On June 20, Dr. Stephen Chanock delivered a presentation titled "Genome-wide Association Studies: A Step in the Right Direction."

1. Genome-wide Association Studies
in Cancer:
A Step in the Right Direction
Stephen Chanock, M.D.
Chief, Laboratory of Translational Genomics
Director, Core Genotyping Facility
June 20, 2012

2. Genetic Predisposition to Breast Cancer
European Population
10 BRCA1
BRCA2
Population genotype relative risk
TP53
PTEN
CHEK2
3 ATM
PALB2
BRIP1
RAD51C
ERCC2
1.5
1.4
1.3
1.2
> Doubling in
2012
1.1
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Population risk-allele frequency
BCAC CGEMS/BCAC WTCCC Other

3. 97 Genes Mutated in Cancer Susceptibility Syndromes
SDHB
MUTYH
ALK FANCD2
MSH2
MSH6 VHL
XPC HRAS
FANCL MLH1 PMS2 FANCF
BAP1 TERT FANCE EGFR CDKN2 WT1
MITF FANCG
A
SDHA DDB2
PHOXB2 POLH EXT2
WRN
TMEM127 CDK4
GATA2 SBDS
NBS1 GALNT1 SDHAF2 PTPN11
HAX1 XPA
2 RET MEN1
SDHC ERCC3 TERC PTCH BMPR1 ATM
MET PTEN
A SDHD
DIS3L2 KIT FANCC SUFO CBL
HRPT2 TSC1
PDGFRA APC
EXT1
RECQL4
T
FH
FANCB
SLX4
TSC2 TP53 ELANE
ERCC4 WRAP53 STK11
FANCN/BRIP FLCN
1
TINF2 CEBPA
NF1 RUNX1 SMARCB1
BRCA2 FANCM ERCC2 CHEK2
BRCA1
RB1 CYLD FANCJ SMAD4 NF2
MAX BUB1B CDH1 RAD51C
FANCI FANCA HOXB13
BLM
DICER1
ERCC5 GPC3
DKC1
C Kratz

4. Genome-Wide Association Studies:
Age of Discovery
• Discovery of New Regions in the Genome Associated
with Diseases/Traits
• New “Candidate Genes & Regions”
• Clues for Mechanistic Insights Into the Contribution of
Common Genetic Variation to Cancer Biology
• Challenge of Genetic Markers for Risk Prediction
• Individual Risk
• Public Health Decisions
• Polygenic Risk Models

5. Published Cancer GWAS Etiology Hits: 5.15.12
KIF1B
1p36
~240 Disease Loci marked by SNPs
DDX1
IRF4
6p22
1 Locus marked by a CNV
SLC4A7/
C2orf43 NEK10 6p21
THADA
ITGA9
TACC3 TERT/
GPRC6A
FOXP4
Another 85 coming soon… DMRT1 10p15.1
LSP1
11p15
TERT
CLPTM1L DLG2
GSTM1
EHBP1 CLPTM1L GABBR1 breast, bladder, kidney, lung,10p14
JAZF1
NAT2
CDKN2A/
BNC2
LMO1 12p11.23
deletion REL 3p12.1 HLA-F
1p11.2 3p11.2
5p15
HLA-A
ovary, prostate, TCGT
IKZF1 8p21 CDKN2B GATA3
MSMB/
ATM ATF7IP
5p12 EGFR
6p21.33 NCOA4 FAM111A
1q22 2q13 5q11.2 11q13 KRT5
ARIDB5
3q13 6p21.32
1q21.1 PDLIM5 5q11 HSD17B12
EEFSEC BAK1 LMTK2 PLCE1
EPAS1 ADH1B 5q13 TYR KITLG
1q21.3 FOXE1
TET2 DDX4 ECHDC1/ ZNF365 11q23.1 12q13.13
1q32.1 ITGA6 RNF146 EIF3H 9q31.2 FGFR2 PHLDB1
3q26 C6orf97/ 7q32 ALDH2
SPRY4 8q24.21(x5)
MYC
CCDC26
8q24.21 ABO CTBP2 11q24.1
TARDBP 2q31 ESR1 7q32
TP63 SLC22A3 PSCA 10q21.2
1q41 CASP8
ERG2
1q42 BARD1 LINC00340
10q22.3
1q42.12 2q35 10q26
FARP2
2q37
19p13 C20orf54
RHPN2 BMP2
NUDT10/
CCNE1
15q15 22q12.25 NUDT11
CEBPE HNF1B x 2 SLC14A1 ASIP MX2
19q13.2 22q13
NKX2-1 GREM1 TOX3
COX11/ SMAD7 PRKD2
15q21.3 21q22 BIK
BMP4 CDH1 STXBP4 RTEL1
KLK2/
15q23 16q24.1 17q24.3 KLK3
KLF5/ RAD51L1
KLF12 CHRNA3/ MC1R 3 Ewing Sarcoma
3 Kidney 4 Thyroid 2 Non-Hodgkin 5 Ovary 3 Gastric 9 Multiple
CHRNA5
13q12.125
3 Hodgkins
13q22 2 Wilms 1 Liver 10 CLL 6 Neuroblastoma 4 Pediatric Acute Lymphoblastic Leukemia 7 Esophageal Squamous
48 Prostateate 26* Breast 13 Colorectal 7 Basal Cell Carcinoma11* Bladde7 Glioma 6 Lung 9 Melanoma 4 Pancreas 7 Nasopharyngeal 6 Testicular
7 Chung & Chanock 2011

6. Lessons from GWAS Discovery
• P values matter- to protect against a sea of
false positives
• Size matters
• Design “sort of” matters…….
• Mapping is required to explore each region
prior to functional work
• Collaboration is central
Epidemiology meets Genetics to Discover Biology
Not for weak of heart or stomach…

7. Lessons from GWAS Discovery
• P values matter- to protect against a sea of
false positives
• Size matters
• Design “sort of” matters…….
• Mapping is required to explore each region
prior to functional work
• Collaboration is central- Can’t do it alone
Not for weak of heart or stomach…

8. CGF & Data Sharing
• Posted first public GWAS datasets for breast & prostate cancer
in 2006
• Aggregate data removed in 2008 in response to NIH policy
change
• Led development of standards for GWAS posting with dbGaP
• Contributed all DCEG GWAS datasets to dbGaP
• CGF was instrumental in addressing privacy issues with GWAS
and other high-dimensional aggregate genomics data
• 11 scans currently listed on dbGaP

9. GWAS Regions for Testicular Cancer Point
Towards Alterations in a Common Pathway
High Estimate for Heritability
Strong Familial Component
MZ Twins= 75 X
DZ Twins= 20-25 X
Strongest Effects Observed in GWAS
KITLG Heterozygote OR= 2.5, Homozygote > 5.0

10. GWAS Studies:
Just the Start……
“This is not the end. It is not even the
beginning of the end. But it is,
perhaps, the end of the beginning.”
Sir Winston Churchill @ Lord Mayor's Luncheon,
Mansion House following the victory at El Alameinin North Africa
London, 10 November 1942.

11. Prostate Cancer Risk Factors
2006
• Age
• Ethnic Background
• Family History

12. Prostate Cancer: 48 as of May 1, 2012
> 24 More to come
2p24.1
THADA
EHBP1
No Clear Evidence of Specific Loci
5p15.33
RFX6
JAZF1
11p15
FOXP4
3
3p12.1
3p11
For Advanced Disease 8p21
MSMB/ FAM111A
NCOA4
11q13
10q11.23 12q13
PDLIM5
EEFSEC LMTK2
TET2
ITGA6 8q24.21 CTBP2
SLC22A3 5 10q26
2q27.3 Suggests Distinct Regions Influence
Etiology & Outcome
NUDT10/
NUDT11
HNF1B 19q13.2
2 BIK
KLK2/
13q22.1 17q24.3 KLK3
Type 2 Diabetes PSA or Prostate Cancer or both??

13. Prostate Cancer Risk Factors
2012
• Age
• Ethnic background
• Family history
• Multiple common alleles-
 48 published & more coming….
 Each common variant explains a small
proportion of risk
 Together 15%

14. Prediction is difficult,
Especially about the future.
Yogi Berra
Dan Quayle
Niels Bohr

15. Discriminatory Power of Genetic Risk
Score for Prostate Cancer
Area Under the ROC
Curve
Model Under Over
65 75
FHx only 0.55 0.51
G only 0.66 0.60
G + FHx 0.68 0.60
PSA 0.87 0.84
P Kraft, S Lindstrom for the BPC3

16. Theoretical Limits of Risk Prediction
Crohn’s Disease Common cancers (Br or Pr)
Sibling relative-risk=20-35 Sibling relative risk=2-3
Park et al., Nat Genet ,2010
Different Diseases Display Distinct Architectures
Random
Using known loci
Using all estimated loci
Ideal (if we could explain all heritability)
Park et al Nature Genetics 2010

17. Next Generation GWAS
• Distinct Populations
– Prostate Cancer in Japan or in African Americans
– Esophageal Cancer in China
• Meta-Analysis Yield Discoveries
– Larger Scans
– Large Scale Replication (iCOGS)
• Shift to lower MAF
– New Arrays vs Low-Pass Sequence Coverage
– Imputation
• Better for Mapping Regions
– Larger Sample Sizes Required

18. Distinct Differences in the
Underlying Genetic Architecture of
Different Cancers
Preliminary Phase of Estimating Differential
Contribution
Common Variants
Log additive Effects
oEpistatic Effects
oUncommon Variants
oRare/Familial Mutations

19. Genetic Predisposition to Breast Cancer
European Population
10 BRCA1
BRCA2
Population genotype relative risk
TP53
PTEN
CHEK2
3 ATM
PALB2
BRIP1
RAD51C
ERCC2
1.5
1.4
1.3
1.2
> Doubling in
2012
1.1
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Population risk-allele frequency
BCAC CGEMS/BCAC WTCCC Other

20. Genomic Architecture of Prostate Cancer
Susceptibility Loci:
BRCA2
5
3 ????
Per allele relative risk
1.4
1.2
1
0 0.05 0.50 0.95 1
Risk-allele frequency

21. Genetic predisposition to neuroblastoma:
Current status and future directions
100 Discovered mutations
ALK Yet to be discovered mutations
Discovered polymorphisms
PHOX2B
Additional mutations and Yet to be discovered polymorphisms
rare polymorphisms to be
TP53
discovered by NGS
Effect size
SDHB
Additional polymorphisms
10
to be discovered by GWAS
PTPN11
FLJ22536
5 DDX4 LMO1
BARD1
HSD17B12
NBPF23 NME7
1
0.001 0.01 0.1 DUSP12 0.5
IL31RA
Modified from Manolio, et al. Nature 2009 Allele Frequency

22. Genomic Architecture of Lung Cancer
Susceptibility Loci
5
3 ????
Per allele relative risk
Smoking….
1.4
Adenocarcinoma specific
Stronger in nonsmokers
1.2
1
0 0.05 0.50 0.95 1
Risk-allele frequency

23. Investigation of GWAS Markers
Non-
Coding
Initial Findings Protein
Variant
Coding
Bioinformatic Regulatory Unannotated Effect on
Element Transcript Protein
Analysis
Alteration of Effect on
Functional Novel mRNA Allelic
Gene Levels Epigenetic Genes
Elements Transcripts Stability Differences
Elsewhere
Expression Expression Gene
Functional Histone
Experimental Elements Methylation
Quantitative miRNA Quantitative Product
Strategy Trait RNASeq Trait Functional
In vitro/vivo Elements
Analysis Analysis Analysis

24. 10q11.2 & Prostate Cancer
Risk
rs10993994 within promoter of
MSMB= b-microseminoprotein
Prostate specific serum marker under study
20,000 subjects
Functional Analysis
Risk Allele “T”
Lower expression levels
Reporter assays
Electrophoretic Mobility Shifts
Levels in Prostate Tissue
Tumor Tissue

25. 10q11.2 Could Be More Complex……
MSMB and NCOA4
Re-sequence
454 FLX
~100 subjects
RNA Expression MSMB and NCOA4
Normal
Tumor Tissue
Anchorage Independent Growth is Specific to Prostate
MSMB- Suppression
NCOA4- Over-expression

26. Characterization of Chimeric Transcript at 10q11.23
MSMB-NCOA4 Fusion
RT-PCR of MSMB-NCOA4 fusion transcripts
in human tissues and 9 prostate cancer cell lines
Expression of MSMB-NCOA4 fusion protein in PC3 transfected cells organization of
Genomic
MSMB and NCOA4
Confirmed by Predicted fusion transcripts
(UCSC)
Western Blot
Immunoprecipitation
anti-N (MSMB) & anti-C (NCOA4)
Fusion transcripts
identified by 5’
RACE
Lou et al in press Hum Genet 2012

27. 11q13: Multi-Cancer Susceptibility Region
Discovery by GWAS & Mapping
CGEMS (Thomas NG 2008)
GWAS
1-> 3 Loci (Chung et al HMG 2011)
Australia/
iCOGS Analysis
J French

28. Admixture Analysis of Native
Asian African American European
GWAS Data:
Use of Differences in
Allele Frequencies to
Map Regions that
Contribute to
Differences in STRUCTURE
Incidence
Population Structure Analysis
Can 1000s of SNPs assist in
Identifying Individuals at
Higher Risk for Poor
Response to Pediatric ALL?
• GWAS Scan as a
Preliminary Biomarker
JJ Yang et al Nature Genetics 2011

29. Association testing
Genome-wide
association studies
Behavioral traits Biometrics Nutrient levels
Tobacco Height, Weight, BMI, Vitamins D, B12
Caffeine Menarche/Menopause Carotene
Alcohol
>200 regions & Height/Weight

30. Loci Associated with BMI
Rethinking “Pathways”
Opportunities for:
1. Pathway Analyses
2. Polygenic Model
GIANT CONSORTIUM

31. Unexpected Findings
Genetic Mosaicism
& the Aging Genome
Genome-wide
association studies
Large chromosomal abnormalities, structural
variation, aneuploidy in germline DNA
Rodriguez-Santiago AJHG 2010
Jacobs et al Nature Genetics 2012
Laurie et al Nature Genetics 2012

32. Looking at LRR/BAF graphs
B-allele
Homozygous
σLRR=0.24 σBAF=0.044
Heterozygous
A-allele
Homozygous
LRR – log ratio of observed probe intensity to expected intensity
– Significant deviation from zero is evidence for copy number change
BAF – B allele frequency – ratio of B probe intensity to total intensity
- Expected values for diploid loci are 0, ½ and 1. Other values can indicate allelic
imbalance and suggest copy number changes or mosaicism.

33. Definition of Genetic Mosaicism
Co-existence of distinct subpopulations of
cells regardless of the clonal or
developmental origin
Presence of large structural genomic events
(> 2 Mb)
Resulting in alteration of
• Copy number (gain or loss)
• Loss of heterozygosity

34. Mosaicism in the Extreme
• Age-old explanation for developmental disorders
and catastrophic diseases (NF)
• Trisomy 21, Turners (XO)
• Rare, Highly Penetrant Mutations lead to
Variegated Aneuploidy
• BUB1B
• CEP57
• Complex Syndromes
• Proteus Syndrome & AKT1 (NEJM 2011)
• Ollier Disease & IDH1/IDH2 (Nature Genetics 2011)
• HRAS- Skin/Cancer (NEJM 2011)

35. Validation for 42 Events Observed
100%
1.7% Overall
No Association with Bladder Cancer Risk
Rodríguez-Santiago et al. Am J Hum Genet. 2010;87:129-38

36. Genetic Mosaicism of the Autosomes
• Analysis of 13 GWAS 57,853
• Cancer cases 31,717
• Cancer-free controls 26,136
• Mosaic events detected 681
• Autosomal chromosomes 641
• Individuals 517
• Individuals with multiple events 69
Jacobs et. al. Nature Genetics 2012

37. Genetic Mosaic Events
0.4
0.3 gain
neutral LOH
0.2
loss
0.1
log2 intensity ratio (LRR)
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Proportion of abnormal cells (p)

38. Age at DNA Collection is the Strongest
3.0%
2.5% Predictor of Genetic Mosaicism
2.0%
Frequency
1.5%
1.0%
0.5%
0.0%
<45 45‐49 50‐54 55‐59 60‐64 65‐69 70‐74 75‐
Age at DNA collection
Cancer Free Cancer DX
Jacobs et. al. Nature Genetics 2012

39. Higher Frequency in Men
Compared to Women
Females Males
Cancer Free 0.56% 0.87%
Cancer 0.79% 1.21%
Overall 0.65% 1.04%

40. Frequency of Mosaic Events by Type & Location
Mosaic Chromosome Count Mosaic Chromosome Frequency (%)
Event Location gain loss cnloh mixed Total gain loss cnloh mixed Total
chromosome 62 11 42 5 120 9.7 1.7 6.6 0.8 18.7
telomeric P 11 13 114 1 139 1.7 2.0 17.8 0.2 21.7
telomeric Q 9 10 149 0 168 1.4 1.6 23.2 0.0 26.2
interstitial 14 185 2 1 202 2.2 28.9 0.3 0.2 31.5
span centromere 1 1 2 0 4 0.2 0.2 0.3 0.0 0.6
complex 0 3 0 5 8 0.0 0.5 0.0 0.8 1.2
Total 97 223 309 12 641 15.1 34.8 48.2 1.9

41. Number of chromosomes with mosaic events
• 69 individuals have two or more events
– 46 cancer cases
– 23 cancer-free
Count of cancer
mosaic Likely Possibly cancer- Cancer
chromosomes Untreated Treated Total free type(s)
1 166 111 277 171
2 15 13 28 18
3 7 0 7 3
4 3 0 3 1
5 0 2 2 1
6 0 1 1 0 prostate
7 1 0 1 0 CLL & ovary
11 0 1 1 0 gastric
16 1 0 1 0 lung
20 0 2 2 0 gastric

42. Circos Plot of mosaic events in 57,583
individuals (681 events)
0.4
0.3 neutral LOH
0.2
gain
0.1
loss
tio (LRR)
0

43. Adjusted analysis of association between
mosaicism & cancer
All cancer cases Likely Untreated Possibly Treated
Site of first cancer OR 95% CI p value OR 95% CI p value OR 95% CI p value
non-hematologic cancer 1.27 (1.05-1.52) 0.012 1.45 (1.18-1.80) 5.4E-04 1.03 (0.81-1.30) 0.804
bladder 1.30 (0.90-1.89) 0.164 1.50 (1.01-2.23) 0.043 0.73 (0.32-1.68) 0.455
breast 0.72 (0.41-1.27) 0.256 0.49 (0.18-1.32) 0.159 0.90 (0.46-1.79) 0.770
endometrium 1.27 (0.64-2.50) 0.494 1.35 (0.42-4.30) 0.611 1.24 (0.54-2.82) 0.610
esophagus 0.86 (0.34-2.18) 0.751 3.51 (0.45-27.58) 0.232 0.76 (0.29-2.03) 0.590
glioma 0.88 (0.45-1.74) 0.717 0.95 (0.44-2.05) 0.892 0.70 (0.17-2.86) 0.622
kidney 1.98 (1.27-3.06) 2.3E-03 2.32 (1.46-3.69) 3.6E-04 0.95 (0.30-3.03) 0.931
lung 1.56 (1.18-2.08) 2.0E-03 1.69 (1.23-2.33) 1.3E-03 1.27 (0.81-1.96) 0.295
osteosarcoma 1.34 (0.39-4.59) 0.637 1.34 (0.39-4.59) 0.637
ovary 1.18 (0.48-2.93) 0.718 1.09 (0.27-4.47) 0.903 1.27 (0.40-4.04) 0.690
pancreas 0.89 (0.60-1.33) 0.574 0.55 (0.14-2.24) 0.406 0.93 (0.62-1.41) 0.735
prostate 1.14 (0.79-1.64) 0.485 1.28 (0.85-1.92) 0.243 0.92 (0.51-1.66) 0.781
stomach 1.43 (0.68-3.03) 0.345 3.35 (0.74-15.13) 0.116 1.32 (0.61-2.88) 0.481
testis 3.29 (0.59-18.46) 0.176 3.29 (0.59-18.46) 0.176
other sites 1.49 (0.55-4.05) 0.438 1.49 (0.55-4.05) 0.438

44. Hematological Cancers and “Mosaicism”
A Tale of Two Studies
NCI Study (Jacobs et al 2012) GENEVA (Laurie et al 2012)
43 Hematological cancers Subanalysis in 4 cohorts for
Higher frequency Heme cancers
15.8% Myeloid Hazard ratio estimate for mosaic
26.3% CLL status =10.1 (95% CI=5.8 - 17.7)
Untreated leukemia vs. p=3 x 10-10
cancer-free controls
OR=35.4 (14.7-76.6 95% CI)
p=3.8×10-11
Jacobs et al., 2012 Laurie et. al., 2012

45. Chromosome Y Mosaic Aneuploidy
Pre-Correction Post-Correction
Example of whole chromosome loss in ~60% of cells
• Uncorrected appears to be segmental loss
• Corrected results are clearly whole chromosome loss

46. SRY
RPS4Y1
ZFY
qPCR Assay Panel for chrY loss
• 15 probes designed to determine copy
AMELY
TBL1Y
PRKY
number of single-copy genes relative
to RNaseP
USP9Y
• Coverage across p- and q-arms
DDX3Y
UTY
TMSB4Y
NLGN4Y
CYorf15A
CYorf15B
KDM5D
EIF1AY

47. Calling whole chromosome Y-loss from Infinium GWAS
data calibrated using qPCR Ratios for 15 Regions
1.40
y = 0.8599x + 0.132
R² = 0.8121
Uncalibrated Y-loss from Infinium LRR
1.20
1.00
Possible
0.80 Gain
No Loss
0.60
Undetermined
0.40
Probable Loss
0.20
0.00
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40
Y-loss from 15 qPCR Probes

48. The Aging Genome:
Implications for Cancer Studies
• Importance of thorough characterization of
‘germline’ DNA in parallel with somatic analyses
• Insights into Genomic Instability
Early vs. Late Events
• Genetic biomarkers for early detection of
hematological cancers

49. Two Hypotheses for
Mosaicism in the Aging Genome
Early Event Late Event
Embryonic Progenitors with Somatic Increase in Somatic Alterations with
Alterations Are Below Threshold Age
of Detection PLUS
Unknown Events Trigger Survival Decreased Genomic Stability due to
Bottleneck Telomere Attrition
LEADS TO LEADS TO
Proliferation of Suppressed
Positive Selection with Rapid Populations of Somatically
Expansion of Second Clonal Altered Clones
Population Decreased Cellular Diversity with
Aging and Cell Populations
Become Increasingly Oligoclonal
Co-existence of Distinct Clonal Populations

50. Using Population Studies to Gain Biological
Insights into Genetic Mosaicism:
• Map breakpoint sites (Mitelman/ENCODE)
• Analyze paired tumors in identified studies
• Investigate timing and dynamics (serial samples)
• Proportions & cell type
• Blood Bank Study at NIH
• Australian Twin Registry (2000)
• Confirm Non-hematological Association
• New lung/kidney studies
• Sex Chromosomes
• Y chromosome in TGS
• X has challenge of Lyonization

51. Detectable Genetic Mosaicism &
Hematological Cancers
• PLCO heme cancers (> 700)
• Serial samples for ~25%
• NHL GWAS
• 9000 Cases (5 subtypes)
• Aplastic Anemia (NHLBI- N Young)
• 20% MDS/Leukemia
• CLL/Waldenstrom families (GEB)
• MBL/MGUS serial samples
• CLL Study (L Staudt)

52. Milestones at the Core Genotyping Facility
2001 2002 2004 2006 2008 2010 2012 2014 & beyond
Whole Genome
Candidate SNP Sequencing
Functional Data Genome Wide
Association
Population-based
Studies Exome
Candidate Genes Sequencing
Sequencing
Biological Plausibility
Genetic Markers
Candidate Pathway
Biological Plausibility Regional Sequencing
GWAS & Linkage

53. Human Genetics:
Thresholds and Significance
• Each new technology has brought us to a new ‘crisis’
Linkage and LOD scores in too few families
Candidate Gene/SNPS in small studies
GWAS in insufficiently powered studies
Required replication/validation
CNVs and power outages together with unstable calling
algorithms
Exome sequencing, oligogenic models with insufficient
number of families
Whole genome sequencing and the rising tide of
‘uninterpretable’ variants…

54. Let’s not fool ourselves about
germline genetics…
• First it is about……..Discovery
Biology
Targets
Possible risk variants
• Then…………………….Validation
• Followed by…………Characterization
• Later…………………….Clinical application
• Distant future………Targets
Don’t forget lifestyle/environment…..

55. Mapping Genetic Architecture
• Comprehensive map will emerge across
spectrum of variation
• GWAS Common Variants
• Sequencing Uncommon and Rare Variants
• Search for “Pathways”
• Moderate penetrance breast cancer genes
• Develop Risk Profiles in Age of Sequencing
• Can Prevention or Early Detection Improve
Outcome?

56. In the not too distant future
• We will look back and think of GWAS as the
“Golden Age”
• The temptation is to continue to do GWAS
– Should not be abandoned (esp with imputation)
– Think bigger in size for comprehensively exploring
MAF spaces
• > 10%
• 1-10%
• < 1% Can it be done by association testing??
But the allure of sequencing is at hand…..

57. Emerging Impact of Population Genetics
in the Search for ‘Elusive’ Variants
• In GWAS era- we tolerated minor mismatching
because sample sizes and MAFs are large
• As MAFs fall, challenge of population private
variants ‘sky-rockets’
• How do we know if a rare variant in one
population is non-contributory whereas in a
second population it influences risk/outcome?
• Value of functional validation
• Challenge of Environmental Exposure/Lifestyle

58. Advances will be accelerated by
“Collective Intelligence”
“I not only use all of the brains
I have, but all I can borrow”
Woodrow Wilson

59. Acknowledgements
NCI-DCEG HSPH
LTG Joseph Fraumeni David Hunter
Renee Chen Peggy Tucker Pete Kraft
Charles Chung Gilles Thomas Sara Lindstrom
Jean- Nicolas Cornu Robert Hoover
Jun Fang Meredith Yeager BPC3 & CGEMS
Phoebe Lee Kevin Jacobs ACS (M Thun)
Lea Jessop Sharon Savage ATBC (D Albanes-DCEG)
Hye Kim Nilanjan Chatterjee CAPS (H Gronberg/J Xu)
Joe Kovacs Nat Rothman CeRePP (O Cussenot)
Tim Myers JuHyun Park CONOR (L Vatten)
Nilabja Sikdar Sonja Berndt EPIC (E Riboli)
Sharon Savage JHU (W Issacs/J Xu)
Strategic Support Lindsay Morton MEC (B Henderson)
MJ Horner Zhaoming Wang PLCO (R Hayes)
Tammy Bell NCI-CCR WHI (R Prentiss)
Mike Dean
Investigators Hong Lou
DFCI
Laufey Amundadottir Institut Curie Matt Freedman
Kevin Brown Olivier Delattre Mark Pomerantz
Mila Prokunina-Olsson Carlo Lucchesi

60. Acknowledgements
Glioma
Preetha Rajamaran (NCI,) Laura Beane Freeman (NCI), Christine
Berg (NCI), Julie Buring, Ulrika Andersson, Mary Butler, Tania
Carreon, Maria Feychting, Anders Ahlbomm J Michael Gaziano,
Graham Giles, Goran Hallmans, Wei Zheng, Susan E Hankinson,
189 authors from 48 participating studies: Roger Henriksson, Peter D Inskip, Christoffer Johansen
Annelie Landgren, Roberta McKean-Cowdin, Dominique
Kevin Jacobs Michaud, Beatrice Melin, Ulrike Peters, Avima Ruder, Howard
Upper GI Sesso, Gianluca Severi, Xiao-Ou Shu, Kala Visvanathan, Emily
Meredith Yeager Christian Abnett, Alisa White, Alicja Wolk, Anne Zeleniuch-Jacquotte,
Margaret Tucker Goldstein, Phil Taylor, Wei Zheng, Manolis Kogevinas
Neal Freedman, Linda
Nathaniel Rothman Liao, Ti Ding, You-Lin
Sholom Wacholder Qiao, Yu-Tang Gao,
African-American Lung Cancer Consortium
Woon-Puay Koh, Yong-
Luis Perez-Jurado Bing Xiang, Ze-Zhong Krista Zanetti (NCI), Melinda Aldrich, Chris Amos,
Joseph Fraumeni Tang, Jin-Hu Fan, Jian- William Blot, Cathryn Bock, Elizabeth Gillanders, Curt
Min Yuan Harris, Chris Haiman, Brian Henderson, Laurence
Kolonel, Loic Le Marchand, Lorna McNeill, Benjamin
Rybicki, Ann Schwartz, Lisa Signorello, Margaret
Breast (CGEMS) Spitz, John Wiencke, Margaret Wrensch, Xifeng Wu
Prostate Cancer (CGEMS) David Hunter
Robert Hoover, Gilles Thomas, Peter Kraft
Sonja Berndt, Weiyin Zhou, Xiang Louise A Brinton,
Lung
Deng, Chenwei Liu, Michael Cullen, Neal Caporaso, Teresa Landi, Lynn Goldin, Dario
Jolanta Lissowska,
Ann Hsing, Caroline Epstein, Laurie Consonni, Pier Alberto Bertazzi, Melissa Rotunno
Beata Peplonska
Burdett, Nilanjan Chatterjee, Joshua Regina Ziegler
Sampson, Amanda Black, Michael
PanScan
Dean, Charles, Chung, Joseph
Patricia Hartge, Laufey Amundadottir, Rachael
Kovaks, Nan Hu, Kai Yu, MJ Horner
Stolzenberg-Solomon (NCI), Demetrius Albanes (NCI),
Renal Jarmo Virtamo, Zhaoming Wang, Amy Hutchinson, Alan
American Cancer Society Mark Purdue, Wong-
A Arslan, H Bas Bueno-de-Mesquita, Charles Fuchs,
Ho Chow, Lee E
Susan Gapstur, Victoria Stevens, Steven Gallinger, Myron D Gross, Elizabeth Holly, Alison
Moore, Kendra
Lauren Teras, Mia Gaudet Klein, Andrea LaCroix, Margaret Mandelson, Gloria
Schwartz, Faith Davis
Petersen, Marie-Christine Boutron-Ruault, Paige M
Bracci, Federico Canzian, Kenneth Chang,Michelle
Cottercho, Ed Giovannucci, Michael Goggins, Judith
Bladder Hoffman Bolton, Mazda Jenab, Kay-Tee Khaw, Vittorio
Montse Garcia-Closas, Debra Krogh, Robert Kurtz, Robert McWilliams, Julie B
Silverman, B. Rodriguez-Santiago, Testis, Ovary & Mendelsohn, Kari Rabe Elio Riboli, Anne Tjonneland,
Nuria Malats,, Francisco Real, Jonine Endometrium Geof Tobias, Dimitrios Trichopoulos, Joanne Elena,
Figueroa, Ludmila Prokunina-Olsson, Christian Kratz, Katherine Herbert Yu, Fredrick Shumacher, Daniel Stram, Lisa
Dalsu Baris, Gaelle Marenne, Manolis McGlynn, Mark Greene, Mirabello, Juan R Gonzalez, Olaya Villa, Donghui Li, Eric
Kogevinas, Molly Schwenn, Alison Michael Cook, Barry J Duell, Harvey A Risch, Sara H Olson, Charles
Johnson Graubard, Ralph Erickson, Kooperberg, Brian M Wolpin, Li Jiao, Manal Hassan,
Nicolas Wentzensen William Wheeler
Osteosarcoma
Sharon Savage, Irene Andrulis,
Jay Wunder, Ana Patiao-Garcia,
Luis Sierrasesumaga, Donald A
Barkauskas, Richard Gorlick

61. 61
Chromosomal mosaicism from birth to
old age and its relationship to
hematological cancer
Investigators:
Cathy C. Laurie, Cecelia A. Laurie, Kenneth Rice, Kimberly F. Doheny, Leila R. Zelnick, Caitlin P.
McHugh, Hua Ling, Kurt N. Hetrick, Elizabeth W. Pugh, Chris Amos, Qingyi Wei, Li-e Wang,
Jeffrey E. Lee, Kathleen C. Barnes, Nadia N. Hansel, Rasika Mathias, Denise Daley, Terri H.
Beaty, Alan F. Scott, Ingo Ruczinski, Rob B. Scharpf, Laura J. Bierut, Sarah M. Hartz, Maria Teresa
Landi, Neal D. Freedman, Lynn R. Goldin, David Ginsburg, Jun Li, Karl C. Desch, Sara S. Strom,
William J. Blot, Lisa B. Signorello, Sue A. Ingles, Stephen J. Chanock, Sonja I. Berndt, Loic Le
Marchand, Brian E. Henderson, Kristine R Monroe, John A. Heit, Mariza de Andrade, Sebastian M.
Armasu, Cynthia Regnier, William L. Lowe, M. Geoffrey Hayes, Mary L. Marazita, Eleanor
Feingold, Jeffrey C. Murray, Mads Melbye, Bjarke Feenstra, Jae Hee Kang, Janey L. Wiggs, Gail
Jarvik, Andrew N. McDavid, Venkatraman E. Seshan, Daniel B. Mirel, Andrew Crenshaw, Nataliya
Sharopova, Anastasia Wise, Jess Shen, David R. Crosslin, David M. Levine, Xiuwen Zheng,
Jenna I Udren, Siiri Bennett, Sarah C. Nelson, Stephanie M. Gogarten, Matthew P. Conomos,
Patrick Heagerty, Teri Manolio, Louis R. Pasquale, Christopher A. Haiman, Neil Caporaso, Bruce
S. Weir

63. Do mosaic subjects have increased
incidence of hematological cancer?
Mosaic Non-mosaic
Event 15 90
No event 134 8,323
Cox proportional hazards regression:
Stratified analysis of the four cohorts
Time to event ~ Age + non-hematological cancer status (time-
dependent) + ethnicity + sex (within the PLCO stratum)
The hazard ratio estimate for mosaic status is 10.1 (95% CI=5.8 - 17.7)
and the p-value is 3 x 10-10
Meta-analysis gave very similar estimate.

64. CGF
Metrics
360,214 DNA profiles 7.6 x 1013 SNP genotypes
(Identifiler)
5.7 x 10-6 STRs
150 Gbps regional sequencing
80 whole genomes + 328 samples whole-exome
80 on the way sequencing