Cis-regulation Trans-regulation 5 Objective: pathway reconstruction.

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Presentation transcript:

Cis-regulation

Trans-regulation 5

Objective: pathway reconstruction

Identify candidate causal genes within the eQTL confidence interval around a marker by (partial) gene expression correlation analysis

Target gene Genome with potential candidate genes

Target gene Marker

Target gene Bootstrap confidence interval

Target gene Significant correlation with target gene

Target gene Significant correlation with target gene

Correlation  Partial correlation

direct interaction common regulator indirect interaction co-regulation Distinguish between direct and indirect interactions A and B have a low partial correlation

Target gene Significant correlation with target gene Method of Bing and Hoeschele

Target gene Keep only the strongest correlation, if significant Method of Bing and Hoeschele

Target gene Compute 1 st -order partial correlations Method of Bing and Hoeschele

Target gene Keep only the strongest partial correlation, if significant Method of Bing and Hoeschele

Target gene Compute 2 nd –order partial correlations Method of Bing and Hoeschele

Target gene Discard 2 nd -order partial correlation if not significant Method of Bing and Hoeschele

Target gene Resulting network Method of Bing and Hoeschele

Network reconstruction, part 1 For each gene included in the gene list of an eQTL confidence interval  compute correlation coefficient with the gene expression profile of the gene affected by the eQTL. Test for significant departure from zero via a t- test with Bonferroni correction (threshold p- value: 0.05/n, n: number of genes in the eQTL confidence interval) If significant: Identify the gene with the most significant correlation coefficient  Gene 1.

Network reconstruction, part 2 Compute first-order partial correlation coefficients between the other genes and the gene affected by the eQTL, conditional on Gene 1. Test for significant departure from zero via a t-test with Bonferroni correction (threshold p-value: 0.05/(n-1), n: number of genes in the eQTL confidence interval). If significant: Identify the gene with the most significant partial correlation coefficient  Gene 2.

Network reconstruction, part 3 Compute second-order partial correlation coefficients between the other genes and the gene affected by the eQTL, conditional on Genes 1 & 2. Test for significant departure from zero via a t-test with Bonferroni correction (threshold p-value: 0.05/(n-2), n: number of genes in the eQTL confidence interval). If significant: Identify the gene with the most significant partial correlation coefficient  Gene 3. And so on …

Shortcomings Iterative, heuristic piecemeal approach No conditioning on the whole system, but on a set of pre-selected genes

Friedman et al. (2000), J. Comp. Biol. 7, Marriage between graph theory and probability theory

Hyperparameter β trades off data versus prior knowledge KEGG pathway Microarray data β Bayesian analysis: integration of prior knowledge

Hyperparameter β trades off data versus prior knowledge KEGG pathway Microarray data β small

Hyperparameter β trades off data versus prior knowledge KEGG pathway Microarray data β large

Input: Learn: MCMC

Protein signalling network from the literature

Predicted network