Relative impact of multi-layered genomic data on gene expression phenotypes in serous ovarian tumors

Kyung Ah Sohn, Dokyoon Kim, Jaehyun Lim, Ju Han Kim

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)

Abstract

Background: The emerging multi-layers of genomic data have provided unprecedented opportunities for cancer research, especially for the association study between gene expressions and other types of genomic features. No previous approaches, however, provide an adequate statistical framework for or global analysis on the relative impact of different genomic feature layers to gene expression phenotypes. Methods: We propose an integrative statistical framework based on a sparse regression to model the impact of multi-layered genomic features on gene expression traits. The proposed approach can be regarded as an integrative expression Quantitative Traits Loci approach in which not only the genetic variations of SNPs or copy number variations but also other features in both genomic and epigenomic levels are used to explain the expression of genes. To highlight the validity of the proposed approach, the TCGA ovarian cancer dataset was analysed as a pilot task. Results: The analysis shows that our integrative approach has consistently superior power in predicting gene expression levels compared to that from each single data type-based analysis. Moreover, the proposed method has the advantage of producing a substantially reduced number of spurious associations. We provide an interesting characterization of genes in terms of its genomic association patterns. Important genomic features reported in previous ovarian cancer research are successfully identified as major hubs in the resulting association network between heterogeneous types of genomic features and genes. Conclusions: In this paper, we model the gene expression phenotypes with respect to multiple different types of genomic data in an integrative framework. Our analysis reveals the global view on the relative contribution of different genomic feature types to gene expression phenotypes in ovarian cancer.

Original languageEnglish
Article numberS9
JournalBMC Systems Biology
Volume7
DOIs
StatePublished - 1 Jan 2013

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Gene expression
Phenotype
Gene Expression
Genomics
Tumors
Tumor
Neoplasms
Ovarian Neoplasms
Ovarian Cancer
Genes
Gene
Heterogeneous networks
Quantitative Trait Loci
Research
Epigenomics
Single Nucleotide Polymorphism
Genetic Variation
Global Analysis
Multilayer
Cancer

Cite this

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title = "Relative impact of multi-layered genomic data on gene expression phenotypes in serous ovarian tumors",
abstract = "Background: The emerging multi-layers of genomic data have provided unprecedented opportunities for cancer research, especially for the association study between gene expressions and other types of genomic features. No previous approaches, however, provide an adequate statistical framework for or global analysis on the relative impact of different genomic feature layers to gene expression phenotypes. Methods: We propose an integrative statistical framework based on a sparse regression to model the impact of multi-layered genomic features on gene expression traits. The proposed approach can be regarded as an integrative expression Quantitative Traits Loci approach in which not only the genetic variations of SNPs or copy number variations but also other features in both genomic and epigenomic levels are used to explain the expression of genes. To highlight the validity of the proposed approach, the TCGA ovarian cancer dataset was analysed as a pilot task. Results: The analysis shows that our integrative approach has consistently superior power in predicting gene expression levels compared to that from each single data type-based analysis. Moreover, the proposed method has the advantage of producing a substantially reduced number of spurious associations. We provide an interesting characterization of genes in terms of its genomic association patterns. Important genomic features reported in previous ovarian cancer research are successfully identified as major hubs in the resulting association network between heterogeneous types of genomic features and genes. Conclusions: In this paper, we model the gene expression phenotypes with respect to multiple different types of genomic data in an integrative framework. Our analysis reveals the global view on the relative contribution of different genomic feature types to gene expression phenotypes in ovarian cancer.",
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Relative impact of multi-layered genomic data on gene expression phenotypes in serous ovarian tumors. / Sohn, Kyung Ah; Kim, Dokyoon; Lim, Jaehyun; Kim, Ju Han.

In: BMC Systems Biology, Vol. 7, S9, 01.01.2013.

Research output: Contribution to journalArticleResearchpeer-review

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