Abstract
BackgroundAssociation studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multiple omics data is needed.ResultsWe propose a novel method, LIPID (likelihood inference proposal for indirect estimation), that uses both single nucleotide polymorphism (SNP) and DNA methylation data jointly to analyze the association between a trait and SNPs. The total effect of SNPs is decomposed into direct and indirect effects, where the indirect effects are the focus of our investigation. Simulation studies show that LIPID performs better in various scenarios than existing methods. Application to the GAW20 data also leads to encouraging results, as the genes identified appear to be biologically relevant to the phenotype studied.ConclusionsThe proposed LIPID method is shown to be meritorious in extensive simulations and in real-data analyses.
Highlights
Association studies using a single type of omics data have been successful in identifying diseaseassociated genetic markers, but the underlying mechanisms are unaddressed
Motivated by the data provided by GAW20, in which single nucleotide polymorphism (SNP) and DNA methylation data for integrative analysis are available, we aim to characterize the effects of SNPs into direct and indirect effects
We propose a novel method, Likelihood inference proposal for indirect estimation (LIPID), to use both SNP and DNA methylation data to test whether there is an indirect effect of SNPs on a phenotype
Summary
Association studies using a single type of omics data have been successful in identifying diseaseassociated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multiple omics data is needed. Genome-wide association studies (GWAS) [1] and epigenome-wide association studies [2] have been successful in identifying disease-associated single-nucleotide polymorphisms (SNPs) and DNA methylation loci. The mechanism of how these genetic loci affect the disease status remains unknown. To provide a possible explanation of the causal mechanisms of these genetic factors, integrative analyses using both types of data are important. Even though integration of multiple types of data sets is a promising method as it is generally more powerful than ordinary association studies [3], the method of integration itself is challenging.
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