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

DNA – ´the stuff of life´

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!

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

´SNPing away´ at the genome.... 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 1.The US Human Genome Project (HGP) 2. The SNP Consortium (TSC)

15 February 2001 Nature 409, (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)

SNP fact sheet  number of loci : HGP 4.2 million TSC 1.8 million (year 2002)  estimated density: every 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

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

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

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

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

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

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

disease gene SNP allele Alzheimer apolipoprotein E (APOE)  4 allele Diabetis mellitus peroxisome proliferator- pro 12 ala Type 2 activated recepto-  PPARG  Venous thrombosis Factor V Leiden G 1691 A SNPs associated with complex diseases

Problems with association studies Example: Factor V Leiden patients controls (venous thrombosis) 50 % % venous thrombosis other geneslifestyle oral contraceptives Factor V mutation

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

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

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

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

´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 Conclusions (2) The full clinical potential of SNPs has yet to be realized

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