Transcript Genome Wide Association Study (GWAS) and Personalized Medicine

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)
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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
February 15 & 16, 2001
Science and Nature
Genome
Implications:
• Scientific advancement
• Enhanced public health
• Potential social issues
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
• 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
Genome
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
Genetics loads the gun, but environment pulls the trigger
• Interaction between
disease genes and
patients’ life style
and/or environment
Genome
A Realistic Picture
+
+
= Diverse responses
to treatment
Same (similar) symptom
+ One-fits-all
Diverse response to a one-fits-all treatment
One-fits-all treatment
Optimal
responders
Suboptimal
responders
Nonresponders
Adverse
Events
From One-Fits-All to Personalized Medicine
Based on patients’ genetic profile,
selecting patients  treatment
Optimal
responders
Suboptimal
responders
Nonresponders
Adverse
Events
A New Way to Determine Genetic Profile
- Whole Genome Scanning
Search all possible SNPs,
not mutations, in all genes;
Yah, right !
Genome
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
Case
Matched/unmatched
Control
Association:
1. Individual SNPs
2. Alleles
3. Haplotype (combination of SNPs)
Disease related:
1. Genes
2. Pathways
3. 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
LD
Hyplotype Block
Selecting SNPs
• 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
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
GWAS
GWAS
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 gene
Approach
GWAS
Genotyping
Phase I
Phase II
690 genes
26,613 SNPs
SNP/gene=40
266,722 SNPs Association analysis of SNPs
with elevated ALT:
• Matched and unmatched
case-control analysis
• Fisher’s Exact test, ANOVA,
145 genes
76 genes
logistic regression analysis;
Multiple testing correction
(FDR)
• Haplotype and linkage
42,742 SNPs
disequilibrium (LD) analysis
SNP/gene=200
28 SNPs
Representing 20 top-ranked genes
Drill-Down and Knowledge-Driven Analysis
HLA-DRB1
region
Phase I
690 genes
26,613 SNPs
SNP/gene=40
A lowest
p-value SNP
Candidate gene
Approach
145 genes
Phase II
DRB1*07
76 genes
42,742 SNPs
SNP/gene=200
28 SNPs
HLA-DQA1
region
Haplotype
DQB1*02
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