Abstract
BackgroundBayesian networks have been proposed as a way to identify possible causal relationships between measured variables based on their conditional dependencies and independencies. We explored the use of Bayesian network analyses applied to the GAW20 data to identify possible causal relationships between differential methylation of cytosine-phosphate-guanine dinucleotides (CpGs), single-nucleotide polymorphisms (SNPs), and blood lipid trait (triglycerides [TGs]).MethodsAfter initial exploratory linear regression analyses, 2 Bayesian networks analyses were performed. First, we used the real data and modeled the effects of 4 CpGs previously found to be associated with TGs in the Genetics of Lipid Lowering Drugs and Diet Network Study (GOLDN). Second, we used the simulated data and modeled the effect of a fictional lipid modifying drug with 5 known causal SNPs and 5 corresponding CpGs.ResultsIn the real data we show that relationships are present between the CpGs, TGs, and other variables—age, sex, and center. In the simulated data, we show, using linear regression, that no CpGs and only 1 SNP were associated with a change in TG levels, and, using Bayesian network analysis, that relationships are present between the change in TG levels and most SNPs, but not with CpGs.ConclusionsEven when the causal relationships between variables are known, as with the simulated data, if the relationships are not strong then it is challenging to reproduce them in a Bayesian network.
Highlights
Bayesian networks have been proposed as a way to identify possible causal relationships between measured variables based on their conditional dependencies and independencies
A typical approach to understanding the identified relationships between phenotype and associated genetic factors is to use public databases to see if the observed association can be explained by gene expression or DNA methylation patterns in tissue types relevant to the phenotype in question
The GAW20 real data are based on a previous study into the association between differential methylation of cytosine-phosphate-guanine dinucleotides
Summary
Bayesian networks have been proposed as a way to identify possible causal relationships between measured variables based on their conditional dependencies and independencies. We explored the use of Bayesian network analyses applied to the GAW20 data to identify possible causal relationships between differential methylation of cytosine-phosphate-guanine dinucleotides (CpGs), single-nucleotide polymorphisms (SNPs), and blood lipid trait (triglycerides [TGs]). A typical approach to understanding the identified relationships between phenotype and associated genetic factors is to use public databases to see if the observed association can be explained by gene expression or DNA methylation patterns in tissue types relevant to the phenotype in question. With the increase in different data types comes the desire to model more complex causal relationships beyond using just 2 or 3 variables at a time This is possible with the use of Bayesian networks, where many variables can be modeled simultaneously in an exploratory manner, providing a natural extension to 3-variable causal modeling. The GAW20 real data are based on a previous study into the association between differential methylation of cytosine-phosphate-guanine dinucleotides
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