1. Shrinking cost of your genome Million-fold in 6 years

2. 2 What does $0 to the consumer mean? 2001 Wikipedia 1998 Search, Maps, Translation.. Web 2.0, Crowd-sourcing But these new technologies (cell phone, fax, PC) are only as good as their communities.

3. The Personal Genome: Do I want to know? 1. Expensive 2. Discriminative 3. Worthless Genetic Information Nondiscrimination Act of 2008 (GINA) Employment & health insurance

4. Destigmatization vs enhanced hiding (addressing causes vs symptoms) Ethnicity Sexuality GLBT Cancer Facebook.com PatientsLikeMe.com - HIV-AIDS - Neurodegenerative Disorders - Psychiatric Meds

5. DNA Explorer, $80 (Ages 10 and up) Genographic $99 DIY Bio 23andme $399

6. Newborns are tested for up to 40 traits (e.g. PKU) 1526 Highly Predictable & actionable gene tests (not SNP chips) As with security/insurance purchases, we are all at risk, even though we don’t expect to see direct payback.

7. The Personal Genome: Do I want to know, if there will be no medical action? 50% to 75% get good news and for the rest: Planning – family, geography Research activism

8. 8 Individually rare collectively common: Breast cancer deCODEme: “does not include the high-risk but rare BRCA1 and BRCA2 breast cancer risk variants”. Navigenics: “Mutations in BRCA1 or BRCA2 are less common in the population and are only present in approximately 5 – 10% of families with breast and ovarian cancer.” 23andme: “Hundreds of cancer-associated BRCA1 and BRCA2 mutations have been documented, but three specific BRCA mutations are worthy of note because they are responsible for a substantial fraction of hereditary breast cancers and ovarian cancers among women with Ashkenazi Jewish ancestry”. 1M vs 3G

9. 9 Valuable Personal Genome Sequences 1464 genes are highly predictive & medically actionable (inherited & cancer) at ~$2K per gene. **Very few of these are on DTC SNP chips.** Why? PKU, Tay Sachs, Cystic Fibrosis, BRCA1/2, etc. Pharmacogenomic drug/allele combinations: Herceptin, Iressa,.. Also: Ancestry, Forensics, Social Networking, Education, Research

10. 10 snp.med.harvard.edu

11. 11 Anonymity vs Open-access? Trends in laws to make data public (not just at elite institutions): e.g. H.R. 2764, SEC. 218. 26-Dec-07 open-access publishing for all NIH-funded research. (12) Identify individual case/control status from pooled SNP data Homer et al PLoS Genetics 2008 as this became known, NCBI pulled dbGAP data (11) Re-identification after “de-identification” using public data. Group Insurance list of birth date, gender, zip code sufficient to re-identify medical records of Governor Weld & family via voter-registration records (1998) Self identification trend (10) Unapproved self-identification. e.g. Celera IRB. (Kennedy Science. 2002) (9) Obtaining data about oneself via FOIA or sympathetic researchers. (8) DNA data CODIS data in the public domain. even if acquitted

12. 12 Anonymity vs Open-access? Accessing “Secure data” (7) Laptop loss. 26 million Veterans' medical records, SSN & disabilities stolen Jun 2006. (6) Hacking. A hacker gained access to confidential medical info at the U. Washington Medical Center -- 4000 files (names, conditions, etc, 2000) (5) Combination of surnames from genotype with geographical info An anonymous sperm donor traced on the internet 2005 by his 15 year old son who used his own Y chromosome data. (4) Identification by phenotype. If CT or MR imaging data is part of a study, one could reconstruct a person’s appearance. Even blood chemistry can be identifying in some cases. (3) Inferring phenotype from genotype Markers for eye, skin, and hair color, height, weight, geographical features, dysmorphologies, etc. are known & the list is growing. (2) “Abandoned DNA bearing samples (e.g. hair, dandruff, hand-prints, etc.) (1) Government subpoena. False positive IDs and/or family coercion index

13. 13 Who can contribute to cures & prevention? Huntington's Nancy Wexler (psychologist) Adrenoleukodystrophy Odone (World Bank) Parkinson’s Brin family Hugh Rienhoff, (MD) MyDaughtersDNA.org ALS Jamie Heywood (engineer) PatientsLikeMe.com Motivating, donating, raising consciousness LRRK2 G2019S HFE Aull (engineer)

14. 14 Genes environments traits, cells 1) First & only open access data 2) Avoid over-promising on de-identification 3) 100% on Exam to assure informed consent (*Educate pre-consent rather than post-discovery*) 4) Low cost whole genome sequences 5) Multiple-traits: images, stem cells, etc. 6) IRB approval for 100,000 diverse volunteers 15,000 since May 2009 0431 1070 1660 1677 1687 1833 1846 1731 1730 1781 501(c)(3)

15. Generic Health Advice Exercise Drink your milk Eat your beans & your grains & your iron Get more rest

16. UNLESS … ExerciseHCM Drink your milkMCM6 Eat your beansG6PD & your grainsHLA-DQ2 & your ironHFE Get more restHLA-DR2

17. 17 Diagnostics Systems Biology Challenge TRAITS (Phenome) Genome 6 Gbp 3M Alleles NOT going from ONLY Genome Sequence to Prediction

18. 18 PersonalGenomes.org Inherited, Somatic, Environmental Genomics VDJ-ome TRAITS (Phenome) Personal stem-cells epigenome (RNA,mC) PERSONAL GENOME 6 Gbp 3M alleles One in a life-time genome + yearly ( to daily) tests Public Health Bio-weathermap.org : Allergens, Microbes, Viruses Microbiome ~5 new non-synonymous Alleles per generation

19. Even far from hospitals & farms Gautam Dantas Morten Sommer

20. 20 Even far from hospitals & farms are multi-drug resistant microbes Researchers Find Bacteria That Devour Antibiotics

21. 21 Microbiome vs VDJ-ome Microbe tests: Detect Drug resistance spectrum Earlier warning (e.g. meningitis) Immune tests: Focus on response to exposure Longer times to detect exposure (e.g. HIV, TB)

22. 22 Microbiomes: What limits diagnostics -Standard of practice: skip diagnostics; guess at pathogen & antibiotics -If diagnostic is used typically a fingerprint rather than cauastive sequences. -Ideally targeted sequencing of pathogenicity and resistance – and broad community updating mechanism. - Assay 25 microliters or 6 liters?

23. 23 Vaccination VDJ-ome HMS/MIT: Francois Vigneault, Uri Laserson, Erez Lieberman-Aiden, George Church Roche: Michael Egholm, Birgitte Simen

24. 24 Time Series Vaccine Experiment Tracking human dynamic response to vaccination to 11 strains: Hepatitis A+B, Flu A/Brisbane/59/2007 (H1N1)-like, 10/2007 (H3N2)-like, B/Florida/4/2006-like virus Polio, Yellow fever Meningococcus Typhoid, Tetanus Diptheria, Pertussis Collect samples at -14d, 0d, +1d, +3d, +7d, +14d, +21d, +28d

25. V and J usage – CDR3 size distribution 25 SR1+SR2+TR1 IMGT/LIGM FV

26. Self Organizing Map (SOM) clustering 26

27. 27 Isotypes

28. 28 Query: FXQ8H8O01DXEUI rank=0514859 x=1493.0 y=2520.0 length=408 Target: I55621 | anti-hepatitis B virus (HBV) surface antigen (HBsAg) (human) Model: affine:local:dna2dna Raw score: 1740 Query range: 8 -> 398 Target range: 27 -> 423 9 : CGCGTTGCTCTTTTAAGAGGTGTCCAGTGTCAGGTGCAGCTGGTGGAGTCTGGGGGAGGCGTGG : 72 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 28 : CTCGTTGCTCTTTTAAGAGGTGTCCAGTGTCAGGTGCAGCTGGTGGAGTCTGGGGGAGGCGTGG : 91 73 : TCCAGCCTGGGAGGTCCCTGAGACTCTCCTGTGCAGCCTCTGGATTCACCTTCAGTAGCTATGG : 136 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||| ||||| 92 : TCCAGCCTGGGAGGTCCCTGAGACTCTCCTGTGCAGCCTCTGGATTCACCTTCAGTAGGTATGG : 155 137 : CATGCACTGGGTCCGCCAGGCTCCAGGCAAGGGGCTGGAGTGGGTGGCAGTTATATCATATGAT : 200 ||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||||| 156 : CATGCACTGGGTCCGCCAGGCTCCAGGCAAGGGGCTGGAGTGGGTGGCAGTGATATCATATGAT : 219 201 : GGAAGTAATAAATACTATGCAGACTCCGTGAAGGGCCGATTCACCATCTCCAGAGACAATTCCA : 264 ||||||||||||| ||||||||||||||||||||||||||||||||||||||||||||||||| 220 : GGAAGTAATAAATGGTATGCAGACTCCGTGAAGGGCCGATTCACCATCTCCAGAGACAATTCCA : 283 265 : AGAACACGCTGTATCTGCAAATGAACAGCCTGAGAGCTGAGGACACGGCTGTGTATTACTGTGC : 328 ||||||| |||| |||||||||| ||||||||||||||| |||||||| ||| ||||||||||| 284 : AGAACACTCTGTTTCTGCAAATGCACAGCCTGAGAGCTGCGGACACGGGTGTATATTACTGTGC : 347 329 : GAGAGA---ACTT-ACTATGGTTCGGGGAGTTCCTG--ACTACTGGGGCCAGGGAACCCTGGTC : 386 || ||| |||| ||| ||||||| |||| || | ||||||||| |||||||||||||||| 348 : GAAAGATCAACTTTACTTTGGTTCGCAGAGTCCCGGGCACTACTGGGTCCAGGGAACCCTGGTC : 411 387 : ACCGTCTCCTCA : 398 |||||||||||| 412 : ACCGTCTCCTCA : 423 aln_summary: FXQ8H8O01DXEUI 408 8 398 + I55621 423 27 423 + 1740 390 372 95.38 UL

29. 29 21-Jan-2010 Emphasis on Protein/cell function, Integration & Interpretation -Personal Genome issues: cost, unfriendly databases, consent, multiple genes + environmental factors -Personal stem cells 3 uses: Diagnostic/inheritance, therapeutic cells, test pharmaceuticals -Microbiomes: What limits diagnostics -VDJ-omes: How to generalize immune diagnostics

30. 30 PGP skin to stem cells to... Lee J, Park IH, Gao Y, Li JB, Li Z, Daley G, Zhang K, Church GM (2009) A Robust Approach to Identifying Tissue-specific Gene Expression Regulatory Variants Using Personalized Human Induced Pluripotent Stem Cells. PLoS Genetics Nov 2009

31. 31 PGP iPSC allele specific expression

32. 32 iPSC-derived hepatic proteins & activity Generation of Functional Human Hepatic Endoderm from Human Induced Pluripotent Stem Cells Gareth et al Hepatology. 2010 Jan;51:329-35.

33. The Personal Genome: Do I want to know? 1. Expensive: “If you think education is expensive.. try ignorance” 2. Discriminative: Destigmatize, pass laws, educate 3. Worthless: If we share,.. priceless

34. 34 Four open-source resources snp.med.harvard.edu (Genes + Environment = Trait prediction) Polonator.org