5. Cis-regulation

6. Trans-regulation 5

8. Objective: pathway reconstruction

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

13. Target gene Genome with potential candidate genes

14. Target gene Marker

15. Target gene Bootstrap confidence interval

16. Target gene Significant correlation with target gene

17. Target gene Significant correlation with target gene

18. Correlation  Partial correlation

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

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

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

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

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

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

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

27. Target gene Resulting network Method of Bing and Hoeschele

28. 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.

29. 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.

30. 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 …

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

36. Friedman et al. (2000), J. Comp. Biol. 7, 601-620 Marriage between graph theory and probability theory

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

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

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

42. Input: Learn: MCMC

43. Protein signalling network from the literature

44. Predicted network