Integrative modeling of multi-platform genomic data under the framework of mediation analysis
Open accessAbstract
Given the availability of genomic data, there have been emerging interests in integrating multi‐platform data. Here, we propose to model genetics (single nucleotide polymorphism (SNP)), epigenetics (DNA methylation), and gene expression data as a biological process to delineate phenotypic traits under the framework of causal mediation modeling. We propose a regression model for the joint effect of SNPs, methylation, gene expression, and their nonlinear interactions on the outcome and develop a variance component score test for any arbitrary set of regression coefficients. The test statistic under the null follows a mixture of chi‐square distributions, which can be approximated using a characteristic function inversion method or a perturbation procedure. We construct tests for candidate models determined by different combinations of SNPs, DNA methylation, gene expression, and interactions and further propose an omnibus test to accommodate different models. We then study three path‐specific effects: the direct effect of SNPs on the outcome, the effect mediated through expression, and the effect through methylation. We characterize correspondences between the three path‐specific effects and coefficients in the regression model, which are influenced by causal relations among SNPs, DNA methylation, and gene expression. We illustrate the utility of our method in two genomic studies and numerical simulation studies. Copyright © 2014 John Wiley & Sons, Ltd.
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The testing procedure can be applied when methylation and/or expression data are not collected in GWAS subjects but their associations with SNPs can be consistently estimated from external meQTL and eQTL studies.
“Thus, the testing procedure in Huang (2015) can be applied in settings where methylation and/or expression data are not collected in the subjects of GWAS but their associations with SNPs (i.e., μM, μE and μME) can be consistently estimated from external meQTL and eQTL studies.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Huang (2015)
Huang (2015) extended the earlier work to jointly analyze SNP, DNA methylation, and gene expression data with respect to a disease outcome.
“Later, Huang (2015) extended the work of Huang et al. (2014) to jointly analyze SNP, DNA methylation, and gene expression data with respective to a disease outcome, adding the layer of DNA methylation data to the existing framework.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Huang (2015)
Huang (2014) extended the methodologies to analyze data from different subjects.
“While Huang et al. (2014) and Huang (2015) jointly analyze multi-omics data from the same subjects, Huang (2014) extended the methodologies to analyze the data from different subjects.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Huang (2015)
Huang (2015) studied path-specific effects by jointly modeling SNPs, DNA methylation, gene expression, and disease outcome.
“In the later work, Huang (2015) studied path-specific effects, as depicted in the causal diagram (Figure 3), by jointly modeling a set of SNPs within a gene, the DNA methylation and expression of the gene, and the disease outcome as a biological process.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Huang (2015)
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References 48 · Cited by 17
- Authors
- Huang, Y.
- Affiliations
- Brown University (US)
- Corresponding author
- Huang, Yen‐Tsung
- ORCID
- Huang, Yen‐Tsung
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2014-10-15
- Volume
- 34
- Issue
- 1
- Pages
- 162-178
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- PMC
- PMC4262570
- OpenAlex
- W2133381849
- Cited by
- 17
- References
- 48
- Citations by year
- 2022: 2 · 2019: 5 · 2018: 1 · 2017: 1 · 2016: 3 · 2015: 5
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5R03CA182937-02)
- Brown University (219247)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- causal mediation analysis, single nucleotide polymorphisms, DNA methylation, gene expression, characteristic function inversion, perturbation method, path-specific effects, phenotypic traits
- MeSH terms
- Asthma, Computer Simulation, Glioblastoma, Humans, Membrane Proteins, Models, Genetic, Regression Analysis, Gene Expression, Genome, Human, Survival Analysis, DNA Methylation, Polymorphism, Single Nucleotide, GRB10 Adaptor Protein, Genome-Wide Association Study, Epigenomics
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