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 1st-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 2nd –order partial correlations

Method of Bing and Hoeschele

Target gene

Discard 2nd-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, 601-620

Marriage between

graph theory

and

probability theory

Hyperparameter β trades off data versus prior knowledge

KEGG pathwayMicroarray data

βBayesian analysis: integration of prior knowledge

Hyperparameter β trades off data versus prior knowledge

KEGG pathwayMicroarray data

β small

Hyperparameter β trades off data versus prior knowledge

KEGG pathwayMicroarray data

β large

Input:Learn:MCMC

Protein signalling network from the literature

Predicted network