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

BRCA1

BRCA2 TP53

PTEN CHEK2

ATM

PALB2 BRIP1

RAD51C

ERCC2

> Doubling in

2012

1

1.1

1 0 0.1 0.9

3

10

1.2

1.3

1.4

1.5

0.2 0.3 0.4 0.5 0.6 0.7 0.8

Po

pu

lati

on

gen

oty

pe

rela

tive r

isk

Population risk-allele frequency

Genetic Predisposition to Breast Cancer European Population

BCAC CGEMS/BCAC WTCCC Other

ATM PTCH

FLCN

BLM

BRCA1 BRCA2

T

MLH1

MSH2

PMS2

MSH6 HRAS

PTEN

DKC1

TERC

TERT

TINF2

WRAP53 TP53

NF1

EXT1

EXT2

FANCA

FANCB

FANCC

FANCD2

FANCE FANCF

FANCG

FANCI

FANCL

FANCJ

FANCM

FANCN/BRIP

1

RAD51C

TSC2

CDH1

KIT PDGFRA

HRPT2

RUNX1 CHEK2

CDKN2

A

CDK4

BUB1B

MEN1 RET

ALK

PHOXB2

NF2

NBS1

SDHD SDHC

SDHB

STK11

APC

BMPR1

A

SMAD4

MUTYH

FH

MET

RB1

SMARCB1

RECQL4

GPC3

TSC1

ERCC4

VHL

WRN

WT1

XPA ERCC3

XPC

ERCC2

DDB2

SLX4

ERCC5

POLH

GATA2

CEBPA

SDHA

SDHAF2

TMEM127

MAX

DICER1

BAP1

ELANE

HAX1

CBL

PTPN11

MITF

GALNT1

2

HOXB13

CYLD

EGFR

SUFO DIS3L2

SBDS

C Kratz

97 Genes Mutated in Cancer Susceptibility Syndromes

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

PSCA

BNC2

ADH1B

C20orf54

ALD

FOXE1

NKX2-1

6p21.33

CTBP2

11p15 THADA

EHBP1

ITGA6

3p11.2

EEFSEC PDLIM5

TET2

SLC22A3

JAZF1

LMTK2

8p21

8q24.21(x5)

MSMB/

NCOA4

17q24.3

19q13.2

KLK2/

KLK3

BIK

NUDT10/

NUDT11

48 Prostateate

C2orf43

5p15

FOXP4

GPRC6A

13q22

10p14

11q23.1

BMP4

GREM1

CDH1 SMAD7

RHPN2 BMP2

EIF3H

8q24.21

13 Colorectal

PRKD2 15q21.3

15q23 16q24.1

11q24.1

2q13

FARP2

IRF4

8q24.21

CDKN2A/

CDKN2B

RTEL1

PHLDB1

TERT

CDKN2A/

CDKN2B

CCDC26

7 Glioma

1p36

1q42

KIF1B

TERT/

CLPTM1L

7q32

7 Basal Cell Carcinoma

TYR

KR

ASIP

MC1R

CDKN2A/

CDKN2B

TYR PLCE1

ASIP

9 Melanoma

1q21.1

BARD1

6 Neuroblastoma

6p22

KLF5/

KLF12

ABO

1q32.1

4 Pancreas

CLPTM1L

ITGA9

7 Nasopharyngeal

GABBR1

HLA-F

HLA-A IKZF1

ARIDB5

CEBPE

CHRNA3/

CHRNA5

6p21.32

TERT/

CLPTM1L

6 Lung

SPRY4

BAK1 KIT

6 Testicular

GSTM1

deletion

11* Bladde7

TACC3

NAT2

PSCA

MYC

FGFR2

LSP1

TOX3 COX11/

STXBP4

RAD51L1

1p11.2

2q35

SLC4A7/

NEK10

5p12

5q11.2

ECHDC1/

RNF146

C6orf97/

ESR1

26* Breast

4 Pediatric Acute Lymphoblastic Leukemia 7 Esophageal Squamous

4 Thyroid 2 Non-Hodgkin 5 Ovary 3 Gastric

10 CLL

1 Locus marked by a CNV

Chung & Chanock

ZNF365

10p15.1

10q22.3

DMRT1

ATF7

9 Multiple

1q22

1 Liver

2q31

19p13

12q13

3q26

1q41 TP63

GATA3 REL

3 Hodgkins

11q13

22q13

CCNE1

2q37

HNF1B x 2

10q21.2 7q32

9q31.2

1q42.12

1q21.3

CASP8

ATM

MX2

LMO1

SLC14A1

5q11

6p21

21q22

13q12.125

22q12.25

5q13

3q13

EGFR

3 Ewing Sarcoma

TARDBP

ERG2

15q15

HSD17B12

LINC00340

DDX4

3p12.1

10q26

FAM111A

EPAS1

3 Kidney

12p

Another 85 coming soon… breast, bladder, kidney, lung, ovary, prostate, TCGT

2 Wilms

DDX1

DLG2

H2

T5

LG

Published Cancer GWAS Etiology Hits: 5.15.12

~240 Disease Loci marked by SNPs

2011

IP

.13

11.23

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…

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…

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

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

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.

Prostate Cancer Risk Factors 2006

• Age

• Ethnic Background

• Family History

CTBP2

11p15 THADA

EHBP1

ITGA6

3p12.1

EEFSEC PDLIM5

TET2

SLC22A3

JAZF1

LMTK2

8p21

8q24.21

MSMB/

NCOA4

11q13

HNF1B

17q24.3

19q13.2

KLK2/

KLK3

BIK

NUDT10/

NUDT11

Prostate Cancer: 48 as of May 1, 2012

> 24 More to come

PSA or Prostate Cancer or both?? Type 2 Diabetes

3

5

10q11.23

5p15.33

13q22.1

FOXP4

2p24.1

RFX6

3p11

2q27.3

12q13

2

FAM111A

10q26

No Clear Evidence of Specific Loci

For Advanced Disease

Suggests Distinct Regions Influence

Etiology & Outcome

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%

Prediction is difficult,

Especially about the future.

Yogi Berra

Dan Quayle

Niels Bohr

Model Under

65

Over

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

Area Under the ROC

Curve

Discriminatory Power of Genetic Risk

Score for Prostate Cancer

Theoretical Limits of Risk Prediction

Crohn’s Disease

Sibling relative-risk=20-35

Common cancers (Br or Pr)

Sibling relative risk=2-3

Random

Using known loci

Using all estimated loci

Ideal (if we could explain all heritability)

Park et al., Nat Genet ,2010

Park et al Nature Genetics 2010

Different Diseases Display Distinct Architectures

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

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

1

1.1

1 0 0.1 0.9

3

10 BRCA1

BRCA2 TP53

PTEN

ATM CHEK2

PALB2 BRIP1

RAD51C

ERCC2

1.2

1.3

1.4

1.5

0.2 0.3 0.4 0.5 0.6 0.7 0.8

Po

pu

lati

on

gen

oty

pe

rela

tive r

isk

Population risk-allele frequency

> Doubling in

2012

Genetic Predisposition to Breast Cancer European Population

BCAC CGEMS/BCAC WTCCC Other

Per

allele

rela

tive r

isk

Risk-allele frequency

1

1.2

1 0 0.05 0.95

3

5

1.4

0.50

????

BRCA2

Genomic Architecture of Prostate Cancer

Susceptibility Loci:

Effe

ct s

ize

1

100

0.001 0.01 0.1 0.5

5

10

DUSP12

NBPF23

HSD17B12

DDX4

NME7

LMO1

FLJ22536

BARD1

Additional polymorphisms to be discovered by GWAS

Additional mutations and rare polymorphisms to be

discovered by NGS

Discovered mutations

Yet to be discovered mutations

Discovered polymorphisms

Yet to be discovered polymorphisms

ALK

PHOX2B

TP53

SDHB

PTPN11

Genetic predisposition to neuroblastoma: Current status and future directions

Modified from Manolio, et al. Nature 2009 Allele Frequency IL31RA

Smoking….

Per

allele

rela

tive r

isk

Risk-allele frequency

1

1.2

1 0 0.05 0.95

3

5

1.4

0.50

????

Adenocarcinoma specific

Stronger in nonsmokers

Genomic Architecture of Lung Cancer

Susceptibility Loci

Non-

Protein

Coding

Bioinformatic

Analysis

Experimental

Strategy

Unannotated

Transcript

Regulatory

Element

Epigenetic

Alteration of

Gene Levels

Effect on

Genes

Elsewhere

Novel

Transcripts

Functional

Elements

In vitro/vivo

miRNA

RNASeq

Expression

Quantitative

Trait

Analysis

Histone

Methylation

Elements

Investigation of GWAS Markers

Functional

Elements

Coding

Variant

Effect on

Protein

Allelic

Differences

mRNA

Stability

Gene

Product

Functional

Analysis

Initial Findings

Expression

Quantitative

Trait

Analysis

10q11.2 & Prostate Cancer

Risk rs10993994 within promoter of

MSMB= b-microseminoprotein

Prostate specific serum marker under study

20,000 subjects

Risk Allele “T”

Lower expression levels

Reporter assays

Electrophoretic Mobility Shifts

Levels in Prostate Tissue

Tumor Tissue

Functional Analysis

10q11.2 Could Be More Complex……

MSMB and NCOA4

RNA Expression MSMB and NCOA4

Normal

Tumor Tissue

Anchorage Independent Growth is Specific to Prostate

MSMB- Suppression

NCOA4- Over-expression

Re-sequence

454 FLX

~100 subjects

Predicted fusion transcripts (UCSC)

Genomic organization of MSMB and NCOA4

Fusion transcripts identified by 5’ RACE

Lou et al in press Hum Genet 2012

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 Confirmed by Western Blot Immunoprecipitation anti-N (MSMB) & anti-C (NCOA4)

11q13: Multi-Cancer Susceptibility Region

Australia/ iCOGS Analysis J French

Discovery by GWAS & Mapping

CGEMS (Thomas NG 2008)

1-> 3 Loci (Chung et al HMG 2011)

GWAS

Admixture Analysis of

GWAS Data:

Use of Differences in

Allele Frequencies to

Map Regions that

Contribute to

Differences in

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

African

Native

American European Asian

STRUCTURE

Genome-wide

association studies

Association testing

Biometrics Nutrient levels Behavioral traits

Height, Weight, BMI,

Menarche/Menopause

>200 regions & Height/Weight

Tobacco

Caffeine

Alcohol

Vitamins D, B12 Carotene

Opportunities for:

1. Pathway Analyses

2. Polygenic Model

GIANT CONSORTIUM

Loci Associated with BMI

Rethinking “Pathways”

Genome-wide association studies

Large chromosomal abnormalities, structural

variation, aneuploidy in germline DNA

Unexpected Findings

Rodriguez-Santiago AJHG 2010

Jacobs et al Nature Genetics 2012

Laurie et al Nature Genetics 2012

Genetic Mosaicism & the Aging Genome

Looking at LRR/BAF graphs

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.

B-allele

Homozygous

A-allele

Homozygous

Heterozygous

σLRR=0.24 σBAF=0.044

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

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)

Validation for 42 Events Observed

100%

Rodríguez-Santiago et al. Am J Hum Genet. 2010;87:129-38

1.7% Overall

No Association with Bladder Cancer Risk

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

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

log 2

inte

nsi

ty r

atio

(LR

R)

Proportion of abnormal cells (p)

gain

neutral LOH

loss

Genetic Mosaic Events

Age at DNA Collection is the Strongest

Predictor of Genetic Mosaicism

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

<45 45‐49 50‐54 55‐59 60‐64 65‐69 70‐74 75‐79

Frequency

Age at DNA collection

Cancer Free Cancer DX

Jacobs et. al. Nature Genetics 2012

Females Males

Cancer Free 0.56% 0.87%

Cancer 0.79% 1.21%

Overall 0.65% 1.04%

Higher Frequency in Men Compared to Women

Frequency of Mosaic Events by Type & Location

gain loss cnloh mixed Total gain loss cnloh mixed Total

62 11 42 5 120 9.7 1.7 6.6 0.8 18.7

11 13 114 1 139 1.7 2.0 17.8 0.2 21.7

9 10 149 0 168 1.4 1.6 23.2 0.0 26.2

14 185 2 1 202 2.2 28.9 0.3 0.2 31.5

1 1 2 0 4 0.2 0.2 0.3 0.0 0.6

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

interstitial

span centromere

complex

Mosaic Chromosome Count Mosaic Chromosome Frequency (%)

Event Location

chromosome

telomeric P

telomeric Q

Number of chromosomes with mosaic events • 69 individuals have two or more events

– 46 cancer cases

– 23 cancer-free

Count of mosaic

chromosomes

cancer Likely

Untreated Possibly Treated Total

cancer- free

Cancer 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

Circos Plot of mosaic events in 57,583 individuals (681 events)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

log 2

inte

nsi

ty r

atio

(LR

R)

Proportion of abnormal cells (p)

neutral LOH

gain

loss

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

All cancer cases Likely Untreated Possibly Treated

Adjusted analysis of association between mosaicism & cancer

Hematological Cancers and “Mosaicism” A Tale of Two Studies

NCI Study (Jacobs et al 2012)

43 Hematological cancers Higher frequency

15.8% Myeloid

26.3% CLL

Untreated leukemia vs. cancer-free controls

OR=35.4 (14.7-76.6 95% CI)

p=3.8×10-11

GENEVA (Laurie et al 2012)

Subanalysis in 4 cohorts for

Heme cancers

Hazard ratio estimate for mosaic status =10.1 (95% CI=5.8 - 17.7)

p=3 x 10-10

Jacobs et al., 2012 Laurie et. al., 2012

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

SRY RPS4Y1 ZFY

AMELY TBL1Y

PRKY

USP9Y DDX3Y UTY TMSB4Y

NLGN4Y

CYorf15A CYorf15B KDM5D

EIF1AY

qPCR Assay Panel for chrY loss • 15 probes designed to determine copy

number of single-copy genes relative to RNaseP

• Coverage across p- and q-arms

y = 0.8599x + 0.132 R² = 0.8121

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40

Un

calib

rate

d Y

-lo

ss f

rom

In

fin

ium

LR

R

Y-loss from 15 qPCR Probes

Calling whole chromosome Y-loss from Infinium GWAS data calibrated using qPCR Ratios for 15 Regions

Probable Loss

Undetermined

No Loss

Possible Gain

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

Two Hypotheses for Mosaicism in the Aging Genome

Early Event

Increase in Somatic Alterations with Age

PLUS Decreased Genomic Stability due to

Telomere Attrition

LEADS TO Proliferation of Suppressed

Populations of Somatically Altered Clones

Decreased Cellular Diversity with Aging and Cell Populations Become Increasingly Oligoclonal

Late Event

Embryonic Progenitors with Somatic Alterations Are Below Threshold of Detection

Unknown Events Trigger Survival

Bottleneck

LEADS TO Positive Selection with Rapid

Expansion of Second Clonal Population

Co-existence of Distinct Clonal Populations

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

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)

Milestones at the Core Genotyping Facility

2001 2002 2004 2006 2008 2010 2012 2014 & beyond

Candidate SNP

Functional Data

Candidate Genes

Biological Plausibility

Genetic Markers

Candidate Pathway

Biological Plausibility

Genome Wide

Association

Studies

Regional Sequencing

GWAS & Linkage

Exome

Sequencing

Whole Genome

Sequencing

Population-based

Sequencing

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…

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…..

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?

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…..

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

Advances will be accelerated by

“Collective Intelligence”

“I not only use all of the brains

I have, but all I can borrow”

Woodrow Wilson

Acknowledgements

NCI-DCEG Joseph Fraumeni

Peggy Tucker

Gilles Thomas

Robert Hoover

Meredith Yeager

Kevin Jacobs

Sharon Savage

Nilanjan Chatterjee

Nat Rothman

JuHyun Park

Sonja Berndt

Sharon Savage

Lindsay Morton

Zhaoming Wang

NCI-CCR Mike Dean

Hong Lou

Institut Curie Olivier Delattre

Carlo Lucchesi

HSPH David Hunter

Pete Kraft

Sara Lindstrom

BPC3 & CGEMS ACS (M Thun)

ATBC (D Albanes-DCEG)

CAPS (H Gronberg/J Xu)

CeRePP (O Cussenot)

CONOR (L Vatten)

EPIC (E Riboli)

JHU (W Issacs/J Xu)

MEC (B Henderson)

PLCO (R Hayes)

WHI (R Prentiss)

DFCI Matt Freedman

Mark Pomerantz

LTG Renee Chen

Charles Chung

Jean- Nicolas Cornu

Jun Fang

Phoebe Lee

Lea Jessop

Hye Kim

Joe Kovacs

Tim Myers

Nilabja Sikdar

Strategic Support MJ Horner

Tammy Bell

Investigators Laufey Amundadottir

Kevin Brown

Mila Prokunina-Olsson

Acknowledgements 189 authors from 48 participating studies:

Kevin Jacobs

Meredith Yeager

Margaret Tucker

Nathaniel Rothman

Sholom Wacholder

Luis Perez-Jurado

Joseph Fraumeni

African-American Lung Cancer Consortium Krista Zanetti (NCI), Melinda Aldrich, Chris Amos,

William Blot, Cathryn Bock, Elizabeth Gillanders, Curt

Harris, Chris Haiman, Brian Henderson, Laurence

Kolonel, Loic Le Marchand, Lorna McNeill, Benjamin

Rybicki, Ann Schwartz, Lisa Signorello, Margaret

Spitz, John Wiencke, Margaret Wrensch, Xifeng Wu

Lung Neal Caporaso, Teresa Landi, Lynn Goldin, Dario

Consonni, Pier Alberto Bertazzi, Melissa Rotunno

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,

Roger Henriksson, Peter D Inskip, Christoffer Johansen

Annelie Landgren, Roberta McKean-Cowdin, Dominique

Michaud, Beatrice Melin, Ulrike Peters, Avima Ruder, Howard

Sesso, Gianluca Severi, Xiao-Ou Shu, Kala Visvanathan, Emily

White, Alicja Wolk, Anne Zeleniuch-Jacquotte,

Wei Zheng, Manolis Kogevinas

Osteosarcoma Sharon Savage, Irene Andrulis,

Jay Wunder, Ana Patiao-Garcia,

Luis Sierrasesumaga, Donald A

Barkauskas, Richard Gorlick

Testis, Ovary &

Endometrium Christian Kratz, Katherine

McGlynn, Mark Greene,

Michael Cook, Barry

Graubard, Ralph Erickson,

Nicolas Wentzensen

Bladder Montse Garcia-Closas, Debra

Silverman, B. Rodriguez-Santiago,

Nuria Malats,, Francisco Real, Jonine

Figueroa, Ludmila Prokunina-Olsson,

Dalsu Baris, Gaelle Marenne, Manolis

Kogevinas, Molly Schwenn, Alison

Johnson

PanScan Patricia Hartge, Laufey Amundadottir, Rachael

Stolzenberg-Solomon (NCI), Demetrius Albanes (NCI),

Jarmo Virtamo, Zhaoming Wang, Amy Hutchinson, Alan

A Arslan, H Bas Bueno-de-Mesquita, Charles Fuchs,

Steven Gallinger, Myron D Gross, Elizabeth Holly, Alison

Klein, Andrea LaCroix, Margaret Mandelson, Gloria

Petersen, Marie-Christine Boutron-Ruault, Paige M

Bracci, Federico Canzian, Kenneth Chang,Michelle

Cottercho, Ed Giovannucci, Michael Goggins, Judith

Hoffman Bolton, Mazda Jenab, Kay-Tee Khaw, Vittorio

Krogh, Robert Kurtz, Robert McWilliams, Julie B

Mendelsohn, Kari Rabe Elio Riboli, Anne Tjonneland,

Geof Tobias, Dimitrios Trichopoulos, Joanne Elena,

Herbert Yu, Fredrick Shumacher, Daniel Stram, Lisa

Mirabello, Juan R Gonzalez, Olaya Villa, Donghui Li, Eric

J Duell, Harvey A Risch, Sara H Olson, Charles

Kooperberg, Brian M Wolpin, Li Jiao, Manal Hassan,

William Wheeler

Upper GI Christian Abnett, Alisa

Goldstein, Phil Taylor,

Neal Freedman, Linda

Liao, Ti Ding, You-Lin

Qiao, Yu-Tang Gao,

Woon-Puay Koh, Yong-

Bing Xiang, Ze-Zhong

Tang, Jin-Hu Fan, Jian-

Min Yuan

Breast (CGEMS) David Hunter

Peter Kraft

Louise A Brinton,

Jolanta Lissowska,

Beata Peplonska

Regina Ziegler

Prostate Cancer (CGEMS) Robert Hoover, Gilles Thomas,

Sonja Berndt, Weiyin Zhou, Xiang

Deng, Chenwei Liu, Michael Cullen,

Ann Hsing, Caroline Epstein, Laurie

Burdett, Nilanjan Chatterjee, Joshua

Sampson, Amanda Black, Michael

Dean, Charles, Chung, Joseph

Kovaks, Nan Hu, Kai Yu, MJ Horner

American Cancer Society Susan Gapstur, Victoria Stevens,

Lauren Teras, Mia Gaudet

Renal Mark Purdue, Wong-

Ho Chow, Lee E

Moore, Kendra

Schwartz, Faith Davis

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

61

Chromosomal mosaicism from birth to old age and its relationship to

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.

Do mosaic subjects have increased incidence of hematological cancer?

360,214 DNA profiles (Identifiler)

5.7 x 10-6 STRs

7.6 x 1013 SNP genotypes

150 Gbps regional sequencing

80 whole genomes + 80 on the way

328 samples whole-exome sequencing

CGF Metrics