Post on 15-Jan-2016
description
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