Physiological Genomics from Rats to Human

Monika Stoll, Ph.D

Director, Genetic Epidemiology of vascular disorders Leibniz-Institute for Arteriosclerosis Research, Münster

Genome-oriented Medicine

Genetic Variation influences - disease susceptibility - disease progression - therapeutic response - unwanted drug effects The use of genetic variation for diagnostic purposes and targeted treatment

“Heterogeneity “ of complex diseases

Gene “polygenic with genetic Heterogeneity” Gene +

Epistasis

Gene +

complex phenotype

Gene Gene + Gene + Salt intake others Psychosocial Stress Diet “Environmental factors”

Gene-environment interactions and CVD

Genetic factors Environment Risk factors Diet, Smoking, Stress Hypertension, Diabetes, Obesity, Age, Lipids, Genetic Background Trait Atherosclerosis Phenotype Myocardial infarction Stroke Peripheral vascular disease

Complex Diseases do not have a clear phenotype but may or may not share some features

Example: metabolic syndrome (syndrome X) dislipidemia athero sclerosis vascular disease Insulin resistance obesity hypertension hyperglycemia

Genetics of Multifactorial Diseases

Difficulties

Human Linkage Analysis Family studies/ Sib-Pair Analysis: large number of patients (2,500 sibpairs) Modest resolution Multiple Genes: Interaction, Epistasis

Difficulties

Disease Etiology Polygenic: modest effects of single genes Incomplete penetrance Age-of-onset Environmental component Genetic Heterogeneity

Lack of Power High Complexity

Genetics of Multifactorial Diseases

Solutions

Association studies Large scale association studies Transmission Disequilibrium Tests Sib - TDT Association studies on quantitative traits Increased statistical power High density typing necessary

Solutions

Reduction of complexity Animal models e.g. rat Controlled genetic background Controlled environment Controlled experimental setting Large number of progenies Decreased heterogeneity Provide candidate regions

Comparative Maps

Positional candidate loci for high density genotyping

Comparative Genomics with Biology

Human

Genes and Genetic Manipulation relevant to human disease Genes, Physiology and Pharmacology relevant to human disease

Mouse

Ability to avoid many biological barriers unique to one species

Rat

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 (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)

Experimentelles Modell Monogene Erkrankung

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

Backcross SHR-SP

Cross design

SHR or WKY F1 F2

F1 F2

SHR-SP x

SHR or WKY

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

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 Fawn-hooded Hypertensive Rat (FHH) Systolic hypertension Renal failure LH x LN 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

Analysis of QTL Clustering

5 4 1 0 3 2 D 1M it1 D 1M it2 1 D 1M it4

QTL #1

LS N D 1M it5 5 4.5

4 3.5

3 2 1.5

1 0.5

0 D 1M it1 D 1M it2 1 D 1M it4

QTL #2 Drop of 1.6 LOD units = 95% confidence interval

LS N D 1M it5 2 1 0 5 4 3 D 1M it1 D 1M it2 1 D 1M it4 LS N

QTL cluster

D 1M it5 Reihe1 3.5

3 2.5

2 1.5

1 0.5

0 D 1M it1 D 1M it2 1 D 1M it4

QTL #3

LS N D 1M it5 Reihe1 Reihe2 Reihe3 Reihe1

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

68 blood pressure QTLs total

LOD score > 4.3

LOD score 2.8-4.3

LOD score 2.5-2.8

13 44 11 Baseline BP Max. response MAP, DBP, SBP, PP Salt MAP, DBP, SBP, PP Drug challenge Delta BP 7 13 QTL clusters total 7 QTL clusters 2 or more crosses 6 QTL clusters within one cross 10 single QTLs First priority regions Second priority regions 19 22 2 7 11 Coverage of rat genome in cM 500 cM (31%)

Syntenic Regions in Human 36 syntenic regions total

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

Identification of Syntenic Regions and Evolutionary Breakpoints 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 RATMAP server http://ratmap.gen.gu.se

Oxford Maps http://www.well.ox.ac.uk

MIT Maps http://www.genome.wi.mit.edu/rat/ Preliminary comparative maps of genes in common on the genetic maps of mouse and human 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

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

Comparative Mapping

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

5 4 1 0 3 2

Comparative mapping of BP QTLs

D18Rat85

Series1 Series2 Series3

D18Mgh3 18 MBP MC5R FECH 10 D18Rat9 13 1.5

D HS - LS SBP D HS - LS MAP HS basaler DBP HS aktiver MAP Tag 2 DBP TPM Alpha2 HS Prot Excr HDL Ratte Chr. 18 16.2

6.7

2.5

6.7

2.7

2.5

1.6

3.4

7.6

D18Rat57 D18Rat18 D18Mit5 D18Mgh9 D18Mgh7 D18Mit3 D18Mit14, D18Mgh8 D18Mit1 D18Mit12 5q ADRB2 DRD1 PDGFRB GRL1 FGF1 EGR1 Humane Homologie

Predicted susceptibility loci in the human genome Mouse Rat 39,40,41,42 20 , 21,22 , 23, 24,25,26 Chr.1

20 ,21,22 , 23, 24,25,26 51 , 52,53,54 Mansfield et al.

Chr.2

30,31,32,33,38 34,35,36,37 51, 52,53,54 27 ,28,29 Krushkal et al.

45,46,47, 48 13,14,15 ,16 17,18,19 13,14,15 ,16 17,18,19 Chr.3

Chr.4

Stoll et al. Genome Res. 10: 473 – 482, 2000 http://www.genome.org/cgi/content/full/10/4/473 Free access

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

Traditional

Genetic Methods

Genomwide linkage (ca. 400 Mikrosatellites, 10cM) Fine mapping (Saturation with Mikrosatellites, 1cM)

2.5

2 1.5

1 0.5

0

Chromosome 10 linkage * Association and Linkage Disequilibrium ( SNPs, 3-50kB, Transmission Disequilibrium, LD, Haplotype analysis ) 2 2 2 1 2 1 Association in Case/Control Design (SNPs, Haplotype Case/Controls, ethnically divergent populations) 2 1

Linkage analysis Linkage Disequilibrium

Linkage analysis

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

Non-Parametric Linkage Analysis m1

Chromosome Disease gene

m2 m3

LOD= log 10 [L(



)/L(1/2)] = log 10 [Prob. Linkage/Prob. No Linkage] 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

m4

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 significant number of families linkage AND association Cosegregation of a specific allele with the disease in a

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 McNemar‘s Chi-square test = (b-c)

2

/(b+c) Allele 1 Allele 2 Trans Untrans 211 138 138 211 Chi-square= 15.27

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

What‘s all that Fuzz about Haplotypes ?

Linkage Disequilibrium decays with time (No. of recombinations)

m2 m1 X 2 2 1 1 X 1 2 1 2 X X 1 2 1 2 LD



= (1-



) t t 2 1 1 2 2 1 1 2 2 1 2 2 1 2 2 1

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 http://www.nature.com/ng/journal/v36/n5/index.html

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ALOX5AP is a susceptibility gene for MI and stroke See figure from

Helgadottir A. et al. Nature Genetics 36 (3): 233-239 (2004) http://www.nature.com/ng/journal/v36/n3/index .html

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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

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

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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

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Conclusion

Success stories for Comparative Genomics

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

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