Pharmacogenomics and Personalized Medicine‐ advances and possible roles of bioinformatics

1. Jan 15, 2001 Pharmacogenomics and Personalized Medicine‐ advances and possible roles of bioinformatics Caroline Lee, PhD Associate Professor, Department of Biochemistry, National University of Singapore Associate Professor, DUKE-NUS Graduate Medical School Principal Investigator, National Cancer Centre

2. Lab Members Steven Theng Jingbo Jingbo Wang Yu Peiyun Cheryl Zihua Lester Thuan Tzen Steven Wolf Soo Ting Yin Yee Jianwei Grace Naajia Way Champ Yiwei

3. Similar Yet Not the Same! • Genotypically, we are similar • 99.9% of the genome between different individuals are identical • Phenotypically, we are different • Predisposition to diseases • Response to drugs • Hence the 0.1% difference in our genome (polymorphisms) is important in determining our differences

4. Inter-Individual Variation??? • Various forms of variations (polymorphisms) exist….e.g. dinucleotide repeats, microsatellite repeats, copy-number repeats….. • Commonest are SINGLE NUCLEOTIDE POLYMORPHISMS (SNPs) • Account for 90% of inter-individual variation

5. Why are there so much interest in SNPs?

6. The Promise of Personalized Medicine!! Disease Prevention, Management and Treatment Tailored according to the Individual’s Genetic Profile!!

7. Why is “Personalized Medicine” so hotly pursued??

8. Because Current Medicine are Optimized for Many, Not for Everyone! BUT people differ in susceptibility to disease and response to medicine

9. “One-Size-Fits-All” Doesn’t Really Fit • In the US alone, > 1 billion prescriptions for tens of billions of drug doses are written for >10,000 different medications • Yet, a drug that is effective for an individual may not be effective for another individual…or worse another individual may suffer from ADVERSE DRUG REACTIONS(ADR) • ADR is one of the leading cause of hospitalization and death in the US • Enormous economic cost on health care providers • 1994 in the US • ADR account for >2.2 million serious cases and over 100,000 deaths • FDA Reports: • 8% of Phase I Trial Drugs are ultimately approved • 4% of approved drugs are withdrawn Caskey, 2007. The Drug Development Crisis. Efficiency and Safety. Ann Rev Med 58:1-6

10. John is diagnosed with hypertension Current approach Personalized medicine Reactive Proactive Drug response prediction test The hypertension unresolved Serious side effect Drug 1 Hypertension eased Side effect Drug 2 The hypertension controlled No side effects. Drug 3 Drug 3 The hypertension is controlled without significant side effects In many visits! With 3 drugs! In one visit! With one drug!

11. CURRENT PROBLEM about SNPs !! • ~56 million SNPs deposited in NCBI !! • >14 million non-redundant SNPs • ~6.6 million validated SNPs • However, NOT all the SNPs are functionally significant • Affect function • Associated with Disease / Drug Response Shi (2001) Enabling Large-Scale Pharmacogenetic Studies by High Throughput Mutation Detection and Genotyping Technologies. Clinical Chemistry 47(2):164-172

12. Current Strategies for Association Studies • Whole Genome Association (WGA) • Primarily tag-SNPs or t-SNPs (e.g. llumina) or ‘quasi-random’ (QR-SNPs) (e.gAffymetrix) • Successful for some diseases (e.g. Diabetes) but not for others (e.g. Parkinsons) • Major Problems: • Does not directly interogate causative SNP • Multiple Testing / Type I error • Candidate Gene Approach • Even a single gene e.g. ABCB1 which is ~210 kb long has >1000 SNPs. • Selection of SNPs for association based on • Non-synonymous SNPs • Not all non-synonymous SNPs are functionally important • Some synonymous /intronic SNPs may be functional / causative e.g changes ESE sites or codon usage/folding of protein (e.g. ABCB1 synonymous SNP3435) • “gut” feeling??

13. Current Strategies for the selection of a subset of SNPs for Association Study • Quasi-random SNPs (QR-SNPs) (Affymetrix) • SNPs selected “unbiasedly” and quasi-randomly to cover the entire genome • Selection of SNPs limited by sequence constraints of the Affymetrix genotyping technology • High Type I error • Frequently NOT interogating the functionally significant SNPs • Tagging-SNPs (t-SNPs) • Subset of SNPs that still retains as much of the genetic variation as the full set. • Not all genes are amenable to tagging-SNP strategy (e.g. low LD genes and recent positively selected SNPs) • Still High Type I error • Frequently NOT interogating the functionally significant SNPs

14. Concept of tag-SNPs (t-SNPs) • Tagging SNPs • Genetic variants that are near each other tend to be inherited together (Linkage Disequilibrium or LD) • Architecture of Genetic Variants show that there are blocks of SNPs in high LD • Determine the extent and number of blocks in the human genome • Genotype representative SNP in each block. www.rikenresearch.riken.jp/frontline/400/

15. Our Approach Integrate Different Resources and Computational Approaches to Predict Potentially Functionally Significant SNPs • Reported Functional Significance • OMIM, PubMed, dbGAP, HGMD • Inferred / Predicted Functional Significance • SNPs under Natural Selection • SNPs predicted to reside in functionally important domains • SNPs predicted to alter consensus sequences of functionally important sites

16. Potential Functional Significant SNPs • SNPs that show functionality at mRNA Level • Promoter SNPs • Change TF Binding Sites • Coding Region SNPs • Affect Nonsense Mediated Decay (NMD) • Change ESE/ESS sites • Intronic SNPs • Reside in Splice Junction • Change ISRE • 3’UTR SNPs • Change miRNA binding • Reside on conserved 3’UTR region • Change TF Binding Sites • General • SNPs reported to be associated with disease or function • OMIM, PubMed, HGMD, dbGAP • SNPs that show evidence of selection • Recent Positive Selection (RPS) • Population Differentiation • Conserved Non-coding Regions (CNS) • SNPs residing in miRNA coding region • Seed, mature non-seed, stem, loop • SNPs in coding region • Non-synonymous SNPs • Domain SNPs reside in • Affect glycosylation/phosphorylation, etc • Predicted to be deleterious • Synonymous SNPs • Show significant codon-usage differences

17. Characteristics of Potential Functional Significant SNPs (pfs SNPs) Non-synonymous Synonymous Non-synonymous Synonymous Coding Coding • 930,324 out of 14,171,351 total SNPs (~6.6%) in the genome were found to be potentially functionally significant. • Pfs SNPs are enriched in coding and promoter regions Non-coding Genic Genic Intergenic Intergenic Non-coding Intron Intron Promoter 3’UTR Promoter 3’UTR pfsSNPs ALL SNPs (dbSNP129)

18. SNPs within the pfsSNPs database is different from those in the t-SNP and QR-SNP databases • The 3 datasets are UNIQUE with less than 10% of SNPs that are common amongst the different methods of SNP selection.

19. Gross chromosome structure of the 3 datasets are similar • SNP coverage for pfsSNPs (red), t-SNPs (green) and QR-SNPs (blue) are similar evenly distributed across the chromosomes

20. pfSNPs dataset has more ungenotyped and monomorphic SNPs than t-SNP / QR-SNP datasets CHB JPT CEU YRI

21. pfsSNPs are More Enriched in Regions of Functional Significance

22. t-SNP and QR-SNP datasets contain low percentage of pfSNPs • t-SNPs contain more putatively functionally significant SNPs than QR-SNPs

23. Which SNP selection Method is Better Associated with Differences in Gene Expression? pfsSNPs are better associated with Differences in Gene Expression than t-SNPs /QR-SNPs • 2 publications associated genotype (from HapMap) with expression: • Stranger: 1218 expression associated SNPs from 1,156,868 SNPs in HapMap I, r16. • Kwan: 412 transcript-isoform associated SNPs from 4,000,107 SNPs in HapMap II, r21 Stranger et al (2007) Science 315:848-853 Kwan et al (2008) Nat Genetics 40:225

24. Web Resource that provide detailed description of potential functionality of pfs SNPs

25. Target Audience of the Web-Resource • Scientists interested in • Whole Genome Association Studies • Gene-Based Association Studies • Identify pfsSNPs in • Specific Genes • Genes expressed in Specific Tissues • Genes expressed in Specific Chromosome Region • Genes in Specific Pathways/Gene Ontology Terms • Genes associated with Specific Disease • Genes associated with Specific Drugs • Specific genomic regions • Specific populations at certain threshold frequency. • Designing Experiments to address functionality of SNPs • Identifying pfsSNPs that are in LD with the scientist’s SNP-of-interest. • Useful for scientist who has performed GWAS using the Affymetrix QR-SNPs or the Illumina t-SNPs

26. Query Page

27. Query Page

28. Query Page

34. Biologist-Friendly Features of the Web-Resource • Highly Customizable Query Interface. • Auto-Complete Prompt-As-You-Type feature in the Query Interface. • SNP ID can be identified through information with regards to the gene, specific genomic region or amino acid number. • Excel-Like Presentation of Results with In-Depth Resource Reference • Information Sharing and Contribution by the Scientific Community.

35. Typical Results (in Excel Format)

37. Demonstration of Web-Resource http://pfs.nus.edu.sg/

38. Acknowledgements • Wang Jingbo (RA and Part-Time Graduate Student) • Mostafa Ronaghi (Stanford University)