This document discusses standards and open access for genome, environment, and trait data. It covers reference materials, sequencing technologies, bioinformatics, and performance metrics. It provides examples of rare protective alleles and case studies showing how genome data can help diagnose and treat diseases. It also discusses technologies like nanopore sequencing, fluorescent in situ sequencing, and organ chips that can advance personalized and precision medicine.

1. Standards & Open-Access Genome-Environment-Trait Data
NIST 10:15-10:45 AM 16-Aug-2012
Thanks to:
.gov
|| NIGMS NHGRI
||
.edu
|| LSRF
ArmRev.org
||
.org Oppenheimer
Foundation
||
||
.com
|| Azco
|| Gen9
Read = = = = = = = = I/O = = = = = = = Write 1

2. Technology & Genome Standards
(1) Reference Material : cell lines, primary cells,
synthetic DNA spiking
(2) Sequencing: Haplotype, nanopore, in situ
other methods: Forensic, immune DNA
(3) Bioinformatics, Data Integration, & Data
Representation: methods to analyze &
integrate the data
(4) Performance Metrics & Figures of Merit
2

3. Individually Rare -- Collectively Common (10%)
2443 diseases (~6000 genes) are highly predictive &
medically actionable
1963 PKU
1991 BRCA
2010 HCM
PGEd.org Genetests.org 3

4. Case studies
Nic Volker: not intestinal surgery à cord blood
Beery twins: not cerebral palsy à Diet 5HTP
Dr. Lukas Wartman: Leukemia à Sunitinib
Ivacaftor: treat CFTR G551D à 3 months in FDA
PGEd.org

5. Genomes = Traits
Standards and improved QC:
Cohorts approved for global & commercial sharing
PERSONAL TRAITS
GENOME (Phenome)
3M alleles
5

6. Genomes + Environments = Traits
PersonalGenomes.org
Standards and improved QC :
Cohorts approved for global & commercial sharing
Therapies
Immunome
Immunome Epigenome TRAITS
PERSONAL RNA,mC
GENOME (Phenome)
Proteome
3M alleles
Microbiome 4D-Imaging
Stem-cells
Metabolome/Tox
Cancer
Food 6

7. Genomes + Environments = Traits
PersonalGenomes.org
US, Korea, Israel, Germany, Canada
Individuals willing to have their genomes, cells
(saliva, blood, skin, iPS), extensive trait data
Open-access : CC0
16K volunteers registered in 74 countries
Harvard IRB approval for 100K
2,418 achieved 100% on entrance exam
7

8. Wireless environment, drug &
physiology monitors
PGP#1
Kim et al Science 2011, GE Vscan ultrasound, Piix cardiac monitor
8

9. Rare Protective alleles e.g. Myostatin
Enhanced muscle growth, decreased body fat &
decreased atherosclerosis (2009 Endocrine Society, Bhasin, et al BU)
Flex Wheeler
MSTN -/-
9

10. Correlation → Cause → Cure/Prevention
Rare Protective alleles
• MSTN -/- Lean muscles <0.001%
• LRP5 -/+ Extra-strong bones 0.001-8%
• PCSK9 -/+ Lower coronary disease 3, 0.06%
• CCR5 -/- HIV-resistant (Pox/Plague) ~0, 1%
• FUT2 -/- Stomach flu resistant 20%
Embrace the extremes: informative, easy, powerful
blog.personalgenomes.org 10

11. Precise Genome Therapy:
prevent/cure HIV
"Long-Term Control of HIV by
CCR5 Δ32/Δ32 Stem-Cell
Transplantation" 2009
New England J Medicine
Sangamo Phase 2 clinical trial
U.S. District Court rules that stem cells are drugs 11

12. Microbiome & Immunome
Impact Metabolome
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)
12

13. PGP Immunome time series
Harvard/MIT: Vigneault, Laserson,
Lieberman-Aiden, Church 13
Roche: Egholm, Simen

14. Rare (therapeutic) antibodies
Broadly reactive antibody … potent
neutralization of HIV-1 … unusually
long, 28-amino acids (84 bp),
CDR3… towers above the antibody
surface. Pejchala et al. PNAS 2010
Antibody-based protection against
HIV infection by vectored immunoprophylaxis
Balazs, Baltimore et al. Nature 2012
RNAi & Drug alternative #2 14

15. PGP CDR3 size distribution
84 bp
15
Laserson, Vigneault

16. PGP Vaccination 3 year time series.
-8 -2 -0.04 1 3 7 14 21 28 days
16

17. Proteome
Antigen
Libraries
Human,
bacterial,
viral, food,
allergens
Larman et al
Nature
Biotech 2011 17

18. $1000 bp/$
Genome
When?
2040
2004-6: $400M
------
Moore’s law 2000-4: $3 billion
1.5x/yr for 0.1
electronics 0.01
2015 2020…
2025 2030 2035 2040
18

19. Factors of bp/$ 2012 $0.8K
10/yr 2011 $4K
$1000
2007: $2M
When?
2012
2004-6: $400M
------
Moore’s law 2000-4: $3 billion
1.5x/yr for 0.1
electronics 0.01
19

20. How? Next-generation technologies
1. Polonator MA 33. Nanophotonics Biosci CA
18. GnuBio MA
2. Roche-454 CT 34. Network Biosystems MA
19. Bionanomatrix PA
3. AB-SOLiD MA 35. SeiraD NM
20. Halcyon CA
4. Illumina UK,CA 36. Affymetrix CA
21. ZS Genetics NH
5. CGI CA 37. Population Gen Tech UK
22. Electron Optica CA
6. Helicos MA 38. AQI Sciences AZ
23. Genizon BioSci QC
7. Pacific Bio CA 39. Base4innovation UK
24. LaserGen TX
8. IntelligentBioSys MA 40. Li-Cor NE
25. GE Global NY
9. Ion Torrent CT 41. U.S. Genomics MA
26. Stratos Genomics WA
17. LightSpeed CA 42. Mobious Genomics UK
27. Reveo NY
10. Genapsys CA 43. Visigen TX
28. Firebird FL
11. Electronic Biosci CA 44. Starlight CA
29. Zeiss MA
12. Nabsys RI
30. Lucigen WI
13. OxfordNanopore UK
31. Adv. Liquid Logic NC
14. IBM-Roche NY
32. Caerus Molec Diag CA
15. NobleGen MA
16. Genia CA
20
http://arep.med.harvard.edu/gmc/nexgen.html

21. Nanopore : Polymer vs Monomer 1995: “use a
polymerase …
ONT & Genia while recording
conductance
changes”
Church, Deamer,
Branton, Baldarelli,
Kasianowicz.
2009 Clarke, Bayley, et al
2010 Derrington, Gundlach, et al
2012 Cherf, Akeson, et al
21

22. 2012 Sequencing ONT/Genia Danaher/IBS
$/device 0-30K 150K
$/PG 100k-2K 1K
Read length 100K 70
Speed (days) 0.1 30
Size (kg) 0.2 50
Sorting No Yes
In situ No Yes June 2012
22
http://arep.med.harvard.edu/gmc/nexgen.html

23. Clinical Importance of Haplotype vs WGS/Exome
2 mutations in cis vs trans!
386 kb fosmid Kitzman, et al Nat Biotech 2011
1429 kb LFR CGI Peters, et al. Nature July 2012
65pg =10 cells à 60-300 kb in 384 aliquots.
1 false positive SNV per 10 Mb. (Q70)

24. Why long haplotypes -- gaps in the
reference genome
20Mb Multiple
gap Sclerosis
Reich et al. Nature Genetics 2005
24

25. Stretched DNA Fiber FISh/FISSEQ
3Mbp

26. In Situ Sequencing: metaphase haplotypes
Zhang et al Nature Gen 2006
Mitra & Church NAR 1999 (FISSEQ) 26

27. Rare cells: Resistance in Leukemia ABL Tyr-Kinase
Nardi, Raz, Chao, Wu, Stone, Cortes, Deininger, Church, Zhu, Daley. Oncogene
M244V
T315I
E255K

28. Personal Genome Project &
Biobanks: iPS (with Coriell)
iPS-derived teratoma
Endoderm
Ectoderm
Mesoderm

29. Personalized organs-on-chip
+ neural,
blood-brain-
barrier, skin,
testis
Huh, Ingber et al. Science. 2010 29
Trends in Cell Biol 2011

30. Read: Fluorescent in situ Sequencing (FISSEQ)
60 cycles x 4 colors
Single base
differences
30
Lee, Yang, Terry, Nilsson, Church et al.

31. 1. Fix cells
Epigenom, 2. Reverse transcribe
Transcriptome in situ 3. Cross-linking cDNA
4. RNA digestion
5. Enzymatic
circularization

32. Fluorescent In situ Sequencing (FISSEQ)
3-D reconstruction of in situ RNA-seq in In situ sequence bar-coded FISH probe
human iPS cells showing DMNT3b, sequencing using confocal microscopy in
GAPDH, EEF1alpha and GAL human fibroblasts

33. Signal to noise ratio remains
stable over 60 cycles
Reference Cycle 1
iPS FISSEQ (Manual cycling)
Probe hyb
Probe strip
(average of 10 spots; 3 pixel x 3 pixel area
per spot from 20x epifluorescence imaging)
Cycle 25 Cycle 50
(Richard Terry and Chao Li)

34. Overcoming the imaging
resolution barrier #1

35. Super-resolution #2: Polonies beads & Rolony grid
35
Synthetic Aperture Optics

36. Challenge of QC of (epi) genetic programming
72/101 Non-silent changes in 20 hiPS cell lines
Not random mutations during reprogramming (p < 8E-50)
Gore, Zhang, et al. Nature.
ABCA3, AKR1C4, ANKRD12, ANKRD12, ARHGEF5, ASB3,
ATM, C14orf174, C1orf100, CABC1, CACNG3, CALN1,
CARM1, CCKBR, CELSR1, DLG3, DNAH3, DSC3, DYNC1H1,
FAT2, GDF3, GOLGA4, GSG1, GTF3C1, HK1, HK1, IFNGR1,
IFT122, INTS4, IQGAP3, ITCH, KLRG2, LINGO2, LRP4,
MARCKSL1, MMP26, MYRIP, MYRIP, NEK11, NEK5, NTRK3,
NTRK3, OR6Q1, OSBPL3, PBLD, POLE, POLR1C, PPP1R2,
PRICKLE1, PTPRM, RANBP3L, RASEF, RFX6, RGS8, RP4,
SAL1, SCN1A, SCN1A, SDR16C5, SEMA6C, SH3PX3, SLC1A3,
SLC1A3, SORCS3, SPATA21, SPEN, TM9SF4, TMEM40, TNR,
UBA2, VAC14, VMO1, ZER1, ZNF16, ZNF471, ZZZ3
36

37. Technology & Genome Standards
(1) Reference Material : cell lines, primary cells,
synthetic DNA spiking
(2) Sequencing: Haplotype, nanopore, in situ
other methods & validation:
(3) Bioinformatics, Data Integration, & Data
Representation: methods to analyze and
integrate the data
(4) Performance Metrics & Figures of Merit
37

38. .

39. .

40. 1977 cm Q20 2012 600 nm Q70
55 bp 1000 genomes /month
50X 6Gbp CGI
Drmanac et al. Science 2009
nobelprize.org/nobel_prizes/chemistry/ 40
laureates/1980/gilbert-lecture.pdf

41. Sequencing Technology: Next
In Situ - Clinical - Portable
Lauerman, Bloomberg
In situ Sequencing
Lauerman, Bloomberg
Intelligent BioSystems Oxford Nanopore
Genia
Hairy =red, Kruppel=green,
Giant=blue. Kozlov et al
41
In Silico Bio 2002

42. John Lauerman (PGP#16)
JAK2
polycythemia
vera, essential
thrombocythemia,
myelofibrosis
Steve Pinker (PGP#6) HCM

43. DIY Bio
DNA Explorer
(Ages 10 and up)
PGP#14 John West
Factor V Leiden
OCTOBER 1, 2010
Obsessed With Genes (Not Jeans),
This Teen Analyzes Family DNA