Genome Wide Association Study (GWAS) and Personalized Medicine

1. Genome Wide Association Study (GWAS) and Personalized Medicine

2. 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

3. Human Genome Project – Hunting for disease genes February 15 & 16, 2001Science and Nature • Implications: • Scientific advancement • Enhanced public health • Potential social issues Genome

4. AGCT AGGGCCTT Relationship between genes and diseases - Single Gene-Driven Diseases • Rare and familial diseases caused by mutations in a single gene (e.g., cystic fibrosis and sickle-cell anemia) Genome

5. 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

6. 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

7. 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

8. 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

9. A Realistic Picture + + = Diverse responses to treatment Same (similar) symptom + One-fits-all

10. Diverse response to a one-fits-all treatment One-fits-all treatment Optimal responders Suboptimal responders Non- responders Adverse Events

11. From One-Fits-All to Personalized Medicine Based on patients’ genetic profile, selecting patients  treatment Optimal responders Suboptimal responders Adverse Events Non- responders

12. A New Way to Determine Genetic Profile - Whole Genome Scanning Search all possible SNPs, not mutations, in all genes; Yah, right ! Genome

13. 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

14. 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

15. GWAS Approach Case Control Matched/unmatched • Association: • Individual SNPs • Alleles • Haplotype (combination of SNPs) • Disease related: • Genes • Pathways • Loci

16. 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

17. 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) Requirements

18. 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

19. Selection of SNPs for GWAS

20. 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

21. 7 positions • 2 alleles • 2 strands • 2 probes (PM/MM) • Total 56 features

23. Downstream Analysis (QC)

24. Current Practice: A Combination of Candidate Gene Approach and GWAS GWAS Candidate gene approach

25. 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

26. 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

27. Experiment Design and Process Candidate gene Approach GWAS Genotyping 690 genes 26,613 SNPs SNP/gene=40 Phase I 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 42,742 SNPs SNP/gene=200 Phase II 28 SNPs Representing 20 top-ranked genes

28. Drill-Down and Knowledge-Driven Analysis Candidate gene Approach HLA-DRB1 region HLA-DQA1 region A lowest p-value SNP 690 genes 26,613 SNPs SNP/gene=40 Phase I DRB1*07 Haplotype 145 genes 76 genes DQB1*02 42,742 SNPs SNP/gene=200 Phase II 28 SNPs

29. 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

30. 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

31. 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