Genome Wide Association Study (GWAS) and Personalized

Medicine

Outline

• Gene discovery and personalized medicine – Family linkage-based approach– Candidate gene-based approach– Whole genome scan (Genome-wide association study)

• Genome wide association study (GWAS)– Objectives and approaches– Benefits and challenges – Resources and requirements– Technologies

• A case study – Genome-Wide Study of Exanta Hepatic Adverse Events

Human Genome Project – Hunting for disease genes

Genome

Implications:• Scientific advancement • Enhanced public health • Potential social issues

February 15 & 16, 2001Science and Nature

Relationship between genes and diseases - Single Gene-Driven Diseases

AGCT

AGGGCCTT

Genome

• Rare and familial diseases caused by mutations in a single gene (e.g., cystic fibrosis and sickle-cell anemia)

Identify Genetic Profile Through Gene Discovery- Approaches and Technologies

• Family Linkage-Based Approach– Use the linkage principle to study families in which the

disease occur frequently• Identify disease-susceptibility genes in rare familial diseases

– More successful for diseases caused by a single gene (e.g., Huntington’s disease)

– More successful for genes strongly increasing risk– Need a well documented family tree and disease

history– Successful far less likely for some heritable diseases

caused by interaction of many weak genes

Relationship between genes and diseases - Multiple Gene-Driven Diseases

Genome

• Many genes interact each to cause disease

• No single gene has strong effect• Must search for multiple genes

functionally involved in putative disease-associated biomedical pathways

Identify Genetic Profile Through Gene Discovery- Approaches and Technologies (cont.)

• Candidate Gene-Based Approach– Process

• Select genes from known disease-related pathways

• Search for causative mutations in the genes • e.g., ACH/Charlotte Hobbs

– Knowledge-based approach– Drawbacks:

• Constrained by existing knowledge• Constrained by genes examined

A More Complicated Picture

Genome

• Interaction between disease genes and patients’ life style and/or environment

Genetics loads the gun, but environment pulls the trigger

A Realistic Picture

Same (similar) symptom

+ One-fits-all

+ +

= Diverse responses to treatment

Diverse response to a one-fits-all treatment

Optimal responders

Suboptimal responders

Non-responders

Adverse Events

One-fits-all treatment

Based on patients’ genetic profile, selecting patients treatment

Optimal responders

Suboptimal responders

Non-responders

Adverse Events

From One-Fits-All to Personalized Medicine

A New Way to Determine Genetic Profile - Whole Genome Scanning

Genome

Search all possible SNPs, not mutations, in all genes;

Yah, right !

Genetic Profile – From Mutation to SNPs

• Mutations and SNPs are both genetic variation– <1% of genetic variations are disease related, &

called mutations; – Mutations considered harmful and disease related– The majority of genetic variation is not disease related

(>1%),& called SNPs– SNPs comprise “harmless” genetic variation

(personalized)– SNPs can be used as markers for disease genes

• GWAS is searching for SNPs marking disease causing mutations

The Era of the Genome Wide Association Study (GWAS)

• A brute force approach of examining the entire genome

to identify SNPs that might be disease causing mutations• Far exceeds the scope of family linkage and candidate

gene approaches• Must obtain a comprehensive picture of all possible

genes involved in a disease and how they interact• Objective: Identify multiple interacting disease genes and

their respective pathways, thus providing a

comprehensive understanding of the etiology of disease

GWAS Approach

CaseCase ControlControlMatched/unmatched

Association:1. Individual SNPs2. Alleles3. Haplotype (combination of SNPs)

Disease related:1. Genes2. Pathways3. Loci

Benefits and Challenges

• Challenges: the uncertainty between SNPs and the

disease-causing mutation requires large sample size– 2000 – 4000 sample sizes– Minimum 1000– Unfortunately, most experiments have < 500 samples

• Why the enthusiasm about GWAS:– Comprehensive scan of the genome in an unbiased fashion has

potential to identify totally novel disease genes or susceptibility

factors

– Potential to identify multiple interacting disease genes and their

respective/shared pathways

Requirements

Success factors• Experimental: large

sample size• Platform: accurate

genotyping technology• Analysis

– Comprehensive SNP maps

– Rapid algorithm • IT

– Sophisticated IT infrastructure

– Powerful computers

Expertise (NCTR)• Medical doctors (NA)• HTP genotyping

platforms (NA)• Population genetics (NA)• Biostatistics (Yes)• Bioinformatics (Yes)• Statistics (Yes)

SNP Map

• Current technology not advanced enough to encompass all SNPs; not even close

• Selecting SNPs based on haplotype block

• Issues related to haplotype– A SNP pattern consistent across a

population– Population-dependent– Analysis method-dependent

• One of the objectives of HapMap

LD

Hyplotype Block

Selecting SNPs

Selection of SNPs for GWAS

High-Throughput Genotyping Technology• Several diverse technologies, but moving to array-based approaches• Array-based technologies: Illumina, Affymetrix, Perlegen and

NimbleGene• Very similar to the technology used for gene expression microarray

• 7 positions• 2 alleles• 2 strands• 2 probes (PM/MM)• Total 56 features

Downstream Analysis (QC)

Current Practice: A Combination of Candidate Gene Approach and GWAS

GWASGWAS Candidate gene

• Data-driven

• Generates new knowledge

• Relies on a SNP map

• Hypothesis-driven

• Constrained by knowledge

• Allows systematic scanning

Candidate gene approach

Case Study: Genome-Wide Study of Exanta Hepatic Adverse Events

• Ximelagatran, marketed as ExantaTM, developed by AZ

• Developed/tested– Prevention of stroke in atrial fibrillation

– Treatment of acute venous thromboembolism

• Withdrawn from clinical development in 2006 because of ALT elevation:– Idiosyncratic nature: occurred in 6-7% of patients with ALT> 3 x

upper limit normal (ULN)

– Geographic dependent: high incidence in Northern Europe compared with Asia

• Hypothesis: Genetic factors could be involved

• Approaches: GWAS and candidate gene approaches

Samples (Subjects or Patients)

• The original set (Training set)– 248 subjects from 80 regions in Europe (Denmark,

Finland, Germany, Noway, Poland, Sweden and the UK)

– 74 Cases = ALT elevation > 3 x ULN

– 132 Control = ALT elevation < 1 x ULN

– 39 Intermediate Control = ALT elevation >1 x ULN and <3 x ULN

• An independent data set available late time– 10 Cases and 16 Treated Controls

Experiment Design and Process

Candidate geneApproach

GWAS

690 genes26,613 SNPsSNP/gene=40

266,722 SNPs Association analysis of SNPs with elevated ALT:• Matched and unmatched case-control analysis• Fisher’s Exact test, ANOVA, logistic regression analysis; Multiple testing correction (FDR)• Haplotype and linkage disequilibrium (LD) analysis

145 genes 76 genes

28 SNPs

Phase I

Phase II

Genotyping

42,742 SNPsSNP/gene=200

Representing 20 top-ranked genes

Drill-Down and Knowledge-Driven Analysis

Candidate geneApproach

690 genes26,613 SNPsSNP/gene=40

145 genes 76 genes

28 SNPs

Phase I

Phase II42,742 SNPs

SNP/gene=200

HLA-DRB1 region

DRB1*07

A l

ow

est

p-v

alu

e S

NP

HLA-DQA1 region

DQB1*02

Haplotype

Validated by the Test Set• Test set (replication study)

– 10 Cases and 16 Controls

• Both DRB1*07 and DQB1*02 are significant

• Only 2 of 28 SNPs are significant, might be due to:– False positive in Phase I

– Lack of power

• A note:– Phases I and II genotyping using the Perlegen technology

– Replication study using the TaqMan assay

Summary• Emphasis more on the candidate gene approach; candidate genes

were selected from

– Involved in MOA of Exanta

– Associated with elevated liver enzyme (e.g., ALT)

– Derived from preclinical studies for Exanta

– Found to be genetically associated with adverse effects

• Supported by the findings in Phase I

– Some evidence obtained from the candidate gene approach (select 145

genes from among 690)

– No evidence from GWAS (76 genes were selected)

• Reflected in the drill-down approach

– Focused on the gene/region with the lowest p-value SNP from the

candidate gene approach; both SNPs identified this way are significant

– 2 out of 28 SNPs are significant from GWAS

My general impression• This study presents the evidence from a

comparative analysis between two approaches– Knowledge-guided vs high-throughput screening

– Hypothesis driven vs data driven

• Less emphasis on GWAS and more reliance on the results from the candidate gene approach– Due to lack of power

– Multiple testing correction issue

• Is GWAS ready for the prime time? – Results from this study are not encouraging

– Further investigation/survey is urgently needed