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Population Genomics Friend or Foe? Tim Shank 4/2/03 Woods Hole Oceanographic Institution.

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1 Population Genomics Friend or Foe? Tim Shank 4/2/03 Woods Hole Oceanographic Institution



4 Genome Projects Microbial Genomics Genome- Genome Interactions Comparative Genomics

5 Population Genomics Projects Microbial Genomics Functional Genomics Pharmaco genomics Population Genomics- A View

6 Population Genomics- definitions The study of forces that determine patterns of DNA variations in populations (Michel Veuille, European Consortium) Field of genomics that links complex genotypes and phenotypes by comparing the flow of genotypic and phenotypic information in breeding and natural populations (Andrew Benson, U. Neb) Genomic variation within species permitting the construction of detailed linkage maps using polymorphic markers, and through crossing experiments between individuals with different phenotypes, identification of genes responsible for phenotypic variation (e.g, disease susceptibility, drug toxicity) (Andrew Clark, PSU)

7 Questions in Marine Population Genetics How do marine larvae disperse between localities that may be isolated? Do topographic and hydrographic features like transform faults and currents disrupt or facilitate gene flow between demes? What role do larval retention and stepping stone habitats play in species maintenance? Does the pattern of colonization and mode of dispersal affect the retention of genetic diversity in marine animals? Characterization of genetic relationships of populations important for understanding: Genetic management of protected or threatened populations (e.g. Jones et al. 2002) Historical migrations and connectivity of populations (e.g. Eizirik et al. 2001) Kin selection and social behavior (e.g. Morin et al. 1994) Mating systems (e.g. Engh et al. 2002) Dispersal, temporal and spatial genetic structure (e.g. Goodisman & Crozier 2001)

8 Dispersal models Continuous populations Isolation-by-distance Discrete populations Stepping-stone Island model

9 F ST -approaches Nm F ST Wright (1951) [The genetical structure of populations. Ann. Eugen. 15:323-354.] noted the following relationship holds when populations reach an equilibrium between genetic drift and migration: where N is the variance effective population size of the average population, and m is the average proportion of immigrants in each population Problem: Useful parameter space is for F ST values between 0.1 and 0.4 Nm is a virtual number

10 Galapagos Rift N S EW 2° 13° 11° 9° East Pacific Rise Fst. Migration rate Guaymas 21° DISTANCE (Km) 100010,000 20 10 5 100 Reject expectations of "island model" Consistent with stepping-stone model Inference: a species with more limited dispersal abilities Black et al. 1994 Gene flow among vestimentiferan tube worm (Riftia pachyptila) populations from hydrothermal vents of the Eastern Pacific. Marine Biology 120: 33-39. The giant tubeworm, Riftia pachyptila

11 Molecular Toolkit: markers for inferring population structure and gene flow  Allozymes  multiple, independent, codominant loci; relatively easy; low cost  need to freeze samples; state characters  RFLPs  variation in restriction fragment lengths  polymorphic due to restriction site mutation  mtDNA  relatively easy; maternally inherited; effectively haploid; non-recombining; modest cost; amenable to genealogical analysis  linked loci and psuedoreplication  nuclear DNA sequences  amenable to genealogical analysis  diploid; recombination; start-up time may be considerable  AFLPs  can get 100s of loci relatively easily  dominance; recombination; state characters; mutation models not available  minisatellites  repeats of 10-40 bp units  polymorphic due to unequal crossing over

12  DNA microsatellites  Repeat unit 2-3 bp; nuclear; can get dozens of loci relatively easily; method of choice for parentage  recombination; state characters; start-up time is great; issues of homoplasy in geographical studies; mutation must be taken into account in gene flow models  Single-Nucleotide Polymorphisms (SNPs)  Most simple form and most common source of genetic polymorphism in most genomes.  large amount of sequencing effort in nonmodel organisms  Violation of analyitcal assmumption of independence among marker loci  Sequence Tagged Sites (STSs) (physical marker)  A short DNA segment that occurs only once in the genome and whose exact location and order of bases are known. (They can be used as primers for PCR reaction).  Very labor intensive; very few loci  Expressed Sequence Tags (ESTs) (physical marker)  Short (100-300bps) part a cDNA which can be used to fish the rest of the gene out of the chromosome by matching base pairs with part of the gene.  large amount of sequencing effort Molecular Toolkit: markers for inferring population structure and gene flow

13 = arbitrary primer (e.g. ggcattactc)  High Variability: Probably due to mutations in priming sequences  PCR-based method Amplify regions between priming sites by polymerase chain reaction Analyze PCR products by agarose gel electrophoresis. Marker is dominant (presence/absence of band). Molecular Markers:Random Amplified Polymorphic DNA, AP-PCR Target Sequence No prior sequence knowledge required Many variations on the theme (e.g., RAMP, ISSR)

14 uPolymorphism based on gain or loss of restriction site, or selective bases uTechnically demanding and expensive uMany markers generated, mostly dominant uMore reliable than RAPD, less so than SSR uNo prior sequence knowledge required Amplified Fragment Length Polymorphism (AFLPs)

15 Single-Strand Conformational Polymorphism  Highly sensitive to DNA sequence: can detect single base changes  Simple process but can be difficult to repeat 1. Amplify Target Sequence 2. Denature product with heat and formamide 3. Analyze on native (nondenaturing) polyacrylamide gel 4. Base sequence determines 3-dimensional conformation

16 Denaturing Gradient Gel Electrophoresis 4. Denaturing gradient gels can be difficult to produce: use perpendicular gradient to identify optimal conditions, move to CDGE: constant denaturant gel electrophoresis 1. Amplify Target Sequence 2. Run product on gel with denaturing gradient (parallel or perpendicular to direction gel runs) 3. Product begins denaturing at a certain point, depending on base sequence: greatly retards migration and allows discrimination of alleles based on small sequence differences

17 Cleaved Amplified Polymorphic Sequence (CAPS)  Fairly simple analysis (cutting can be a hassle)  Requires sequence information from several alleles (or luck) 1. Amplify Target Sequence 2. Cut with a restriction enzyme that differentiates alleles 3. Alleles can be differentiated by size based on loss or gain of restriction site; May be able to analyze on agarose gel X Allele 1 Allele 2

18 Allele Discrimination via Quantitative PCR (Taqman)

19 Microsatellites (Simple Sequence Repeats)


21 Microsatellites “…reiterated short sequences [of DNA] tandemly arrayed, with variations in copy number accounting for a profusion of distinguishable alleles” - (Avise 1994) Locations: - Nuclear DNA - Chloroplast

22 Microsatellite Types 1.Dinucleotide Animals - CA Plants - TA, GA 2.Trinucleotide  GTG, CAG, and AAT  Related to disease and cancers 3.Tetranucleotide  GATA/GACA  Highly polymorphic

23 Microsatellite Uses 1.Population Genetics 1.Gene flow 2.Stock Structure 2.Genetic Probes 1.Larvae 2.Gut contents 3.Scat 4.Source populations 3.Pedigree Maps 4.Understanding Diseases

24 Microsatellite Advantages 1. Highly Polymorphic 2.Codominant 3.In every organism examined to date 4.Very abundant 5.Random spacing in the genome 6.Can find same loci in closely related species 7.Easy and reliable scoring 8.Highly sensitive 9.Neutral markers

25 Microsatellite Disadvantages 1.Expensive 2.Time consuming 3.Several loci are needed to obtain sufficient statistical power 4.Current analyses methods do not distinguish between changes in flanking regions vs. changes within the microsatellite regions 5.Different rates of evolution at different loci

26 Mutation Mechanisms 1.Slippage in DNA at Replication (Slip-Strand Mispairing, SSM)  increases or decreases the repeat by one unit  most supporting evidence 2.Recombination A.Unequal crossing over (UCO) B.Gene conversion

27 Microsatellite Mutations u10 -3 to 10 -6 events per locus per generation (point mutation 10 -9 to 10 -10 ) uVaries by repeat type base composition of the repeat taxonomic group length of the allele umost common - addition or deletion of a single repeat  occasionally 2 to several repeats ustrong evidence that the number of repeats is limited

28 Mutation Models 1.Infinite Allele Model (IAM) gain or loss of any number of repeats and always results in an allelic state not present in the population 2.Stepwise Mutation Model (SMM) gain or loss of a single repeat 3.Two-Phase Model (TPM) gain or loss of X repeats 4.K-allele Model (KAM) Intermediate step in the IAM (IAM = KAM with infinite K) K possible allelic states

29 Creating A Microsatellite-Enriched Library DNA Extraction DNA Library Genomic DNA PCR Add Linkers Digestion

30 CACA Enriching Microsat Library Hybridize to Beads GTGT Microsatellite-Enriched DNA Library PCR

31 Microsatellite Library Screening Cloning Dot Blot Hybridizations Blots/ Hybridizations Enzyme Digest Plasmid Preps Check Insert Size Isolated Plasmids

32 References Avise, J.C. 1994. Molecular Markers, Natural History and Evolution. Chapman and Hall, New York. 511 pp. Balloux, F. and N. Lugon-Moulin. 2002. The estimate of population differentiation with microsatellite markers. Molecular Ecology. 11: 155-165. Goldstein, D.B. and C. Schloterrer (Editors). 1999. Microsatellites: Evolution and Applications. Oxford University Press, Oxford, 352 pp. Jarne, P and P.J.L. Lagoda. 1996. Microsatellites, from molecules to populations and back. Trends in Ecology and Evolution 11(10): 424-429. Slatkin, M. 1995. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139: 457-462.

33 uAcrylamide gel with 5 microsatellite loci and internal size standard  Simultaneous analysis of a dozen loci Fluorescent Labeling of Microsatellites

34 Comparing “Genomic” Methods for Population Studies Method Polymorphism Codominance Prior Seq.Knowledge Difficulty Repeatability DNA Quality DevelopmentCost GenotypingCost/Locus Ease ofscoring Gel RAPD ++NN+- -++Agarose AFLP ++NN+ ++ Polyacryl. Microsats +++YY++ ++++++ Polyacryl. CAPS +YY++++(+) * ++Agarose DGGE, SSCP +++YN++++ Polyacryl. TaqMan +++YY ++ +++ ++None * Depends on cost of restriction enzymes employed

35 All population genetic/genomic markers are vulnerable to violations of assumptions- linkage equilibrium, mendelian inheritance, neutrality. Linkage Disequilibrium- alleles at different loci are found together more or less often than expected based on their frequencies (and location in the genome). Goldstein and Weale 2001 Population genomics: linkage disequilibrium holds the key. Current Biology 11:576-579

36 Population Genomics Research  Understandings population structure, historical migrations, and gene flow among populations (e.g. SNP density distribution, coalescent approaches)  Need relatively moderate polymorphism, low cost per sample  mtDNA, Microsatellites, SNPs  Understanding current gene flow and mating systems by direct methods (e.g., maternity analysis, paternity analysis)  Need high polymorphism, codominance, repeatability, low cost per sample  Microsatellites, SNPs  Pharmacogenomics: polymorphism-based approaches for the discoveryand development of new medications; translating polymorphisms into “new genomic medicine”*  Need rapid, low-cost, repeatable way to distinguish alleles  screening large numbers of individuals; SNPs and Sequencing *New York Times, Nov. 2002


38 Neighbor-joining phylogram based on complete mtDNA genome sequences (excluding D-loop). 1000 bootstrap replicates shown on nodes. Asterisk refers to the MRCA of the youngest clade containing both African and non-African individuals. uTwo main hypotheses for human evolution:  “Recent African origin” hypothesis- modern humans originated in Africa 100 - 200k years ago, and spread  “Multi-regional” hypothesis- modern humans evolved in different parts of the world uMtDNA favored out of Africa hypothesis but lacked statistical support for deep African branches  53 human mtDNA sequences (16,500 bp)  examined timing of evolutionary events  mtDNA evolving in a “clocklike” fashion  Linkage Disequilibrium not evident  3 deepest branches lead exclusively to sub-Saharan  Note star-like vs deep branching topology- larger Ne or longer genetic history in Africa; bottleneck in non-Affican

39 Mismatch distributions of pairwise nucleotide differences between a) African and b) non-African mtDNA mismatch distributions for Africans and non-Africans Individuals of African origin show a ragged distribution consistent with constant population size Individuals of non-African origin show a bell-shaped distribution strongly suggests a recent population expansion Exodus from Africa began 100 million years ago Divergence of Africans and non-Africans occurred 52,000  28,000 years ago

40 Human genome mining to produce 507,152 high-confidence SNP candidates as uniform resource for describing nucleotide diversity and regional variation within and between human populations

41 So What’s a SNP? u A mutation that causes a single base change is known as a Single Nucleotide Polymorphism (SNP)  SNPs are the most simple form and most common source of genetic polymorphism in the human genome  90% of all human DNA polymorphisms;1SNP in 1000 bp; 1.42 million  SNP Haplotype is a particular pattern of sequential SNPs (or alleles) found on a single chromosome  Microarrays, mass spectrometry and sequencing are all used to accomplish grouping or blocking of SNPs= haplotyping  Haplotype Determination Problem- find all haplotypes given a genome and all identified SNPs (algorithm development)

42 Approaches to SNP discovery and Genotyping Many and numerous! (Reviewed Pui-Yan Kwok Annu. Rev. Genomics Hum Genet. 2001. 2:235-258 Polymorphism ratio sequencing: a new approach for SNP discovery and genotyping Blazej et al. (2003) Genome Research 13:287-293. Dideoxy-terminator extension ladders generated from a single sample and reference template are labeled with fluorescent dyes and coinjected into a separation capillary for comparison of relative signal intensities. SNP discovery can be based on expressed sequence tags (ESTs), genomic restriction fragments, aligned BAC sequences, random shot gun clone sequences, overlapping genomic clone sequences Parallel genotyping of SNPs using generic high-density oligonucleotide tag arrays Fan et al. (2000) Genome Research 10:853-860. (see Stickney et al 2002 for zebrafish SNP arraying) PCR + single base extension chimeric primers, allele specific (labeled) dideox NTPs and then hybridized to arrays containing thousands of preselected 20-mer oligonucleotide tags A novel method for SNP detection using a new duplex-specific nuclease from crab hepatopancreas Shagin et al. (2002) Genome Research 12:1935-1942. “ Duplex Specific Nuclease Preference ” - SNP region amplified, template, signal probe, and matched duplexes are then cleaved by DSN to generate sequence-specific fluorescence

43 GenBank has a dbSNP One year ago: dbSNP had 2,842,021 SNP submissions total Today, 2003, dbSNP has 6,250,820 submissions for human 1,368,805 submissions for mosquito 197,414 submissions for mouse 2,031 submissions for zebrafish It is possible to search dbSNP by BLAST comparisons to a target sequence

44 The SNP Consortium is an alliance of pharmaceutical and computer companies managed by Lincoln Stein at Cold Spring Harbor Lab. “The SNP Consortium Ltd.. is a non-profit foundation organized for the purpose of providing public genomic data. Its mission is to develop up to 300,000 SNPs distributed evenly throughout the human genome and to make the information related to these SNPs available to the public without intellectual property restrictions. The project started in April 1999 and is anticipated to continue until the end of 2001.”

45 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.

46 Built a set of pairwise sequence alignments by analyzing the over- lapping regions of large insert clones SNP marker density grouped by overlapping regions Looked for mismatches; SNPs if Polybayes probability was 0.80 Modeled the marker density distribution

47 Marker density distributions predicted under competing population genetic models r indicates the per nucleotide, per generation recombination rate No demographic history Poisson distribution driven by mutation rate Distribution of polymorphic sites profoundly impacted Increased pop size yields abundance of new lineages with more mutation Decreased pop size raises likelihood of relatedness resulting in over-representation of sequence identity Collapse followed by a phase of recent population recovery Evaluated degree of fit between observed density distribution and probability predicted using the log likelihood of the data for a given model

48 Superior fit of the modeled parameters (with or without recombination) suggests a severe, 2- to 7 fold, collapse of population size 40,000 years (1600 generations) ago ….followed by a modest recovery % of successful trials for each model, at each data fraction; Assessments based on the amount of data required for rejection by X 2 test. Interestingly, data fit between observations and best-fitting models decays with more data.

49 History of the inbred laboratory mouse Compared the C57BL/6J Mouse genome sequence with 59 finished segments of the 129/Sv inbred strain Discovered nearly 70,000 SNPs on blocks of high SNP density (40 SNPs per 10kb) separated by blocks of low density (0.5 SNPs per 10kb) Surveyed panels of inbred mouse strains to find that distinct SNP haplotypes were shared among common inbred populations. Surveyed wild strains showed that 67% of each of the inbred genomes are derived from European mice and 33% from Asian mice

50 Three-Spined Sticklebacks  morphological evolution  populations isolated after last glaciation, have diverged morphologically and in sequence (CA n microsatellites)  strategy: cross benthic and limnetic fish; intercross F 1 s, follow morphological traits and polymorphisms in F 2 s  see Peichel et al (2001) The genetic architecture of divergence between threespine stickleback species. Nature 414: 901-5. How about other organisms? or new ‘model’ organisms; organisms that exemplify phenomena not well studied in human/worm/mouse?

51 Stickleback genetic map (Woods et al. 2000)  227 polymorphisms  1 SNP marker per 4 cM  took ~4 person-years  now mapping genetic basis of morphological variations

52 Zebrafish Genes Postlehwait et al. 1994 A genetic linkage map for zebrafish. Science 264: 699-703. Woods et al. 2000 A comparative map of zebrafish genome. Genome Research 10: 1903-1914. Geisler et al. 1999 A radiation hybrid map of the zebrafish genome. Nature Genetics 23: 86-89. First zebrafish SNP map 5 months ago 2102 SNPs for mutation mapping Hundreds of SNPs on single array Stickney et al. 2002 Rapid mapping of zebrafish mutations with SNPs and oligonucleotide microarrays. Genome Res. 12: 1929-1934. Vertical lines = 25 linkage groups Red dots correspond to SNPs represented on the olig. microarray Microsatellites Shimoda et al. 1999 Zebrafish genetic map with 2000 microsatellite markers. Genomics 58: 219-232.

53 Population Genomics Research  Understandings population structure, historical migrations, and gene flow among populations (e.g. SNP density distribution, coalescent approaches)  Need moderate polymorphism, low cost per sample  Allozymes, mtDNA, RAPDs, Microsatellites, AFLPs, RFLPs, SNPs  Understanding current gene flow and mating systems by direct methods (e.g., maternity analysis, paternity analysis)  Need high polymorphism, codominance, repeatability, low cost per sample  Microsatellites, SNPs  Pharmacogenomics: polymorphism-based approaches for the discovery and development of new medications; translating polymorphisms into “new genomic medicine”*  Need rapid, low-cost, repeatable way to distinguish alleles  screening large numbers of individuals; SNPs and Sequencing *New York Times, Nov. 2002

54 Inferring Pairwise Relationships with SNPs (in Your Favorite Metazoan) (Glaubitz, Rhodes, and Dewoody 2003 Molecular Ecology 12: 1039-1047) Problem : Need to determine genetic relationships in populations without known pedigrees Microsatellites current methods of choice among close kin within a population, but the number of independently segregating microsatellite markers is limited SNPs may provide large number of segregating loci with a large number of alleles at even frequencies Goal: To assess known pairwise relationships - via single nucleotide polymorphisms where already have parallel microsatellite results. Recent advances in microarray technology permit genotyping of large #s of individuals at 100s to 1000s of SNP loci (reviewed by Kwok 2001)- this could be big! Need to know if SNPs equal or exceed the power of practical numbers of microsatellite loci in estimating relationships?

55 Glaubitz et al. 2003- Computer simulations designed to evaluate SNPs ability to discriminate a variety of (pairwise) relationships likely to occur in natural populations, comparisons to microsatellites from Blouin et al 1996 SNPs segregate independently, ideal genome with 20 autosomes, 5 SNPs per chromosome, 10,000 individuals random genotypes Constructed 5 catagories of relationships types Constructed an array of pedigrees estimated pairwise relatedness at a single locus (r 1 ) Evaluated the performance of 100 simulated SNPs by estimating misclassification (rate) of relationships

56 illustrates that different pairwise relationships can have different amounts of inherent variance in relatedness the parent offspring (PO) and unrelated (U) relationships have 0 inherent variance (share one or no alleles) FS has largest variance; second order relatives can not be distinguished from each other via estimation of r Even under the optimistic scenario of 100 independent loci, results show little promise for discriminating higher order relationships on the basis of pairwise relatedness. Microsatellite approaches are still better… 100 independently segregating SNPs determinined parent-offspring pairs as well as about 16 or fewer microsatellite loci when both parents are unknown

57 Conclusion: “SNPs have limited potential for the delineation of genealogical relationships…” Based on 1) assumption of independence among the sampled SNP loci 2) that the microsatellites themselves are independent (not linked) My two cents: In the absence of a linkage map, the number of microsatellite or SNP loci scored must be increased to compensate for the loss of information as a result of nonindependence between markers An alternative to using independently segregating SNPs is to use independently segregating haplotypes, with each haplotype defined by a cluster of tightly linked SNPs. (e.g., Heaton et al 2002 sequenced regions around 32 cattle SNPs; additional 183 polymorphic sites; and more haplotypes for better resolution)

58 To take full advantage of the “vast” abundance of SNPs in metazoan genomes and their potential automation, we will need analytical methods that account for tight genetic linkage (McPeek and Sun 2000) and known recombination frequencies…. until then, SNP population genomics will likely only be used on model organisms.

59 Population Genomics Research  Understandings population structure, historic migrations, and gene flow among populations (e.g., Fst, coalescent approaches)  Need moderate polymorphism, low cost per sample  Allozymes, mtDNA, RAPDs, Microsatellites, AFLPs, RFLPs, SNPs  Understanding current gene flow and mating systems by direct methods (e.g., maternity analysis, paternity analysis)  Need high polymorphism, codominance, repeatability, low cost per sample  Microsatellites, allozymes  Pharmacogenomics: polymorphism-based approaches for the discovery and development of new medications; translating polymorphisms into “new genomic medicine”*  Need rapid, low-cost, repeatable way to distinguish alleles  screening large numbers of individuals; SNPs and Sequencing *New York Times, Nov. 2002


61 Pharmacogenomics  The use of DNA sequence information to measure and predict the reaction of individuals to drugs.  Pharmacogenetics is the study of this variation at the level of a single gene, while pharmacogenomics studies variation at the genome wide level.  Observation that there is great individual variation in response to drugs- genetically determined.  It is possible to measure many thousands of SNPs simultaneously in a small blood sample from a patient  Can compare “genotypes” for SNP markers linked to virtually any trait

62 Evolving Paradigm for Discovery of Genetic Polymorphisms associated with aberrant drug disposition or effects Observed phenotype - family studies- inherited basis More discoveries thru polymorphisms in candidate genes (metabolism; transport; targets of candidate medication

63 New Drug Targets Expected from the Human Genome Project Number of Drug Targets 12,000 0 6,000 4,000 2,000 10,000 8,000 Approx. 500 5,000–10,000 Cumulative Number of Targets Known Today New Targets Expected from Human Genome Project Source: Drews J. Nat Biotechnol 1996;14.

64 The Gene for…

65 Disease Genes Discovered  For 1100 genes at least one disease-related mutation has been identified

66 Clinical disorders and gene mutations  Different mutations in the same gene can give rise to more or less distinct disorders, so total number of diseases for which there are known mutations is ~1500

67 Functional Classifications  Disease genes classed by function and their relative representations

68 Some Diseases Involve Polygenic Effects  There are a number of classic “genetic diseases” caused by mutations of a single gene –Huntington’s, Cystic Fibrosis, Tay-Sachs, PKU, etc.  There are also many diseases that are the result of the interactions of many genes: –Asthma, Heart disease, Cancer  Each of these genes may be considered to be a risk factor for the disease.  Groups of SNP markers may be associated with a disease without determining mechanism

69 u There are proteins that chemically activate or inactivate drugs. u Other proteins can directly enhance or block a drug's activity. u There are also genes that control side effects. Gene Product- Drug Interaction

70 Some Examples  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.

71  0.04% of individuals are homozygous for alleles of psedocholineseterase that are unable to inactivate the muscle relaxant drug succinylcholine, leading to respiratory paralysis. Succinylcholine Toxicity

72  There are many polymorphic alleles of the N-acetlytransferase (NAT2) gene with reduced (or acclerated) ability to inactivate the drug isoniazid.  Some individuals developed peripheral neuropathy in reaction to this drug  Some alleles of the NAT2 gene are also associated with succeptibility to various forms of cancer Isoniazid Metablolism

73 Cytochrome P450  ~10% of the Caucasian population is homozygous for alleles of the Cytochrome P450 gene CYP2D6 that do not metabolize the hypertension drug debrisoquine, which can lead to dangerous vascular hypotension.

74 ACE  Patients homozygous for an allele with a deletion in intron 16 of the gene for angiotensin-converting enzyme (ACE) showed no benefit from the hypertension drug enalapril while other patients benefit.

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

76  Scan these populations with a large number of SNP markers.  Find markers linked to drug response phenotypes. Make Genetic Profiles

77 Use the Profiles u Genetic profiles of new patients can then be used to prescribe drugs more effectively & avoid adverse reactions. u Can also speed clinical trials by testing on those who are likely to respond well.

78 Major pharmacogenetics approaches in post-genomic era  Identifying SNP variations in the genome and populations  Study of differential gene expression  Chips with mRNAs from different tissue types or normal and diseased tissue  Can detect expression of a target gene among 50,000-300,000 transcripts on a microarray  Possibility of simultaneously monitoring expression of every gene in any tissue will be possible  Detecting new metabolic disease pathways  Based on comparisons with other model organisms

79 Micro-Array technology to analyze gene expression The principle behind this is to look at differences in gene expression when variables are changed - eg. Yeast cells grown in the presence of EtOH- what genes are turned on or off in response to that change in the environment Another variable could be normal versus diseased tissue Pool the cDNAs

80 The cDNAs are hybridized to microarrays on which every gene that has been cloned is present [the DNA is spotted on the microslides and each spot corresponds to DNA from a different gene] If a particulatr gene is expressed, then it will be present and labelled in the the cDNA pool. It can then hybridize to the spot of the plate corresponding to that particular gene

81 The results from such an experiment look like this where the color of the spot tells you something about that gene expression and drug therapy optimization.

82 The data can then be analyzed and sorted into tables that show which genes are expressed in response to the stimulus and which are turned off This sort of experiment can be done with any collection of RNAs that you want to compare- particularly useful to compare ‘normal’ to mutant/disease state- eg. tells you what genes are turned on in cancerous cells, may give you a clue as to how cancer works

83 Link Gene Expression to Genome Sequence  Identify promoter and 5' sequence for a group of co-expressed genes.  Scan for known transcription factor binding sites.  Predict new regulatory sites based on common sequence elements.

84 Diagnostic arrays -Examples of factors showing variability that could be detected on arrays -Provide information of status of SNPs and gene expression profiles

85 Pharmacogenomics - The Future  Ultimate goal is to personalize drug treatment regimes  $  Faster clinical trials  $  Less drug side effects  $  Identify how genetic factors interact to affect variation in drug outcomes  Inactivation or activation by oxidation by cytochrome P450a  Clearance from bloodstream through kidney  Target sensitivity  Toxicity  Heterogeneity of disease mechanisms

86  Mutations in coding sequences will probably only play a small role in disease susceptibility between individuals  Variations affecting splicing and gene regulation will play a greater role  We know very little about the the importance that variations in regulatory and intronic sequences have and how they differ between populations  Issues:  associating sequence variations with heritable phenotypes  how genotypes affect common diseases, drug responses, and other complex phenotypes Pharmacogenomics - The Future…continued

87 The promise is to deliver “personalized” medicine Science News Focus Booming Population Databases


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