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Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression

Jinghui Li1, Tianjing Zhao1, Dailu Guan1

  • 1Department of Animal Science, University of California, Davis, Davis, CA 95616, USA.

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This study introduces DeepGCF, a deep learning method to assess functional genomic conservation between humans and pigs. It identifies key conserved regions crucial for gene regulation and human complex traits.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Comparative Biology

Background:

  • Pigs are valuable biomedical models, but functional genomic conservation with humans requires deeper understanding.
  • Assessing functional genomic conservation is key to enhancing pigs' utility in human disease research.

Purpose of the Study:

  • To develop a novel deep learning approach, DeepGCF, for assessing functional genomic conservation between humans and pigs.
  • To identify functionally conserved genomic regions and their roles in gene regulation and human complex traits.

Main Methods:

  • Developed a deep learning-based approach (DeepGCF) integrating human and pig functional genomic profiles.
  • Utilized chromatin states, sequence ontologies, and regulatory variants to score functional conservation.
  • Compared DeepGCF performance against previous methods.

Main Results:

  • DeepGCF achieved superior prediction performance in assessing functional genomic conservation.
  • Identified a core set of functionally conserved genomic regions between humans and pigs.
  • These regions are enriched for gene regulation, heritability of complex traits, and human diseases.

Conclusions:

  • Cross-species functional genomic comparison is vital for understanding complex phenotypes.
  • DeepGCF provides a powerful tool for identifying conserved functional elements, advancing biomedical research.
  • The identified conserved regions offer insights into the genetic and evolutionary underpinnings of human traits and diseases.