Acknowledgements

1. BioinformaticsSNPs and haplotypesKristel Van Steen, PhD, ScD(kristel.vansteen@ulg.ac.be)Université de Liege - Institut Montefiore2008-2009

2. Acknowledgements Parts of theseslides have been adapted or taken over fromexisting course notes and online material: Practical: Heather Cordell Slides: Stuart M Brown

3. Outline • Practical in R on genetic association analysis • SNPs and Haplotypes • A tour in FBAT

4. Genetic Association Analysis in R

5. Computer Practical Exercise • Heather Cordell http://www.staff.ncl.ac.uk/heather.cordell/WTACcasecon2007.html • Using R for • Case-control association • Gene-gene interactions (future class)

6. SNPs and Haplotypes A gentle introduction of relevant issues

7. Mutationscreate Alleles • Mutations occur randomly throughout the DNA • Most have no phenotypic effect (non-coding regions, equivalent codons, similar AAs) • Some damage the function of a protein or regulatory element • A very few provide an evolutionary advantage

8. Human Alleles • The OMIM (Online Mendelian Inheritance in Man) database at the NCBI tracks all human mutations with known pheontypes. • It contains a total of about 2,000 genetic diseases [and another ~11,000 genetic loci with known phenotypes - but not necessarily known gene sequences] • It is designed for use by physicians: • can search by disease name • contains summaries from clinical studies

10. Population Genetics • Chromosome pairs segregate and recombine in every generation. • Every allele of every gene has its own independent evolutionary history (and future!) • Frequencies of various alleles differ in different sub-populations of people.

11. SNPs Single nucleotide polymorphisms (SNPs) are DNA sequence variations occurring when a single nucleotide (A, C, T, G) in the genome is altered. The inherited allelic variation must have >1% population frequency. SNPs can occur in both coding and non-coding regions, making up 90% of all human genetic variation Frequency: roughly, every 100 to 300 bases along the about 3 billion base human genome Remark: Some definitions include methylated and deaminateddinucleotides

12. Distribution of SNPs and Power

13. SNPs are Very Common • SNPs are very common in the human population. • Between any two people, there is an average of one SNP every 1000 bases. • Most of these have no phenotypic effect • only <1% of all human SNPs impact protein function (non-coding regions) • Selection against mis-sense mutations (think about what would happen to dominant lethal mutations?) • Some are alleles of genes.

14. Why are SNPs Important? • Alleles of health related genes • Genetic Markers that are linked to every gene (and to non-transcribed loci that may also affect health) • Fast, cheap, accurate genotypes • Population diversity & history • Genetic Association studies in populations • Pharmacogenomics

15. Genome Sequencing finds SNPs • The Human Genome Project involves sequencing DNA cloned from a number of different people. • Even in a library made from from one person’s DNA, the homologous chromosomes have SNPs • This inevitably leads to the discovery of SNPs - any single base sequence difference

17. We describe a map of 1.42 million single nucleotide polymorphisms (SNPs) distributed throughout the human genome, providing an average density on available sequence of one SNP every 1.9 kilobases. These SNPs were primarily discovered by two projects: The SNP Consortium and the analysis of clone overlaps by the International Human Genome Sequencing Consortium. The map integrates all publicly available SNPs with described genes and other genomic features. We estimate that 60,000 SNPs fall within exon (coding and untranslated regions), and 85% of exons are within 5 kb of the nearest SNP. Nucleotide diversity varies greatly across the genome, in a manner broadly consistent with a standard population genetic model of human history. This high-density SNP map provides a public resource for defining haplotype variation across the genome, and should help to identify biomedically important genes for diagnosis and therapy.

18. GenBank has a dbSNP “As of Mar. 2007 , dbSNP has submissions for 31,035,607 human SNPs” • It is possible to search dbSNP by BLAST comparisons to a target sequence

19. >gnl|dbSNP|rs1042574_allelePos=51 total len = 101 |taxid = 9606|snpClass = 1 Length = 101 Score = 149 bits (75), Expect = 3e-33 Identities = 79/81 (97%) Strand = Plus / Plus Query: 1489 ccctcttccctgacctcccaactctaaagccaagcactttatatttttctcttagatatt 1548 ||||||||||||||||||||||||||||||||||||||||||||||| || ||||||||| Sbjct: 1 ccctcttccctgacctcccaactctaaagccaagcactttatattttcctyttagatatt 60 Query: 1549 cactaaggacttaaaataaaa 1569 ||||||||||||||||||||| Sbjct: 61 cactaaggacttaaaataaaa 81 If a matching SNP is found, then it can be directly located on the Genome map

20. Linkage • Meiosis (sexual cell division) involves a process of crossing over, which gives new combinations of alleles • Genes that are located close to each other on the chromosome rarely show recombination of alleles

21. HapMapProject • The HapMap Project tests linkage between SNPs in various sub-populations. • For a group of linked SNPs recombination may be rare over tens of thousands of bases • A few "tagSNPs" can be used to identify genotypes for groups of linked SNPs • Makes it possible to survey the whole genome with fewer markers (1/3-1/10th)

22. Haplotype • Linkage is common in the human population, particularly in genetically isolated sub-populations. • A group of alleles for neighboring genes on a segment of a chromosome are very often inherited together. • Such a combination of linked alleles is known as a haplotype. • When linked alleles are shared by members of a population, it is called a linkage disequilibrium.

23. Haplotype Map of the Human Genome • Goals: • Define patterns of genetic variation across human genome • Guide selection of SNPs efficiently to “tag” common variants • Public release of all data (assays, genotypes) • Phase I: 1.3 M markers in 269 people • Phase II: +2.8 M markers in 270 people

24. HapMap Samples • 90 Yoruba individuals (30 parent-parent-offspring trios) from Ibadan, Nigeria (YRI) • 90 individuals (30 trios) of European descent from Utah (CEU) • 45 Han Chinese individuals from Beijing (CHB) • 45 Japanese individuals from Tokyo (JPT)

25. Recombination hotspots: widespread - LD structure 7q21

26. Common Haplotypes • For a single locus in a population, 55 percent of people may have one version of a haplotype, 30 percent may have another, 8 percent may have a third, and the rest may have a variety of less common haplotypes. • These haplotype blocks may contain 5-20 SNPs

27. Common Haplotypes • All of these halplotypes can be identified by genotyping 1-3 "tag SNPs" • Tag SNPs that contain most of the information about the patterns of human genetic variation are estimated to be about 300,000 to 600,000, which is far fewer than the 10 million common SNPs.

28. Applications of HapMap • Pick better SNPs for genotyping study • Choose SNPs with high heterozygosity in target population • Whole genome coverage with reduced set of "tag SNPs" (capture all "common variants") • Interpret genotyping results • What genes are in LD with this SNP? • What coding variants and putative functional variants are in LD with this SNP?

29. Example: Complement Factor H - AMD rs380390

30. SNP Testing • Genotyping • SNPs are permanent features of genomic DNA • May be homozygous or heterozygous • Many different technologies are available

31. Genotyping Technologies • Sequencing (whole genome or targeted) • PCR (allele specific primers) • Oligonucleotide ligation • Primer extension (incorporate labeled nucleotides) • Hybridization (microarray)

32. TaqMan - rtPCR • 4 oligos must be designed and tested for each SNP • Fast & cheap for lots of samples

33. Primer Extension

34. Oligonucleotide Ligation (ABI) can multiplex 48 SNPs

35. Preliminary data from Affy 10K SNP

36. Microarrays • Screening large numbers of SNP markers on a sample of genomic DNA is one highly promising application for microarray technology. • Many other “high-throughput” SNP genotyping technologies are under development. • Affymetrix 1million SNP product on sale now!

37. Comparison of Methods? • Array-based methods can cover the whole genome • PCR (& variants) are cheaper for defined numbers of SNPs on lots of samples • Whole genome: may be too much data • false positives • privacy concerns • Whole genome may work for discovery research, but clinical applications favor targeted assays

38. Pharmacogenomics • The use of DNA sequence information to measure and predict the reaction of individuals to drugs. • Personalized drugs • Faster clinical trials • Less drug side effects

39. Some Gene Products Interact with Drugs • There are proteins that chemically activate or inactivate drugs. • Other proteins can directly enhance or block a drug's activity. • There are also genes that control side effects

40. Example • 10% of African Americans have polymorphic alleles of Glucose-6-phosphate dehydrogenase that lead to haemolyitic anemia when they are given the anti-malarial drug primaquine.

41. Collect Drug Response Data • These drug response phenotypes are associated with a set of specific gene alleles. • Identify populations of people who show specific responses to a drug. • In early clinical trials, it is possible to identify people who react well and react poorly.

42. Make Genetic Profiles • Scan these populations with a large number of SNP markers. • Find markers linked to drug response phenotypes. • It is interesting, but not necessary, to identify the exact genes involved. • Can work with “associated populations,” does not require detailed information on disease in family history(pedigree).

43. Huge Database Problem • Physicians collect tons of data • patient age, sex, weight, blood pressure, family disease history, date of symptom onset • Cancer data: tumor size, location, stage, etc. • Data specific to each type of disease • Now integrate thousands (or 100K’s) of SNPs that are correlated with some of these clinical factors in complex relationships

44. Use the Profiles • Genetic profiles of new patients can then be used to prescribe drugs more effectively & avoid adverse reactions. • Can also speed clinical trials by testing on those who are likely to respond well. • Can "rescue" drugs that don't work well on everybody, or that have bad side effects on a few.

45. Real World Applications • Most of the major pharmaceutical companies are currently collecting pharmacogenomic data in their clinical trials. • Data is yet to be published. • Genetic indications for drug use are becoming available. • Plan to sell the drug with the gene test

46. Multi-locus SNP Profiles • There will be a few hundred to a few thousand SNPs linked to medically important alleles in the next ~10 years. • Haplotypes will reduce the number that need to be screened (one SNP gives information about a group of linked genes) • Some genes will turn out to be involved in many important pathways

47. Will People Want This Information?? • Genetic determinism and possible discrimination. • Even a simple test to see what drug you should take could reveal information about your risk of cancer or heart disease.

48. A tour in FBAT testing

49. A tour in Python

50. Homework Assignment 4 (R) check website for exercise and supplementary info: due 28 Oct