Dr. Almut Nebel Dept. of Human Genetics University of the Witwatersrand Johannesburg South Africa

1. Significance of SNPs for human disease Dr. Almut Nebel Dept. of Human Genetics University of the Witwatersrand Johannesburg South Africa

2. DNA – ´the stuff of life´

3. Human genomic variation On average, the difference between any two homologous human DNA sequences has been estimated to be < 0.1%. For the human genome, this translates into ~ 3 million nucleotides!

4. AGAGATTAGTCTGCATC-CG AGTGATTAGTTTGCATCGCG Single nucleotide polymorphisms ( = SNPs) account for ~ 90% of all human DNA variation. SNP = a locus in the DNA at which different people have a different nucleotide (allele)

5. ´SNPing away´ at the genome .... • The US Human Genome Project (HGP) • 2. The SNP Consortium (TSC) • Aims: • to identify informative SNPs • to create SNP maps across the genome • to determine SNP allele frequencies in different populations • to make the data publicly and freely available

6. Nature409, 928 - 933 (2001) 15 February 2001 A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms The International SNP Map Working Group (HGP, TSC and others)

7. SNP fact sheet • number of loci : HGP 4.2 million TSC 1.8 million (year 2002) • estimated density: every 300 - 1000 bp through- out the genome, except sex chromosomes • only ~ 1% of SNPs are in genes and ~ 0.1% of SNPs are functional (= mutations) • mostly bi-allelic – suitable for automated analysis

8. ´in silico´ SNP discovery and screening to identify new SNPs in the genome to type many DNA samples for known SNPs • automated high-throughput technologies • software for efficient database management • availability of and access to sequence data • bioinformatic tools

9. SNPs as genetic signposts for human disease Research mapping disease genes (monogenic, complex) Diagnostics diagnosing predisposition to complex diseases Pharmacogenetics predicting responses to drugs

10. Linkage Disequilibrium (LD) SNP 1 SNP 2 SNP 2 SNP 1 SNP 1 SNP 2 haplotype

11. 1. linkage analysis to map genes responsible for highly penetrant disorders (monogenic) 2. association studies to examine the genetic basis of complex (multifactorial) diseases Strategies for gene mapping

12. ~ location SNPs in linkage analysis identify SNP haplotypes that segregate together with the disease family pedigree + fine mapping SNP typing using DNA of affected and unaffected family members candidate gene

13. SNPs in association studies to test whether a particular SNP allele / haplotype is enriched in patients compared to healthy controls SNP X alleleA allele C frequency of C in patients > controls

14. SNPs associated with complex diseases disease gene SNP allele Alzheimer apolipoprotein E (APOE) e4 allele Diabetis mellitus peroxisome proliferator- pro 12 ala Type 2 activated recepto-g (PPARG) Venous thrombosis Factor V Leiden G 1691 A

15. patients controls (venous thrombosis) 50 % 3 - 4 % Problems with association studies Example: Factor V Leiden Factor V mutation Factor V mutation venous thrombosis other genes lifestyle oral contraceptives

16. SNPs and pharmacogenetics (1) = the study of variability in drug responses due to genetic factors in individuals drug efficacy adverse effects (acute toxic events, drug interactions)

17. SNPs and pharmacogenetics (2) Clinical trial of a drug association study: testing SNPs in genes coding for drug- metabolizing enzymes (eg. cytochrome P450 mono-oxygenase gene family) to identify a SNP allele / haplotype that predisposes individuals to an adverse drug effect

18. SNPs and population genetics There are considerable differences in SNP allele frequencies among populations classified acc. to geographic, racial and ethnic criteria = ´population-specific SNPs´ Allele Frequency Project of TSC

19. Conclusions (1) ´SNP revolution´ SNPs are being used to identify genes involved in both monogenic and complex diseases SNPs have the potential for predicting disease and for identifying individuals at risk for drug toxicities, but there is still uncertainty surrounding their use in clinical molecular diagnostics

20. Conclusions (2) ´SNP revolution´ SNPs are being used to identify genes involved in both monogenic and complex diseases SNPs have started to play an important role in the administration of drugs and in identifying individuals at risk for toxicities SNPs have the potential for predicting disease, but there is uncertainty surrounding their use in clinical molecular diagnostics The full clinical potential of SNPs has yet to be realized

21. Prospects for the post-genomic era SNP analysis + gene expression + (SNP-related) functional proteomics • more accurate predictive models for complex diseases • ´tailored´ or personalized medicine with better, safer medication • financial, ethical, personal issues