1. Selecting Initial GWAS and replication studies
David Hunter
Harvard School of Public Health
Brigham and Women’s Hospital
Broad Institute of MIT and Harvard

2. Initial Study for GWAS
Cases and controls well matched with respect to ancestry to minimize population stratification
(restriction to one self-identified group)
Genomic control or other methods
e.g. Eigenstrat (Price et al, 2006), may compensate for looser matching

3. 45, 19 and 19 SNPs (respectively) with p<10-7 not shown
Control of population stratification e.g. hair color in Nurses’ Health Study (European ancestry)
Chi-squared inflation factors and Q-Q plots of –log10 p-values with no adjustment for population stratification and adjusting for the top four and fifty eigenvectors (Price et al, 2006)
45, 19 and 19 SNPs (respectively) with p<10-7 not shown
Kraft P, unpublished

4. Article
Nature 447, (7 June 2007) | doi: /nature05911; Received 26 March 2007; Accepted 11 May 2007
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium

5. Conclusions Broad matching on ancestry and region adequate for
discovery of strongest hits
Statistical methods for control of population stratification
(within populations of European ancestry) adequate to
assist in discovery of strongest hits
Will more rigorous designs permit discovery of weaker
associations?
When signal-noise is low, how does noise due to
multiple comparisons compare with noise due to
poor matching of controls? False negatives the biggest
problem (can deal with false +ves via replication).

6. Criteria for follow-up of initial reports of genotype–phenotype associations
Replication studies should be of sufficient sample size to convincingly distinguish the proposed effect from no effect
Replication studies should preferably be conducted in independent data sets, to avoid the tendency to split one well-powered study into two less conclusive ones
The same or a very similar phenotype should be analysed
A similar population should be studied, and notable differences between the populations studied in the initial and attempted replication studies should be described
Similar magnitude of effect and significance should be demonstrated, in the same direction, with the same SNP or a SNP in perfect or very high linkage disequilibrium with the prior SNP (r2 close to 1.0)
Statistical significance should first be obtained using the genetic model reported in the initial study
When possible, a joint or combined analysis should lead to a smaller P-value than that seen in the initial report
A strong rationale should be provided for selecting SNPs to be replicated from the initial study, including linkage-disequilibrium structure, putative functional data or published literature
Replication reports should include the same level of detail for study design and analysis plan as reported for the initial study
Chanock, Maniolo et al. Nature, June 7th 2007

7. Initial Study for GWAS: technical issues
Standard advice – case and control samples handled exactly the same at every stage
Source of DNA
Blood/buffy coat mostly good results
Buccal cell variable results (Feigelson et al. CEBP, encouraging)
Whole genome amplified DNA (Affy OK, Illumina in development)

8. Replication studies
For statistical replication, prefer:
Similar phenotype
Similar ancestry
For generalizability, prefer
Different populations
Different ancestry backgrounds (may
also help with fine mapping)

9. Study design?
Prospective
Protect from survivor bias
Protect from selection bias
Interpretability of gene-environment analyses
Possibility of interpretable biomarkers

10. Study quality? Importance depends on strength of signal
To date – little apparent relation between
probability of replication and quality
May matter more for weak signals
Sample size may trump quality
(within limits)

11. NCI BPC3 Results: 7909 cases, 8683 controls
Rs : Overall p, trend 4 x 10-19
Schumacher et al. Can Res, April 2007

12. a, rs2981582; b, rs3803662; c, rs889312; d, rs13281615; and e, rs3817198
FGFR2
Forest plots of the per-allele odds ratios for each of the five SNPs reaching
genome-wide significance for breast cancer. Easton et al. Nature, May 2007

13. Cancer Genetic Markers of Susceptibility (CGEMS):

14. General Strategy for Multistage analysis of Prostate & Breast Cancer
Initial GWAS Study
1150 cases/1150 controls
540,000 Tag SNPs
Follow-up Study #1
4500 cases/ 4500 controls
~28,000 SNPs
Follow-up Study #2
3500 cases/ 3500 controls
at least 1,500
SNPs
30 ±20
loci
Fine Mapping

15. Committed Studies CGEMS
Breast Cancer
NHS (GWAS)
PLCO
WHI
Polish C/C
ACS
EPIC
MEC
Prostate Cancer
PLCO (GWAS)
ACS
HPFS
PHS
ATBC
CeRePP
EPIC
MEC

16. CGEMS: caBIG Posting Pre-Computed Analysis
No Restrictions
Raw Genotype
Case/control
Age (in 5 yrs)
Family Hx (+/-)
Registration

17. Association Tests Prostate 10/06 Breast 04/07 ~528,000 SNPs
Illumina 550k
Instant
Replication!

18. Additional In silico replication possibilities
dbGAP ncbi.nlm.nih.gov/dbgap
Framingham nhlbi.nih.gov/about/framingham
WTCCC wtccc.org.uk
DGI broad.mit.edu/diabetes

19. Chromosomes Log10(p-value) 1 2 3 4 5 6 7 8 -2 -3 -4 -5 9 10 11 12 13q p q -2 -3 -4 -5 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X p q p q -2
Log10(p-value) -3 -4
FGFR2 -5 -6

20. The six SNPs with the smallest P values of the 528,173 tested among 1,145 cases of postmenopausal invasive breast cancer and 1,141 controls (full results available at ).
SNP ID Χ2* P* ORhet* ORhomo* Chromosome Gene rs
rs FGFR2
rs RELN
rs FGFR2
rs TLR1,TLR6 rs
*From analyses adjusting for age, matching factors (see Methods), and three eigenvectors of the principal components identified by Eigenstrat. P value obtained by a score test with 2df.
Hunter et al, Nat Gen, May 2007

21. Scatterplot of P values for the FGFR2 locus from the GWAS.

22. Results of associations of rs in the Nurses Health Study, Nurses’ Health Study 2, and the PLCO study.
Study Population Allele Frequency ORhet ORhomo Ptrend
(N cases/N controls) Cases Controls (95% CI) (95% CI)
(%) (%)
Nurses’ Health Study (1,145/1,141)
x 10-6
( ) ( )
Nurses’ Health Study 2 (302/594)
( ) ( )
PLCO (919/922)
( ) ( )
ACS CPS-II (555/556)
( ) ( )
Pooled estimates (2,921/3,213) x 10-10
( ) ( )

23. UNFINISHED AGENDA Where is the causal variant?
Results of associations of rs in the Nurses Health Study, Nurses’ Health Study 2, and the PLCO study.
Study Population Allele Frequency ORhet ORhomo Ptrend
(N cases/N controls) Cases Controls (95% CI) (95% CI)
(%) (%)
Nurses’ Health Study (1,145/1,141)
x 10-6
( ) ( )
Nurses’ Health Study 2 (302/594)
( ) ( )
PLCO (919/922)
( ) ( )
ACS CPS-II (555/556)
( ) ( )
Pooled estimates (2,921/3,213) x 10-10
( ) ( )
UNFINISHED AGENDA Where is the causal variant?
What does this tell us about mechanisms of breast carcinogenesis?

24. THE HITS KEEP COMING….
UNFINISHED EPIDEMIOLOGIC/PUBLIC HEALTH AGENDA
Gene-environment interaction, what do the genes tell us about environmental exposures?
Gene-gene interaction
Pathway analysis
Clinical implications – risk stratification for screening? Intervention?
Health policy implications?
Much of the substrate data – publicly available or relatively cheap.

25. NHS/HPFS/PHS GENETIC STUDIES
Immaculata De Vivo NHS/HPFS:
Peter Kraft Sue Hankinson
Hardeep Ranu Shelley Tworoger Crystal Arnone Eric Rimm
Carolyn Guo Frank Hu
Pati Soule Meir Stampfer
Craig Labadie Walt Willett
Carolyn Guo Frank Speizer
Jiali Han Charles Fuchs
Monica Macgrath Ed Giovannucci
Chunyan He Andy Chan, Debra Patrick Dennett Schaumberg
David Cox Fran Grodstein, Jae Tim Niu Hee Kang
Aditi Hazra PHS: Jing Ma
Fred Schumacher Mike Gaziano, P Ridker

26. NCI BPC3 STEERING COMMITTEE: SECRETARIAT: David Hunter, Elio Riboli
Harvard cohorts
EPIC cohorts
ACS cohort
Multiethnic Cohort
PLCO cohort
ATBC cohort
BROAD
INSTITUTE
NCI Core Gen Facility
CEPH
NCI BPC3 STEERING COMMITTEE:
Harvard David Hunter, Michael Gaziano, Julie Buring, Graham Colditz, Walter Willett
EPIC,CEPH, Cambridge Elio Riboli, Rudolf Kaaks, Federico Canzian, Gilles Thomas,
ACS Michael Thun, Heather Feigelson, Jeanne Calle
NCI Richard Hayes, Demetrius Albanes, Bob Hoover, Stephen Chanock; Program - Mukesh Verma
MEC & Broad Brian Henderson, Laurence Kolonel, David Altshuler, Malcolm Pike
SECRETARIAT: David Hunter, Elio Riboli
GENOMICS subgroup:
David Altshuler (Chair)
Steve Chanock
Gilles Thomas
Genotyping
subgroup:
Chris Haiman (Chair)
Federico Canzian
Alison Dunning
Steve Chanock
David Cox
David Hunter
Loic LeMarchand
James Mackay
STATISTICS subgroup:
Dan Stram (Chair)
Peter Kraft
Rudolf Kaaks
Paul Pharoah
Malcolm Pike
Gilles Thomas
Shalom Wacholder
PUBLICATIONS
COMMITTEE:
Michael Thun (Chair)
Elio Riboli
Brian Henderson
David Hunter
Graham Colditz
Richard Hayes
Demetrius Albanes

27. CGEMS Acknowledgements
HSPH
David Hunter
Peter Kraft
Fred Schumacher
David Cox
ACS
Heather Feigelson
Carmen Rodriguez
Eugenia Calle
Michael Thun
PLCO
Regina Ziegler
Chris Berg
Saundra Buys
Chris MacCarty
NCI
Stephen Chanock
Gilles Thomas
Robert Hoover
Joseph Fraumeni
Daniela Gerhard
Kevin Jacobs
Zhaoming Wang
Meredith Yeager
Robert Welch
Richard Hayes
Sholom Wacholder
Nilanjan Chatterjee
Kai Yu
Margaret Tucker
Marianne Rivera-Silva
NCICB

29. Selecting initial and replication samples from existing studies
I. What studies of the same phenotype exist?
II. Can a consortium or collaborative approach provide a study with adequate power for the initial GWAS, along with pre-planned replication studies?
III. Do any of these studies have pre-existing data that would increase power e.g. “free” controls for a prior GWAS of another phenotype?
IV. Is the phenotype defined in the same or similar manner?
V. Are covariate data available, and defined similarly?
VI. Do any of the studies have additional phenotypic information e.g. biomarkers that would create opportunities for “added value” analyses, if these are the subjects of the GWAS?