1. Towards an understanding of Genotype-Phenotype correlations Paul Fisher et al.,

2. The entire genetic identity of an individual that does not show any outward characteristics, e.g. Genes, mutations Genotype DNA ACTGCACTGACTGTACGTATATCT ACTGCACTGTGTGTACGTATATCT Mutations Genes

3. The observable expression of gene’s producing notable characteristics in an individual, e.g. Hair or eye colour, body mass, resistance to disease Phenotype vs. Brown White and Brown

4. Genotype to Phenotype

5. GenotypePhenotype ? Current Methods 200 What processes to investigate?

6. ? 200 Microarray + QTL Genes captured in microarray experiment and present in QTL (Quantitative Trait Loci ) region Genotype Phenotype Metabolic pathways Phenotypic response investigated using microarray in form of expressed genes or evidence provided through QTL mapping

7. The Pathway approach GenotypePhenotype Pathway(s) Obtain a global view of what is happening in the phenotype Pathways allow for experimental verification in the lab Provides a driving force for functional discovery

8. CHR QTL Gene A Gene B Pathway A Pathway B Pathway linked to phenotype – high priority Pathway not linked to phenotype – medium priority Pathway C Phenotype literature Gene C Pathway not linked to QTL – low priority Genotype

9. Issues with current approaches

10. Huge amounts of data 200+ Genes QTL region on chromosome Microarray 1000+ Genes How do I look at ALL the genes systematically?

11. Hypothesis-Driven Analyses 200 QTL genes Case: African Sleeping sickness - parasitic infection - Known immune response Pick the genes involved in immunological process 40 QTL genes Pick the genes that I am most familiar with 2 QTL genes Biased view Result: African Sleeping sickness -Immune response -Cholesterol control -Cell death

12. Manual Methods of data analysis Navigating through hyperlinks No explicit methods Human error Tedious and repetitive

13. Implicit methods

14. Issues with current approaches Scale of analysis task User bias and premature filtering Hypothesis-Driven approach to data analysis Constant flux of data - problems with re-analysis of data Implicit methodologies (hyper-linking through web pages) Error proliferation from any of the listed issues

15. So what do we want to do? Decrease scale of manual analysis task for user Limit user bias Remove premature filtering Data-driven approach to hypothesis generation Analyse the data whenever I want or after an update Create explicit methodologies that can be re-used Reduce the overall errors Solution – Automate using workflows

16. PhD - Hypothesis Utilising the capabilities of workflows and the pathway-driven approach, we are able to provide a more: - systematic - explicit - scalable - un-biased the benefit will be that new biology results will be derived, increasing community knowledge of genotype and phenotype interactions.

17. Pathway Resource QTL mapping study Microarray gene expression study Identify genes in QTL regions Identify differentially expressed genes Wet Lab Literature Annotate genes with biological pathways Select common biological pathways Hypothesis generation and verification Statistical analysis Genomic Resource

18. Replicated original chain of data analysis

19. http://www.genomics.liv.ac.uk/tryps/trypsindex.html Trypanosomiasis in Africa Andy Brass Steve Kemp + many Others

20. Results A strong candidate gene was found –Daxx gene not found using manual investigation methods –The gene was identified from analysis of biological pathway information –Possible candidate identified by Yan et al (2004): Daxx SNP info –Re-sequencing of the Daxx gene identified mutations –Mutation was published in scientific literature, –affect on the binding of Daxx protein to p53 protein –p53 plays direct role in cell death and apoptosis, one of the Trypanosomiasis phenotypes

21. Shameless Plug! A Systematic Strategy for Large-Scale Analysis of Genotype-Phenotype Correlations: Identification of candidate genes involved in African Trypanosomiasis Fisher et al., (2007) Nucleic Acids Research PubMed ID: 17709344 Explicitly discusses the methods we used for the Trypanosomiasis use case Discussion of the results for Daxx and shows mutation Sharing of workflows for re-use, re-purposing

22. Recycling, Reuse, Repurposing Here’s the e-Science ! Trypanosomiasis mouse workflow reused without change in Trichuris muris infection in mice Identified biological pathways involved in sex dependence Previous manual two year study of candidate genes had failed to do this. More to follow with additional data Additional workflows constructed for looking at cattle and human Used mouse workflows as basis for development 1 web service changed in entire workflow (BioMart) Exactly the same methods

23. Recycling, Reuse, Repurposing http://www.myexperiment.org/ Share Search Re-use Re-purpose Execute Communicate Record

24. …. and share to get recognition for your work Prove your methods can be replicated

25. What next? More use cases for QTL and microarray –African Trypanosomiasis –Trichuris muris –Possibly Lung cancer ??? Text Mining !!! –Aid biologists in identifying novel links between pathways –Link pathways to phenotype through literature

26. Pathway Resource QTL mapping study Microarray gene expression study Identify genes in QTL regions Identify differentially expressed genes Wet Lab Literature Annotate genes with biological pathways Select common biological pathways Hypothesis generation and verification Statistical analysis Genomic Resource

27. CHR QTL Gene A Gene B Pathway A Pathway B Pathway linked to phenotype – high priority Pathway not linked to phenotype – medium priority Pathway C Phenotype literature Gene C Pathway not linked to QTL – low priority Genotype DONE MANUALLY

28. It can’t be that hard, right? PubMed contains ~17,787,763 journals to date Manually searching is tedious and frustrating Can be hard finding the links Computers can help with data gathering and information extraction – that’s their job !!!

29. What Does the Text Hold? Protein Info Related Proteins Protein-Protein Interactions Biological processes Pathways

30. What Next ? Biological processes Generate a Profile for Pathway / Phenotype Apoptosis Cell Death Stress response ……..

31. Score and Rank Terms Phenotype Terms Apoptosis Cholesterol Diabetes Apoptosis JNK pathway Another pathway Apoptosis Cholesterol JNK pathway Apoptosis Cell Death JNK pathway Common terms 28.35 13.27 0.15 Score pathway links based on occurrence of phenotype term in pathway abstracts

32. The Workflows

33. http://www.genomics.liv.ac.uk/tryps/trypsindex.html Trypanosomiasis in Africa Andy Brass Steve Kemp + many Others

34. Preliminary results – a preview Glycolysis, reactive oxygen species, alternatively activated macrophages TH1 TH2 Parasite macrophage Reactive oxygen species (NO) IFN-Gamma Glycolysis Alternative macrophage N.B. It’s not as linear as this !!! glycolysis156.87 ATP107.24 antimycin102.53 glycolytic enzymes93.27 apoptosis89.17 reactive oxygen85.02 oxidative stress80.25 glycolytic intermediates67.31 H2O264.02 Sample of ranked workflow results

35. Text Mining A means of assisting the researcher –Time –Effort –Narrow searches Hypothesis generation and verification –Suggested links –Limited corpus, but its specific NOT A REPLACEMENT FOR DOMAIN EXPERTISE

36. The Final Result GenotypePhenotype Pathway(s) Tools (workflows) to allow easier transition between genotype and phenotype

37. Many thanks to: including: Joanne Pennock, EPSRC, OMII, myGrid, and lots more people