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Physiological Genomics from Rats to Human

Physiological Genomics from Rats to Human. Monika Stoll, Ph.D Director, Genetic Epidemiology of vascular disorders Leibniz-Institute for Arteriosclerosis Research, Münster. Genome-oriented Medi c ine. Genetic V ariation influences - disease susceptibility - disease progression

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Physiological Genomics from Rats to Human

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  1. Physiological Genomics from Rats to Human Monika Stoll, Ph.D Director, Genetic Epidemiology of vascular disorders Leibniz-Institute for Arteriosclerosis Research, Münster

  2. Genome-oriented Medicine Genetic Variation influences - disease susceptibility - disease progression - therapeutic response - unwanted drug effects The use of genetic variation for diagnostic purposesand targeted treatment

  3. “Heterogeneity “ of complex diseases “polygenic with genetic Heterogeneity” Gene+ Gene- Epistasis Gene+ Gene + complex phenotype Gene - Gene+ others Salt intake Psychosocial Stress Diet “Environmental factors”

  4. Gene-environment interactions and CVD Genetic factors Environment Diet, Smoking, Stress Hypertension, Diabetes, Obesity, Age, Lipids, Genetic Background Risk factors Trait Atherosclerosis Phenotype Myocardial infarction Stroke Peripheral vascular disease

  5. Example: metabolic syndrome (syndrome X) athero-sclerosis hypertension vascular disease dislipidemia hyperglycemia Insulin resistance obesity Complex Diseases do not have a clear phenotype but may or may not share some features

  6. Genetics of Multifactorial Diseases Difficulties Difficulties Human Linkage Analysis Disease Etiology Family studies/ Sib-Pair Analysis: large number of patients (2,500 sibpairs) Modest resolution Multiple Genes: Interaction, Epistasis Polygenic: modest effects of single genes Incomplete penetrance Age-of-onset Environmental component Genetic Heterogeneity Lack of Power High Complexity

  7. Large scale association studies Transmission Disequilibrium Tests Sib - TDT Association studies on quantitative traits Increased statistical power High density typing necessary Animal models e.g. rat Controlled genetic background Controlled environment Controlled experimental setting Large number of progenies Decreased heterogeneity Provide candidate regions Genetics of Multifactorial Diseases Solutions Solutions Association studies Reduction of complexity Comparative Maps Positional candidate loci for high density genotyping

  8. Human Genes and Genetic Manipulation relevant to human disease Genes, Physiology and Pharmacology relevant to human disease Mouse Rat Ability to avoid many biological barriers unique to one species Comparative Genomics with Biology

  9. Why ‚Comparative Genomics‘? Take advantage of the wealth of genome information from the various Genome Projects Genomic regions are evolutionary conserved between mammalian species (Synteny) Sequence is highly conserved between species (Homology) The genomic sequence of human, rat and mouse genomes are available QTLs/Genes identified in rodent models are predictive for human loci Rodent models can help to elucidate the function of novel disease genes e.g. implicated by human linkage studies or expression profiling

  10. Map ‚novel‘ genes identified e.g. in expression profiling and anchor on existing comparative maps (www.rgd.edu/VCMap) Sequence positional candidate genes in mouse, rat and human to identify conserved mutations and/or regulatory elements Predict potential target regions for human linkage studies based on model organisms Characterize candidate genes from human studies in representative experimental model (inbred strains, congenics, transgenics, conditional knock-outs) Strategies for ‚comparative genomics‘

  11. ExperimentellesModell Monogene Erkrankung Geschwisterpaar-Untersuchungen: Bestätigung Kandidatengen-Locus Assoziationsstudien: Identifizierung von Kandidatengen-Polymorphismen (polygene) komplexe Erkrankung

  12. Cross design SHR-SP SHR or WKY Backcross F1 F2

  13. SHR or WKY SHR-SP x F1 F2

  14. Human Chromosome Regions Implicated in Hypertension via a Cross-Species Comparison

  15. 27 independent blood pressure phenotypes • Baseline Blood Pressure • Maximal Response • MAP, DBP, SBP, PP • MAP, DBP, SBP, PP after salt-load • Drug Challenges • Delta BPs Blood Pressure Phenotypes

  16. Rat Models for Genetic Hypertension Spontaneously Hypertensive Rat (SHR) High blood pressure Cardiovascular disease SHR x WKY SHR x DNY SHR x BN Genetically Hypertensive Rat (GH) Hypertension, cardiac hypertrophy Vascular disease, not salt-sensitive GH x BN Dahl Salt-Sensitive Rat (SS) Salt-sensitive hypertension Hyperlipidemia, insulin resistance SS x BN Lyon Hypertensive Rat (LH) Mild hypertension, hyperlipidemia LH x LN Fawn-hooded Hypertensive Rat (FHH) Systolic hypertension Renal failure FHH x ACI

  17. Linkage Analysis for Blood Pressure QTLs Independent total genome scans in 7 intercrosses representing a model for genetic hypertension 200-300 SSLP markers 10-20 cM spacing 57- 390 animals Linkage analysis using MAPMAKER/QTL computer package LOD score >2.8 suggestive LOD score >4.3 significant Integration of QTLs on integrated map based on genotyping information from crosses used for linkage analysis

  18. Analysis of QTL Clustering QTL #1 QTL #2 QTL #3 Drop of 1.6 LOD units = 95% confidence interval QTL cluster

  19. Establishment of Syntenic Regions in Human Genome Identification of syntenic regions and evolutionary breakpoints using comparative maps between rat, mouse and human Definition of positional candidate regions in human genome based on QTLs identified in rat models of hypertension Designation of ‘first priority’ and ‘second priority’ regions second priority region based on QTLs from single rat cross first priority region based on QTLs from multiple rat crosses

  20. QTLs identified in Rat 68 blood pressure QTLs total Baseline BP 2 Max. response 7 MAP, DBP, SBP, PP 19 Salt MAP, DBP, SBP, PP 22 Drug challenge 7 Delta BP 11 LOD score > 4.3 13 LOD score 2.8-4.3 44 LOD score 2.5-2.8 11 13 QTL clusters total 7 QTL clusters 2 or more crosses 6 QTL clusters within one cross 10 single QTLs Coverage of rat genome in cM 500 cM (31%) First priority regions Second priority regions

  21. Syntenic Regions in Human 36 syntenic regions total Confidence level Classification highest: 7 regions (14 QTLs) high: 20 regions (38 QTLs) moderate: 5 regions (10 QTLs) conversion incomplete or impossible 6 QTLs 23 ‘first priority’ regions 13 ‘second priority’ regions Coverage of human genome in cM ~800 cM (~24%)

  22. Identify homologous genes mapped in rat, mouse and/or human Preliminary comparative maps of genes in common on the genetic maps of rat and mouse Preliminary comparative maps of genes in common on the genetic maps of mouse and human Identification of Syntenic Regions and Evolutionary Breakpoints RATMAP server http://ratmap.gen.gu.se Oxford Maps http://www.well.ox.ac.uk MIT Maps http://www.genome.wi.mit.edu/rat/ RATMAP server Mouse Genome Database http://www.informatixs.jax.org UniGene http://www.ncbi.nlm.nih.gov/ UniGene/index.html Genome Database http://gdbwww.gdb.org Framework comparative maps

  23. VC-MAP : Bioinformatics-‘Tool‘ for comparative maps Stoll et al., Genome Res. 10: 473 – 482, 2000 http://www.genome.org/cgi/content/full/10/4/473 Free access Kwitek et al. Genome Res. 11: 1935 – 1943, 2001 http://www.genome.org/cgi/content/full/11/11/1935 Free access www.rgd.mcw.edu

  24. Comparative Mapping Human chr. 22 and its homologies to rat chr. 11, 20, 6, 14 and 7

  25. Comparative mapping of BP QTLs D18Rat85 MBP MC5R FECH 19.0 cM 18 D18Mgh3 10 D18Rat9 ADRB2 DRD1 PDGFRB GRL1 FGF1 EGR1 13 GJA1, D18Mit16 5q 1.5 D18Mit8 D HS - LS SBP D HS - LS MAP HS basaler DBP HS aktiver MAP Tag 2 DBP TPM Alpha2 HS Prot Excr HDL 16.2 D18Rat57 6.7 D18Rat18 2.5 D18Mit5 6.7 D18Mgh9 2.7 D18Mgh7 2.5 D18Mit3 1.6 Humane Homologie D18Mit14, D18Mgh8 3.4 D18Mit1 Ratte Chr. 18 7.6 D18Mit12

  26. Predicted susceptibility loci in the human genome Mouse Rat 39,40,41,42 30,31,32,33,38 34,35,36,37 45,46,47,48 20,21,22,23, 24,25,26 51,52,53,54 27,28,29 13,14,15,16 17,18,19 Krushkal et al. 20,21,22,23, 24,25,26 13,14,15,16 17,18,19 51,52,53,54 Mansfield et al. Chr.4 Chr.3 Stoll et al. Genome Res. 10: 473 – 482, 2000 http://www.genome.org/cgi/content/full/10/4/473 Free access Chr.2 Chr.1

  27. Conclusion The regions in the human genome implicated for hypertension may be useful as primary targets 1. Large scale testing in human populations Association studies TDT, Sib-TDT Linkage studies 2. High density mapping Targeted genome scans Single Nucleotide Polymorphisms (SNPs)

  28. Genetic studies in human populations

  29. Mendelian Disease: Exhibits Mendelian mode of inheritance Complex Disease: Appears to cluster in families Family, twin, adoption studies show greater risk to relatives of affecteds than the population incedence Segregation analysis can provide estimates of genetic and environmental contribution to disease Is there a genetic component ?

  30. Linkage analysis: Cosegregation of mapped marker with the disease Fine mapping to narrow the region In Complex Disease: Requires a defined genetic model Requires classifying people as affects and unaffecteds Allele sharing methods (sib pairs etc.) Population association studies Where is the gene ?

  31. 2 2 2 2 2 1 * 1 1 Genomwide linkage (ca. 400 Mikrosatellites, 10cM) Traditional Fine mapping (Saturation with Mikrosatellites, 1cM) Association and Linkage Disequilibrium (SNPs, 3-50kB, Transmission Disequilibrium, LD, Haplotype analysis) Association in Case/Control Design (SNPs, Haplotype Case/Controls, ethnically divergent populations) Genetic Methods

  32. Linkage analysis Linkage Disequilibrium

  33. Non-parametric linkage studies 1/2 3/4 1/2 3/4 1/2 3/4 1/2 3/4 1/3 1/4 1/3 2/4 1/3 2/4 1/3 2/4 Looking at a marker Association in between families Affected sib pairs Extended families Affected relative pairs Discordant pairs Linkage analysis Problem: late onset of CAD

  34. Chromosome LOD= log10 [L()/L(1/2)] = log10 [Prob. Linkage/Prob. No Linkage] m1 Disease gene m2 See Figure 1 from Broekel et al. Nature Genetics 30, 210 - 214 (2002) http://www.nature.com/ng/journal/v30/n2/full/ng827.html Free access m3 m4 Non-Parametric Linkage Analysis

  35. Several examples for hypertension linkage in human study populations

  36. How to get from linkage to the causative gene variant ?

  37. Linkage-property of the relative position of loci, not their alleles. Linkage is the cosegregation of a disease or trait with a specific genomic region in multiple families (it can involve any allele at the marker locus in a given family) Association- property ofalleles: a specific allele of a gene or marker is found with a disease or trait in a population Linkage Disequilibrium– the presence oflinkage AND association Cosegregation of a specific allele with the disease in a significant number of families What is Linkage Disequilibrium ?

  38. It is a tool for fine mapping Affected sib pair analysis may not be sensitive enough to detect minor genes Association test may be sensitive but the association detected may not be due to linkage disequilibrium. It could be caused by population stratification(confounding due to race, admixture, heterogeneity in the population for some other reason) Why do we care about Linkage Disequilibrium ?

  39. Transmission Disequilibrium Test (TDT): TDT tests for equal numbers of transmissions of specific alleles and all others from heterozygous parents to an affected offspring GENEHUNTER: Transmitted vs. Untransmitted alleles TRANSMIT: Expected vs. Observed alleles TDT test is McNemar‘s Chi-square test = (b-c)2/(b+c) Trans Untrans Allele 1 211 138 Chi-square= 15.27 Allele 2 138 211 p=0.000093 Limitations: locus heterogeneity, allelic heterogeneity, need for specific polymorphisms, can only detect linkage in the presence of association, need to be very close to disease gene How do you analyze for Linkage Disequilibrium ?

  40. 1 1 2 2 2 1 2 2 2 2 1 2 2 1 2 2 2 1 2 2 1 2 m2 LD X  = (1-)t 1 m1 t X X X 1 1 1 1 1 Linkage Disequilibrium decays with time (No. of recombinations) What‘s all that Fuzz about Haplotypes ?

  41. L. Kruglyak (1998): need 1 SNP/3kb for genomewide association • D. Reich (2001): haplotype block size in Caucasians 60-120kb due to • bottle neck in population history 50,000 years ago • haplotype block size in Africans 10-30 kb • M. Daly (2001): haplotype block structure in human genome • 2003: haplotype structure varies. Blocks of long range LD • interspersed with recombination hot spots • Human Haplotype Map – will be finished in 2005 Size of Haplotype blocks depends on population history

  42. Hierachical Linkage Disequilibrium Mapping See figures from Stoll et al. Nature Genetics 36 (5): 476-480, 2004 http://www.nature.com/ng/journal/v36/n5/index.html Subscription access only

  43. ALOX5AP is a susceptibility gene for MI and stroke 296 multiplex icelandic families (713 individuals) Linkage on 13q12-13 LOD score: 2.86 14 additional microsatellites LOD score 2.48 (p=0.0036) at D13S289 Haplotype based case-control association using 150 microsatellites Haplotype with association to MI (p=0.00004) Gene within haplotype ALOX5AP 144 SNPs identified by resequencing 97 individuals 2 haplotype blocks in strong LD Association testing in case/control study design See figure from Helgadottir A. et al. Nature Genetics 36 (3): 233-239 (2004) http://www.nature.com/ng/journal/v36/n3/index.html Subscription required

  44. ALOX5AP is a susceptibility gene for MI and stroke See Table 1 from Helgadottir A. et al. Nature Genetics 36 (3): 233-239 (2004) http://www.nature.com/ng/journal/v36/n3/index.html Subscription required See Table 2 from Helgadottir A. et al. Nature Genetics 36 (3): 233-239 (2004) http://www.nature.com/ng/journal/v36/n3/index.html Subscription required

  45. Conclusion

  46. Obesity: Discovery of Leptin as the human homologue of the mouse (ob) mutant Leptin receptor and db/db mice (diabetes and obesity phenotype) Melanocortin-4 receptor and severe obesity in mice and man Diabetes: Cd36 as a susceptibility factor for insuline resistance in the SHR rat Cblb (ubiquitin-protein ligase) as susceptibility factor for Type I Diabetes Atherosclerosis: APOAI/CIII/AIV gene cluster and lipid metabolism in mice and man Hypertension: Predictive power of QTLs from rodents for human hypertension Success stories for Comparative Genomics

  47. Total Genome Scan Case-control Studies Phenotype Candidate Gene Approach Positional Cloning Congenics Consomics ENU-Mutagenesis Transgenics Knock-outs Knock-ins Gene

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