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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 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