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Comparing variants related to chronic diseases from genome-wide association study (GWAS) and the cancer genome atlas (TCGA).
Jeon, Soohyun; Park, Chaewon; Kim, Jineui; Lee, Jung Hoon; Joe, Sung-Yune; Ko, Young Kyung; Gim, Jeong-An.
Affiliation
  • Jeon S; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, South Korea.
  • Park C; School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea.
  • Kim J; Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, South Korea.
  • Lee JH; Department of Microbiology, Institute for Viral Diseases, College of Medicine, Korea University, Seoul, 02841, South Korea.
  • Joe SY; Department of Pharmacology, College of Medicine, Korea University, Seoul, 02841, South Korea.
  • Ko YK; School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea.
  • Gim JA; Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, South Korea.
BMC Med Genomics ; 16(1): 332, 2023 12 19.
Article in En | MEDLINE | ID: mdl-38114957
ABSTRACT

BACKGROUND:

Several genome-wide association studies (GWAS) have been performed to identify variants related to chronic diseases. Somatic variants in cancer tissues are associated with cancer development and prognosis. Expression quantitative trait loci (eQTL) and methylation QTL (mQTL) analyses were performed on chronic disease-related variants in TCGA dataset.

METHODS:

MuTect2 calling variants for 33 cancers from TCGA and 296 GWAS variants provided by LocusZoom were used. At least one mutation was found in TCGA 22 cancers and LocusZoom 23 studies. Differentially expressed genes (DEGs) and differentially methylated regions (DMRs) from the three cancers (TCGA-COAD, TCGA-STAD, and TCGA-UCEC). Variants were mapped to the world map using population locations of the 1000 Genomes Project (1GP) populations. Decision tree analysis was performed on the discovered features and survival analysis was performed according to the cluster.

RESULTS:

Based on the DEGs and DMRs with clinical data, the decision tree model classified seven and three nodes in TCGA-COAD and TCGA-STAD, respectively. A total of 11 variants were commonly detected from TCGA and LocusZoom, and eight variants were selected from the 1GP variants, and the distribution patterns were visualized on the world map.

CONCLUSIONS:

Variants related to tumors and chronic diseases were selected, and their geological regional 1GP-based proportions are presented. The variant distribution patterns could provide clues for regional clinical trial designs and personalized medicine.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Genome-Wide Association Study / Neoplasms Limits: Humans Language: En Journal: BMC Med Genomics Journal subject: GENETICA MEDICA Year: 2023 Document type: Article Affiliation country: Korea (South) Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Genome-Wide Association Study / Neoplasms Limits: Humans Language: En Journal: BMC Med Genomics Journal subject: GENETICA MEDICA Year: 2023 Document type: Article Affiliation country: Korea (South) Country of publication: United kingdom