1. Final Biology Group Presentation December 9-11, 2009 Biophysics 101 Anugraha Raman, Jacqueline Nkuebe and Ridhi Tariyal

2. Trait-o-maticPhenotype MODEL LIT

3. MODEL Phenotype GWAS Data Proposal to the Rotterdam Management Team Creation of Test Data with the Modeling Team Correspondence with various scientists to begin to create a sane model for gene-gene interactions Hypothesis-building tool SNP query tool

4. Overview Recap from Eye Color Presentation of 11/24 Thinking of How Everything Fits Together: – Thought-projects realized by the Infrastructure Team – Working with the Modeling Group – Dataset Creation Future Directions

5. Just a recap from last time….

6. WHO? Target Consumer: – High school student with mathematical skills, discretionary time and a keen sense of curiosity OR – Biologists with very specific, high end needs OR – Experimental geneticists OR – Clinical geneticists

7. Let’s think about how everything fits together…

8. To make edit/add features to Trait-o-Matic based on our bio-stream research – Research Friendly – Increased Utility Goal 1

9. Querying for SNPs by Chromosomal Location “We realized that it would be helpful to be able to type in a particular SNP location and get a listing of all of the genotypes for that location for everyone in the Trait-o-matic database…”

10. SNP specific Data by Allele/Trait

11. Goal 2 Provide test case details: We decided that thinking about LD (linkage-disequilibrium) in a math model was ultimately unnecessary… Complicate problem too much This can be a future direction once basic models are in place

12. Where do we find this new dataset?

13. Dataset Creation (“Toy” Story) HapMap data? (HaploView) Discussion with the Math Modeling Group about an ideal dataset Rotterdam Study Correspondence (and others) (Dr. Liu)—Wrote a proposal to the Management Team The Process

14. Literature Search Yielded… ~34 Eye-Color SNPs

15. Combining Biological and Modeling Group Requirements 1. Read a CSV(i.e. spreadsheet on excel) file of SNP/phenotype data and process it into python (it is general enough to deal with arbitrarily many SNPs and multiple phenotypes so long as they are ordinal (ie. as long as there are phenotypes we can call 0, 1,...)). 2. Process these arrays into conditional probabilities. 3. Take logits of probabilities, make an array of these logits where each genotype maps to its corresponding probabilities. 4. Link into a scipy ols package, and perform a regression 5. Take in a new genotype and provide the predicted phenotype (probably using PGP 10 genotype inputs)

16. Enter HapMap

17. Chromosome 15 (Eye Color Specific Region)

18. What’s missing? Corresponding phenotypic data…

19. Data-Set Creation

20. 123456789101112Rule 1Phenotype 1Rule 2Phenotype 2 ATTTCGGGCGGGCGGGATTTATTTATTTCGGGCGGGCGGGATTTATTT 10110111100111011010110016intermediate2.17blue 211010101100100000111101015brown1.5blue 301010110100011101010111115brown9.4intermediate 401000111100110011110001015brown5.5intermediate 510101011101100111010110015brown6.57intermediate 60011100111110010000001113blue2.57blue 700011110111111100010111117brown5.4intermediate 810010111101001100110000017brown6.5intermediate 911001001010010111011101013brown5.5intermediate 100010101001111111100110003blue9.67intermediate 1101100110001010101010000018brown4.67blue 121011000011100010000100103blue0.67blue 130110000111011011010001011blue2.57blue 141110010011101010100011006intermediate5.07intermediate 1501111101111001110111101118brown7.17intermediate 160100011010100100010011006intermediate5.4intermediate 171011000101000000100011110blue6.57intermediate 181000110101100011000101105intermediate1.9blue 1911101101101010101111110118brown6.57intermediate 200100010001001001110100003blue4 2101101010001101100010000016brown5.67intermediate 2210001010011111100110110115brown5.4intermediate 230011000111110010010100003blue2.17blue 241000110000101000110011005intermediate4.4blue 250111001100111110010110104blue7.17intermediate 123456789101112Rule 1Phenotype 1Rule 2Phenotype 2 ATTTCGGGCGGGCGGGATTTATTTATTTCGGGCGGGCGGGATTTATTT 11100001010101010110011104blue4.4blue 21001100011110010010110103 0 31000011010100101110110115intermediate9 41111010011101111100010106 9.67intermediate 50100001111101100010000114blue6.5intermediate 61100001100010010100001011blue5.9intermediate 70010010111100010000110015 2.17blue 801100101110010001110000115brown6.17intermediate 91101110101010001100001103blue5.9intermediate 1010000101001000011010001117brown5.5intermediate 1100011101101111100111010017brown6.9intermediate 120110001101101011110100114blue6.17intermediate 1300111111111010000111110017brown2.57blue 141010111000111110100111005intermediate10.07intermediate 151101011100101101010110106intermediate6.5intermediate 160111111110110101010101116intermediate7.57intermediate 171100111011101000000000006intermediate0blue 181001100101001110100101000blue10.9intermediate 191000001000101111000011103blue5.4intermediate 2001111011011101010110111016brown7.57intermediate 2100010000001101100011010115brown5.4intermediate 2210011100001111010111000017brown5intermediate 231010110001111110010100115intermediate5.67intermediate 241110110011100001110011016intermediate5.07intermediate 251010010111101110010100115intermediate7.17intermediate Phenotypic Ranges: (0-4)= Blue (5-12)= Intermediate (13-19)= Brown Rules: (.5*homozygous recessive SNP1 + 2*homozygous recessive SNP3+ 3*heterozygous SNP6+ 12*heterozygous SNP10) (.67*heterozygous SNP2+ 1.5*homozygous recessive SNP4+ 5*homozygous recessive SNP7+ 4*heterozygous SNP9+.4*homozygous recessive SNP11)

21. 123456789101112Rule 1Phenotype 1Rule 2Phenotype 2 ATTTCGGGCGGGCGGGATTTATTTATTTCGGGCGGGCGGGATTTATTT 111011111100001101011011115brown10.9intermediate 21100100101010010110101111blue5.9intermediate 31001010111001011110111112blue5.9intermediate 41101111101001110110011113blue10.9intermediate 50010011111101111010110115 7.17intermediate 610101010111011010010010115brown6.07intermediate 700001010010000001010110112brown4.4blue 800111001101010100111100115brown2.17blue 90011001010001001000111110 1.07blue 100001100010111110000101003blue5.4intermediate 1111001101100011011111110115brown10.9intermediate 120000010010100110100000105intermediate9 131000111111101011000100115intermediate1.5blue 1410100001011010110110010015brown2.57blue 1511010001111111001110101016brown10.5intermediate 160101011010101110000010106intermediate5 171111011000110110100100016intermediate9.67intermediate 181010110011000111100110012blue9.67intermediate 191110111110000100110100103blue11.17intermediate 2000100001101111111010111115brown11.57intermediate 2110000000101001000110010115brown5.4intermediate 220011100100100001100010113blue6.17intermediate 2311011000010011010011001013brown5intermediate 240001101010011000000011000blue0.4blue 2510001010101001101111001115brown9intermediate

22. Future Directions

23. Finding info for Future Trait Investigations Now, since we can download HapMap based data from dbSNP, this population diversity info can be thoroughly evaluated in future tools

24. Factoring in Environmental Factors Way to combine human phenome project, environmental knowledge, genotype and Trait-o-Matic in a consistent, usable way

25. Protein-Protein Interactions If goal is to truly model epistasis, you need to understand all protein-protein interactions Gene, ChromosomeFunction of Protein ProductInvolved in the the Following Metabolic PathwaysPredicted Interacting Partners (Protein ID) SNP_1 SNP_2 SNP_3 SNP_4 Above we see a matrix for protein products of these genes. Sometimes we have to look at surrounding protein interactions as well (ABO Blood Typing) Bombay Phenotype makes phenotypic determination of offspring difficult If the recessive form of H antigen (found on surface of rbcs) is inherited from two parents a child can have blood type O even if both parents do not have O. H antigen is precursor to A and B antigens in blood

26. Future Directions Tutorial on how to use Trait-o-Matic add-ons SNP location based query tool 3-D visualization (student appeal) click on a different portions a human body to look at traits associated with that particular area Potential Forensics Application (expanding target audience) Choose list of traits known in suspect  creation of potential DNA sequence/ Image

27. And More… Exploring the question of chromosomal location standardization (Bruce Birren) – in progress Improving collection of phenotype data from PGP participants – what does the current questionnaire look like? Organization of phenotype-outputs in T-O-M Pharmaco-genetics Direction

28. Final Progress and Contributions

29. THANKS! Professor Church and Harris Sasha Dr. Fan Liu and Manfred Kayser (Rotterdam) Dr. Bruce Birren, Amy Carmargo (Broad) Biophysics 101 (’09)