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Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks.

Publication ,  Journal Article
Kim, M; Huffman, JE; Justice, A; Goethert, I; Agasthya, G; VA Million Veteran Program, ; Danciu, I
Published in: BMC Med Genomics
July 6, 2022

BACKGROUND: Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation. RESULTS: This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration's Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness. CONCLUSIONS: To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies.

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Published In

BMC Med Genomics

DOI

EISSN

1755-8794

Publication Date

July 6, 2022

Volume

15

Issue

1

Start / End Page

151

Location

England

Related Subject Headings

  • Reproducibility of Results
  • Prostatic Neoplasms
  • Phenotype
  • Male
  • Humans
  • Genomics
  • Genome-Wide Association Study
  • Genetics & Heredity
  • 3205 Medical biochemistry and metabolomics
  • 3105 Genetics
 

Citation

APA
Chicago
ICMJE
MLA
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Kim, M., Huffman, J. E., Justice, A., Goethert, I., Agasthya, G., VA Million Veteran Program, ., & Danciu, I. (2022). Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks. BMC Med Genomics, 15(1), 151. https://doi.org/10.1186/s12920-022-01298-6
Kim, Minsu, Jennifer E. Huffman, Amy Justice, Ian Goethert, Greeshma Agasthya, Greeshma VA Million Veteran Program, and Ioana Danciu. “Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks.BMC Med Genomics 15, no. 1 (July 6, 2022): 151. https://doi.org/10.1186/s12920-022-01298-6.
Kim M, Huffman JE, Justice A, Goethert I, Agasthya G, VA Million Veteran Program, et al. Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks. BMC Med Genomics. 2022 Jul 6;15(1):151.
Kim, Minsu, et al. “Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks.BMC Med Genomics, vol. 15, no. 1, July 2022, p. 151. Pubmed, doi:10.1186/s12920-022-01298-6.
Kim M, Huffman JE, Justice A, Goethert I, Agasthya G, VA Million Veteran Program, Danciu I. Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks. BMC Med Genomics. 2022 Jul 6;15(1):151.
Journal cover image

Published In

BMC Med Genomics

DOI

EISSN

1755-8794

Publication Date

July 6, 2022

Volume

15

Issue

1

Start / End Page

151

Location

England

Related Subject Headings

  • Reproducibility of Results
  • Prostatic Neoplasms
  • Phenotype
  • Male
  • Humans
  • Genomics
  • Genome-Wide Association Study
  • Genetics & Heredity
  • 3205 Medical biochemistry and metabolomics
  • 3105 Genetics