Association of Human DNA Association of Human DNA Variation with Complex TraitsVariation with Complex Traits

David R. Cox M.D., Ph.D.David R. Cox M.D., Ph.D.Chief Scientific OfficerChief Scientific OfficerPerlegen Sciences Inc.Perlegen Sciences Inc.

david_cox@perlegen.comdavid_cox@perlegen.com

Genes, through the proteins they encode, Genes, through the proteins they encode, interact with challenges from the interact with challenges from the

environmentenvironment

…………………………..February, 2005

……………..October, 2005

Whole-Genome Patterns of Common Human DNA Variation Have Been Characterized

27 October 2005

18 February 2005

Genetic AssociationCases- drug toxicityCases- drug toxicity Controls- no toxicityControls- no toxicity

If a segment of the genome is “associated” with toxicity, casesIf a segment of the genome is “associated” with toxicity, caseswill have a different SNP allele frequency than controls.will have a different SNP allele frequency than controls.

40% Green and 60% red 50% Green and 50% red

Lessons Learned From SNPAssociation Studies To DateLessons Learned From SNPAssociation Studies To Date

SNP associations can lead to novel biological insights

It is not possible to predict the fraction of variation of a complex trait determined by a SNP prior to performing an association study

The majority of SNP associations account fora small fraction trait variability

SNP associations can lead to novel biological insights

It is not possible to predict the fraction of variation of a complex trait determined by a SNP prior to performing an association study

The majority of SNP associations account fora small fraction trait variability

Questions That Remain Unanswered

What is the relative role of common versus rareWhat is the relative role of common versus raregenetic variation in complex human traits?genetic variation in complex human traits?

What is the relative role of population specificWhat is the relative role of population specificversus global genetic variation in complex humanversus global genetic variation in complex humantraits?traits?

Which segments of the human genome play theWhich segments of the human genome play themost important role in human phenotype variation?most important role in human phenotype variation?

Will genetic associations of modest effect haveWill genetic associations of modest effect haveclinical utility?clinical utility?

How will Genetic Knowledge Impact Health Outcomes and

the Practice of Medicine?

Automobiles

Organization example

• None • Rolls-Royce • Ford • General Motors

• Rolls Royce

• -• Individuals • Rolls, CS Royce, FH

• Henry Ford • Alfred SloanKey person

• -• Ad-hoc methods

• Job-shop • Line • Multiple-lineMethod of production

• -• Non-motorized vehicle

• Silver Spirit • Model-T • PontiacProduct example

Timing Pre-1900 1900-1920 1920-1940 1940-present

While mass customization developed soon after mass production, personalization never occurred in auto industry

Not formalized on large scale

Individualizeto small degree

Mass produced Mass customized Personalized

Visual

Basically never…

Clothing

Organization example

• None • Bespoke tailor

• US Army • Major retailer • Levi Jeans Co.

• IC3D• None • Davies & Son (1803)

• Alexander, M (1850)

• O’Brien, R & Shelton, WC (1941)

Key person

• Levi’s personalized jeans

• None • Bespoke suit • Civil War uniform

• 1950 shirtProduct example

Timing Pre-history Pre-1850 1850-1940 1940-present

As with automobiles, apparel became mass customized relatively quickly but has also never become personalized on large scale

Not formalized on large scale

Individualized to small degree

Mass producedMass customized

Personalized

2000 (but it didn’t work out…)

Visual

Medicine

Genetic knowledge will reclassify disease based onbiological causality

Individuals will receive “group” assignmentsbased on this information

What is the “right” phenotype to study?

A Genome-Wide Association Study of Breast Cancer

Douglas F Easton, Alison M Dunning, Karen Pooley, Paul DP Pharoah, Dennis Ballinger, Deborah Thompson, D Gareth Evans, Diana Eccles, Nazneen Rahman, Michael R Stratton, Julian Peto, Olivia Fletcher, David R. Cox, Bruce AJ Ponder, The Breast Cancer Association Consortium

Low Frequency Germline Gene Mutations Associated with Increased Breast Cancer Risk

High risk BRCA1, BRCA2

Two-fold risk CHEK2, ATM, BRIP1, and PALB2

“High-risk” breast cancer cases (n=408)

Compare genotype frequencies P<.05?

Phase I: 227,876 tag SNPs

Female controls EPIC (age>50) (n=400)

Study Design

P<.0001?

3,916 controls (EPIC)3,990 breast cancers (ABC)

Phase II: 13,023 SNPs

21,668 invasive breast cancers 20,973 controls (BCAC) 967 carcinoma in-situ cases

Phase III: 30 SNPs

Breast Cancer Association Consortium

kConFab/AOCS: Georgia Chenevix-Trench, Mandy Spurdle, Jonathan Beesley, Xiaoqin Chen ABCFS: John Hopper, Margaret McCredie, Melissa Southey, Graham GilesMCCS: Graham Giles, Melissa Southey, John Hopper, Chris SchroenNurses Health Study: David Hunter, Sue Hankinson, David Cox Mayo Clinic BCS: Fergus Couch, Ellen Goode, Janet OlsonUS Radiologic Technologists Study: Alice Sigurdson, Jeff StruewingMulti-ethnic Cohort: Chris HaimanThailand/IARC: Paul BrennanSoeul Breast Cancer Study: Daehee KangTaiwan Breast Cancer Study: Chen-Yang Shen

CNIO, Madrid : Roger Milne Gloria Ribas Ana Gonzalez Javier Benitez SASBAC: Per Hall, Sara Wedren, JJ Liu, Low Yin Lin Copenhagen BCS: Stig Bojesen, Borge NordestgaardLeiden BCS: Rob Tollenaaer, C.E. Jacobi, J.G.M. Klijn, Peter DevileeRotterdam BCS: Hanne Meijers-Hiejboer, André UnterlindenSheffield BCS: Angie CoxHelsinki BCS: Heli NevanlinnaKuopio BCS: Arto Mannermaa, Veli-Matti Kosma, Vesa Kataja, Jaana Hartikainen GENICA: Hiltrud BrauchHannover BCS: Thilo DörkPolish BCS: Montse Garcia-Closas

Level ofSignificance

Observed ObservedAdjusted1

Expected Ratio

.01-.05 1087 1005 939.7 1.07

.001-.01 509 463 342.2 1.35

.0001-.001 103 88 57.3 1.54

.00001-.0001 13 11 3.5 3.14

<.00001 14 12 0.48 25.0

1Adjusted for inflation of the test statistic by the genomic control method

Observed numbers of associations after stage 2 by level of significance, before and after adjustment for

population stratification, and expected numbers under the null hypothesis of no association

Locus Maf2 HetOR(95%CI)

HomOR(95%CI)

P-trend

Stages1&2

Stage3 Combined

FGFR2 0.38(0.30)

1.22 (1.17-1.27)

1.63 (1.53-1.73)

4x10-16 5x10-62 2x10-76

TNRC9/LOC643714

0.44(0.20)

1.10 (1.05-1.16)

1.19 (1.12-1.27)

4x10-6 4x10-8 10-12

MAP3K1 0.28(0.54)

1.13 (1.09-1.18)

1.27 (1.19-1.36)

4x10-6 3x10-15 7x10-20

LSP1 0.30(0.14)

1.06 (1.02-1.11)

1.17 (1.08-1.25)

8x10-6 10-5 3x10-9

8q 0.40(0.56)

1.06 (1.01-1.11)

1.18 (1.10-1.25)

2x10-7 6x10-7 5x10-12

2 Minor allele frequency in Search (UK) study. Combined allele frequency from three Asian studies

in italics

Five SNPs Selected for Stage 3 Five SNPs Selected for Stage 3 With Strong Evidence of AssociationWith Strong Evidence of Association

Results for Five Loci from 22 Studies(21,668 cases/ 20,973 controls)

Locus Per allele OR (95%CI)

HetOR(95%CI)

HomOR(95%CI)

p-trend(CIS vs

controls)

p-trend(CIS vs

invasive)

FGFR2 1.25 (1.14-1.37)

1.11 (0.96-1.30)

1.62 (1.34-1.96)

6x10-7 .67

TNRC9/LOC643714

1.18 (1.05-1.33)

1.14 (0.97-1.35)

1.45 (1.10-1.91)

.0006 .95

MAP3K1 1.30 (1.17-1.44)

1.31 (1.14-1.51)

1.66 (1.31-2.09)

10-7 .007

LSP1 1.07 (0.96-1.19)

1.18 (1.00-1.39)

1.10 (0.88-1.37)

.06 .21

8q 1.07 (0.97-1.19)

1.04 (0.90-1.20)

1.18 (0.94-1.49)

.25 .71

Odds Ratios for Carcinoma in situ vs ControlsOdds Ratios for Carcinoma in situ vs Controls

1 1

Additive Genetic Variance Predicts A Subset of the Population Additive Genetic Variance Predicts A Subset of the Population At Increased Risk For An Adverse Response To TreatmentAt Increased Risk For An Adverse Response To Treatment

112

23344555

2 2 3 3 4 4 5 5

Risk StratifierRisk Stratifier

Ris

k

Low

ris

kH

igh

risk

Frequency In UK Population

Breast cancerRisk by age 70

Copies of FGFR2 Risk Allele

2 1 0

14% 47% 39%

10.5% 6.7% 5.5%

A High Proportion of Women in the General PopulationCarry the FGFR2 Breast Cancer Risk Allele

American Cancer Society Guidelinesfor Breast Screening with MRI as an

Adjunct to MammographyDebbie Saslow, PhD; Carla Boetes, MD, PhD; Wylie Burke, MD, PhD; Steven Harms, MD; Martin O. Leach, PhD; Constance D. Lehman, MD, PhD; Elizabeth Morris, MD; Etta Pisano,MD; Mitchell Schnall, MD, PhD; Stephen Sener, MD; Robert A. Smith, PhD; Ellen Warner,MD; Martin Yaffe, PhD; Kimberly S. Andrews; Christy A. Russell, MD (for the American Cancer Society Breast Cancer Advisory Group)

CA Cancer J Clin 2007;57:75–89.

ABSTRACT New evidence on breast Magnetic Resonance Imaging (MRI) screening hasbecome available since the American Cancer Society (ACS) last issued guidelines for the earlydetection of breast cancer in 2003. A guideline panel has reviewed this evidence and developednew recommendations for women at different defined levels of risk. Screening MRI is recommendedfor women with an approximately 20–25% or greater lifetime risk of breast cancer,including women with a strong family history of breast or ovarian cancer and women who weretreated for Hodgkin disease. There are several risk subgroups for which the available data areinsufficient to recommend for or against screening, including women with a personal history ofbreast cancer, carcinoma in situ, atypical hyperplasia, and extremely dense breasts on mammography.Diagnostic uses of MRI were not considered to be within the scope of this review.

The Missing Piece

An international network collecting long termAn international network collecting long termtreatment outcomes for a wide range of disorderstreatment outcomes for a wide range of disorders

Genetic association analysis using data collectedGenetic association analysis using data collected

by such a network would provide an importantby such a network would provide an importantscientific body of knowledge that could bescientific body of knowledge that could beused to improve treatment efficacy and toused to improve treatment efficacy and to

reduce adverse treatment events in reduce adverse treatment events in individual patientsindividual patients

Prohibiting Genetic DiscriminationKathy L. Hudson Ph.D.

The Enhancing Drug Safety and Innovation Act of 2007

Passed the US Senate with a vote of 93 to 1.

Requires the FDA to link electronic health caredatabase to answer questions about the safety ofdrugs on the market.

Gives FDA the authority to require a drugcompany to conduct any post-approval studynecessary to answer a question that the FDA’sown surveillance system will not answer

Online May 21, 2007 N ENGL J MED 10.1056/NEJMoa072761

ConclusionsRosiglitazone was associated with a significant increase in the risk of myocardial infarction and with an increase in the risk of death from cardiovascular causes that had borderline significance. Our study was limited by a lack of access to original source data, which would have enabled time-to-event analysis. Despite these limitations, patients and providers should consider the potential for serious adverse cardiovascular effects of treatment with rosiglitazone for type 2 diabetes.

Conclusions

Genetic knowledge can be used in conjunction withother risk factors to help individual patients and their physicans tochoose between exisitng treatment options, thereby maximizing treatment efficacy and minimizing adverse events

Large scale outcome studies, performed as an intergral componentof the healthcare system, will be essential for the short termapplication of genetic knowledge to human health outcomes.