Abstract
BackgroundThere has been significant interest in investigating genome-wide and epigenome-wide associations with lipids. Testing at the gene or region level may improve power in such studies.MethodsWe analyze chromosome 11 cytosine-phosphate-guanine (CpG) methylation levels and single-nucleotide polymorphism (SNP) genotypes from the original Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, aiming to explore the association between triglyceride levels and genetic/epigenetic factors. We apply region-based tests of association to methylation and genotype data, in turn, which seek to increase power by reducing the dimension of the gene-region variables. We also investigate whether integrating 2 omics data sets (methylation and genotype) into the triglyceride association analysis helps or hinders detection of candidate gene regions.ResultsGene-region testing identified 1 CpG region that had been previously reported in the GOLDN study data and another 2 gene regions that are also associated with triglyceride levels. Testing on the combined genetic and epigenetic data detected the same genes as using epigenetic or genetic data alone.ConclusionsRegion-based testing can uncover additional association signals beyond those detected using single-variant testing.
Highlights
There has been significant interest in investigating genome-wide and epigenome-wide associations with lipids
Eight CpG sites achieved significance (p value < 10− 7) with the top 4 sites the same as those found in the GOLDN study in CPT1A (Table 1)
We detect gene CPT1A using gene-region testing, but in addition we find 2 other genome-wide significant regions: AP006216.5 using methylation data, and BUD13 using genetic data (Table 2)
Summary
There has been significant interest in investigating genome-wide and epigenome-wide associations with lipids. Yoo et al [1] report that region tests can be more sensitive to genetic architectures with multiple causal components, and find that reduced-dimension test statistics, such as that proposed by Gauderman et al [2], can improve power compared to tests in full multivariable regressions. To some extent, this argument applies to genome-wide epigenetic studies, but conclusive evidence is. Our aims are to explore the association between baseline triglyceride (TG) levels and genetic/epigenetic factors using gene-region analysis methods, and to investigate 1 approach to integration of 2 omics data types (SNP genotype and CpG methylation) by comparing the integrated approach with separate analyses
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