deep learning model to predict functional variants affecting gene expression via chromatin interactions. The model prioritizes causal single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWASs).

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Area of Science:

  • Genomics
  • Computational Biology
  • Artificial Intelligence

Background:

  • Genome-wide association studies (GWASs) have identified numerous trait-associated variants, but causal variants and their mechanisms remain largely unknown.
  • Understanding non-coding variants' functional impact, particularly on gene expression through chromatin interactions, is crucial for deciphering genetic associations.

Purpose of the Study:

  • To develop and validate a novel deep learning model for predicting functional non-coding variants.
  • To identify variants affecting gene expression by analyzing their impact on long-range chromatin interactions.

Main Methods:

  • Proposed a deep learning model integrating Hi-C data to classify interacting and non-interacting DNA fragment pairs.
  • Predicted the functional effects of single nucleotide alterations on chromatin interaction probability and subsequent gene expression.
  • Assessed model performance in classifying interacting fragments and prioritizing causal single nucleotide polymorphisms (SNPs).

Main Results:

  • The deep learning model effectively classified interacting and non-interacting DNA fragment pairs.
  • Predicted causal SNPs with greater impact on chromatin interaction were more likely to be detected by GWAS and expression quantitative trait loci (eQTL) analyses.
  • Demonstrated that integrating AI with chromatin interaction data prioritizes functional variants in disease-associated loci.

Conclusions:

  • The developed AI-deep learning approach, combined with experimental evidence of chromatin interactions, effectively prioritizes functional variants.
  • This integrative strategy accelerates the discovery of biological mechanisms underlying genetic associations identified in genomic studies.
  • The model facilitates a deeper understanding of non-coding variant function and its role in human traits and diseases.