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Oncotarget

Predicting the Functions of Regulatory Genetic Variants Associated with Cancer

Nov 20, 2023
Researchers discuss computational methods for predicting the molecular functions of genetic variants associated with cancer and the challenges hindering our understanding. They explore the integration of genomic, epigenomic, transcriptomic, and machine learning approaches in identifying and characterizing functional genetic variants.
02:54

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Quick takeaways

  • The effect size of genetic variants associated with cancer is often small, making it challenging to detect their impact on macroscopic phenotypic differences.
  • Integrating genomic, epigenomic, transcriptomic, and machine learning methods can help identify functional genetic variants and understand their mode of action in regulating target genes, with significant implications for cancer research and precision medicine.

Deep dives

Predicting the Molecular Functions of Genetic Variants Associated with Cancer

In a recent editorial paper, researchers from the University of Illinois at Urbana-Champaign discuss the computational methods of genetic analysis and the prediction of the molecular functions of regulatory genetic variants associated with cancer. They explore the challenges in understanding the effects of genetic variants, emphasizing that the effect size of these variants is often small, making it difficult to detect their impact on macroscopic phenotypic differences. Additionally, the majority of reported variants are found in non-protein coding regions of the genome, suggesting their involvement in regulating the expression of unknown target genes. To overcome these challenges, the researchers propose integrating genomic, epigenomic, transcriptomic, and machine learning methods to identify functional genetic variants and understand their mode of action in regulating target genes.

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