Dataset Information

Gaussian graphical models for phenotypes using pedigree data and exploratory analysis using networks with genetic and nongenetic factors based on Genetic Analysis Workshop 18 data.

ABSTRACT: Graphical models are increasingly used in genetic analyses to take into account the complex relationships between genetic and nongenetic factors influencing the phenotypes. We propose a model for determining the network structure of quantitative traits while accounting for the correlated nature of the family-based samples using the kinship coefficient. The Gaussian graphical model of age, systolic blood pressure, diastolic blood pressure, hypertension, blood pressure medication use, and smoking status was derived for three time points using real data. We also explored binary sparse graphical models of single-nucleotide polymorphisms (SNPs), covariates, and quantitative traits for exploratory analysis of the data. We validated the applicability of this method by producing a network graph using 20 causal variants, 21 noncausal variants, and 6 binary and quantitative phenotypes using the simulated data. To improve the model's ability to identify associations between the causal variants and the phenotypes, we intend to conduct follow-up studies investigating how to use the relationships between SNPs and between SNPs and phenotypes when analyzing genome wide association data with multiple phenotypes.

SUBMITTER: Talluri R

PROVIDER: S-EPMC4143694 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Gaussian graphical models for phenotypes using pedigree data and exploratory analysis using networks with genetic and nongenetic factors based on Genetic Analysis Workshop 18 data.

Talluri Rajesh R Shete Sanjay S

BMC proceedings 20140617 Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo

Graphical models are increasingly used in genetic analyses to take into account the complex relationships between genetic and nongenetic factors influencing the phenotypes. We propose a model for determining the network structure of quantitative traits while accounting for the correlated nature of the family-based samples using the kinship coefficient. The Gaussian graphical model of age, systolic blood pressure, diastolic blood pressure, hypertension, blood pressure medication use, and smoking ...[more]

PMID: 25519421

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Project description:BackgroundWith the advent of high-throughput targeted metabolic profiling techniques, the question of how to interpret and analyze the resulting vast amount of data becomes more and more important. In this work we address the reconstruction of metabolic reactions from cross-sectional metabolomics data, that is without the requirement for time-resolved measurements or specific system perturbations. Previous studies in this area mainly focused on Pearson correlation coefficients, which however are generally incapable of distinguishing between direct and indirect metabolic interactions.ResultsIn our new approach we propose the application of a Gaussian graphical model (GGM), an undirected probabilistic graphical model estimating the conditional dependence between variables. GGMs are based on partial correlation coefficients, that is pairwise Pearson correlation coefficients conditioned against the correlation with all other metabolites. We first demonstrate the general validity of the method and its advantages over regular correlation networks with computer-simulated reaction systems. Then we estimate a GGM on data from a large human population cohort, covering 1020 fasting blood serum samples with 151 quantified metabolites. The GGM is much sparser than the correlation network, shows a modular structure with respect to metabolite classes, and is stable to the choice of samples in the data set. On the example of human fatty acid metabolism, we demonstrate for the first time that high partial correlation coefficients generally correspond to known metabolic reactions. This feature is evaluated both manually by investigating specific pairs of high-scoring metabolites, and then systematically on a literature-curated model of fatty acid synthesis and degradation. Our method detects many known reactions along with possibly novel pathway interactions, representing candidates for further experimental examination.ConclusionsIn summary, we demonstrate strong signatures of intracellular pathways in blood serum data, and provide a valuable tool for the unbiased reconstruction of metabolic reactions from large-scale metabolomics data sets.

Dataset Information

Gaussian graphical models for phenotypes using pedigree data and exploratory analysis using networks with genetic and nongenetic factors based on Genetic Analysis Workshop 18 data.

Publications

Gaussian graphical models for phenotypes using pedigree data and exploratory analysis using networks with genetic and nongenetic factors based on Genetic Analysis Workshop 18 data.

Similar Datasets

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