Cis-regulation

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

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

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

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

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

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

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

27. Method of Bing and Hoeschele Target gene Resulting network

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

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

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

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

42. Input: Learn: MCMC

43. Protein signalling network from the literature

44. Predicted network