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Shrinking cost of your genome

Shrinking cost of your genome. Million-fold in 6 years. What does $0 to the consumer mean?. Web 2.0, Crowd-sourcing. 2001 Wikipedia 1998 Search, Maps, Translation. But these new technologies (cell phone, fax, PC) are only as good as their communities. 1. Expensive 2. Discriminative

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Shrinking cost of your genome

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

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

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

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

  5. DNA Explorer, $80 (Ages 10 and up) DIY Bio Genographic $99 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. 50% to 75% get good news • and for the rest: • Planning – family, geography • Research activism The Personal Genome: Do I want to know, if there will be no medical action?

  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. 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. snp.med.harvard.edu

  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 2008as 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. 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 genotypeMarkers 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. Motivating, donating, raising consciousness Who can contribute to cures & prevention? Huntington'sNancyWexler(psychologist) HFE Aull (engineer) Adrenoleukodystrophy Odone(World Bank) ALSJamie Heywood(engineer) PatientsLikeMe.com Parkinson’s Brin family Hugh Rienhoff, (MD) MyDaughtersDNA.org LRRK2 G2019S

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

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

  16. Exercise HCM • Drink your milk MCM6 • Eat your beans G6PD • & your grains HLA-DQ2 • & your iron HFE • Get more rest HLA-DR2 UNLESS …

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

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

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

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

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

  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 SR1+SR2+TR1 IMGT/LIGM FV

  26. Self Organizing Map (SOM) clustering

  27. Isotypes

  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. 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 29

  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. PGP iPSC allele specific expression

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

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

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