Single Nucleotide Polymorphisms Arthur M Lesk Bologna Winter

Single Nucleotide Polymorphisms Arthur M. Lesk Bologna Winter School 2011 1

What are SNPs and why are they important? • SNP = Single nucleotide polymorphism, an isolated change in a single nucleotide • SNPs are one type of mutation • Some have obvious functional consequences • Sickle-cell haemoglobin: gag→gtg (β 6 Gln→Val) • First “molecular disease” sickle-cell anaemia • Some are ‘silent’ • Some are in non-coding regions • affect splice sites? • affect regulatory sites? • some have no known phenotypic effect • 2

What is a SNP? • The genomes of individuals in a population contain a particular base at some position most of the time. • That is, there is a “normal” sequence • A SNP is a deviation from the normal sequence. – Many people require that a variation occur in at least 1% of the population, to be considered a SNP • But: what population? What if two distinct populations have a consistent polymorphism? 3

SNPs in human genomes • SNPs are about 90% of all inter-human variation • Occur on the average once in every 300 bases • 2/3 of SNPs are C→T changes (perhaps because C can easily deaminate) cytosine → uracil 4

SNP density varies across human genome • Some high-density patches • Some ‘deserts’ • SNPs in coding regions ~1/3 as many as in noncoding regions • SNP density correlated with recombination rate (which causes which? ? ) • AT microsatellites: long (AT)n repeat tracts tend to appear in regions of low SNP density 5

Figure 14 SNP density in each 100 -kbp interval as determined with Celera-PFP SNPs. J C Venter et al. Science 2001; 291: 1304 -1351 Published by AAAS

What is normal? • Obviously we all differ genomically • Swedes and Chinese have obviously different phenotypes • Most Swedes and Chinese are healthy indviduals • Therefore genetic differences do not necessarily cause disease • Pointless to check for differences from a single ‘reference sequence’ • Of course, many genetic differences not just SNPs 7

Variation in human and other species • Any two humans ~99. 5% identical in sequence • Chimpanzees, gorillas: twice as variable, despite much smaller population size • Implies prehistoric bottleneck in human population, recent common origin • Most SNPs (> 5%) shared among human populations from around the world • Most populations (e. g. British) contain 85 -90% of all known variation 8

Variation in human and other species • Some variation is population-specific • In some cases, there is local selective pressure • For example, adult lactose tolerance, malaria resistance • African populations have greatest genetic diversity • Supports ‘Out of Africa’ theory of human origin and migration 9

Identification of geographical origin, phenotype • A criminal leaves a blood sample at a crime scene • How much can we tell about him or her? • Not perfectly, but: – Ethnic group – Eye and hair colour (hair colour easier to change) – Family name? 10

Types of SNPs • Transitions: – purine ↔ purine – pyrimidine ↔ pyrimidine (cytosine→uracil) • Transversions: – purine ↔ pyrimidine • Transitions are more common than transversions 11

Prevalence of SNPs in human genomes • approximately 1 in 300 bp (0. 001%) • compare difference between human / chimpanzee genomes: • 4% different (not all SNPs!) 12

‘Life cycle’ of a SNP • Generation of a mutation • Initial survival, against ‘sampling loss’ • Increase in frequency – survival until become homozygous in some individuals; • chance of loss reduced (helped by bottlenecks, founder effects – population size dependent) • Fixation 13

Initial survival of a SNP • Suppose a person is heterozygous for a novel, selectively-neutral mutation. • Suppose the person has 2 children that survive to reproductive age. The probability of loss of the mutation is 25%. • If each descendant has 2 children that survive to reproductive age, probability of loss in 200 years = 94% 14

Where do SNPs occur in the human genome? • Distributed throughout the genome • 50% in non-coding regions – NOT the same as non-functional!!! • 25% missense mutations (amino acid substitution) • 25% silent (amino acid unchanged) – silent = no change in encoded amino-acid sequence – NOT the same as no phenotypic effect!!! – would be better to call them synonomous SNPs rather than silent SNPs 15

SNPs in non-human genomes • Of course other species have SNPs • Here we will focus on human SNPs because of relevance to human disease • However, SNPs in pathogens are sometimes associated with antibiotic resistance, and therefore related to human disease • SNPs in some plants give clues to domestication 16

Organised efforts to collect SNPs • The Hap. Map is a catalogue of common human genetic variants • Hap. Map Project = international collaboration among Japan, the United Kingdom, Canada, China, Nigeria, and the United States • NOT Europe • Carry out measurements, provide database • Other projects collect SNPs in other species 17

Hap. Map project • International consortium: International Hap. Map Project – http: //hapmap. ncbi. nlm. nih. gov/ • Catalogue of human genetic variants : – What sites? – How distributed – frequency in different populations – Raw material for linking genomics with disease 18

Origin of samples • • • Total of 270 people. The Yoruba people of Ibadan, Nigeria Japan (Tokyo) China (Beijing) U. S. residents with Northern and Western European ancestry 19

What is a haplotype? • Often, a set of SNPs appear nearby on the same chromosome • In absence of recombination, they will be inherited in blocks • Pattern of SNPs in a block is called a haplotype • A block may contain many SNPs, but only a few are needed to identify a haplotype • These signature SNPs within a haplotype block are called `tag SNPs’ 20

http: //www. riken. go. jp/engn/r-world/info/release/news/2003/nov/image/frol_06. gif 21

http: //img. medscape. com/fullsize/migrated/553/400/ncpcard 553400. fig 1. gif 22

Guide to SNP databases • SNPlinks: http: //www. snpforid. org/snpdata. html • NCBI db. SNP http: //www. ncbi. nlm. nih. gov/entrez/query. fcgi? db=snp • The SNP Consortium http: //snp. cshl. org/ • Hap. Map http: //www. hapmap. org/ • Applied Biosystems http: //myscience. appliedbiosystems. com/cds. Entry Assays-on-Demand /Form/assay_search_basic. jsp • Ensembl http: //www. ensembl. org/Homo_sapiens/ • HGVBase http: //hgvbase. cgb. ki. se/ • Seattle. SNPs https: //gvs. gs. washington. edu/GVS/ 23

db. SNP database at NCBI • non-redundant dataset • nomenclature: rs number • rs = reference SNP. 24

General human mutations • Human Gene Mutation Database http: //www. hgmd. cf. ac. uk • over 100000 mutations, in 3700 genes • 6. 2% of total ~23000 genes • about 10000 new mutations found per year • OMIM (Online Mendelian Inheritance in Man) – database of mutations associated with human disease • OMIA (Online Mendelian Inheritance in Animal)

Databases with important related information • Online Mendelian Inheritance in Man (OMIM) [NCBI] – Comprehensive compendium of human genes and associated phenotypes – Not limited to SNPs • SNPs 3 D http: //www. snps 3 d. org/ – SNPs 3 D assigns molecular functional effects to nonsynonymous SNPs based on structure and sequence analysis. • SNPper http: //snpper. chip. org/ – Retrieve SNPs by position or gene association 26

Quality of sequence information is important • SNPs appear in human genome at approximately 1 in 300 bases • Obviously error rate in resequencing must be substantially lower than this if SNP data are to be meaningful • Measure of DNA sequencing quality: PHRED 27

PHRED – measure of sequence quality • Phred scores accepted to characterize the quality of DNA sequences • Originally Phred was a program, that determined accurate quality scores indicating error probabilities. • Accepted as general standard • Phred quality score Q. Let P = probability of base error Q = -10 log 10 P 28

Phred quality score Q 10 20 30 40 50 Probability of incorrect base call 1 in 10000 1 in 100000 Base call accuracy 90% 99. 9% 99. 999% 29

Phred quality score Q Base call accuracy 10 Probability of incorrect base call 1 in 10 20 30 40 50 1 in 100000 99% 99. 999% 90% A method that gave an averaged phred score Q = 30 would give approximately as many errors as there are SNPs! 30

What can SNPs tell us? • Causes of disease -- dysfunctional protein • Correlation with disease prognosis, success of particular treatment • Useful genetic markers, to locate some gene of phenotypic interest; for instance, a gene correlated with a disease • Characterise individuals • Characterise populations (SNP distribution) • Applications in anthropology -- tracing of migrations, human evolution 31

Use of SNPs as genetic markers Before 1980, genetic maps were constructed by measuring recombination frequencies between genes giving measurable phenotypic traits This goes back at least to Sturtevandt and Morgan, if not to Mendel At that time, phenotypes were the only visible aspect of the genome

Use of SNPs as genetic markers In 1980, Botstein, Davis, Skolnick & White proposed using polymorphic DNA markers for genetic mapping, even if they had no known phenotypic effect Example: (then) restriction sites SNPs → restriction fragment length polymorphisms (RFLPs) Did linkage mapping with restriction sites Now we can use SNPs

Traits depending on multiple loci • Use of SNPs to identify traits, including but not limited to diseases, that depend on multiple loci • Single genes for diseases showing simple Mendelian inheritance (for instance, cystic fibrosis) can be isolated • Diseases that depend on interaction with multiple loci can be studied with enough SNP linkage information 34

SNPs tell us about human history • Development of ability to digest lactose past infancy correlated with domestication of cattle, increased (non-fermented) dairy products in human diet • Source of calcium and calories • Many Asian populations retain adult lactose intolerance • Where do they get calcium? “The soybean is the cow of Asia. ” 35

Ability to digest lactose in adulthood • Digestion of lactose depends on enzyme lactase-phlorizin hydrolase, which catalyzes hydrolysis of lactose → glucose + galactose 36

Ability to digest lactose in adulthood • In many people, the ability to digest lactose is a juvenile characteristic • Expression declines after age 2 – varies among individuals • Consistent with lifestyle involving breast feeding until this age, followed by weaning followed by diet not including (nonfermented) milk and other dairy products – To form yoghurt, bacteria cleave lactose 37

Evolution of adult lactase expression • Domestication of cattle, with concomitant rise of milk in the diet, led to selective pressure for lactose tolerance • Mutation arose among cattleraising people: – the Funnel Beaker culture – north-central Europe ~5, 0006, 000 years ago • Most common mutations in Europeans: SNPs – C/T-13910 – G/A-22018 • Not surprisingly, in control regions for lactase gene 38

http: //gseorlando. files. wordpress. com/2010/09/j. jpg Prevalance of lactose-tolerance SNP Danes and Swedes Spanish and French Chinese 90% 50% 1% Group Study Exchange 39

Multiple development of lactose tolerance • Development of lactose tolerance apparently appeared four times, independently – Europe: C/T-13910 and G/A-22018 • Pastoral areas of Africa – three independent mutations: – G/C-14010 East Africa – T/G-13915 North Sudan – C/G-13907 North Kenya 40

http: //www. ncbi. nlm. nih. gov/pmc/articles/PMC 2672153/bin/ukmss-4417 -f 0002. jpg 41

http: //www. ncbi. nlm. nih. gov/pmc/articles/PMC 2672153/bin/ukmss-4417 -f 0001. jpg 42

SNPs in anthropology • Useful in tracing relationships between populations, migration routes • Initially used mitochondrial DNA (16569 bp) • Maternal inheritance only – (Y chromosome gives paternal inheritance only) • Important argument for “out of Africa” theory of human origins and dispersal • Can choose non-selected regions, in contrast to previous work on blood groups, MHC haplotypes 43

Migration routes into Asia and the Pacific based on SNPs http: //i 49. tinypic. com/2 d 0 j 2 py. jpg 44

DNA sequences and language groups • Proposal by L. L. Cavalli-Sforza • Showed consistency between trees based on genetic markers and trees based on linguistic groupings • Controversial! • In some cases, genomics has confirmed hypotheses of population affinity based on language similarity / dissimilarity • Basques are outliers in both genes and language 45

Recommended reading Tomasz Kamusella The Politics of Language and Nationalism in Modern Central Europe Palgrave Macmillan, 2008 46

What happens after invasions? • Hungary invaded by Magyars in 896 AD. Country converted to speaking Uralic language • Rome fell to vandals in 476 AD but did NOT impose their language. (Perhaps recognising superiority of Italian culture – which their descendants don’t) • England invaded by Anglo-Saxons in about 5 th century. Anglo-Saxon pushed Celtic languages to far reaches of British Isles + Brittany • Norman invasion of 1066 did NOT entirely replace Anglo-Saxon by French. 47

Possible effects of SNPs • In protein-coding sequences – silent – missense – coding → stop codon – stop codon → coding – SNPs can → dysfunctional proteins • In splice sites – 15% of disease-causing mutations in human genome are point mutations in vicinity of m. RNA splice junctions • In regulatory sequences 48

What are possible effects of SNPs in coding sequences? • Change in amino acid • Example: sickle-cell anaemia • sense codon → stop codon – protein truncated • stop codon → sense codon – protein extended 49

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SNPs in coding regions can do more than change one amino acid • Change of codon for an amino acid to STOP codon produces truncated protein – Example: common mutation causing phenylketonuria • Change of STOP codon to codon for an amino acid produces extended protein • Example: haemoglobin Constant Spring – α-chain variant – termination codon TAA is mutated to CAA (glutamine) – produces extension of haemoglobin α-chain from 142 to 172 amino acids – causes mild anaemia 51

Possible consequences of silent (synonymous) SNPs • Nothing detectable • Change in proportions of variable spliced proteins • Change in stability of m. RNA • Effect on protein folding (translational pausing) 52

SNPs can affect variable splicing • Almost all multiexonic genes show variable splicing • Change in isoform can have severe effects • Susceptibility to West Nile Virus – SNP in 2', 5'-oligoadenylate synthetase-like gene common in susceptible individuals – oligoadenylate synthetase implicated in viral resistance – SNP present in exonic splice enhancer – Increases level of truncated protein → enhanced susceptibility to virus 53

SNPs can affect m. RNA stability • Expression levels of proteins depend on m. RNA half-life (among other things) • ATP-binding-cassette (ABC) transporters are membrane proteins • function in translocation of compounds out of cells • Disease associates with SNP in this family 54

Dubin-Johnson syndrome • autosomal recessive disorder • increase in conjugated bilirubin • defect in hepatocyte secretion of conjugated bilirubin into bile • many patients asymptomatic • hormonal birth control or pregnancy can → jaundice • Some cases caused by synonymous SNP in gene for ABCC 2 → increased m. RNA stability → increased expression levels 55

Synonymous SNPs can affect protein folding and even native structure • Synonymous SNPs do not affect amino acid sequence • Therefore should not alter native structure • However, affects kinetics of folding • m. RNA secondary structure affect translational pausing 56

Cotranslational folding is affected by translational pausing • Can affect not only kinetics but tertiary structure • Example: SNPs in Multidrug Resistance 1 MDR 1 – Encodes P-glycoprotein, an ABC transporter – Function to pump molecules out, including chemotheraputic agents used in cancer – Haplotype C 1236 T, G 2677 T (nonsynonymous), C 3435 T – Affects interactions of protein with: • cyclosporine A -- fungal cyclic peptide, immunosuppressant, used post-transplant • verapamil -- calcium channel blocker, used in treatment of high blood pressure Ref: Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, Calcagno AM, Ambudkar SV, Gottesman MM (2007). A "silent" polymorphism in the MDR 1 gene changes substrate specificity. Science. 315, 525 -528. 57

Verapamil Cyclosporin A 58

References • Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, Calcagno AM, Ambudkar SV, Gottesman MM (2007). A "silent" polymorphism in the MDR 1 gene changes substrate specificity. Science. 315, 525 -528. • Erratum in: * Science. 2007 Nov 30; 318(5855): 1382 -3. • Comment in: Science. 2007 Jan 26; 315(5811): 466 -7. • Bioessays. 2007 Jun; 29(6): 515 -9. • * Epilepsia. 2007 Dec; 48(12): 2369 -70. 59

Prediction of functional effects of non-synonymous SNPs • Poly. Phen: (EMBL, Heidelberg) http: //coot. embl. de/Poly. Phen/ • SNPs 3 D (Baltimore) http: //www. snps 3 d. org/ • Pmut (Barcelona) http: //mmb 2. pcb. ub. es: 8080/PMut/ • SIFT (University of Washington) http: //blocks. fhcrc. org/sift/SIFT. html • MAPP (Stanford) http: //mendel. stanford. edu/Sidow. Lab/downloads/MAPP/index. htm l 60

Sorting Intolerant From Tolerant • Database and server at University of Washington • SIFT predicts whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids • Limited to non-synonymous SNPs (or more generally, amino acid substitutions) 61

SNPs in Medicine Genomic sequence analysis can provide a lot of information about health risks of any individual So far, part of the problem is that sequences usually just give bad news Indications of optimal therapy useful: the U. S. health care industry faces huge costs in treatment of side effects of medication

SNPs and disease • Some SNPs (and of course other mutations) are consistent with a healthy life, and typical life-span, provided the individual carries on a reasonable lifestyle. • Some SNPs directly and unavoidably cause disease • Others cause disease only in combination with unusual lifestyle or specific events – Example: fever in children with Z-mutation of α 1 -antitrypsin – protein somewhat unstable, denatures and aggregates – Essential to keep infants free of high fever • In many cases we can’t tell extent of genetic basis of disease or how it interacts with environmental effects 63

Copy-number variations may mask disease genes • Genes in which nonsense SNPs detected belong to gene families of higher than average size. • Genetic robustness • Every individual is heterozygous for some deleterious mutations that, if homozygous, would be lethal.

Interaction of SNPs with environment/experience • α 1 -antitrypsin is a natural elastase inhibitor in the lung • elastase in lung protects against bacteria • inhibitor prevents elastase from acting on human tissues, notably elastin in the lung • Z-mutation of α 1 -antitrypsin: glu 342→lys • Causes enhanced risk of emphysema • Z-mutation + smoking = GUARANTEE of early death from emphysema “Genetics loads the gun; environment pulls the trigger” (J. Stern)

Discussion of following diseases • • Sickle-cell anaemia Phenylketonuria Alzheimer’s disease Cancer 66

SNP causing disease: Sickle-cell anaemia • β 6 Val→Gln creates hydrophobic (sticky) patch on surface of β chains of haemoglobin • Common SNP: gag → gtg • causes aggregation of deoxyhaemoglobin 67

Phenylketonuria • Inborn deficiency in phenylalanine hydroxylase • Autosomal recessive (12 q 24. 1) • 1/10000 sufferers • 1/50 carriers • Subject of neonatal screening in many countries 68

Mutations causing PKU • Phenylalanine hydroxylase is a tetramer • Known mutations include: – Over 200 affecting catalysis – About 50 affecting regulation – About 10 affecting tetramerization (Some involve cofactor -- tetrahydrobiopterin -- processing) • Most common mutation in Caucasians: – g→a in intron 12 – causes truncation (sense codon to stop codon) – fails to tetramerize • Mc. Gill database: http: //www. pahdb. mcgill. ca/ 69

Testing for PKU • Phenylalanine, and degradation products such as phenylpyruvate build up in blood and urine (Phenylpyruvate is a ketone, hence the name of the disease. ) • Blood sample from neonate, mass spec to detect phe, tyr levels • Can also do genomic sequencing – detection of carriers, counselling of potential parents 70

Symptoms if untreated • developmental defects: – mental retardation – microcephaly • seizures 71

• Low phenylalanine diet – not entirely satisfactory (unpalatable? ) – tricky to manage PKU women in pregnancy • Gene therapy (works in mice …) • Enzyme replacement therapy http: //newenglandconsortium. org/wp-content/uploads/2009/12/PKU-Food-Diagram-copy. jpg Treatment 72

PKU in pregnancy • Remember that PKU is an autosomal recessive trait • A woman with PKU must be homozygous for defective phenylalanine hydroxylase (not necessarily same mutation) • If such a woman becomes pregnant, it is likely that the foetus is only a carrier (unless father also a carrier) • Tricky to control phe levels in mother to give foetus adequate nutrition but not toxic levels 73

Enzyme replacement therapy for PKU (1) administer functional phenylalanine hydroxylase itself But: requires cofactor, complex regulatory controls (2) phenylalanine ammonia-lyase • converts phenylalanine to trans-cinnamic acid • trans-cinnamic acid: – has low toxicity and does not cause developmental defects – converted by liver to benzoic acid, detoxified and excreted in urine – stable • phenylalanine ammonia-lyase found in many plants; and fungi, including yeasts • Anabaena variabilis enzyme in phase II clinical trials 74

Comparison of reactions catalysed by phenylalanine hydroxylase (PAH) and phenylalanine ammonia-lyase (PAL) http: //www. nature. com/mt/journal/v 10/n 2/images/mt 20041219 f 1. jpg 75

Genomics of phenylalanine hydroxylase • Rhesus macaque and chimpanzee phenylalanine hydroxylases differ from normal human PAH • On difference: Human Y 356 = H in macaque and chimp • The mutant is in the list of mutations in the PKU database: http: //www. pahdb. mcgill. ca/ • But chimps and rhesus macaques do not suffer from PKU • Why not? 76

Alzheimer’s disease • Loss of cognitive function, characterised by: – Loss of train of thought – Progressive memore problems – Miss important appointments • Early-onset – Appears at age < 65 • Late-onset Alzheimer’s – most common type – Affects people over the age 65 – ~50% of people over age 85 suffer from it. • Familial Alzheimer’s – < 1% of cases, appears at age 40 -60

Alzheimer’s disease • Early-onset -- age < 65 – associated with mutations in presenilin 1, presenilin 2 and amyloid precursor protein • Late-onset Alzheimer’s – age > 65 – most common type: ~50% of people over age of 85 suffer from it. – Propensity associated with Apo. E (apolipoprotein E SNPs) • Familial Alzheimer’s – < 1% of cases, appears at age 40 -60

Apo. E SNPs and risk of late-onset Alzheimer disease • Apo. E = apolipoprotein E – Gene on chromosome 19; therefore we have two alleles – Basic function: remove cholesterol from blood • Four common alleles, differ by SNPs: – Apo. E 1 [minor variant], Apo. E 2, Apo. E 3 [~55%], Apo. E 4 • E 3 most prevalent(“ normal”) • At least one E 4 allele increased risk of Alzheimer’s • At least one E 2 allele decreased risk of Alzheimer’s

Apo. E alleles • Apo. E = 317 -residue protein • Four common Apo. E alleles, differ by SNPs: – Apo. E 1 = rs 429358(C) + rs 7412(T) [minor variant] – Apo. E 2 = rs 429358(T) + rs 7412(T) – Apo. E 3 = rs 429358(T) + rs 7412(C) [~55%] – Apo. E 4 = rs 429358(C) + rs 7412(C) Allele E 1 E 2 E 3 112 Arg Cys 168 Cys Arg E 4 Arg

SNPs and Cancer SNPs are relevant to cancer research and treatment in several ways: Mutations detectable in the genome indicate propensity for development of cancers Mutations in BRCA 1 and BRCA 2, as indicators for likelihood of breast/ovarian cancer development probably best known Sequence analysis can predict progression and outcome Sequence analysis can help choose optimal treatment Progression of tumour often involves mutations and divergence of cell lines

Formation of cancer associated with loss of genome integrity Cancer results from accumulated mutations that break down the controls on cell growth Three classes of genes can promote cancer: Genes that regulate cell proliferation Genes required for repair of DNA damage Genes that control apoptosis

Retinoblastoma Rare childhood tumour of eye Sporadic / familial (30 -40%) Characteristics of familial retinoblastoma: Early onset Multiple tumours Affect both eyes Autosomal dominant inheritance pattern

‘Two-hit’ hypothesis Non-familial cases require inactivation of both copies of retinoblastoma gene Require separate and independent mutations Familial cases inherit one defective copy, one functional copy That is, ‘first hit’ is inherited, all that is needed is ‘second hit’

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SNPs and cancer • A number of genes are known as ‘tumour suppressor genes’ – Well-known examples: BRCA 1, BRCA 2 – Not all common mutations are SNPs • Some SNPs in tumour suppressor genes cause predisposition to development of cancer • Other SNPs correlated with – Progression of disease – Efficacy of certain drugs 86

Tumour suppressor genes Encode proteins that inhibit tumor formation Normal function: inhibit cell growth Mutations take “foot off cell-growth brake”

Mutations in BRCA 1 and BRCA 2 • In general population: ~ 12% of women will develop breast cancer • Women with harmful mutation in BRCA 1 or BRCA 2: ~ 60% will develop breast cancer • In general population: ~1. 4% of women will develop ovarian cancer • Women with harmful mutation in BRCA 1 or BRCA 2: ~ 15 -40% will develop breast cancer 88

Some common BRCA 1 mutations • Varies with population: showing strong ‘founder’ effect • Many not SNPs • Not all of these are necessarily harmful mutations Population Common mutation Ashkenazi Jewish 185 del. AG, 188 del 11, 5382 ins. C Italian 5083 del 19 African-Americans 943 ins 10, M 1775 R Spanish R 71 G French 3600 del 11, G 1710 X French Canadians C 4446 T 89

Other tumour suppressor genes correlated with predisposition to develop cancer • TP 53, PTEN, STK 11/LKB 1, CDH 1, CHEK 2, ATM, MLH 1, and MSH 2 • But BRCA 1 and BRCA 2 have the strongest correlation with predisposition to breast and ovarian cancer • Importance of early detection in treatment of cancer • At-risk individuals should be sure to undergo frequent checkups 90

Pharmacogenomics • Tailoring of treatment to individual patient, based on genetic sequences 91

Pharmacogenomics • Tailoring of treatment to individual patient, based on genetic sequences • Choice of optimal drug, and dosage • Use of drugs inappropriate for the patient: – risks side effects: discomfort or even death – loses time in treating a condition which may become progressively worse – at best, wastes money and health care resources; may require additional resources to cure side effects and more severe conditions 92

Thiopurine methyltransferase • Acute lymphoblastic leukemia is a childhood cancer treated by thiopurines • Thiopurine methyltransferase breaks down the drugs • Genetic variant leading to inactive enzyme threatens toxic levels of drug in patient • Screening patients for deficiency allows monitoring to determine appropriate dosage levels 93

Sensitivity to abacavir • Abacavir used in treatment of AIDS • 4 -8% of patients have serious, potentially-fatal hypersensitivity reaction • Hypersensitivity correlated with MHC allele HLA-B*5701 • Genetic screening can detect, guide treatment 94

Cytochrome P 450 and drug metabolism • Cytochrome P 450 is a family of enzymes in the liver • Responsible for metabolizing a wide variety of drugs • Variations in sequences affect activity of these enzymes • Lowered activity or loss of activity can cause drug toxicity • Genetic tests for variations in cytochrome P 450 genes warn of potential overdose dangers • Pharmaceutical companies screen compounds for rates of metabolism by cytochrome P 450 enzymes 95

J. D. Watson – lessons from genome • The sequence of J. D. Watson’s DNA has been determined. • He is homozygous for an unusual allele of the important drug metabolizing cytochrome gene (CYP 2 D 6) • Individuals with his genotype metabolise some drugs more slowly than other people. • Watson has been taking β blockers to lower his blood pressure. • Side effect: made him unacceptably sleepy. • Now he is taking a lower dosage. 96