Towards an understanding of Genotype-Phenotype correlations

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

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

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

4. Genotype to Phenotype

5. Current Methods Genotype Phenotype 200 ? What processes to investigate?

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

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

9. Issues with current approaches

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

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

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

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

18. Replicated original chain of data analysis

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

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 • Share • Search • Re-use • Re-purpose • Execute • Communicate • Record http://www.myexperiment.org/

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

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

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

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 Pathways Biological processes

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

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

32. The Workflows

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

34. Preliminary results – a preview • Glycolysis, reactive oxygen species, alternatively activated macrophages Parasite Sample of ranked workflow results IFN-Gamma TH1 macrophage glycolysis 156.87 ATP 107.24 antimycin 102.53 glycolytic enzymes 93.27 apoptosis 89.17 reactive oxygen 85.02 oxidative stress 80.25 glycolytic intermediates 67.31 H2O2 64.02 Reactive oxygen species (NO) Glycolysis Alternative macrophage TH2 N.B. It’s not as linear as this !!!

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 Genotype Phenotype 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