Dataset Information

Disease-Specific Integration of Omics Data to Guide Functional Validation of Genetic Associations.

ABSTRACT: Unbiased genetic association studies, including genome-wide association and whole-genome sequencing studies, have uncovered many novel disease-associated variants. Relatively few of these associated regions, however, have led to insights that are biologically mechanistic or clinically actionable due in part to the difficulty in designing appropriate functional validation studies to understand how variants contribute to disease. Asthma is a complex inflammatory lung disease for which many genetic associations have been identified. Using asthma as a disease model, we designed Reducing Associations by Linking Genes And transcriptomic Results (REALGAR), an app that facilitates the design of functional validation studies by integrating cell- and tissue-specific results of diseaserelevant gene expression and other omics studies. Via specific examples, we demonstrate how integrated gene- centric and disease-specific information leads to asthma insights, and more broadly, can help understand complex diseases.

SUBMITTER: Shumyatcher M

PROVIDER: S-EPMC5977601 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Disease-Specific Integration of Omics Data to Guide Functional Validation of Genetic Associations.

Shumyatcher Maya M Hong Rui R Levin Jessica J Himes Blanca E BE

AMIA ... Annual Symposium proceedings. AMIA Symposium 20170101

Unbiased genetic association studies, including genome-wide association and whole-genome sequencing studies, have uncovered many novel disease-associated variants. Relatively few of these associated regions, however, have led to insights that are biologically mechanistic or clinically actionable due in part to the difficulty in designing appropriate functional validation studies to understand how variants contribute to disease. Asthma is a complex inflammatory lung disease for which many genetic ...[more]

PMID: 29854229

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Project description:BackgroundRapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation.ResultsA simulation study to assess the performance of O2PLS showed positive results in both low and higher dimensions. More noise (50 % of the data) only affected the systematic part estimates. A data analysis was conducted using data on metabolomics and transcriptomics from a large Finnish cohort (DILGOM). A previous sequential study, using the same data, showed significant correlations between the Lipo-Leukocyte (LL) module and lipoprotein metabolites. The O2PLS results were in agreement with these findings, identifying almost the same set of co-varying variables. Moreover, our integrative approach identified other associative genes and metabolites, while taking into account systematic variation in the data. Including orthogonal components enhanced overall fit, but the orthogonal variation was difficult to interpret.ConclusionsSimulations showed that the O2PLS estimates were close to the true parameters in both low and higher dimensions. In the presence of more noise (50 %), the orthogonal part estimates could not distinguish well between joint and unique variation. The joint estimates were not systematically affected. Simultaneous analysis with O2PLS on metabolome and transcriptome data showed that the LL module, together with VLDL and HDL metabolites, were important for the metabolomic and transcriptomic relation. This is in agreement with an earlier study. In addition more gene expression and metabolites are identified being important for the joint covariation.

Dataset Information

Disease-Specific Integration of Omics Data to Guide Functional Validation of Genetic Associations.

Publications

Disease-Specific Integration of Omics Data to Guide Functional Validation of Genetic Associations.

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