1 9-9:30 am 20-oct nrb room 350 thanks to: personal genome projectile
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9-9:30 AM 20-Oct NRB Room 350
Thanks to:
Personal Genome Projectile
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Agenda 9:00 am Introduction: George Church 9:30 am PGP-10 comment period10:00 am Genomic Consultation : Joseph Thakuria MD, MGH10:30am PGP Cells: Jay Lee MD, In Hyun Park 11:00 am Response of Patients to ApoE info: Robert Green MD, Boston Univ.11:30am PGP-10 & Staff -saliva specimen collection for microbiomics12:00 pm Lunch12:45 pm Tour featuring Polonator instruments 1:00 pm Sharing: John Wilbanks, Science Commons 1:30 pm Association Studies: David Altshuler MD, Broad Institute 2:00 pm PGP-10 & Staff Flex Time 3:00 pm International PGP: Jeantine Lunshof, Amsterdam, Margret Hoehe,MD Berlin 3:30 pm PGP-10 comment period and public data release 4:00 pm Closing Remarks: George Church 4:30 pm Press conference NRB Rotunda 6:00 pm Public event sponsored by Nova scienceNow
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Major points#1: Thank you #2: Today is a start, not a final product#3: PGP is research, not a genetics service #4: We are providing some interpretations, but mainly to initiate study & discussion. Decisions about releasing data should be largely based on other considerations.#5: Today: PGP-10: 50K exons, SNPs, CNVs,#6: 2009: PGP>100: 200K exons, RNA, microbiome, VDJome; full genomes for 10.
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PGP Education, ELSI, InternationalMeetings:PG-0 28-Jun-06 Toronto, AnnArbor, PG-1 18-Jul-07 Boston, BrooklinePG-2 20-Oct-08 BostonPG-3 ??
Education: pgEd.org, NecessaryFilms.comOppenheimerfoundation.org
PGP inquiries: UCSD, JCVI, UNM, Yale, ISBBerlin, Toronto, Seoul, Shenzhen, Singapore
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PersonalGenomes.org : gene/environment/trait data
1) Open access (very low barrier to participation) 2) Avoid over-promising on de-identification 3) 100% on Exam to assure informed consent4) Low cost coding sequence + regulatory data 5) Multi-traits: imaging, iPS stem cell RNA, microbes 6) Cells available for personal functional genomics7) IRB approval for 100,000 diverse volunteers 2003 to 2008 International consortium
0431
1070
1660
1677
1687
1833
1846
1731
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Lunshof JE, Chadwick R, Vorhaus DB, Church GM. From genetic privacy to open consent. Nat Rev Genet. 2008 Lunshof JE, Chadwick R, Church GM (2008) Hippocrates revisited? Old ideals and new realities. Genomic Med. 2(1-2):1-3.
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Inherited Genomics
TRAITS(Phenome)
PERSONAL GENOME1 to 98%
Once in a life-time genome sequence
to Predictive Medicine
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Inherited + Environmental Genomics
VDJ-ome
TRAITS(Phenome)
Microbiome
Multi-tissue
Epigenome
(RNA,mC)
PERSONAL GENOME1 to 98%
Once in a life-time genome + yearly ( to daily) tests
Public Health Bio-weather map : Allergens, Microbes, Viruses
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9K chem/drugs
Omic combinatorics
VDJ-ome1M receptors
4000 disorders + non-medical (quant)traits
Microbiome1M species
>>250 tissues
epigenome (RNA,mC)
PERSONAL GENOME3M alleles
(Alleles^n * environments^m) vs. (lumping via pathways)
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Multiple hypothesis testingY= Number of Sib Pairs (Assocation)
X= Number of Alleles (Hypotheses) Tested
GRR=1.5, p= 0.5 (population frequency)
0
200
400
600
800
1,000
1,200
1,400
1,600
1E+4 1E+7 1E+10 1E+13 1E+16 1E+19 1E+22
|
= Genotypic relative risk
based on Risch & Merikangas (1996) Science 273: 1516
Pool some alleles by pathway & mutation type(not LD or chromosome position)
Allele & environmentcombinations
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Sequencing tracked Moore’s law (2X / 2 yr) until 2004-8 (10X / yr)
40X 98% genome $5K in 2008 ($50 for 1%?)
0.0000001
0.000001
0.00001
0.0001
0.001
0.01
0.1
1
10
1990 1995 2000 2005 2010
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G
A
C
T
Multiplex Cyclic Sequencing by Synthesis Polonator: multiple chemistries: polonies on slides or beads
Polymerase -or- LigaseShendure,
Porreca, et al. 2005 Science
Illumina, IBS*
AB-SOLiD*, CGI*
Mitra, et al. 2003 Analyt.
Biochem.1999NAR
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Open-architecture hardware, software, wetware
Polonator
$150K - 2 billion beads/run
e.g.1981IBM PC
Rich Terry
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6 Next Generation Sequencing Platforms
Roche Illumina AB-SOLiD Helicos Polonator
$500K $680K $690K $1350K $155K
.001G/0.03h 0.2 G /2.6h 0.3 G /4h 2.8 G/2h 2G/2h
VDJ-grant Exomes Co-develop SAB Co-develop
SAB & PGP10 in 2009
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Association Studies & Direct to Consumer$350K 98% genome
$2500 0.02% 23 clinical
$999 0.02% 31 diseases
$400 0.02% 10 clinical
68 researchsubsidized 0.02% de-identified
subsidized 0.02% researcher subset
subsidized Varies -- no trait data
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The number of human genes
Broad Inst.: 20,500 genes with conservation pattern indicative of function
Genecard annotated: 29,479; name/location: 38,891
Genetests: 1347; #included in today’s exome: 953
1% of the genome is protein coding = 2x30Mbp
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Selective genome sequencing
Shendure, et al. Science 309:1728 Porreca et al 2007 Nat Methods 4:931 Nilsson et al. (2006) Trends Biotechnol
24:83.
Red=Synthetic; Yellow=genome/cDNA
How do we optimize >100K 100mers ?
3 ways to capture alleles from genomic or c-DNA
In vitro Paired-end-tags (PET)
Science Science 20052005
Hybridiz.selection
Zhang, Chou, Shendure, Li, Leproust, Dahl, Davis, Nilsson, Church
For rearrangements
2. 3.1.
GapFill
Nat Methods 2007
3.
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Induced Pluripotent Stem Cell Generation & Transdifferentiation (Oct4/Sox2/Myc/Klf4)
Retroviral Infection
Tissue Culture on a Mouse Feeder Layer
ES Cell Colony Identification
Clonal Isolation and Propagation
Embryoid Body Induction&
Guided Differentiation
Adenoviral Infection
Mixture of differentiated cell types
&Guided Differentiation
2 monthsMultiple integration sites
1 week
Yamanaka, Daley, ThomsonHochedlinger, Jaenisch labs
Jay Lee
In HyunPark
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Inherited + Environmental Genomics
VDJ-ome
TRAITS(Phenome)
Multi-tissue
Epigenome
(RNA,mC)
PERSONAL GENOME1 to 98%
One in a life-time genome + yearly ( to daily) tests
Public Health Bio-weather map : Allergens, Microbes, Viruses
Microbiome
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Human B &T lymphocyte cDNA : VDJ Polonies
http://www.infobiogen.fr/services/chromcancer/Genes/TCRBID24.html
2-4 E6 / ml * 5L = 1E10 cells (blood) 46*23*6*67*5 = 2M combinations (24 bits vs 750 bp)
V D J C
IGH38-46
23 6 9
IGK31-35
- 5 1
IGL 29-32 - 4-5 4-5
TRA
45-47
- 50 1
TRB 39-46 2 13 2
TRD/A 5 3 4 1
TRG 4-6 - 5 2
Uri Laserson,
Francois Vigneault
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VDJ(H) 16 antigens &3 PG-B cells
combinations24x86
ImMunoGeneTics database http://imgt.cines.fr/
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Major points#1: Thank you #2: Today is a start, not a final product#3: PGP is research, not a genetics service #4: We are providing some interpretations, but mainly to initiate study & discussion. Decisions about releasing data should be largely based on other considerations.#5: Today: PGP-10: 50K exons, SNPs, CNVs,#6: 2009: PGP>100: 200K exons, RNA, microbiome, VDJome; full genomes for 10.
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Is promising anonymity realistic? Are we in denial?
Trends in laws to make data public (not just at elite institutions): e.g. H.R. 2764, SEC. 218. 26Dec07 open-access for all NIH-funded research. SEC, GINA, etc
(12) Identify individual case/control status from pooled SNP data Homer et al PLoS Genetics 2008
(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 (genome-altruists)(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
index
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Is promising anonymity realistic? Are we in denial?
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