高血压英文ppt精品课件physiologicalgenomics from rats to

Physiological Genomics from Rats to Human Monika Stoll, Ph.D Director, Genetic Epidemiology of vascular disorders Leibniz-Institute for Arteriosclerosis Research, Mnster Genetic Variation influences - disease susceptibility - disease progression - therapeutic response - unwanted drug effects The use of genetic variation for diagnostic purposes and targeted treatment Genome-oriented Medicine “Heterogeneity “ of complex diseases complex phenotype “polygenic with genetic Heterogeneity” “Environmental factors” Salt intake Psychosocial Stress Diet others Gene+ Gene + Gene -Gene+Gene+ Gene - Epistasis Gene-environment interactions and CVD Genetic factors Diet, Smoking, Stress Hypertension, Diabetes, Obesity, Age, Lipids, Genetic Background Atherosclerosis Myocardial infarction Stroke Peripheral vascular disease Environment Risk factors Trait Phenotype Complex Diseases do not have a clear phenotype but may or may not share some features Example: metabolic syndrome (syndrome X) hypertension hyperglycemia dislipidemia obesity athero- sclerosis Insulin resistance vascular disease Polygenic: modest effects of single genes Incomplete penetrance Age-of-onset Environmental component Genetic Heterogeneity High Complexity Difficulties Disease Etiology Family studies/ Sib-Pair Analysis: large number of patients (2,500 sibpairs) Modest resolution Multiple Genes: Interaction, Epistasis Lack of Power Difficulties Human Linkage Analysis Genetics of Multifactorial Diseases 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 Solutions Reduction of complexity Solutions Association studies Comparative Maps Positional candidate loci for high density genotyping Genetics of Multifactorial Diseases Comparative Genomics with Biology Human Mouse Rat Genes, Physiology and Pharmacology relevant to human disease Genes and Genetic Manipulation relevant to human disease Ability to avoid many biological barriers unique to one species 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 Strategies for comparative genomics Map novel genes identified e.g. in expression profiling and anchor on existing comparative maps (/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) Experimentelles Modell Monogene Erkrankung Geschwisterpaar-Untersuchungen: Besttigung Kandidatengen-Locus Assoziationsstudien: Identifizierung von Kandidatengen-Polymorphismen (polygene) komplexe Erkrankung Cross design SHR-SP SHR or WKY F1 Backcross F2 F2 x SHR-SP SHR or WKY F1 Human Chromosome Regions Implicated in Hypertension via a Cross-Species Comparison Blood Pressure Phenotypes 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 Rat Models for Genetic Hypertension SHR x WKY SHR x DNY SHR x BN GH x BN SS x BN LH x LN Spontaneously Hypertensive Rat (SHR) High blood pressure Cardiovascular disease Genetically Hypertensive Rat (GH) Hypertension, cardiac hypertrophy Vascular disease, not salt-sensitive Dahl Salt-Sensitive Rat (SS) Salt-sensitive hypertension Hyperlipidemia, insulin resistance Lyon Hypertensive Rat (LH) Mild hypertension, hyperlipidemia Fawn-hooded Hypertensive Rat (FHH) Systolic hypertension Renal failure FHH x ACI 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 QTL #1 QTL #2 QTL #3 QTL cluster Drop of 1.6 LOD units = 95% confidence interval Analysis of QTL Clustering 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 first priority region based on QTLs from multiple rat crosses second priority region based on QTLs from single rat cross QTLs identified in Rat LOD score 4.3 13 LOD score 2.8-4.3 44 LOD score 2.5-2.8 11 68 blood pressure QTLs total 13 QTL clusters total 7 QTL clusters 2 or more crosses 6 QTL clusters within one cross 10 single QTLs Baseline BP 2 Max. response 7 MAP, DBP, SBP, PP 19 Salt MAP, DBP, SBP, PP 22 Drug challenge 7 Delta BP 11 Coverage of rat genome in cM 500 cM (31%)First priority regions Second priority regions Syntenic Regions in Human 36 syntenic regions total 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%) Classification Confidence level Identification of Syntenic Regions and Evolutionary Breakpoints Framework comparative maps RATMAP server http:/ratmap.gen.gu.se Oxford Maps http:/www.well.ox.ac.uk MIT Maps /rat/ RATMAP server Mouse Genome Database UniGene / UniGene/index.html Genome Database 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 VC-MAP : Bioinformatics-Tool for comparative maps Stoll et al., Genome Res. 10: 473 482, 2000 /cgi/content /full/10/4/473 Free access Kwitek et al. Genome Res. 11: 1935 1943, 2001 /cgi/content /full/11/11/1935 Free access Comparative Mapping Human chr. 22 and its homologies to rat chr. 11, 20, 6, 14 and 7 D HS - LS SBP D HS - LS MAP HS basaler DBP HS aktiver MAP Tag 2 DBP TPM Alpha2 HS Prot Excr HDL D18Rat85 D18Mgh3 D18Rat9 GJA1, D18Mit16D18Mit8 D18Rat57 D18Rat18 D18Mit5 D18Mgh9 D18Mgh7 1.5 19.0 cM 6.7 D18Mit3 D18Mit14, D18Mgh8 D18Mit12 D18Mit1 10 13 16.2 2.5 6.7 2.7 2.5 1.6 3.4 7.6 18 5q ADRB2 DRD1 PDGFRB GRL1 FGF1 EGR1 Ratte Chr. 18 Humane Homologie MBP MC5R FECH Comparative mapping of BP QTLs Chr.1 Chr.2 51,52,53,54 30,31,32,33,38 34,35,36,37 45,46,47,48 13,14,15,16 17,18,19 20,21,22,23, 24,25,26 Mansfield et al. Krushkal et al. 39,40,41,42 20,21,22,23, 24,25,26 51,52,53,54 27,28,29 13,14,15,16 17,18,19 Chr.3 Chr.4 Predicted susceptibility loci in the human genome Stoll et al. Genome Res. 10: 473 482, 2000 /cgi/content/full/10/4/473 Free access Mouse Rat 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) Genetic studies in human populations Is there a genetic component ? 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 Where is the gene ? 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 Genetic Methods Genomwide linkage (ca. 400 Mikrosatellites, 10cM) 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) Traditional 1 12 2 2 2 2 1 * Linkage analysis Linkage Disequilibrium Linkage analysis Non-parametric linkage studies Looking at a marker Association in between families 1/2 3/4 1/3 2/4 1/2 3/4 1/3 2/4 Extended families Affected relative pairs Discordant pairs 1/2 3/4 1/3 1/4 1/2 3/4 1/3 2/4 Affected sib pairs Problem: late onset of CAD Non-Parametric Linkage Analysis m1 m2 m3 m4 Disease gene Chromosome LOD= log10 L()/L(1/2) = log10 Prob. Linkage/Prob. No Linkage See Figure 1 from Broekel et al. Nature Genetics 30, 210 - 214 (2002) /ng/journal/v30/n2/full/ng827.html Free access Several examples for hypertension linkage in human study populations How to get from linkage to the causative gene variant ? What is Linkage Disequilibrium ? 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 of alleles: a specific allele of a gene or marker is found with a disease or trait in a population Linkage Disequilibrium the presence of linkage AND association Cosegregation of a specific allele with the disease in a significant number of families Why do we care about Linkage Disequilibrium ? 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) How do you analyze for Linkage Disequilibrium ? 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 McNemars 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 Whats all that Fuzz about Haplotypes ? Linkage Disequilibrium decays with time (No. of recombinations) X X X 1 2 2 11 1 1 1 2 2 2 2 2 1 1 X 2 1 2 1 1 12 2 2 2 2 2 m1 m2 1 LD t = (1-)t Size of Haplotype blocks depends on population history 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 Hierachical Linkage Disequilibrium Mapping See figures from Stoll et al. Nature Genetics 36 (5): 476-480, 2004 /ng/journal/v36/n5/index.html Subscription access only 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 res