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MOLECULAR GENETICS OF PSORIASIS K

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MOLECULAR GENETICS OF PSORIASIS K Powered By Docstoc
					          HELSINKI UNIVERSITY BIOMEDICAL DISSERTATIONS, No. 27
                      Helsinki Biomedical Graduate School




                          Department of Medical Genetics
                              University of Helsinki
                                     Finland




              MOLECULAR GENETICS OF PSORIASIS




                                 Kati Asumalahti




                               Academic dissertation




To be publicly discussed with the permission of the Faculty of Medicine, University of
 Helsinki, in Biomedicum Lecture Hall 2, Haartmaninkatu 8, Helsinki, on the 4th of
                               April 2003, at 12 noon.


                                   Helsinki 2003
Supervised by:
Juha Kere, MD, PhD
Professor
Department of Biosciences at Novum
and Clinical Research Center
Karolinska Institute
Sweden
and
Department of Medical Genetics
University of Helsinki




Reviewed by:
Irma Järvelä, MD, PhD
Docent in Medical Molecular Genetics
HUCH-Laboratory Diagnostics
Laboratory of Molecular Genetics
Helsinki University Hospital

Aarne Oikarinen, MD, PhD
Professor
Department of Dermatology
University of Oulu




Official opponent:
James T. Elder, MD, PhD
Professor of Dermatology
Department of Dermatology
University of Michigan
Michigan, USA



ISBN 952-10-1000-2
ISBN 952-10-1001-0 (pdf version: http://ethesis.helsinki.fi)
ISSN 1457-8433

Yliopistopaino
Helsinki 2003
CONTENTS


LIST OF ORIGINAL PUBLICATIONS...................................................................5

ABBREVIATIONS ......................................................................................................6

ABSTRACT..................................................................................................................8

INTRODUCTION......................................................................................................10

REVIEW OF THE LITERATURE .........................................................................12

   1. Mapping of complex diseases ..............................................................................12
      1.1. Difficulties in mapping complex diseases .....................................................12
      1.2. Allelic structure of complex diseases ............................................................12
      1.3. Single nucleotide polymorphisms (SNPs) .....................................................14
      1.4. Association studies........................................................................................14
      1.5. Genome-wide linkage analysis .....................................................................16
      1.6. Linkage disequilibrium mapping ..................................................................17
      1.7. Use of population isolates.............................................................................19
   2. Clinical features of psoriasis ...............................................................................20
      2.1. Clinical characteristics and related diseases ...............................................20
      2.2. Prevalence.....................................................................................................22
      2.3. Age of onset...................................................................................................22
   3. Pathogenesis of psoriasis.....................................................................................23
      3.1. Keratinocytes ................................................................................................24
      3.2. Inflammatory cells ........................................................................................24
      3.3. Cytokines.......................................................................................................25
      3.4. Pathogenetic models .....................................................................................26
   4. Genetics of psoriasis ............................................................................................27
      4.1. Family and twin studies ................................................................................27
      4.2. Inheritance models........................................................................................28
      4.3. The HLA association.....................................................................................30
         4.3.1. The Major Histocompatibility Complex.................................................30
         4.3.2. The HLA association with psoriasis ......................................................31
      4.4. Linkage analyses ...........................................................................................33
   5. The PSORS1 locus in the MHC region ................................................................37
      5.1. Refinement of PSORS1..................................................................................37
      5.2. Candidate genes at PSORS1 .........................................................................38

AIMS OF THE STUDY.............................................................................................41

MATERIALS AND METHODS ..............................................................................42
 1. Study subjects.......................................................................................................42
    1.1. Finnish psoriasis families .............................................................................42
    1.2. Foreign psoriasis families.............................................................................42
    1.3. Guttate psoriasis and PPP patients ..............................................................43
    1.4. Skin biopsies..................................................................................................43
 2. Reverse Transcriptase (RT)-PCR ........................................................................43
 3. PCR amplification and direct sequencing ...........................................................43
   4. SSCP analysis ......................................................................................................44
   5. In situ hybridization .............................................................................................44
   6. Genotyping of SNPs .............................................................................................44
   7. Production of antibodies......................................................................................45
   8. Western blotting ...................................................................................................45
   9. Immunohistochemistry .........................................................................................46
   10. Genotyping of microsatellite markers................................................................46
   11. Statistical analyses.............................................................................................46
      11.1. Linkage analysis..........................................................................................46
      11.2. Haplotype association analysis...................................................................47
      11.3. Transmission Disequilibrium Test (TDT) ...................................................47
      11.4. Other tests for allele association ................................................................48
      11.5. Linkage disequilibrium tests .......................................................................48

RESULTS AND DISCUSSION ................................................................................49
 1. Characterization of the HCR (Pg8) gene structure (I, II) ...................................49
 2. Detection and screening of HCR polymorphisms (I, III).....................................49
    2.1. Nomenclature of the HCR SNPs (I-IV) .........................................................50
 3. Case-control association analysis of HCR SNPs (I)............................................50
 4. Association and haplotype analysis of PSORS1 susceptibility alleles (II) ..........52
 5. PSORS1 allele associations in GP and PPP (III)................................................54
    5.1. GP shares genetic background with PV .......................................................54
    5.2. PPP is not associated with PSORS1.............................................................56
 6. HCR mRNA is overexpressed in psoriatic skin (I)...............................................56
 7. Altered structure and expression of HCR protein in psoriasis (II, III)................57
 8. HCR gene orthologues in primates (unpublished)...............................................58
 9. Defining the PSORS1 risk gene ...........................................................................58
 10. A minor locus for psoriasis on 18p in PSORS1 negative families (IV) .............60

CONCLUSIONS AND FUTURE PROSPECTS.....................................................63

ACKNOWLEDGEMENTS ......................................................................................65

REFERENCES...........................................................................................................67

ORIGINAL COMMUNICATIONS.........................................................................80
                  LIST OF ORIGINAL PUBLICATIONS


This thesis is based on the following original publications, which are referred to in the
text by their Roman numerals. In addition, some unpublished data are presented.



I      Asumalahti, K., Laitinen, T., Itkonen-Vatjus, R., Lokki, M-L., Suomela, S.,
       Snellman, E., Saarialho-Kere, U. and Kere, J. A candidate gene for psoriasis
       near HLA-C, HCR (Pg8), is highly polymorphic with a disease-associated
       susceptibility allele. Human Molecular Genetics, 2000; 9 (10), 1533-42.

II     Asumalahti, K.*, Veal, C.*, Laitinen, T., Suomela, S., Allen, M., Elomaa, O.,
       Moser, M., de Cid, R., Ripatti, S., Vorechovsky, I., Marcusson, J.A.,
       Nakagawa, H., Lazaro, C., Estivill, X., Capon, F., Novelli, G., The Psoriasis
       Consortium, Saarialho-Kere, U., Barker, J., Trembath, R. and Kere, J. Coding
       haplotype analysis supports HCR as the putative susceptibility gene for
       psoriasis at the MHC PSORS1 locus. Human Molecular Genetics 2002; 11
       (5), 589-97.

III    Asumalahti, K.*, Ameen, M.*, Suomela, S., Hagforsen, E., Michaëlsson, G.,
       Evans, J., Munro, M., Veal, C., Allen, M., Leman, J., Burden, A.D., Kirby, B.,
       Connolly, M., Griffiths, C.E.M., Trembath, R.C., Kere, J., Saarialho-Kere, U.
       and Barker, J.N.W.N. Genetic analysis of PSORS1 distinguishes guttate
       psoriasis and palmoplantar pustulosis. Journal of Investigative Dermatology
       (in press).

IV     Asumalahti, K., Laitinen, T., Lahermo, P., Suomela, S., Itkonen-Vatjus, R.,
       Jansen, C., Karvonen, J., Karvonen, S-L., Reunala, T., Snellman, E.,
       Uurasmaa, T., Saarialho-Kere, U. and Kere, J. Psoriasis susceptibility locus on
       18p revealed by genome scan in Finnish families not associated to PSORS1.
       (submitted).

       * equal contribution




                                           5
ABBREVIATIONS

Ala           alanine
APC           antigen-presenting cell
APOE          apolipoprotein E
ASP           affected sibling pair
CARD15        caspase recruitment domain-containing protein 15
CD/CV         common disease/common variant
cDNA          complementary deoxyribonucleic acid
CDSN          corneodesmosin (also known as S gene)
COS-1 cells   African green monkey kidney cells
cpd           chronic proliferative dermatitis (mouse mutation model)
DNA           deoxyribonucleic acid
DZ            dizygotic
ELAM-1        endothelial leukocyte adhesion molecule
fsn           flaky skin (mouse mutation model)
GM-CSF        granulocyte-macrophage colony-stimulating factor
GP            guttate psoriasis
HCR           α-helical coiled-coil rod homologue (also known as Pg8)
HIV           human immunodeficiency virus
HLA           human leukocyte antigen
HPM           haplotype pattern mining
IBD           identical by descent
IBS           identical by state
ICAM-1        intercellular adhesion molecule
IDDM          insulin-dependent diabetes mellitus
IFN           interferon
Ig            immunoglobulin
IL            interleukin
K14           keratin 14
kb            kilobase pairs (1kb=1000bp)
KGF           keratinocyte growth factor
LCT           lactase-phlorizin hydrolase
LD            linkage disequilibrium
LOD           logarithm of odds
Met           methionine
MHC           major histocompatibility complex
M-MLV         Moloney murine leukemia virus
mRNA          messenger ribonucleic acid
MZ            monozygotic
NK            natural killer
NPL           non-parametric linkage
OR (95% CI)   odds ratio (95% confidence interval)
OTF3          octamer-binding transcription factor 3
PCR           polymerase chain reaction
Pg8           putative gene 8 (also known as HCR)
POU5F1        POU-type homeodomain-containing DNA-binding protein
PPAR          peroxisome proliferator-activated receptor
PPP           palmoplantar pustulosis


                                        6
Pro        proline
PsA        psoriatic arthritis
PSORS      psoriasis susceptibility locus
PV         psoriasis vulgaris
RNA        ribonucleic acid
RT-PCR     reverse transcriptase polymerase chain reaction
SCID       severe combined immunodeficiency
SDS-PAGE   sodium dodecyl sulfate-polyacrylamide gel
SNP        single nucleotide polymorphism
SSCP       single-stranded conformational polymorphism
SSP-PCR    sequence specific primer-polymerase chain reaction
TCF19      transcription factor 19
TDT        transmission disequilibrium test
TGF        tumor growth factor
θ          recombination fraction
Thr        threonine
TNF        tumor necrosis factor
VCAM-1     vascular cell adhesion molecule




                                     7
ABSTRACT
Psoriasis is a chronic inflammatory skin disease. Based on family studies, a strong
genetic component exists in the etiology of psoriasis, but environmental factors are
also needed to trigger the disease onset. Both linkage and association analyses have
assigned the major locus for psoriasis susceptibility, PSORS1, to 6p21.3 in all
populations studied. The PSORS1 locus has been narrowed down to an approximately
200 kb region in the centromeric part of the MHC class I region.


In this study, a novel candidate gene at the PSORS1 locus, HCR (Pg8), was cloned
and found to be highly polymorphic. In a large trio family material from seven
different populations, a specific allele of the HCR gene, HCR*WWCC, was shown to
be strongly associated with chronic plaque psoriasis (PV). However, two previously
identified susceptibility alleles of the locus, HLA-Cw*6 and CDSN*5, showed a
similar association. These three susceptibility alleles were found to belong to the same
extended susceptibility haplotype and their separation was not possible even with a
sample size of almost 1700 chromosomes.


The three PSORS1 susceptibility alleles were also studied in two clinical variants of
psoriasis, guttate psoriasis (GP) and palmoplantar pustulosis (PPP), to see whether the
locus was involved in the pathogenesis of subtypes and whether these could be used
to differentiate the three alleles. GP was shown to have a similar but even stronger
association with PSORS1 susceptibility alleles than PV, however, it did not help in
separating the three genes. PPP was not associated with any of the PSORS1 alleles,
suggesting a different etiology or molecular mechanism than for PV or GP.


Functional evidence for HCR as a potential psoriasis gene was also gained.
Expression of the HCR mRNA and protein was altered in the keratinocytes of lesional
psoriatic skin compared with non-lesional psoriatic and normal skin. In addition, the
HCR*WWCC allele was predicted to adopt an altered secondary structure compared
with the wild-type allele, which could affect the antigenic properties of the protein.


The PSORS1 locus is estimated to account for 30-50% of familial psoriasis, thus other
susceptibility loci likely exist. In genome-wide linkage analyses, several minor


                                            8
putative psoriasis susceptibility loci have been identified (PSORS2-7), but similar to
other complex diseases, most of them have not been replicated in subsequent studies
in different populations. In this study, a genome-wide scan in Finnish psoriasis
families not associated with PSORS1 was performed to find other susceptibility loci.
A minor susceptibility locus for psoriasis was mapped to 18p. The 18p locus has
previously been suggested as a susceptibility locus for psoriasis in a British
population. Taken together, these two mapping results yield sufficient evidence to
name 18p as a new candidate locus for psoriasis.




                                          9
INTRODUCTION
Psoriasis is a chronic inflammatory skin disease affecting approximately 1-3% of
Caucasians. Clinically, psoriasis is characterized by well-defined red, scaly plaques
typically located on the scalp, knees or elbows. The main pathological features of
these skin lesions are keratinocyte hyperproliferation and loss of differentiation,
inflammatory cell infiltration and vascular changes. The molecular pathogenetic
mechanisms of psoriasis are still largely unknown. The disease is rarely fatal but has a
potentially devastating effect on the patient’s quality of life. Current treatments are for
the most part palliative and may cause significant side effects.


Based on family and twin studies, the etiology of psoriasis has a significant genetic
component. However, environmental factors, such as infections, drugs, stress or
trauma to the skin, are needed to trigger disease onset in genetically susceptible
individuals. In segregation analyses of large multigenerational families, no clear
inheritance pattern can be seen. Psoriasis thus belongs to a group termed genetically
complex or multifactorial diseases.


Disease gene mapping in complex diseases is far more complicated than expected
based on successful positional cloning studies in monogenic diseases. In genome-wide
linkage analyses, several loci for different complex phenotypes have been mapped,
but replication of results in different populations has usually been unsuccessful.
Psoriasis is a rare example among complex diseases in that one major genetic
susceptibility locus has clearly been detected in all genome-wide linkage analyses
with sufficient power. The major susceptibility locus for psoriasis, PSORS1, has been
mapped to chromosome 6p21.3, and association analyses support its important role in
disease etiology in all populations studied. The PSORS1 locus is estimated to account
for a maximum of 50% of familial psoriasis, thus, other susceptibility loci likely exist.
While several minor putative psoriasis loci have been identified, most of them have
not been replicated in subsequent studies in different populations.


The PSORS1 locus has been narrowed down to an approximately 200 kb region in the
centromeric part of the MHC class I region. When this study was initiated,


                                            10
susceptibility alleles of two candidate genes of the region, HLA-C and
corneodesmosin (CDSN), had been shown to be significantly associated with
psoriasis. At the same time, the genomic sequence of the whole MHC region had
become available to the public databases as one of the first large completely
sequenced genomic regions. With the genomic sequence in hand, new candidate genes
at the PSORS1 locus could be identified.


The aims of this study were to investigate a novel candidate gene at the PSORS1
locus, HCR (Pg8), as a possible candidate gene for psoriasis, and to identify minor
susceptibility loci for psoriasis by a genome-wide scan in families not associated with
PSORS1.




                                           11
REVIEW OF THE LITERATURE


1. Mapping of complex diseases
In the past two decades, many genes underlying monogenic or Mendelian diseases
have been identified using genetic linkage and positional cloning methods. However,
detection of genetic factors behind complex diseases has been far more complicated.
Several features of common diseases complicate susceptibility gene mapping. One
major problem in study design is that we do not know what we are looking for as a
disease variant. Are there a few common alleles or multiple rare alleles underlying
common diseases? The recent advance in defining haplotype structure and extension
of linkage disequilibrium in the human genome has, however, increased our
knowledge of the human genome and genetic variation, and provided new tools for
the mapping of genes associated with the complex diseases.

1.1. Difficulties in mapping complex diseases
One factor complicating linkage analysis in complex diseases is locus heterogeneity.
There are numerous reports of susceptibility genes or loci that might underlie complex
disorders, but replication of findings has proven difficult. In different populations,
different genetic loci may explain genetic susceptibility to a disease. The difficulty in
defining the exact phenotype may also be one reason for locus heterogeneity because
complex diseases typically vary in severity of symptoms and age of onset. By using
stringent clinical diagnosis criteria and dividing patients into subgroups, the problem
may be diminished. However, locus interaction and the existence of phenocopies
complicate the analyses. Moreover, a strong environmental component is present in
complex diseases (Lander and Schork 1994; Risch and Merikangas 1996; Chakravarti
1999).



1.2. Allelic structure of complex diseases
Two models have been proposed to explain the genetic basis for complex diseases.
The common disease-common variant (CD/CV) hypothesis suggests that the genetic
risk for complex diseases is due to disease-predisposing alleles present at relatively
high frequencies at a handful of loci. The opposing genetic heterogeneity hypothesis



                                             12
proposes that numerous loci have rare alleles, each of which can cause the disease
(Lander 1996; Cargill et al. 1999; Chakravarti 1999). Neither of the hypotheses has
thus far been confirmed. However, based on statistical models on human population
expansion and differences in the kinetics of rare and common alleles, the CD/CV
model has gained more support (Pritchard 2001; Reich and Lander 2001; Pritchard
and Cox 2002).


The mutation rate is about the same for rare and common alleles. The rare alleles
behind Mendelian disease mutations are highly penetrant and usually under very
strong selection because of their deleterious effects on fitness. The susceptibility
variants in complex diseases seem to have moderate to low penetrance, are less prone
to selection and thus can reach higher frequency. The common alleles are likely to be
of ancestral origin. For rare alleles, the allele spectrum turnover is more rapid due to
selection and the allele frequency remains low (Pritchard 2001; Reich and Lander
2001; Pritchard and Cox 2002). For study design, knowledge about the allelic
structure of common diseases is crucial. Association studies are likely to be successful
only if there are few predominating alleles. For linkage analysis, allelic heterogeneity
does not cause a problem, but locus heterogeneity may explain unsuccessful
replication of linkage loci in genome scans (Pritchard and Cox 2002; Smith and Lusis
2002).


Although the mapping of susceptibility genes for complex diseases has so far been
generally disappointing, there are a few examples of successful susceptibility gene
mapping; for example, APOEε4 allele in Alzheimer’s disease (Corder et al. 1993;
Martin et al. 2000), PPARγ Pro12Ala (Altshuler et al. 2000) and Calpain-10 (Baier et
al. 2000; Horikawa et al. 2000) in type II diabetes, and NOD2 (CARD15) in Crohn’s
disease (Hugot et al. 2001; Ogura et al. 2001). Common to all of these susceptibility
alleles is that they are fairly prevalent in the population, supporting the CD/CV
hypothesis. Whether this is also the case in other complex diseases remains to be seen.
These disease variants may have higher penetrance and simpler allelic architecture
than other complex diseases (Pritchard and Cox 2002).




                                          13
1.3. Single nucleotide polymorphisms (SNPs)
SNPs are single base pair variations in genomic DNA for which different alleles exist
in normal individuals and the least frequent allele has a frequency of 0.01 or greater in
the general population. SNPs account for approximately 90% of human DNA
polymorphism (Collins et al. 1998). In addition to bi-allelic polymorphisms, the term
is often loosely used to describe tri- and tetra-allelic polymorphisms, sometimes also
insertion and deletion variants. Bi-allelic SNPs comprise four different types, with the
C/T (G/A) transition being the most common, accounting for about two-thirds of
SNPs (Brookes 1999). Approximately three million common SNPs (minor allele
frequency >20%) or one in every 1 kb are expected to exist in the human genome,
which means that there is on average a 0.1% chance of any base being heterozygous
in an individual (Taillon-Miller et al. 1998; Kruglyak and Nickerson 2001;
Sachidanandam et al. 2001). The total number of SNPs is estimated to be as high as
ten million (Kruglyak and Nickerson 2001). On a genome-wide level, region-specific
differences are present in SNP density. SNPs are more frequent in non-coding than in
coding regions, and in coding regions differences exist between genes. In the coding
regions, synonymous SNPs, which do not cause amino acid changes, are more
common than non-synonymous SNPs, probably due to selection against deleterious
alleles (Cargill et al. 1999; Chakravarti 1999).


The low mutation rate and essentially random nature of base-changing events make
the SNP alleles very stable (Gray et al. 2000) and most (>80%) are common to all
human populations, but with different allele frequencies (Sachidanandam et al. 2001).
Because SNPs are so frequent in the genome and can be genotyped with high-
throughput methods, common SNPs are believed to be good markers for genome-
wide mapping of complex diseases.



1.4. Association studies
Association analysis is likely to be an effective tool for studying complex diseases
because it has greater statistical power than linkage analysis when there is locus
heterogeneity (Lander and Schork 1994; Risch and Merikangas 1996). To date, most
association studies with complex traits have been performed as candidate-gene
studies. The candidate genes are usually selected based on their possible biological


                                           14
function in disease pathogenesis and directly causal variants are tested for association
(Risch 2000; Tabor et al. 2002). Numerous positive association results have been
published, but findings have not been replicated in subsequent studies. The reason for
this is often poor study design (e.g. overly small study groups, inadequately matched
controls) or that the alleles examined are not causal to the disease pathogenesis.
Mostly non-synonymous coding SNPs have thus far been used in association analyses
of candidate genes. However, SNPs on promoter regions or other important regulatory
elements may also be disease-causing variants, as seen for example with the LCT
gene variant in hypolactasia (Enattah et al. 2002).


Case-control study design is the most commonly used strategy in association studies.
The advantage of case-control study is that cases and controls are easy to obtain and
genotype. However, the patient and control groups must be carefully selected. If the
control group has not been adequately matched with the patient group, even
statistically significant associations may not reflect the actual influence of the studied
allele. Any systematic allele frequency differences between cases and controls can
appear to be an association (Risch 2000; Cardon and Bell 2001). Isolated populations
have been suggested as a good case-control sample set because of the homogenous
background (Peltonen et al. 2000), even though population substructure must even
then be considered (Kere 2001). Another way to improve control ascertainment is to
use a prospective study cohort, which also allows monitoring of environmental
factors. Sample size should be sufficiently large to detect significant results, and the
findings should be replicated in another population (Cardon and Bell 2001).


To reduce the effect of population stratification, family-based controls can be used
instead of population-based controls in association analyses. The most commonly
used family-based association test is transmission disequilibrium test, TDT (see
Materials and Methods section 11.3.) (Spielman et al. 1993; Spielman and Ewens
1996). Family-based tests, however, have weaker power and thus might require even
larger sample sizes than case-control studies. In addition, as disease onset is late in
many complex diseases, parents of the patients may be deceased (Cardon and Bell
2001).




                                           15
1.5. Genome-wide linkage analysis
Genetic linkage means that alleles from two loci segregate together rather than
independently in meiosis because of their close physical proximity on a chromosome.
The extent of linkage between two loci is measured by the recombination fraction (θ),
which is the fraction of meiotic events that show recombination between the loci of all
possible meiosis. For unlinked loci θ=0.5 and for completely linked loci θ=0 (Ott
1985; Pericak-Vance 1998).


Linkage analysis is used to locate a disease gene based on its close proximity to a
segregating marker allele on the same chromosome. Microsatellite repeats are
commonly used as markers because they are highly polymorphic and informative, and
densely located throughout the genome. The most powerful test for linkage is the
LOD score method, which is a likelihood-based parametric linkage approach to
estimate the recombination fraction and the significance of the evidence for linkage.
The LOD score is the logarithm (log10) of the odds of linkage, which is calculated by
dividing the likelihood of linkage at given θ by the likelihood that there is no linkage
(θ=0.5). LOD score is commonly calculated for different θ values, and the most likely
recombination fraction is the one that gives the highest positive LOD score (Morton
1955; Ott 1985; Pericak-Vance 1998).


Standard LOD score analysis, i.e. parametric linkage analysis, has been successfully
used for mapping Mendelian disease genes. In parametric analysis, the inheritance
pattern, penetrance of the trait and gene frequency must be known or correctly
estimated. Parametric linkage analysis is very sensitive to the given parameters, and if
they are incorrectly defined, the results can be skewed. In complex diseases,
parametric analysis is often done using both recessive and dominant inheritance
models with different gene frequencies and penetrance values to see which model
gives the highest LOD score. However, when doing multiple testing, the threshold for
a significant linkage score has to be increased accordingly. Non-parametric linkage
analysis is the method of choice for mapping complex disease genes since prior
knowledge of the parameters that define the mode of inheritance is not required (Xu et
al. 1998).




                                          16
Non-parametric linkage analysis is based on the higher than expected sharing of
alleles by affected individuals. The predominant method used is ASP analysis, which
monitors alleles that are IBD (identical by descent) between affected sib-pairs (Risch
1990; Goldgar 1998). Other affected relative pairs can also be used. The NPL method
can be used to analyze IBD allele sharing among all affected pedigree members
simultaneously, in addition to merely monitoring affected pairs. Another advantage of
NPL is that data for all markers on a chromosome can be evaluated simultaneously
using a multipoint approach. However, NPL analysis is limited to pedigrees of
moderate size since the computer time increases exponentially with the number of
individuals (Kruglyak et al. 1996). The major disadvantage of all non-parametric
methods is loss of power because it is often impossible to determine whether two
alleles are IBD or IBS (identical by state). As a consequence, more families are
needed than in parametric analyses (Pericak-Vance 1998).


For simple Menedelian traits, a parametric LOD score >3 in two-point analysis has
traditionally been used as significant evidence of linkage. This corresponds to a 5%
significance level for a specific locus and a 9% genome-wide significance level. To
minimize false-positive linkage results in complex diseases, more stringent criteria
have been suggested. According to the widely accepted criteria of Lander and
Kruglyak (1995), the linkage results are classified into the following three categories:
suggestive evidence of linkage, significant evidence of linkage and highly significant
evidence of linkage. These would be expected to occur one, 0.05 or 0.001 times in a
genome scan, respectively. The corresponding LOD scores and point-wise
significance levels in sib-pair analyses are 2.2 (P=7x10-4), 3.6 (P=2x10-5) and 5.4
(P=3x10-7) (Lander and Kruglyak 1995). These criteria have been questioned (Morton
1998) and only replication of a significant linkage result in a further sample can be
interpreted as a confirmed linkage.



1.6. Linkage disequilibrium mapping
Linkage disequilibrium (LD) refers to certain alleles at two or more linked loci
occuring together in the same chromosome more often than expected by chance.
Because genome-wide scans and candidate gene association analyses have proven
rather disappointing in mapping complex diseases, genome-wide LD mapping has


                                          17
been suggested as a method of choice (Lander and Schork 1994; Kruglyak 1999;
Reich et al. 2001; Weiss and Clark 2002). LD is disrupted by recombination, mutation
and gene conversion events. Average LD declines with chromosomal distance, but
there is large variation between different chromosomal regions. The biggest question
in genome-wide LD mapping is the number of markers (SNPs) required. The first
simulation studies estimated the average LD to be about 3 kb and suggested that up to
500 000 SNPs would be required for successful LD mapping (Kruglyak 1999). Recent
studies on the extent of LD in the human genome have given more promising results
for LD mapping. LD is believed to extend on average much longer than previously
thought (Taillon-Miller et al. 2000; Patil et al. 2001; Reich et al. 2001; Gabriel et al.
2002).


Haplotypes are now presumed to be organized into blocks, regions in which there is
little evidence of recombination and LD is large. Within the blocks, a few common
haplotypes account for over 80% of all haplotypes of the block (Daly et al. 2001; Patil
et al. 2001; Gabriel et al. 2002). Considerable variance exists in the size of the blocks
in different genomic regions (from <1kb to almost 200 kb). Studies comparing
different populations have shown that the blocks are smaller in Nigerian Yoruban and
African American populations than in European and Asian populations. All four
populations share over 50% of all haplotypes, and the boundaries of the blocks are
highly correlated across different populations. The European and Asian haplotypes are
almost identical, and of those haplotypes present in only one population, nearly all are
found in the Yoruban sample (Reich et al. 2001; Gabriel et al. 2002). These haplotype
characteristics are in agreement with the ‘out of Africa’ theory. European and Asian
populations are believed to have originated in Africa, with divergence occurring some
100 000 years ago, resulting in a genetic bottleneck (Cavalli-Sforza 1998).


For genome-wide LD mapping, the allelic structure of the blocks is promising. Over
80% of the haplotypes of a block are defined by less than 10% of the total SNPs of the
block. This means that as little as two or three SNPs per block, known as tag SNPs,
are needed to identify a block. However, the haplotype map covering the whole
genome must first be constructed, probably involving the typing of millions of SNPs
to find the tag SNPs. When this is done, a much smaller subset of SNPs is required for



                                           18
whole genome LD mapping (Daly et al. 2001; Johnson et al. 2001; Patil et al. 2001;
Gabriel et al. 2002; Reich et al. 2002).



1.7. Use of population isolates
For mapping and cloning Mendelian disease genes, population isolates have proven to
be very useful. In Finland, several rare recessive diseases have been successfully
mapped utilizing linkage disequilibrium in positional cloning (de la Chapelle 1993;
Peltonen et al. 1999). While expectations for using population isolates to map
complex diseases were initially high, results have been disappointing. The studies of
LD in population isolates of Finland and Sardinia have shown that at least in the
regions studied LD does not extend significantly longer than in outbred populations
(Eaves et al. 2000; Taillon-Miller et al. 2000). In these studies, however, only
common marker polymorphisms in random non-disease chromosomes were studied.
In the case of disease alleles, the extent of LD may differ from this background LD
and be useful in disease mapping (Peltonen et al. 2000; Kere 2001).


The population history and structure of genetic isolates, and the expected disease
allele frequency have to be considered carefully when choosing a population isolate
for complex disease mapping (Kruglyak 1999). In young population isolates (<100
generations), such as Finland, Sardinia and Iceland, the allelic heterogeneity is
reduced, but because of the fairly large number of founders, it may still be too high
for successful mapping of a common disease variant. In these populations, extent of
LD is only slightly elevated for common alleles, but for rare disease alleles the LD is
likely to be longer. Very young isolates (<20 generations), such as sub-isolates in the
Netherlands and French Canada, can provide some further advantages because they
have experienced a very narrow bottleneck following rapid expansion. The allelic
heterogeneity is reduced more and LD is believed to extend longer than in young
isolates (Peltonen et al. 2000; Heutink and Oostra 2002).


Population isolates also offer other advantages such as common environment and
culture. The more uniform environmental factors may help in avoiding some of the
environmental noise surrounding complex diseases. Some population isolates,




                                           19
Finland, for example, have the advantage of good genealogical records and uniform
diagnostic criteria and phenotype definitions (Peltonen et al. 2000).



2. Clinical features of psoriasis
The characteristic skin lesions in psoriasis are red, well-demarcated erythematous
plaques of various sizes covered by grayish-white scale. The most commonly affected
skin areas are the elbows, knees and scalp (Lomholt 1963; Roenigk 1991). Disease
course typically waxes and wanes, and it is impossible to predict on an individual
basis. Forty to fifty per cent of patients experience total remission of symptoms,
lasting from a few months to decades (Lomholt 1963; Farber and Nall 1974, 1991).



2.1. Clinical characteristics and related diseases
There are several clinical subtypes of psoriasis. Chronic plaque psoriasis or psoriasis
vulgaris (PV) is the most common, accounting for approximately 80-90% of cases
(Lomholt 1963). The size and morphology of the plaques vary and they are usually
symmetrically located on the extensor surfaces or scalp. Scaling is typically present.
Guttate psoriasis (GP) is characterized by sudden onset of widely dispersed small, red,
scaly plaques mainly over the trunk and proximal limbs. The lesions are less indurated
and there is less scaling than in PV. GP is the presenting form at disease onset in 14-
17% of patients, but in all affected patients its prevalence is less than 10% (Lomholt
1963; Baker 1986; Roenigk 1991). GP is seen most commonly in children or
adolescents and is often preceded by a streptococcal throat infection (Telfer et al.
1992; Mallon et al. 2000). In most cases, GP is self-limiting (acute GP), but a
significant proportion of patients develop PV later in life. Guttate flares can also be
seen in patients with chronic PV (Naldi et al. 2001). Other clinical subtypes of
psoriasis are inverse, erythrodermic and pustular psoriasis. Typical features of the
various types are shown in Table 1 (Farber and Nall 1991; Wright and Baker 1991a,
b).
Palmoplantar pustulosis (PPP) has some features in common with other forms of
pustular psoriasis and is often classified as a localized form of pustular psoriasis
(Baker 1986; Lever and Schaumburg-Lever 1990). However, the relationship between
PPP and psoriasis is controversial. Up to 24% of PPP patients have psoriasis (Enfors



                                           20
and Molin 1971), which is much higher than the normal population prevalence of
psoriasis (see section 2.2.). PPP is characterized by painful sterile pustules on
erythematous, scaly skin confined to palms and soles. PPP is relatively rare, with a
prevalence of 0.01-0.05% (Lomholt 1963; Hellgren and Mobacken 1971). The age of
onset is usually between 30 and 50 years, and women are more often affected (Enfors
and Molin 1971; Hellgren and Mobacken 1971; Eriksson et al. 1998). PPP is strongly
associated with tobacco smoking (Rosen et al. 1982; Akiyama et al. 1995).


Table 1. Clinical characteristics of different subtypes of psoriasis.

Type of psoriasis                 Clinical characteristics
Plaque psoriasis                  - most common form
                                  - size and morphology of the plaques vary
                                  - usually located on extensor surfaces or scalp
                                  - scaling is typically present
Guttate psoriasis                 - most common in children and adolescents
                                  - often preceded by streptococcal throat infection
                                  - skin lesions are small and usually widely located on the
                                  upper trunk and extremities
Inverse psoriasis                 - occurs as erythema in e.g. groin and axillary regions
                                  - scaling is absent
Erythrodermic psoriasis           - can cover almost the entire cutaneous surface of the body
                                  - can be accompanied by systemic metabolic disturbances,
                                  e.g. hypoalbunemia
Pustular psoriasis                - sterile pustules in erythematous and scaly skin
                                  - localized or generalized

                                  - palmar/plantar psoriasis

Nail changes are common in psoriasis patients, reported in up to 50% of patients.
These include pitting, discoloration, subungual hyperkeratosis and onycholysis (Baker
1986; Scher 1991). Psoriasis can also be associated with a seronegative
spondyloarthropathy referred to as psoriatic arthritis (PsA) (Espinoza et al. 1992;
Kaipiainen-Seppanen 1996; Hohler and Marker-Hermann 2001). Epidemiological
studies on PsA report an estimated overall prevalence of 0.1% and a prevalence of 5-
30% in patients with psoriatic skin disease. Of patients with extensive skin
involvement, up to 40% have PsA (Molin 1976; Espinoza et al. 1992; Braun et al.
1998). The arthritis involved is commonly a symmetric or asymmetric oligoarthritis,
affecting distal interphalangeal joints. It can also manifest as a severe deforming




                                                     21
arthritis that involves multiple small joints in the hands, feet and spine (Moll and
Wright 1973; Molin 1976; Espinoza et al. 1992).


Several factors are known to trigger the onset of psoriasis and to induce flares in
disease course (Lomholt 1963; Farber and Nall 1974, 1991). These include emotional
stress (in 23% of patients), drugs (e.g. beta-blockers, lithium) (in 16%), physical
trauma (in 43%) known as Koebner phenomenon, infections (in 14%) and alcohol
intake (Farber and Nall 1974; Poikolainen et al. 1990; Krueger and Duvic 1994;
Poikolainen et al. 1999; Higgins 2000; Raychaudhuri and Gross 2000). Streptococcal
pharyngitis has been associated with the onset of guttate psoriasis, in particular, but is
also reported to exacerbate chronic plaque psoriasis (Telfer et al. 1992; Leung et al.
1995; Wardrop et al. 1998; Mallon et al. 2000).



2.2. Prevalence
The prevalence of psoriasis is usually reported to be 1-3%. However, studies in
different populations show considerable variation in the occurrence of the disease.
The prevalence is lowest in Chinese (0.4%) and in West African (0.3-0.7%) and
American (0.7%) black populations. In Japan and India, the prevalence is 0.3-1% and
0.8%, respectively. In Caucasoid populations, the prevalence varies between 1 and 5%
(Farber and Nall 1991) being, for example, 3% in Denmark (Brandrup and Green
1981), 2.5% in Australia (Duffy et al. 1993), 2.8% in the Faroe Islands (Lomholt
1963) and 4.8% in Norway (Kavli et al. 1985). When one considers that some people
may have only a single lesion and never seek medical attention, the actual prevalence
may be higher (Christophers 2001). Psoriasis is equally common in both sexes
(Lomholt 1963; Farber and Nall 1974; Brandrup and Green 1981; Kavli et al. 1985;
Henseler and Christophers 1995).



2.3. Age of onset
The average age of onset is 20-30 years, but the disease can erupt at any age (Watson
et al. 1972; Farber and Nall 1974; Duffy et al. 1993). In women, the average age of
onset is somewhat lower than in men (Farber and Nall 1974; Henseler and
Christophers 1985). The age of onset distribution is, however, bimodal and a second



                                           22
peak is seen around 57-60 years, without a significant difference between the sexes
(Henseler and Christophers 1985; Smith et al. 1993). In 35% of patients, the age of
onset is before 20 years, and in 58%, before 30 years (Farber and Nall 1991).
Approximately 70% of psoriasis patients are affected by 40 years of age (Watson et
al. 1972; Henseler and Christophers 1985).


The clinical features of psoriasis and familial aggregation vary according to the age of
onset. When the age of onset is early, the disease course is often unstable, with
frequent relapses and extensive body involvement (Lomholt 1963; Farber and Nall
1974; Henseler and Christophers 1985; Stuart et al. 2002). In addition, most patients
who have an affected family member belong to the early-onset group (Farber et al.
1974; Henseler and Christophers 1985; Smith et al. 1993). Based on the age of onset
and familial occurrence, two clinical types of psoriasis can be separated. Type I
psoriasis has an onset before the age of 40 years, is familial and its clinical picture is
more severe. The HLA-Cw*6 allele association (see section 4.3.2.) is found mainly in
type I patients. Type II psoriasis is sporadic, the age of onset is over 40 years and the
disease course is often milder (Henseler and Christophers 1985; Swanbeck et al.
1995).



3. Pathogenesis of psoriasis
The main pathogenetic features of psoriatic lesions are abnormal keratinocyte
differentiation and hyperproliferation, infiltration of inflammatory cells and vascular
changes. Accordingly, keratinocytes, fibroblasts and cells involved in the immune
response (antigen-presenting and T cells) or vascular system (endothelial cells) are all
suggested as primary defects in the disease process. Interactions between the different
cell types via cytokines play an important role in the disease process, but the
pathogenetic mechanisms are still largely unsolved (Baker and Fry 1992; Kadunce
and Krueger 1995; Bhalerao and Bowcock 1998; Bos and De Rie 1999; Ortonne
1999).




                                           23
3.1. Keratinocytes
In psoriatic skin, keratinocyte differentiation is abnormal, resulting in parakeratosis
and loss of the granular layer. The keratinocytes are also hyperproliferative. These
changes are believed to be secondary to altered growth and maturation kinetics of
keratinocytes. In psoriatic plaques, the cell cycle of proliferating keratinocytes is eight
times shorter and the proliferating cell population is two times greater than in control
skin (Weinstein et al. 1985). Normally, only a small portion of the keratinocyte stem
cells in the basal layer is active in cell cycling. In psoriatic skin, the percentage of
stem cells participating in cell division seems to be increased (Bata-Csorgo et al.
1993). Alternatively, the number of cell cycles in a transiently amplifying cell
population might be increased (McKay and Leigh 1995). The factors leading to these
changes in keratinocytes remain obscure.


There is also a disturbance in keratinocyte adhesion in psoriatic skin lesions. Integrins
are important in cell-cell interactions and in adhesive properties of keratinocytes.
They also play a role in the initiation of terminal differentiation of keratinocytes
(Adams and Watt 1989). The polarized topography of integrin expression and the
integrin-cytoskeleton association are altered in non-lesional and lesional psoriatic skin
compared with normal skin, but whether the changes are primary or secondary to
some other stimulus is not known (Hertle et al. 1992; Pellegrini et al. 1992).



3.2. Inflammatory cells
Strong evidence indicates that inflammatory cells, especially T lymphocytes, have an
important role in the pathogenesis of psoriasis. T lymphocytes accumulate early in the
developing plaque and immunosuppressive drugs targeted against T lymphocytes (e.g.
cyclosporin A) are effective in treating active psoriasis (Griffiths et al. 1986).
Psoriasis can be exacerbated by HIV infection, which is known to infect the CD4+ T
cells (Johnson et al. 1985; Duvic 1990). Experiments with an immunodeficient SCID
mouse model have also supported the important role of immunomechanisms in
pathogenesis. In one study, non-lesional skin from psoriasis patients and skin from
healthy controls were grafted onto SCID mice. After injection of autologous
immunocytes, psoriatic plaques developed in uninvolved skin from psoriasis patients
but not in control skin. Histological and immunohistological staining of the plaques


                                            24
revealed typical features of psoriatic plaques (Wrone-Smith and Nickoloff 1996).
There are also case reports of clearance or development of psoriasis following
allogenic bone marrow transplants (Snowden and Heaton 1997; Kanamori et al.
2002). Whether the inflammatory response in psoriasis is primary or secondary to
some other stimulus remains to be elucidated.



3.3. Cytokines
The expression of several cytokines is altered in psoriatic skin. Because of the
complex interaction of the cytokines, it is unlikely that over- or underexpression of a
single cytokine could be the sole pathogenetic mechanism. IL-6 is a major mediator of
the host response to injury and infection. It also enhances B and T cell proliferation
and activation of B cells, T cells and macrophages. It is present in increased amounts
in psoriatic skin (Grossman et al. 1989; Ohta et al. 1991). IL-8 is a potent T cell and
neutrophil chemoattractant and is also overexpressed in the psoriatic skin (Gillitzer et
al. 1991). Both IL-6 and IL-8 are produced in part by keratinocytes and have been
shown to stimulate keratinocyte proliferation in vitro (Grossman et al. 1989; Tuschil
et al. 1992). TGF-α is produced by keratinocytes and has mitogenic and angiogenic
properties. Both TGF-α mRNA and protein levels are overexpressed in psoriatic
lesions. TGF-β, by contrast, inhibits epithelial growth and its mRNA levels in
psoriatic skin are not significantly different from those in normal skin (Elder et al.
1989).


IFN-γ is produced by activated lymphocytes and has the ability to induce the
expression of the adhesion molecule ICAM-1. TNF-α can also induce ICAM-1 but to
a lesser extent. Both IFN-γ and TNF-α are believed to be important in the trafficking
of T lymphocytes to the psoriatic epidermis (Griffiths et al. 1989; Terajima et al.
1998). The amount of TNF-α in psoriatic lesions is elevated compared with that in
control skin (Bonifati et al. 1994; Ettehadi et al. 1994; Terajima et al. 1998).


IL-1 stimulates production of other cytokines by keratinocytes and induces vascular
endothelial cell adhesion molecules for leukocytes (ELAM-1, VCAM-1, ICAM-1),
which could account for the infiltration of leukocytes into psoriatic lesions. The



                                            25
expression of ELAM-1 and ICAM-1 has been shown to be higher in psoriatic skin
than in normal skin (Gillitzer et al. 1991). Keratinocytes themselves also produce IL-
1, which stimulates the expression of KGF and GM-CSF in fibroblasts. These
fibroblast-derived      factors     in   turn    stimulate     keratinocyte      proliferation     and
differentiation via a paracrine regulation mechanism (Smola et al. 1993; Maas-
Szabowski et al. 1999; Werner and Smola 2001).



3.4. Pathogenetic models
Cytokines together with inflammatory cells and keratinocytes, which synthesize and
secrete the cytokines after appropriate stimuli, form a complex network of signalling.
One proposed immunopathogenetic model is shown in Figure 1 (Krueger 2002).




Figure 1. (1) APCs capture antigens in the epidermis, which leads to their activation and migration to
lymph nodes. In lymph nodes, naive T lymphocytes recognize antigens bound to class I or II MHC
molecules on APCs. T cells are activated, acquire the skin-homing receptor CLA and differentiate into
type 1 or 2 effector lymphocytes. (2) CLA+ memory T cells enter the circulation and exit cutaneous
blood vessels at the site of inflammation. (3) In the dermis and epidermis, T cells become activated to
release cytokines upon encountering the initiating antigen. In psoriasis, type I lymphocytes are
expanded and release several cytokines, e.g. γ-interferon, which cause increased expression of
inflammatory proteins and adhesion molecules for leukocytes on keratinocytes. (4) Intraepidermal T
cells trigger keratinocyte hyperproliferation, but direct stimulation of keratinocytes by e.g. epidermal
injury has also been suggested. (5,6) Cytokine-activated keratinocytes produce cytokines, chemokines
and various other growth factors that stimulate neutophil influx, vascular alterations and keratinocyte


                                                  26
hyperplasia. (CLA, cutaneous lymphocyte-associated antigen; LFA, lymphocyte associated antigen;
PNAd, peripheral lymph node addressin; TCR, T cell receptor; Tc1, type 1 cytotoxic T cell; Th1, type I
helper T cell; V-EGF, vascular endothelial growth factor; KC, keratinocyte; LC, Langerhans cell; DC,
dendritic cell; PMN, polymorphonuclear leukocyte) (Modified from Krueger 2002).



No animal model is available for psoriasis. Some psoriasiform features, such as
epidermal hyperproliferation, a mixed inflammatory infiltrate and vascular changes,
can be seen in mice homozygous for the chronic proliferative dermatitis (cpd) and
flaky skin (fsn) mutations (Schon 1999). However, these mice seem to lack T cell-
based immunopathogenesis, and thus their value for psoriasis research is limited. In
the last ten years, several transgenic mouse models for psoriasis have been developed.
Most of these overexpress some cytokine or growth factor that show altered
expression in psoriasis under the K14 or involucrin promoter (e.g. K14/IL-1α,
K14/IL-6, K14/TGFα, involucrin/IFNγ). Nevertheless, the transgenic mice only
possess some of the pathogenetic features seen in psoriasis. This emphasizes the
importance of the interaction between different cell types and cytokines in the disease
process (Vassar et al. 1991; Turksen et al. 1992; Carroll et al. 1995; Groves et al.
1995; Schon 1999).



4. Genetics of psoriasis
Based on family and twin studies, a strong genetic component is present in the
pathogenesis of psoriasis, but the segregation of the disease in most pedigrees does
not obey simple Mendelian laws. Moreover, environmental factors are needed to
trigger disease onset in a genetically susceptible individual. Further support for the
genetic basis of psoriasis has come from HLA association studies, and in linkage
analyses, several susceptibility loci for psoriasis have emerged. In genome-wide
scans, most of the patients have been diagnosed as having chronic plaque psoriasis
according to the standard diagnostic criteria. However, subtypes of psoriasis may exist
in the psoriasis vulgaris group, accounting for locus heterogeneity.



4.1. Family and twin studies
In family studies, a clear familial aggregation of psoriasis can be seen. In a population
study of psoriasis in the Faroe Islands, Lomholt (1963) examined some 11 000


                                                 27
inhabitants, a third of the entire population, and reported that 91% of patients had an
affected family member. In another large study, 36% of patients had an affected
relative and 47% of first-degree relatives had psoriasis (Farber and Nall 1974). Other
studies have reported a somewhat lower but consistent prevalence of psoriasis in first-
degree relatives of patients, approximately 12-19% in parents and 9-16% in siblings
(Lomholt 1963; Watson et al. 1972; Melski and Stern 1981; Brandrup 1984;
Swanbeck et al. 1994). In a large Swedish family data set, the estimated lifetime risk
of psoriasis was 28% when one parent was affected and 24% when an affected sibling
was present (Swanbeck et al. 1997).


In twin studies, the concordance rates for monozygotic and dizygotic twins are
different. The concordance in MZ twins is much higher, 63-73%, compared with the
17-20% in DZ twins (Farber et al. 1974; Brandrup et al. 1978; Brandrup et al. 1982).
In an Australian population, the concordance rates were lower in both groups (35% in
MZ twins and 12% in DZ twins), but in agreement with earlier studies (Duffy et al.
1993). The much higher concordance in MZ versus DZ twins speaks for a strong
genetic component. In addition, such clinical features as distribution pattern of
plaques, severity, and disease course are similar in MZ twins but not in DZ twins.
However, because the concordance never reaches 100% in MZ twins, environmental
factors are also needed to trigger disease onset.
Based on twin studies, the overall heritability is estimated to be 80-91% (Brandrup et
al. 1978; Brandrup and Green 1981; Brandrup et al. 1982; Duffy et al. 1993). Similar
heritability estimates of 82-87% have been obtained from segregation analyses of
population data from the Faroe Islands and Poland (Pietrzyk et al. 1982b; Iselius and
Williams 1984).



4.2. Inheritance models
Based on family studies, several different inheritance models have been proposed to
explain the segregation of psoriasis. An autosomal dominant inheritance model with
incomplete penetrance was suggested in two studies, both examining one large
pedigree (Ward and Stephens 1961; Abele et al. 1963). Moreover, an autosomal
recessive model with a high gene frequency (25%) explained the inheritance best in a
Swedish population study (Swanbeck et al. 1994). However, a dominant inheritance


                                           28
model with low penetrance (<50%) was also compatible with the Swedish data. This
was probably due to a pseudodominant inheritance pattern, i.e. segregation that
appeared to be dominant in pedigrees, which can result from a recessive mode of
inheritance with very common disease alleles (Swanbeck et al. 1994).


In most studies, the disease segregation in families does not fit simple Mendelian
models. When Lomholt’s family data from the Faroe Islands (Lomholt 1963; Iselius
and Williams 1984) and family data from a Polish population (Pietrzyk et al. 1982a;
Pietrzyk et al. 1982b) were analyzed using a mixed model of complex segregation
analysis, some of the families showed multifactorial and others monogenic
inheritance. Elder et al. (1994) proposed a multilocus model after reanalyzing
Lomholt’s data (Elder et al. 1994). Watson et al. (1972) also concluded that only a
multifactorial inheritance model could explain their material. Furthermore, in a study
of first-degree relatives of Danish psoriatic twins, an autosomal dominant model with
reduced penetrance or multifactorial inheritance was most probable (Brandrup 1984).


Genomic imprinting has also been proposed as a genetic mechanism for psoriasis,
with two supporting observations from population studies. The birth weight of the
children of psoriatic fathers is higher than that of psoriatic mothers or controls
(Traupe et al. 1992; Swanbeck et al. 1994). In addition, the offspring of a psoriatic
father are more often affected than offspring of a psoriatic mother or gene carrier
(Traupe et al. 1992). A model of allelic instability in mitosis has also been suggested
as a possible inheritance mechanism because great variability is present in the age of
onset and severity of the disease, and the magnitude of genetic anticipation is greater
when inherited from the father (Theeuwes and Morhenn 1995).


Based on all of these studies, one can conclude that in some large pedigrees
Mendelian inheritance with lowered penetrance explains the disease segregation, but
in most families, the mode of inheritance is more complicated. Current opinion is that
psoriasis belongs to the complex diseases group and that the inheritance is
multifactorial (Henseler 1998; Barker 2001; Elder 2001).




                                          29
4.3. The HLA association

4.3.1. The Major Histocompatibility Complex
The MHC is an extended region of about 4 million bp on human chromosome 6,
between 6p21.31 and 6p21.32, usually referred to as the HLA (human leukocyte
antigen) system. It contains over 200 genes, more than 40 of which encode for
leukocyte antigens involved in immune response. While some of the other genes in
the region are functionally involved with the HLA genes, a large number have nothing
to do with immunity. The HLA genes that are involved in the immune response are
grouped into the HLA class I and II genes, which are both functionally and
structurally different (Margulies 1999; Klein and Sato 2000a).


The major (or classic) class I genes, HLA-A, HLA-B and HLA-C, are expressed by
most somatic cells. The class II genes are grouped into the main classes of HLA-DM,
-DO, -DP, -DQ and –DR, and further into two families, A or B, based on the chain (α
or β). The class II genes are normally expressed only by immune cells (B cells,
macrophages, dendritic cells, activated T cells and thymic epithelial cells), but in the
presence of γ interferon other cell types can also express them (Margulies 1999; Klein
and Sato 2000a).


The class I and II MHC molecules are cell surface receptors that recognize and bind
antigen fragments, and after processing, present them to the T cells, initiating an
immune response. The peptide fragments of cytosolic proteins (worn-out or defective
proteins of the cell or viral proteins) are presented by HLA class I molecules, and
extracellular proteins (self or foreign) by class II molecules. The HLA-peptide
complexes formed interact with T cell receptors or CD4-CD8 co-receptors of the T
cells. The MHC molecules can also serve as elements for signal transduction for
natural killer (NK) cells and either protect or activate natural killing of the target cell
(Margulies 1999; Klein and Sato 2000a).


The HLA genes exhibit a high degree of polymorphism, which allows the binding and
presentation of a large number of different antigens. In addition to foreign pathogens,
HLA class I molecules in particular have an important role in self-recognition by the
immune system and are largely responsible for the acceptance or rejection of organ


                                            30
transplants. The MHC also plays a role in the etiology of many autoimmune diseases,
but the precise mechanism remains obscure. Certain alleles or haplotypes of HLA
genes are significantly more common in patients compared with controls in some
diseases; for example, HLA-B27 in ankylosing spondylitis, DR4 in rheumatoid
arthritis, DR3 in celiac disease and DQB1*0302 in IDDM. Psoriasis is the only
disease that is known to be associated with the HLA-C gene (Margulies 1999; Klein
and Sato 2000b). The MHC region is composed of blocks within which recombination
appears to be very rare and tight linkage disequilibrium exists between the alleles,
forming extended conserved haplotypes (Marshall et al. 1993).



4.3.2. The HLA association with psoriasis
The first studies reporting an association between psoriasis and the HLA antigens
were published already in the 1970s. They were motivated by the findings that
streptococcal infections often preceded onset of psoriasis and that a protein from
group A haemolytic streptococcus was shown to cross-react with certain HLA
antigens (Tervaert and Esseveld 1970; Farber and Nall 1974). Russel et al. (1972)
found a significant increase of the HLA-B13 (A13) and –B17 (W17) alleles in
psoriasis patients compared with controls (27.3% and 22.7% in psoriasis patients and
3.4% and 9% in controls, respectively) when studying 44 Caucasian patients and 89
controls (Russell et al. 1972). The HLA-B13 (A13) and HLA-B17 (W17) alleles were
also shown to be associated with psoriasis in subsequent studies (White et al. 1972;
Karvonen et al. 1976; Tiilikainen et al. 1980). In a Finnish population sample, 45.9%
of psoriasis patients were HLA-Cw*6 carriers compared with 7.4% of random blood
donors (P<0.0001) (Tiilikainen et al. 1980). The early HLA association studies were
done with a serological typing method that is known to be insensitive to HLA-C and
fails to detect many HLA-C alleles completely (Bunce et al. 1996). With more
specific DNA-based genotyping methods, the HLA-Cw*6 allele association has been
the strongest and most consistently reported HLA association in psoriasis. In a study
of 201 Swedish patients and 77 controls, the frequency of the HLA-Cw*6 allele in
patients (66.7%) was significantly increased compared with controls (11.7%)
(Enerback et al. 1997). In a case-control study of a UK population, the HLA-
Cw*0602 allele was seen in 47% of patients and 20% of controls (P<0.0001) (Mallon
et al. 1997).


                                            31
In two studies, a specific amino acid change, alanine instead of threonine at position
73 of the HLA-C gene, was reported to be associated with psoriasis (Asahina et al.
1991; Ikaheimo et al. 1994). This change is seen in the HLA-Cw*6 allele, but it is not
unique to Cw*6. The HLA-Cw*4, -Cw*7, -Cw*12, -Cw*1503 and –Cw*17 alleles
also have Ala 73 in their coding regions (Kostyu et al. 1997; Mallon et al. 1997). In
the UK population, Ala 73 was present at high frequencies in both psoriasis patients
(88.5%) and the control group (84.3%) and showed a significant association with
psoriasis only in male patients with type I disease (Mallon et al. 1997). The authors
concluded that the HLA-Cw*0602 allele was probably playing the major role in the
association, not Ala 73 (Mallon et al. 1997). In a Japanese population, the HLA-Cw11
allele was significantly more common in patients than in controls (57% vs. 9%), but
HLA-Cw6 and –Cw7 also showed an increased risk (Nakagawa et al. 1990).


In several studies, the frequency of the HLA-Cw*0602 allele has been much higher in
psoriasis patients with early onset disease (before age 40 years) than in those with late
onset disease (after age 40 years) (Tiilikainen et al. 1980; Henseler and Christophers
1985; Enerback et al. 1997; Mallon et al. 1997). In addition, HLA-Cw*6 positive and
negative patients have been reported to have different clinical features. The disease
course is often more severe in patients with the HLA-Cw*6 allele than in HLA-Cw*6
negative patients (Guedjonsson et al. 2002).


HLA class II alleles have also been reported to be associated with psoriasis. In a
Taiwanese population, HLA-DRB1*0701 was seen in 11% of patients and in 2% of
controls (P=0.001) (Jee et al. 1998). In a Caucasian population, in addition to the
HLA-Cw*6, -B*13 and –B*57 alleles, DRB1*0701, DQA1*0201 and DQB1*0303
were over-represented in type I psoriasis patients (Schmitt-Egenolf et al. 1993;
Ikaheimo et al. 1996). Furthermore, an extended haplotype, EH57.1, containing
Cw*6-B*57-DRB1*0701-DQA1*0201-DQB1*0303 was significantly more common
in type I psoriasis patients than in controls (P=0.00021). Individuals carrying class I
alleles of EH57.1 but lacking the class II alleles were significantly more common in
the type I psoriasis patient group (12%) than in the control group (2%) (P=0.0060).
By contrast, possessing only the class II alleles of the haplotype and lacking the class
I alleles was not associated with psoriasis. HLA-Cw*6 and –B*57 appear to be the
actual markers for psoriasis susceptibility, with the class II allele association likely


                                           32
being due to linkage disequilibrium between the alleles (Schmitt-Egenolf et al. 1996).
Jenisch et al. (1998) also reported that the class I haplotype of EH57.1 was selectively
retained among affected individuals and that the HLA-Cw*6 allele was the strongest
predictor of psoriasis risk of the HLA alleles. The authors did, however, suggest that
the HLA-Cw*6 allele itself was unlikely to be the direct determinant of susceptibility
but rather was in tight linkage disequilibrium with it (Jenisch et al. 1998).



4.4. Linkage analyses


17q25 PSORS2
The first genome-wide scan in psoriasis, performed in 1994, localized a susceptibility
locus at 17q25 (PSORS2). Eight large Caucasian families from USA with 65 affected
individuals were analyzed using a dominant inheritance model. A total LOD score of
5.7 (θ=0.15) was obtained from six families, but the score came mostly from one large
family (LOD 5.3) (Table 2) (Tomfohrde et al. 1994). However, in a study of a large
family from Northeast England, the 17q25 linkage was excluded (Matthews et al.
1995). A study of 24 North American multiplex families using 12 microsatellite
markers also failed to detect linkage at 17q25, but there was suggestive evidence of
allele sharing for three distal 17q markers (Nair et al. 1995).


4q PSORS3
In a subsequent study by Matthews et al. (1996), a large Irish family showed some
evidence of linkage at 17q (LOD 1.24, θ= 0.0), but six other multiplex families were
unlinked. The authors continued with a genome-wide scan in one of the unlinked
families using a dominant model with 70% penetrance and got a positive linkage
result at 4q. When the locus was studied with further markers in all the six families
unlinked to 17q, a total two-point LOD score of 3.03 (θ=0.08) was obtained. Non-
parametric linkage analysis supported the linkage at 4q (PSORS3) with a NPL of 4.2
(P=0.0026) (Matthews et al. 1996).


6p21.3 PSORS1
Trembath et al. (1997) performed a two-stage genome scan with 106 affected sib-pairs
from 68 Caucasoid families from the UK. Using non-parametric statistics, they found,



                                            33
for the first time, significant evidence of linkage to the MHC region on chromosome
6p21.3 (PSORS1) (LOD 6.5, P=5.8x10-7). Regions of possible linkage were reported
on chromosomes 2 (LOD 1.27, P=0.008), 8 (LOD 2.6, P=0.0003) and 20 (LOD 2.01,
P=0.001) (Trembath et al. 1997).


In a genome-wide scan of 86 nuclear and 29 extended families with 182 independent
sib-pairs from USA and Germany, significant linkage was also obtained on 6p21.3
(Zmax= 3.52, P=2.9x10-5). Both parametric linkage with several models and non-
parametric linkage were analyzed, and two novel loci at 16q (Zmax=2.50, P=0.00034)
and 20p (Zmax=2.62, P=0.00026) under a recessive model were found. The previously
reported 17q25 locus also showed evidence of linkage (Zmax=2.09, P=0.00097) under
a dominant model allowing heterogeneity (Nair et al. 1997). The 16q locus was
interesting because it overlapped a susceptibility locus for Crohn’s disease (Hugot et
al. 1996; Rioux et al. 2000) and an increased co-occurrence of psoriasis and Crohn’s
disease has been reported (Lee et al. 1990; Nair et al. 1997). Polymorphisms of the
NOD2 gene at the 16q locus have been reported to be associated with Crohn’s disease
(Hugot et al. 2001; Ogura et al. 2001). The NOD2 gene polymorphisms, however,
showed no association with psoriasis (Nair et al. 2001; Plant et al. 2002).


The 6p21.3 locus yielded evidence for linkage also in a family material collected from
Scotland. A maximum LOD score of 4.63 was reached under a dominant inheritance
model with 70% penetrance and 5% phenocopies. There was also evidence of excess
allele sharing between affected siblings. The 4q and 17q loci did not show evidence of
linkage in this cohort of 103 families (301 affected individuals) (Burden et al. 1998).


1q21 PSORS4
No evidence of linkage to 6p or 17q was seen when 22 Italian multiplex pedigrees
were analyzed. Moreover, the other previously reported susceptibility regions on 2p,
4q, 8q, 16q and 20p were excluded in one large family. In a genome-wide scan with
the same family using a dominant model, a putative linkage at 1q21 was observed. All
families taken into the analysis yielded a maximum LOD score of 3.75, θ=0.05. Non-
parametric analysis also showed evidence of linkage at the 1q21 locus (PSORS4)
(NPL=4.07, P=0.0001) (Capon et al. 1999). The PSORS4 is interesting as a psoriasis
candidate locus, because it coincides with the region of the Epidermal Differentiation


                                           34
Complex. In subsequent fine mapping using linkage disequilibrium analysis in two
independent Italian sample sets, the region has been further refined (Capon et al.
2001).


3q21 PSORS5
In a genome-wide scan of 20 Swedish families, a suggestive linkage was found at a
novel locus on 3q using a recessive model with 25% penetrance. In an extended
family material with 47 families, a LOD score of 3.36 was obtained at 3q. The locus
was further analyzed with 153 sib-pairs using non-parametric statistics, yielding a
maximum NPL value of 1.77 (P=0.04). After stratifying all 104 families according to
their origin, a NPL value of 2.77 (P=0.003) at 3q21 (PSORS5) was gained in families
originating from southwest Sweden (Enlund et al. 1999). In another genome scan with
a denser marker map and 134 sib-pairs also included in the previous study of 3q, the
families were stratified according to joint complaints. With non-parametric linkage
analysis, NPL score of 2.83 (P=0.002) was obtained at 6p21.3 in families without
joint symptoms. The 3q21 locus was also linked in non-joint involvement families
(NPL 2.89, P=0.002), and the chromosome 15 locus in families with joint complaints
(NPL 2.96, P=0.0017) (Samuelsson et al. 1999). Recently the PSORS5 locus at 3q21
has been narrowed to a 250 kb interval with TDT analysis (Hewett et al. 2002).


19p13 PSORS6
In a genome-wide study of 32 large German multiplex families with non-parametric
linkage statistics, a novel psoriasis susceptibility locus at 19p13, PSORS6, (Zlr=3.50,
P=0.0002) was reported. The authors also calculated linkage using parametric models
by maximizing the LOD scores over multiple genetic models and obtained a LOD
score of 2.38. When allowing heterogeneity under a recessive model, a LOD score of
4.06 was reached. The genome-wide significance was assessed with simulations, and
suggestive evidence of linkage was also seen at 6p21.3 (Zlr=3.1, P=0.001). Two loci
on chromosomes 8q and 21q were also reported as possible susceptibility loci (Lee et
al. 2000).


1p PSORS7
In a genome-wide screen of 284 affected sib-pairs of UK origin using non-parametric
statistics, a novel susceptibility locus on 1p (NPL=3.6, P=0.00019), was reported


                                          35
        (PSORS7). However, also in this study the PSORS1 locus at 6p21.3 showed the most
        significant linkage, NPL=4.7 (P=2x10-6). In addition, suggestive evidence for linkage
        was detected at two novel loci, 2p and 14q, in families that were linked to the
        PSORS1 locus and at chromosome 7 in all families (Veal et al. 2001).




        Table 2. Genome-wide scans in psoriasis.
Material:                   Studied loci:    Linkage results:                              Reference:
USA                         genome scan      -17q25 PSORS2 (LOD 5.7)                       Tomfohrde et al. (1994)
8 large families                             - dominant inheritance model
65/216 affected
Ireland                     genome scan      - 4q                                          Matthews et al. (1996)
1 large family unlinked                      -dominant model
to 17q

6 large families            4q markers       - 4q35 PSORS3 (LOD 3.03, NPL 4.225,
(unlinked to 17q25)                          P=0.0026)
UK                          two-stage        - 6p21.3 PSORS1 (LOD 6.5, P=5.8x10-7)         Trembath et al. (1997)
1: 254 individuals, 41      genome scan      - 2p
families (66 ASPs)                           - 8q
2: additional 27 families                    - 20p
(40 ASPs)
USA, Germany                genome scan      - 6p21.3 (LOD 3.52, P=2.9x10-5, NPL 2.54)     Nair et al. (1997)
29 large and 86 nuclear                      - 16q
families                                     - 20p
224 ASPs                                     - 17q
(182 independent)
Italy                       genome scan      -1cen-q21 (LOD 1.69)                          Capon et al. (1999)
1 large family

22 families                 6p, 17q and 1q   - 1q21 PSORS4 (LOD 3.75, NPL 4.1,
                            markers          P=0.0001)
Sweden                      genome scan      -3q                                           Enlund et al. (1999)
20 families

153 ASPs                    3q markers       - 3q21 PSORS5 (LOD 3.36, recessive model,
                                             NPL 1.77), (NPL 2.77, P=0.003 in families
                                             from southwest Sweden)
Sweden                      genome scan      - 6p21.3 (NPL 2.83, P=0.002) in families      Samuelsson et al. (1999)
134 ASPs                                     with no joint involvement
                                             - 3q21 (NPL 2.89, P=0.002) in families with
                                             no joint involvement
                                             -15 (NPL 2.96, P=0.0017) in families with
                                             joint involvement
Germany                     genome scan      - 19p13 PSORS6 ( Zlr 3.5, P=0.0002)           Lee et al. (2000)
32 large families                            - 6p21.3 (Zlr 3.10, P=0.001)
(162/357 affected)                           - 8q
                                             - 21q
UK                          genome scan      - 6p21 (NPL 4.7, P=2x10-6)                    Veal et al. (2001)
284 affected sib-pairs                       - 1p PSORS7 (NPL 3.6, P=0.00019)
(222 independent                             -7
sibships)                                    - 2p
                                             - 14q




                                                        36
5. The PSORS1 locus in the MHC region


Significant linkage to the PSORS1 locus at 6p21.3 has been reported in several
studies of different populations (Nair et al. 1997; Trembath et al. 1997; Burden et al.
1998; Samuelsson et al. 1999; Lee et al. 2000; Veal et al. 2001), and the locus fulfils
the criteria for confirmed linkage (Lander and Kruglyak 1995). The contribution of
the PSORS1 locus to the relative risk of developing familial psoriasis is calculated to
be about 35-50% (Nair et al. 1997; Trembath et al. 1997; Burden et al. 1998). HLA
association analyses also support the importance of the PSORS1 locus in psoriasis
susceptibility, especially its tight association with the HLA-Cw*6 allele. The PSORS1
locus is thus the major susceptibility locus for psoriasis.



5.1. Refinement of PSORS1
The PSORS1 locus has been further refined with association analyses of densely
spaced markers across the MHC region to localize the PSORS1 gene. Balendran et al.
(1999) genotyped 14 microsatellite markers to the susceptibility region. Using TDT
analysis, the region was narrowed to a 285 kb interval (Balendran et al. 1999) (Fig. 2).
In haplotype analysis, the most common haplotype among patients contained the
HLA-B*5701 and HLA-Cw*0602 alleles (91 vs. 13 sibs, P<0.0001). In addition, a
haplotype containing HLA-B*1302 and HLA-Cw*0602 was seen more frequently in
patients than controls (23 vs. 3 sibs, P<0.001). A possible explanation for this is that
HLA-Cw*0602 is the psoriasis susceptibility allele or that it is in strong linkage
disequilibrium with the real disease allele. Another explanation is that the associated
haplotypes define the HLA regions that contain alleles at a number of loci, all of
which are required for disease predisposition (Balendran et al. 1999).


Oka et al. (1999) genotyped 11 new microsatellites to a 1060 kb segment around the
HLA-C gene. Three markers displayed significant deviation from Hardy-Weinberg’s
equilibrium in psoriasis patients in the probability and heterozygote deficiency tests,
and the susceptibility locus was narrowed down to a 111 kb region 89 kb telomeric to
the HLA-C gene (Oka et al. 1999) (Fig. 2).



                                            37
Figure 2. Map of the PSORS1 region displaying the positions of known genes. Arrows indicate the
direction of transcription. The minimal refined regions in different studies are shown with solid black
lines and broken lines illustrate the regions when interpreting the results more conservatively (Modified
from Veal et al. 2002).



Nair et al. (2000) genotyped 62 microsatellites across the MHC in 339 families and
refined the locus to a 170 kb region centered 100 kb telomeric to HLA-C with short-
haplotype analysis. The authors next genotyped additional families (altogether 478)
with 34 markers 1.2 Mb across the MHC and dissected the region further by
recombinant ancestral haplotype analysis. They found two risk haplotypes, RH1 and
RH2, which were separated only by a 2 kb interval. They concluded that RH1 was the
most likely haplotype to carry the PSORS1 gene located 60 kb telomeric to HLA-C
(Nair et al. 2000). The RH1 and RH2 haplotypes were otherwise continuous, but
separated only by two microsatellites. This could be explained only by double
recombinations between the two haplotypes, which would be extremely unlikely
especially within MHC. The authors have therefore recently reanalyzed their data by
sequencing the interval between RH1 and RH2 in carriers of different haplotype
clusters. They found that the haplotype seems to be continuous across the interval
between RH1 and RH2. The previous results could be explained by marker mutations
in the intervening microsatellites. The susceptibility region for the PSORS1 gene was
thus expanded to a 143 kb region telomeric to HLA-C (Nair et al. 2002) (Fig. 2).



5.2. Candidate genes at PSORS1
The complete sequence and the gene map of the MHC region have been published
(Guillaudeux et al. 1998; Consortium 1999). Based on refinement studies, the region



                                                   38
of approximately 200 kb telomeric to HLA-C is most likely to contain the PSORS1
gene (Fig. 2). The region contains at least four pseudogenes, NOB5, HCGIX-3,
HCGII-2 and NOB4, and five genes HLA-C, TCF19 (SC1), OTF3 (POU5F1), HCR
(Pg8) and CDSN (the S gene) (Zhou and Chaplin 1993; Krishnan et al. 1995;
Guillaudeux et al. 1998; Consortium 1999). In addition, three poorly characterized
unpublished genes, SEEK1, SPR1 and STG, have been mapped to the region (Fig. 2)
(Oka et al. 1999). The region is interesting as a candidate locus for psoriasis because it
contains several closely located genes, all of which are expressed in skin
keratinocytes.


Corneodesmosin (CDSN, earlier known as the S gene) is located 150 kb telomeric to
HLA-C. The protein has been thoroughly studied and it is known to be expressed only
in cornified squamous epithelia like skin epidermis. It is synthesized at the late stages
of keratinocyte differentiation and is thought to play a major role in corneal layer
cohesion. It is proteolysed during the maturation of the corneal layer of the epidermis,
which is believed to be one of the major biochemical changes that leads to
desquamation, making the gene a strong functional candidate for psoriasis (Zhou and
Chaplin 1993; Haftek et al. 1997; Simon et al. 1997; Guerrin et al. 1998).


In a Japanese population, the CDSN gene was found to be polymorphic with nine
SNPs in the coding region, three of which were non-synonymous. None of the
polymorphisms showed an association with psoriasis (Ishihara et al. 1996). In a case-
control study of a Caucasian population three non-synonymous SNPs, CDSN-619,
-1240 and -1243 were analyzed for association with psoriasis. CDSN-1243*C was
associated significantly with psoriasis; 65% of patients carried the allele but only 47%
of controls (P=2x10-9). The HLA-Cw*6 allele showed, however, an even stronger
association (36% vs. 10%, P=8x10-45). Both associations were stronger among type I
psoriasis patients, and the CDSN-619*T and –1243*C alleles were in tight LD with
HLA-Cw*6 (Tazi Ahnini et al. 1999).


In a study by Allen et al. (1999), the same alleles, collectively called CDSN*5,
showed a significant association with psoriasis in TDT analysis of 152 Caucasian trio
families (P=3x10-6). The authors suggested that the association might be independent
of the HLA-C association (Allen et al. 1999). Jenisch et al. (1999) reported similar


                                           39
results when specifically different alleles of the CDSN gene were studied in a family
material. They found seven alleles encoding six distinct protein forms of CDSN. One
of them, CD2 containing CDSN*5, was associated significantly with psoriasis, again
in strong LD with HLA-Cw*6 (Jenisch et al. 1999).


Using immunohistochemistry and immunoelectron microscopy, the CDSN expression
was found to be different between lesional psoriatic skin and normal or non-lesional
skin. The results were specific for psoriasis and not seen in other studied
inflammatory skin diseases. Whether the changes in CDSN expression in psoriasis
were a fundamental effect contributing to the pathogenesis of the disease or merely a
result of changes already occurring in the psoriatic skin remains to be determined
(Allen et al. 2001).


Of the other genes of the region, the OTF3 gene is a POU transcription factor and acts
as a master regulator of pluripotency in the mammalian embryo (Niwa et al. 2000).
The TCF19 (SC1) gene is preferentially expressed in the G1-S phase of the cell cycle
and is considered to be a possible transactivating factor that could play a prominent
role in the transcription of genes required for the later stages of cell cycle progression
(Ku et al. 1991). Based on their broad and important functions in cell regulation,
neither of the genes is an obvious candidate gene for psoriasis. In fact, sequencing of
the coding regions of the genes failed to reveal any disease-specific variants (Nair et
al. 2000). In a study of 67 Japanese patients and 103 controls, several polymorphisms
in the TCF19 gene were discovered, however, none of them showed an association
with psoriasis (Teraoka et al. 2000). In a Spanish population (95 patients and 104
controls), the β-allele of the OTF3 gene showed a significant association with
psoriasis but was clearly the major allele in both groups (85% vs. 60%, P<0.0003) and
was shown to be in tight LD with the HLA-Cw*0602 allele (Gonzalez et al. 2000).




                                           40
AIMS OF THE STUDY




  1. To characterize the structure of a novel candidate gene at the PSORS1 locus,
     HCR (Pg8), and screen the coding sequence for psoriasis-associated
     polymorphisms.


  2. To compare the associations of the different susceptibility alleles of the
     PSORS1 locus in different populations and subtypes of psoriasis to identify
     the PSORS1 gene.


  3. To study the expression of HCR mRNA and protein in lesional and non-
     lesional psoriatic skin compared to normal skin.


  4. To search for minor susceptibility loci for psoriasis using a genome-wide scan
     in PSORS1 negative families.




                                       41
MATERIALS AND METHODS
1. Study subjects



1.1. Finnish psoriasis families
We recruited 142 psoriasis patients and 210 family members (100 families) from the
central-eastern part of Finland (Kainuu province) during 1995-1997. In addition, 93
population-matched control individuals were recruited. Of the Kainuu families, 100
independent cases and 93 controls were analyzed in Study I. In 1999-2000, we
expanded our sample collection and recruited additional families from all parts of
Finland. In Study IV, large multiplex families (N=31) with three or more affected
individuals per family, and in Study II, nuclear families (N=91) with a proband and
parents (in some families also one parent affected) were analyzed.
Inclusion criteria for probands were age of under 40 years and having chronic plaque
psoriasis. Experienced dermatologists examined all patients and both patients and
family members filled out a health questionnaire. All participants gave written
informed consent and donated blood samples. The sample collection was approved by
the Ethics Review Boards of all participating hospitals and the Department of Medical
Genetics, University of Helsinki.



1.2. Foreign psoriasis families
For association analyses of the PSORS1 candidate alleles (Study II), DNA samples
from psoriasis families of different ethnic origins were obtained. Most of the families
were trios, British (N=175), Swedish (N=64), Italian (N=48) and Gujarati Indian
(N=27), and 52 were multiplex Spanish families. In addition, separate case-control
sample sets of 61 Gujarati Indian patients and 73 population–based controls and 83
Japanese patients and 70 population-matched controls were analyzed. Clinical details
of the patients are described in Table 1 of Study II. The collection of foreign samples
was obtained with informed consent and was approved by the local Ethics Review
Boards in each country. All samples were analyzed anonymously.




                                          42
1.3. Guttate psoriasis and PPP patients
For Study III, 134 British unrelated guttate psoriasis and 106 palmoplantar pustulosis
patients were recruited. In addition, DNA from 309 population-matched controls was
collected for the case-control study. A second PPP sample set (N=50) was collected
from Sweden. Clinical details of the patients are described in Table 1 of Study III.
Collection of foreign samples was obtained with informed consent and approved by
local Ethics Review Boards.



1.4. Skin biopsies
Skin specimens were obtained from paired biopsies of the center of psoriatic plaques
and non-lesional skin (I: N=6, II: N=16) and from healthy control skin from different
parts of the body (I: N=3, II: N=7). For Study III, skin specimens were obtained from
guttate psoriasis skin (N=6), PPP in various stages (N=20) and healthy control sole
skin (N=7). All skin biopsies were fixed in formalin and embedded in paraffin.



2. Reverse Transcriptase (RT)-PCR
To amplify the HCR cDNA, total RNA was extracted from a primary keratinocyte
cell line using RNeasy Mini Kit (Qiagen, Valencia, CA). RT-PCR was carried out
with random hexamer primers using M-MLV reverse transcriptase (Promega,
Madison, WI). The first-strand synthesis of cDNA was performed for 5 min at 70˚C
followed by a 60 min incubation at 37˚C according to the manufacturer’s protocol (M-
MLV Reverse Transcriptase, Promega).



3. PCR amplification and direct sequencing

PCR assays were carried out in 20 µl volumes containing 50 ng of genomic DNA or 2
µl of cDNA, 1x PCR buffer (10 mM Tris-HCl, 50 mM KCl, 0.1% triton X-100), 200
µM dNTPs, 1.5 mM MgCl2, 0.6 µM primer mix and 0.6 U of DNA polymerase
(DyNAzyme, Finnzymes, Espoo, Finland). The samples were denatured for 5 min at
94οC, followed by 35-38 cycles each of 30 s at 94οC, 30 s at 55-68οC and 30-180 s at
72οC. Purification of the PCR products was performed with a gel extraction or PCR




                                          43
purification kit (Qiagen, Valencia, CA). Sequencing was performed by dye-terminator
chemistry in both directions using the ABI 373A and ABI 377 sequencers.



4. SSCP analysis
SSCP gels were prepared in 50 ml volumes containing 10-12.5 ml of 2xMDE gel
solution (FMC BioProducts, Rockland, ME), 6 ml of 5xTBE, 100 µl of 10% APS and
40 µl of TEMED. The PCR-amplified samples were electrophoresed at 2-4 W for 17-
20 h, and the bands were visualized by silver staining.



5. In situ hybridization
The HCR probe was generated by RT-PCR amplification from the keratinocyte cell
line using the primers ATTTAGGTGACACTATACattccctggagcctgagttt and TAATA
CGACTCACTATAcctcctgctggatgaggc. T7 and Sp6 RNA polymerase promoter
sequences were introduced at opposite ends of the 628 bp gene-specific product
(lower case letters in primer sequences). In vitro transcribed antisense and sense RNA
                            35
probes were labelled with        S-marked UTP. In situ hybridization was performed at
50°C overnight on formalin-fixed paraffin-embedded specimens using 4 x 104 cpm/ml
of labelled probe. The slides were then washed under stringent conditions, including
treatment of RNAse A. After autoradiography for 20-40 days, the photographic
emulsion was developed and the slides were stained with haematoxylin and eosin for
microscopy. A sense RNA probe was used as a negative control.



6. Genotyping of SNPs
The SNPs were genotyped using PCR amplification (see primer sequences from Study
II) and altered restriction site recognizing enzymes for the Finnish, Swedish, Italian
and Spanish samples. One primer was fluorescently labelled and the electrophoresis
of the pooled digestion products was run on an ABI 377 sequencer. Allele calling was
done using the Genotyper program (Applied Biosystems, Foster City, CA). PCR
assays were carried out in 10 µl volumes containing 25 ng of genomic DNA, 1x PCR
buffer (10 mM Tris-HCl, 50 mM KCl, 0.1% triton X-100), 200 µM dNTPs, 1.5 mM
MgCl2, 0.5 µM primer mix, 1% DMSO and 0.3 U of DNA polymerase (DyNAzyme



                                            44
II, Finnzymes, Espoo, Finland). Digestion reactions were performed overnight in 10
µl reactions containing 5 µl of PCR product and 0.125-1 U of either BstUI
(HCR*307), AvaII (HCR*325), Tsp509I (HCR*477), BsmFI (HCR*771), MslI
(HCR*1723), HhaI (HCR*1911), MwoI (HCR*2327), MnlI (CDSN*619) or HphI
(CDSN*1243), and the appropriate manufacturer's buffer (New England Biolabs,
Beverly, MA).
The HLA-Cw6 allele was genotyped using restriction enzymes MspA1I and DdeI or
by SSP-PCR (Tonks et al. 1999). The digestion products were electrophoresed on
agarose gels and photographed under UV illumination.
The British, Japanese and Gujarati Indian samples were genotyped in the laboratory of
Professor Richard Trembath using an allele-specific hybridization assay (Jeffreys et
al. 2000). To validate the genotyping results of the two different methods, a control
sample set was blindly genotyped with both assays in both laboratories.



7. Production of antibodies
Antisera against HCR protein were raised in rabbits by immunization with a synthetic
18-mer peptide ERDVSSDRQEPGRRGRSW (amino acids 62-79). The peptide
synthesis and antibody production were purchased from Sigma Genosys (Cambridge,
UK). The antibodies were affinity-purified with the peptide bound to an epoxy-
activated sepharose column according to the manufacturer’s instructions (Pharmacia
Biotech, Uppsala, Sweden).



8. Western blotting
To ascertain the specificity of the HCR antibodies, SDS-PAGE and Western blotting
were carried out according to standard procedures. HCR cDNA was cloned into the
pCMV5 vector and transiently expressed in COS-1 cells using the lipofection
technique (Fugene, Roche, Indianapolis, IN). Affinity-purified HCR antibodies (1
µg/ml) were used as primary and peroxidase-conjugated anti-rabbit IgG as secondary
antibodies and detected with enhanced chemiluminiscence (Boehringer Mannheim,
Mannheim, Germany).




                                         45
9. Immunohistochemistry
Immunostaining was performed on formalin-fixed paraffin-embedded specimens
using the avidin-biotin-peroxidase complex technique for the HCR (II, III) (Vectastain
ABC kit, Vector Laboratories Inc., Burlingame, CA) and Ki67 (II) antibodies
(StreptABComplex/HRP Duet kit, Dako A/S, Glostrup, DK). Affinity-purified HCR
antibodies were used at 4 µg/ml and anti-Ki67 was diluted to 1:200. Paraffin sections
were pretreated with trypsin (10 mg/ml) (HCR) or antigen retrieval (Ki67). Ki67
immunohistochemistry was performed on sections serial to those used for HCR. Both
diaminobenzidine (DAB) and 3-amino-9-ethylcarbazole (AEC) were used as
chromogenic substrates. The tissues were counterstained with haematoxylin. Controls
were performed with preimmune serum.



10. Genotyping of microsatellite markers
Genome wide genotyping was done at the Finnish Genome Center using 377
fluorescent polymorphic microsatellite markers from the Applied Biosystems Linkage
Mapping Set MD-10. PCR assays were done in a 5 µl volume containing 20 ng of
DNA and reagent concentrations and temperature profiles as recommended by the
reagent manufacturer (Applied Biosystems). The electrophoreses were run using a
MegaBace 1000 capillary instrument (Molecular Dynamics, Sunnyvale, CA) and
allele calling was done using Genetic Profiler 1.1 (Molecular Dynamics) software.
For fine-mapping markers, the PCR assays were done in a 10 µl volume. The
electrophoreses were run using an ABI 377 sequencer and allele calling was done
using Genotyper 2.0 (Applied Biosystems).



11. Statistical analyses

11.1. Linkage analysis
Genome-wide linkage was analyzed using non-parametric multipoint linkage analysis
(NPL) with GENEHUNTER 2.1 software (Kruglyak et al. 1996).




                                         46
11.2. Haplotype association analysis
Haplotyping was done within trios using an in-house computer program written by
Petteri Sevon (unpublished). In the case of larger pedigrees, the program divided them
into trios, randomly selecting one trio per pedigree in which at least one of the
members was affected and genotyping information was available for all members
(Spanish families in Study II) or finding the maximal number of independent trios
with different chromosomes (IV). From each trio, four independent chromosomes
were obtained. A chromosome was considered to be trait-associated if it occurred in
any of the affected family members and to be a control if it occurred only in
unaffected individuals. In case of ambiguities (missing genotypes, identical
heterozygotic genotypes in all family members, or Mendel errors), the alleles were
zeroed.
Haplotype association analysis was performed using the Haplotype Pattern Mining
(HPM) algorithm (Toivonen et al. 2000). HPM is a data mining-based method which
searches for combinations of marker alleles that are more frequent in disease-
associated than in control chromosomes using a χ2 test. The haplotypes are allowed to
contain gaps since missing data and genotyping errors, for instance, can disrupt the
continuous haplotypes. The maximum number and length of the gaps can be
determined. The haplotype patterns are ordered by their strength of association with
the phenotype, and all haplotypes exceeding a given threshold level for χ2 are used for
prediction of disease susceptibility gene location using a non-parametric model. The
results obtained by considering marker frequencies can be contrasted against the null
hypothesis that there is no gene effect using permutation tests. The chromosome status
is permutated randomly, keeping the proportions of affected and control chromosomes
constant. Marker-wise P values are approximated using permutations and the disease
gene is predicted to be located near the marker with the smallest empirical P value
(Toivonen et al. 2000).



11.3. Transmission Disequilibrium Test (TDT)
TDT was analyzed with GENEHUNTER 2.1 (Kruglyak et al. 1996). TDT evaluates
the transmission of an associated marker allele from a heterozygous parent to an
affected offspring compared with the transmission of the alternative marker allele. It
tests directly for linkage between a disease and marker locus in the presence of


                                          47
association and can thus distinguish between association due to linkage disquilibrium
and association that arises only from population stratification (Spielman et al. 1993;
Spielman and Ewens 1996). TDT in GENEHUNTER can analyze a maximum of
four-marker haplotypes, and therefore for longer haplotypes, the TDT analyses were
done manually.



11.4. Other tests for allele association
Allele associations for single SNPs or alleles were calculated counting only
individuals who had genotyping data for that allele. The statistical significance
between the case and control groups was calculated using χ2-test or Fisher’s exact test
when the number of expected observations was less than five. The Bonferroni
correction for multiple testing was carried out to reduce type I error (significance was
accepted at the 5% level). Relative risk (RR) [RR=[a/(a+c)]/[b/(b+d)], where a is the
number of patients with the risk allele; c the number of patients without the risk allele;
b and d the equivalent values in the controls, respectively] or odds ratios (OR)
[OR=f(aff)/(1-f(aff)):f(contr)/(1-f(contr))] with 95% confidence intervals (95% CI) were also
calculated for each association.



11.5. Linkage disequilibrium tests
Linkage disequilibrium is the non-random association of alleles at linked loci. LD was
calculated using different statistical measures: D, D´ and r2. The coefficient of linkage
disequilibrium, D, is the difference between the observed haplotype frequency and the
expected haplotype frequency under statistical independence (D=pAB-pApB). D
depends on allele frequencies, and it was standardized to its theoretical maximum to
get the normalized measure of LD, D´ (D´=D/Dmax, where Dmax=is the lesser of pApb
or papB if D is positive or pApB or papb if D is negative). D´ depends strongly on
sample size, and the square of the correlation coefficient, r2, was also calculated,
r2=D2/(pApapBpb). r2 shows much less inflation in small samples than D´ but is
affected by allele frequencies such as D (Jorde 2000; Ardlie et al. 2002; Weiss and
Clark 2002).




                                             48
RESULTS AND DISCUSSION
1. Characterization of the HCR (Pg8) gene structure (I, II)
The structure of the HCR gene was predicted using two different prediction programs,
GENSCAN and FGENES. Primers were designed for the predicted exons and the
coding sequence was verified with RT-PCR from keratinocyte RNA. The HCR gene
consists of 18 exons and the coding sequence is 2349 bp encoding a protein of 782
amino acids (GenBank AY29160). The sizes of the exons vary from 28 to 304 bp and
the gene covers a 15.8 kb genomic region. The translation initiation codon is located
in exon 2.



2. Detection and screening of HCR polymorphisms (I, III)
To detect possible sequence variations, all exons of the HCR gene were screened by
direct sequencing using intronic primers in five Finnish psoriasis patients and one
control. Two patients were HLA-Cw*6 positive and three negative to enrich for
different variants. The genomic clone Y24c027 (GenBank AC004195) was used as a
reference sequence. The gene was found to be highly polymorphic, with 18 SNPs in
the coding region distributed in eight exons. Eleven SNPs caused an amino acid
change and nine of them were non-conservative. Exons 4 and 10 were the most
polymorphic, with five and four SNPs, respectively. All 18 SNPs with corresponding
amino acid changes are shown in Table 3.


In Study III, 14 PPP patients were sequenced to detect possible new PPP-specific
polymorphism of the HCR gene. Two novel non-synonymous SNPs (HCR-326 G->A
and HCR-2315 G->A) were found, but they were extremely rare, seen in one and two
patients of the 106 patients, respectively. In addition, eight SNPs in the HCR gene not
seen in the Kainuu population have since been published (O'Brien et al. 2001). Six of
these SNPs seemed to be very rare also in the Swedish population and were seen in
only one or two individuals of the 80-person study group (O'Brien et al. 2001).
Because only six individuals were screened in our original polymorphism detection,
rare polymorphisms may have gone unnoticed or simply did not exist in the studied
population. However, a polymorphism not seen in any of the five sequenced psoriasis


                                           49
patients is not likely to explain psoriasis in Finnish patients at the major psoriasis
locus.



2.1. Nomenclature of the HCR SNPs (I-IV)
In Study I the detected HCR SNPs were numbered according to the verified cDNA
sequence (GenBank AF216493). Two new exons, adding 135 bp to the 5’ end of the
gene and 26 amino acids to the protein, were subsequently discovered (Study II). The
SNP numbering was changed to correspond to the entire length of cDNA, base
number one being the first base of the first methionine (GenBank AY029160). This
may cause some confusion, but the base numbering starting from the first Met is
commonly used for mutation reporting and was thus used in Studies II-IV. Both
numberings for SNPs are shown in Table 3, but amino acids are numbered only
according to the longer amino acid sequence. In Study I, two SNPs, HCR-307 (251)
and HCR-325 (269), affect the translation and change the codon CGG (R, arginine) to
TGG (W, tryptophan). Mistakenly, the amino acid designations were reversed
throughout Study I (erratum published in Hum Mol Gen 2001, 10 (3) p. 301).



3. Case-control association analysis of HCR SNPs (I)
The 18 HCR SNPs were screened in 100 psoriasis patients and 93 population-matched
controls from the Kainuu subisolate of Finland using parallel SSCP analysis and
direct sequencing. Case-control studies are known to be very sensitive to population
stratification, and by using a population isolate of homogenous background, the
stratification could be minimized (Risch 2000; Cardon and Bell 2001). The allele and
carrier frequencies of the SNPs are shown in Table 3.


Two SNPs in exon 4 (HCR-307 and HCR-325) showed a significant association with
psoriasis in the Kainuu population. Forty-two per cent of patients and 19% of controls
possessed the HCR-307*T and –325*T alleles (P=0.00068, RR 2.2, 95% CI 1.7-2.6),
which invariably occurred together. The two previously reported alleles of PSORS1,
HLA-Cw*6 and CDSN*5, were also analyzed in the case-control sample set. The
HLA-Cw*6 allele showed an even stronger association than the two HCR SNPs.




                                         50
Table 3. Allele and carrier frequencies of HCR coding SNPs in the Kainuu population and corresponding amino acid changes. SNP alleles
in boldface indicate psoriasis susceptibility haplotype.
Exon        SNP number SNP number                     Allele          Allele            Carrier               Carrier          Amino acid change
            (AF216493)        (AY029160)           frequencies     frequencies        frequencies           frequencies
                                                     Patient         Control            Affected              Control
                                                  chromosome      chromosome             N=100                  N=93
                                                      N=200           N=186
Exon 4     +249            +305           G:0.95    A:0.05     G:0.95   A:0.05     G:1.00    A:0.09     G:1.00   A:0.09     Arg -> Gln (R102Q)*
           +251            +307           C:0.77    T:0.23     C:0.88   T:0.12     C:0.96    T:0.42     C:0.96   T:0.19     Arg -> Trp (R103W)*
           +269            +325           C:0.77    T.0.23     C:0.88   T:0.12     C:0.96    T:0.42     C:0.96   T:0.19     Arg -> Trp (R109W)*
           +421            +477           C:0.91    T:0.09     C:0.82   T:0.18     C:1.00    T:0.18     C:0.99   T:0.34     no change (131)
           +436            +492           G:0.76    C:0.24     C:0.76   C:0.24     G:1.00    C:0.47     G:1.00   C:0.47     Arg -> Ser (R164S)*
Exon 6     +715            +771           C:0.77    G:0.23     C:0.77   G:0.23     C:0.96    G:0.43     C:0.97   G:0.43     no change (257)
           +769            +825           A:0.72    C:0.28     A:0.72   C:0.28     A:1.00    C:0.57     A:1.00   C:0.57     Glu -> Asp (E275D)
Exon 10    +1193           +1249          T:0.86    C:0.14     T:0.81   C:0.19     T:0.89    C:0.17     C:0.89   C:0.26     Trp -> Arg (W417R)*
           +1194           +1250          G:0.99    A:0.01     G:0.98   A:0.02     G:1.00    A:0.02     G:1.00   A:0.04     Trp -> Stop
           +1219           +1275          C:0.95    T:0.05     C:0.93   T:0.07     C:0.96    T:0.06     C:0.96   T:0.11     no change (425)
           +1229           +1285          T:0.49    C:0.51     T:0.52   C:0.48     T:0.72    C:0.74     T:0.70   C:0.67     no change (429)
Exon 14    +1667           +1723          G:0.77    T:0.23     G:0.90   T:0.10     G:0.96    T:0.42     G:0.97   T:0.17     Gly -> Cys (G575C)
Exon 15    +1824           +1880          G:0.90    A:0.10     G:0.85   A:0.15     G:0.93    A:0.13     G:0.85   A:0.15     Arg -> Gln (R627Q)*
Exon 16    +1855           +1911          G:0.70    A:0.30     G:0.74   A:0.26     G:0.90    A:0.49     G:0.96   A:0.47     no change (637)
           +1861           +1917          G:0.98    T:0.02     G:0.98   T:0.02     G:0.99    T:0.04     G:1.00   T:0.03     Gln -> His (Q639H)*
Exon 17    +2119           +2175          A:1.00               A:1.00              A:1.00               A:1.00              no change (725)
           +2122           +2178          A:0.89    T:0.11     A:0.84   T:0.16     A:1.00    T:0.22     A:1.00   T:0.31     no change (726)
Exon 18    +2271           +2327          C:0.65    G:0.35     C:0.72   G:0.28     C:0.88    G:0.59     C:0.94   G:0.49     Ser -> Cys (S776C)*
*Non-conservative amino acid change
Thirty-seven per cent of patients and 9% of controls were carriers (P=3.1x10-6, RR
4.3, 95% CI 3.1-5.0). CDSN*5 was the major allele in both groups, with 85-86%
prevalence, and was not associated with psoriasis in the Kainuu population.



4. Association and haplotype analysis of PSORS1 susceptibility alleles (II)
To find out whether the HCR associations detected in the Kainuu population were
associated with psoriasis in other populations as well, a large trio family material was
analyzed. By genotyping families, the haplotype phase for the HCR SNPs could also
be confirmed. Of the analyzed families, 164 were British, 91 Finnish, 62 Swedish, 47
Italian, 26 Spanish and 29 Gujarati Indian trios. In addition to the seven HCR SNPs
(HCR-307, -325, -477, -771, -1723, -1911, -2327), the HLA-Cw*6 and CDSN*5
(CDSN-619 and –1243) alleles were genotyped in all 419 trios.


When all families were pooled together (908 patient chromosomes, 772 control
chromosomes) the HLA-Cw*6, HCR-307*T, HCR-325*T and HCR-1723*T alleles
showed the strongest association with psoriasis in allele association analyses (P<10-10,
OR>2). In haplotype analysis, a new HCR susceptibility allele was found. SNP alleles
HCR-307*T, HCR-325*T, HCR-1723*T and HCR-2327*G were almost always
inherited exclusively in the same chromosomes. The four SNPs were also the only
non-synonymous ones of the studied HCR SNPs. All the other HCR SNP alleles of
the HCR susceptibility haplotype (alleles bolded in Table 3) were clearly the major
alleles in both patients and controls and were not haplotype-specific. The
susceptibility allele, named HCR*WWCC, was seen in 35% of patient and 18% of
control chromosomes (P=10-10, OR 2.5, 95% CI 1.9-3.3), also showing a significant
association in TDT analysis (178 transmitted vs. 82 untransmitted chromosomes,
P=10-11).


However, the HLA-Cw*6 allele showed a similar or even stronger association in
haplotype (P=10-11, OR 2.9, 95% CI 2.1-3.9) and TDT (157 transmitted vs. 48
untransmitted, P=10-13) analyses. The CDSN*5 association remained somewhat lower
(P=10-9, OR 2.0, 95% CI 1.6-2.5 in haplotype analysis and 275 transmitted vs. 180
untransmitted, P=10-7 in TDT analysis) (Fig. 3). All three susceptibility alleles




                                          52
seemed to be in strong linkage disequilibrium with each other, and the 95% CIs for
the ORs were overlapping.
The extended haplotype containing all three alleles was most significantly associated
with psoriasis (OR 3.9, 95% CI 2.1-7.3). The P-value remained somewhat lower than
for individual alleles (P=10-6). This was probably mainly due to the lower overall
number of chromosomes because only chromosomes with genotyping data for all of
the studied alleles of the haplotype were counted in each case.



                                   60
            Allele Frequency (%)




                                                                                     P=10-9
                                   50                       P=10-10                  OR 2.0 (1.6-2.5)
                                                                               54
                                        P=10-11             OR 2.5 (1.9-3.3)
                                   40   OR 2.9 (2.1-3.9)
                                                                                    37           Affected
                                   30                        35
                                                                                                 Control
                                   20     26
                                   10                             18
                                               11
                                    0
                                        HLA-Cw*6           HCR*WWCC            CDSN*5


       Figure 3. Haplotype association results for PSORS1 susceptibility alleles in 908 psoriasis
       patient chromosomes compared with 772 control chromosomes.


In LD analyses, HLA-Cw*6 and HCR*WWCC seemed to be in stronger LD
(D´=0.73, r2=0.46 in patients) than HCR*WWCC and CDSN*5 (D´=0.41, r2=0.08 in
patients). Based on the LD measurements and inspection of the haplotypes, little
recombination occurred between the three genes, especially between HLA-C and
HCR. The genes were probably included in the same haplotype block (Daly et al.
2001; Patil et al. 2001; Reich et al. 2001), and even with this large sample size
(almost 1700 chromosomes), the genes could not be separated with statistical
confidence from each other.


Two studies questioning the role of HCR as a psoriasis susceptibility gene have been
published. In O’Brien et al. (2001), HCR polymorphisms were screened in 42
Swedish patients and 38 controls. Two HCR SNPs, 1249*T and 1285*T, and the
HLA-Cw*6 allele showed the most significant association with psoriasis and the 95%
CI for ORs of the associated alleles overlapped. Based on stratification of the HCR
alleles over HLA-Cw*6 and vice versa, the authors concluded that HCR is unlikely to


                                                                  53
have an independent genetic effect on psoriasis (O'Brien et al. 2001). However,
because of the small sample size and absence of chromosomal data, the study lacked
the power to differentiate between HCR and HLA-C. Furthermore, the two
associating HCR SNP alleles (1249*T and 1285*T) are included in the HCR*WWCC
susceptibility haplotype, and the strong LD between this allele and HLA-Cw*6 has
been confirmed in our large family-based study in different populations.


Chia et al. (2001) suggested that HCR is unlikely to be causal for familial psoriasis
because the HCR susceptibility SNPs (307*T, 325*T) were not seen in two of their
haplotype clusters conferring risk to psoriasis (clusters 17 and 18). The disease
association with cluster 17 shows, however, only a borderline P value (P=0.048)
compared with the two clusters (21 and 25) that show the most significant association
(P=10-4 and P=10-11, respectively) and carry the HLA-Cw*6, HCR 307*T and 325*T
alleles (Nair et al. 2000; Chia et al. 2001). In addition, cluster 17 seems to be very
rare, at least in the Finnish population. In 90 Finnish psoriasis trios, only three
individuals (one affected) carried the cluster, and it is thus unlikely to carry the major
disease variant (unpublished).



5. PSORS1 allele associations in GP and PPP (III)
The susceptibility alleles of the PSORS1 locus were genotyped in GP and PPP
patients to find out whether the two clinical variants of psoriasis share the same
genetic background with PV and whether they might be helpful in differentiating
between the three genes. The HLA-C locus, 5 CDSN SNPs (+619, +1215, +1236,
+1240, +1243), 10 HCR SNPs (+305, +307, +325, +492, +825, +1099, +1249, +1285,
+1723, +2327) and two non-coding SNPs centromeric to HLA-C (SNP 7 and 9, Veal
et al. 2002) were genotyped in 134 British GP, and 106 PPP patients and 309
population-matched controls.



5.1. GP shares genetic background with PV
GP was strongly associated with HLA-Cw*6. Eighty-three per cent of patients and
15% of controls carried the allele (OR 27.7, 95% CI 15.8-48.5, P=10-40). SNPs 7 and
9 (OR 22.0-23.7, 95% CI 12.1-43.0, P=10-30-10-32) and all four HCR*WWCC allele-



                                           54
defining SNPs (OR 7.2-20.6, 95% CI 3.9-43.7, P=10-10-10-21) also showed a strong
association with GP. The CDSN-1243*C allele was also more common in the GP
group than in controls, but the association was weaker (OR 14.1, 95% CI 4.4-45.7,
P=10-7). When examining the HCR*WWCC and CDSN*5 susceptibility alleles as a
whole, which was possible because of the strong LD between the separate SNPs, all
three PSORS1 susceptibility alleles seen with PV showed significant associations and
their 95% CIs for ORs were overlapping. The HCR*WWCC association (OR 13.6,
95% CI 7.7-24.1, P=10-23) was weaker than for HLA-Cw*6 (OR 27.7, 95% CI 15.8-
48.5, 10-40) but stronger than for CDSN*5 (OR 11.9, 95% CI 6.1-22.9, P=10-17) (Fig.
4). The HCR*WWCC and CDSN*5 alleles were more common both in patients and
controls than the HLA-Cw*6 allele, which could partly explain the weaker statistical
significance measured as P values and ORs. All three susceptibility alleles tend to
occur in the same individuals, consistent with a similar association with PSORS1 for
both GP and PV. Thus, GP seems to share a genetic background with PV, and its
PSORS1 association is even stronger. This may be influenced by the earlier onset of
GP because early onset generally has a stronger association with PSORS1 than late
onset (Henseler and Christophers 1985; Guedjonsson et al. 2002; Stuart et al. 2002).
The type of the disease (PV vs. GP) is thus controlled by factors other than PSORS1.
The second aim of the genotyping of the two subtypes was to differentiate the three
genes, but the associations of the three alleles in GP were very similar to those seen in
PV. Despite the stronger association with GP, the extended susceptibility haplotype
remained intact.



                                     100                               93
              Allele Frequency (%)




                                           83           86
                                      80
                                                                                    GP
                                      60
                                                             42             44 47   PPP
                                      40                          31
                                                20 15                               Control
                                      20
                                       0
                                           HLA-Cw*6 HCR*WWCC           CDSN*5


       Figure 4. Frequencies of PSORS1 susceptibility alleles in 134 British GP and 106
       PPP patients compared with 309 population-matched controls.




                                                             55
5.2. PPP is not associated with PSORS1
PPP did not associate with HLA-Cw*6 or with any other HLA-C allele. Neither did
the frequencies of any CDSN or HCR SNPs differ significantly between the PPP
patients and controls (Fig. 4). To verify the negative finding in another population, the
HCR*WWCC SNPs and the HLA-Cw*6 allele were also genotyped in 50 Swedish
PPP patients. The allele frequencies were close to those seen in British PPP patients.
The allele frequencies estimated from 124 Swedish control chromosomes (from Study
II) were also similar to the frequencies seen in Swedish PPP patients.
To exclude possible new PPP-specific variants in the HCR gene, the coding region
was sequenced in 8 British and 6 Swedish patients. Two novel non-synonymous
SNPs, HCR*326 G/A (in one British patient) and HCR*2315 G/A (in two British
patients), were found. However, the SNPs seemed to be extremely rare, not being
detected in any other patients when screening the 106 PPP patient cohort.
The relationship between PPP and PV has been controversial. In this study, no
association with any of the PSORS1 susceptibility alleles was detected. The lack of
association could not be explained by the later onset of PPP compared with GP,
because the frequencies of the PSORS1 risk alleles were in fact slightly lower among
PPP patients with early age of onset (<40 years). PPP thus seems to be a genetic entity
distinct from PV and GP, and its susceptibility is dependent on factors outside the
PSORS1 interval. However, PV and PPP are likely to share common pathogenetic
steps, as suggested by their concurrence in patients. The progression of the disorders
probably involves regulatory genes other than those in the PSORS1 region.



6. HCR mRNA is overexpressed in psoriatic skin (I)
The HCR gene was shown by RT-PCR to be expressed at variable levels in all tissues
tested (Human Multiple Tissue cDNA panel I, Clontech). The most abundant
expression was seen in the heart, liver, skeletal muscle, kidney and pancreas, and
weaker expression in the lung and placenta. Abundant expression was also seen in
keratinocytes. In addition, HCR mRNA expression was studied by in situ
hybridization of skin biopsies. HCR was strongly expressed in keratinocytes of
psoriatic lesions, but normal looking skin of psoriasis patients and control skin were
almost negative, suggesting upregulation of HCR in psoriatic keratinocytes.




                                           56
7. Altered structure and expression of HCR protein in psoriasis (II, III)
The predicted secondary structures of the wild-type and *WWCC susceptibility
alleles of the HCR protein were analyzed with two programs that give the probability
of coils, COIL and PAIRCOIL (Lupas et al. 1991; Berger et al. 1995). The probability
of coil decreased dramatically at the sites of the four non-conservative amino acids of
the HCR*WWCC allele compared with the wild-type form (Fig. 5). The altered
secondary structure might affect the protein interactions or antigenic properties of the
protein. In keratinocytes, this could lead to an altered response to a triggering antigen
and initiate the disease process. However, the structural predictions must be viewed
cautiously and assessed experimentally.




Figure 5. (A) Genomic structure of the HCR gene showing the 18 exons (numbering below the boxes)
and the location of the four non-synonymous SNPs of the HCR*WWCC allele. The introns are not
drawn to scale. (B) Secondary structure predictions of the HCR protein showing the probability of coil.
The wild-type allele is shown as a broken line and the psoriasis susceptibility allele as a solid line. The
locations of the four amino acids of the HCR*WWCC allele are indicated by black arrowheads.



HCR protein expression was studied with immunohistochemistry in skin biopsies
using polyclonal peptide antibodies. In control and non-lesional skin, the basal
keratinocytes were strongly and uniformly stained. In lesional psoriasis, expression
was enhanced in basal keratinocytes at the tips of dermal papillae, whereas basal
keratinocytes at rete ridges were mostly negative. This altered expression pattern was
not seen in other inflammatory (eczema, lichen planus) or acanthotic (pityriasis rubra


                                                   57
pilaris) skin disorders resembling psoriasis. When parallel skin sections were stained
with proliferation marker Ki67, the proliferating cells in psoriatic skin were most
abundant at rete ridges, where HCR was only moderately expressed. HCR and Ki67
showed thus an inverse staining pattern, which suggests that HCR might have a role
in regulating keratinocyte proliferation.
In skin sections from GP patients, HCR protein expression resembled that seen in PV.
In PPP samples, much less HCR expression was detected, with only occasional
staining of basal keratinocytes at the sides of PPP pustules. HCR protein expression is
altered in the same way in both GP and PV. This supports the conclusion drawn from
genetic analysis that GP shares genetic background with PV and suggests that the
HCR protein might have a role in the disease process. Whether the altered expression
is a primary event or is secondary to some other stimulus remains to be solved. PPP
does, however, seem to have a different molecular genetic background.



8. HCR gene orthologues in primates (unpublished)
The coding region of the HCR gene and exon 2 of the HLA-C gene were sequenced
from genomic DNA of the chimpanzee, pygmy chimpanzee, gorilla and orangutan to
see whether the historic allelic structure of PSORS1 might be helpful in determining
the age of the HCR*WWCC allele relative to the HLA-Cw*6 allele.
As expected, the HCR gene was highly homologous with human HCR in all four
apes, with 97-99% homology at both cDNA and amino acid sequence levels. None of
the apes had any of the four SNP alleles defining the HCR*WWCC allele (307*T,
325*T, 1723*T, 2327*G) or SNPs corresponding to the human HLA-Cw*6 allele. All
of these alleles were minor alleles in humans (frequencies 23-35%) and thus expected
to be newer and having emerged after the divergence of humans and great apes.
Altogether 21 non-synonymous SNPs not seen in humans were found in one ape, and
five of them in at least two apes. The amino acid changes did not cause radical
differences in secondary structure predictions (unpublished).



9. Defining the PSORS1 risk gene
The three PSORS1 susceptibility alleles, HLA-Cw*6, HCR*WWCC and CDSN*5,
are all coding variants and possibly directly cause disease pathogenesis. However, the



                                            58
three alleles may show a significant association only because they are in LD with the
actual, so far unidentified, disease allele. The PSORS1 region has been thoroughly
studied and presumably all of the genes and their coding polymorphisms identified,
but non-coding variants can also be causal if located for example on a regulatory
region of a gene.


In a recent study by Veal et al. (2002), a dense map of 59 SNPs across the PSORS1
locus was genotyped in 171 trios. Haplotyping revealed five alternative major
haplotype blocks at PSORS1. Two non-coding SNPs just a few kilobases centromeric
to the HLA-C gene (SNP 7 and 9) showed the most significant association with
psoriasis. In haplotype analysis, the associated alleles of SNPs 7 and 9 were unique to
the overtransmitted chromosomes defining a 10 kb core risk haplotype. When
studying LD between the known PSORS1 susceptibility alleles, LD was strongest in a
block containing HLA-C and HCR but decreased markedly between this block and
CDSN, as also seen in Study II. The SNPs 7 and 9 are non-coding and no known or
predicted genes are located in their close proximity except HLA-C (distance 7 kb and
4 kb, respectively) (Veal et al. 2002). However, all but one rare haplotype of the
overtransmitted haplotypes also carry the HCR susceptibility allele. Moreover, the
haplotype remains intact between SNPs 7 and 9 and the HCR allele in three different
haplotype clusters comprising over half of all the overtransmitted chromosomes. In
addition to the known coding variants of PSORS1, SNPs 7 and 9 are also possible
candidates for PSORS1 disease variants. Because of the strong LD, especially in the
centromeric part of PSORS1, none of the variants can, however, be prioritized based
on current genetic analyses alone.


Functional studies of the PSORS1 susceptibility alleles are sparse. Despite the HLA-
Cw*6 association being known for decades, the expression of HLA-C in psoriatic and
normal skin is poorly characterized. The CDSN protein, by contrast, has been
thoroughly studied. CDSN is a good functional candidate because of its role in the
desquamation process of keratinocytes in the cornified layer, although the genetic
association analyses support HLA-C or HCR instead. The CDSN protein expression
has been shown to differ between lesional psoriatic skin and normal or non-lesional
skin (Allen et al. 2001). HCR mRNA and protein expression was also different
between lesional and non-lesional or control skin, and the HCR*WWCC allele was


                                          59
predicted to adopt a different secondary structure than the wild-type allele (Study II).
Whether the changes in CDSN or HCR expression in psoriasis are primary events in
the pathogenesis of the disease or secondary to changes already occurring in psoriatic
skin remain to be determined. The pathogenetic mechanisms of complex diseases are
still largely unknown. Locus interaction is a possible mechanism and perhaps
interaction between all of the PSORS1 susceptibility alleles is needed in the
development of psoriasis.



10. A minor locus for psoriasis on 18p in PSORS1 negative families (IV)
Based on linkage and association analyses, the PSORS1 locus seems to explain one-
third to one-half of psoriasis. Other minor susceptibility loci are thus likely to exist.
Several minor susceptibility loci have been detected in genome-wide scans, but the
replication of these loci in different populations has been difficult. In addition to locus
heterogeneity, the strong genetic effect of the PSORS1 locus might complicate the
analyses in unselected family material.


To find minor psoriasis susceptibility loci, only families showing no association with
the PSORS1 locus were selected for a genome-wide scan. The PSORS1 association
was excluded by genotyping two HCR SNPs (+325 and +1723) known to be in tight
LD with the other SNPs of the HCR*WWCC haplotype as well as with the HLA-
Cw*6 and CDSN*5 alleles. Individuals having the 325*T and 1723*T alleles were
defined as PSORS1 positive and the carriers of all the other allele combinations as
negative. A PSORS1 negative family was defined as having more than two PSORS1
negative patients and at least one transmission of psoriasis from parent to offspring
without cotransmission of PSORS1.


Altogether 31 families having at least three psoriasis patients per family were
genotyped. Based on these results, nine PSORS1 negative families were selected for
the genome-wide scan. The scan was performed at the Finnish Genome Center using
377 fluorescently labelled microsatellite markers and the genome-wide linkage was
analyzed using non-parametric (NPL) linkage analysis with GENEHUNTER 2.1
(Kruglyak et al. 1996).




                                            60
A total of five loci (11p, 12q, 15q, 18p, 22q) showing some evidence of linkage to
psoriasis (NPL>1.7), and 3p for which a NPL score of 1.21 was observed 10 cM from
the PSORS5 locus were selected for fine mapping with a 5 cM marker interval. The
NPL scores remained essentially unchanged or decreased for all loci other than 18p.
At 18p11, the NPL score increased sharply to 3.50 (P=0.0045), and after adding nine
more markers, increasing the marker density to approximately 1 cM, a NPL score of
3.58 (P=0.0038) was reached (Fig. 6).




Figure 6. NPL curve of chromosome 18 with a peak of NPL 3.58 (P=0.0038) after fine mapping.
Associated haplotypes are shown below the curve. The HPM haplotype is marked with a black box and
the shared haplotype with a gray box.



Haplotype sharing between the linked families gave additional support for the 18p
locus. All patients in families showing the strongest evidence of linkage to 18p11
shared the D18S471*2 allele. In addition, two of the families shared a three-marker
haplotype (D18S458*2-D18S471*2-AFM238yg3*1) across the flanking markers.
Association analysis with the HPM algorithm yielded evidence of an association for a


                                               61
three-marker haplotype (D18S471*2-AFM238yg3*1-D18S967*4) that overlapped the
haplotype shared between the linked families (Fig. 6). The haplotype was found in
6/30 of patient but in 0/26 of control chromosomes (P=0.025).


The significance of the 18p locus as a candidate locus for psoriasis is supported by
two earlier reports. Nominal evidence of linkage and excess allele sharing at 18p has
been reported in British psoriatic sib-pairs (NPL 1.97, P=0.025) (Veal et al. 2001). In
addition, in a Swedish population, a susceptibility locus for an extreme atopic
phenotype was also mapped to 18p (LOD 1.88, P<0.05) (Bradley et al. 2002).
Psoriasis and atopic disease have been shown to share several other susceptibility loci,
suggesting that genes at these loci might have general effects on dermal inflammation
and immunity (Cookson et al. 2001).


The PSORS1 locus has been shown to be associated with psoriasis, especially in
patients with familial background. This was also the case in our material, with only
three completely PSORS1 negative families being found. Because this was the first
genome scan performed in only PSORS1 negative families, it will be interesting to
see whether the locus appears in PSORS1 negative families in other populations. The
markers showing evidence of haplotype association were unfortunately somewhat
uninformative since the major allele was seen in the disease-associating haplotype.
Additional, more informative markers will hopefully strengthen the haplotype
association and refine the linked region.




                                            62
CONCLUSIONS AND FUTURE PROSPECTS

The major locus for psoriasis susceptibility, PSORS1, resides on chromosome 6p21.3
in the MHC region. In this study, the structure of a novel candidate gene at PSORS1,
HCR (Pg8), was defined. The HCR gene was found to be highly polymorphic and to
carry a new susceptibility allele for psoriasis, HCR*WWCC. The two previously
identified susceptibility alleles of PSORS1, HLA-Cw*6 and CDSN*5, showed,
however, very similar disease association. The three candidate genes were in strong
LD with each other, forming an extended risk haplotype similar in all populations
studied, and separation of the genes was not possible with these sample sizes.


The role of the PSORS1 locus in two clinical variants of psoriasis, guttate psoriasis
and palmoplantar pustulosis, was also determined in this study. GP was associated
with the PSORS1 locus even more strongly than psoriasis vulgaris, giving further
support for the notion that GP and PV share the same genetic background. The
stronger association with PSORS1, however, did not help in differentiating the
PSORS1 genes. The relationship between psoriasis and PPP has long been
controversial. In this first systematic study of the PSORS1 susceptibility alleles in
PPP patients, the PSORS1 association could be excluded, which speaks strongly for
different etiologies or molecular mechanisms for PPP as compared with PV or GP.


The PSORS1 locus has been studied intensively by several research groups from all
over the world during the last few years. Recent studies on the PSORS1 locus have
clearly shown that defining the PSORS1 risk gene will require an extremely large
sample size. The studied 1700 chromosomes in this study were insufficient to detect
ample recombinations to separate the genes of the PSORS1 locus from each other.
Within the last year, the first steps towards of a large collaborative study of the
PSORS1 locus with many participating PSORS1 research groups have been taken.
This study will offer a comprehensive analysis of the PSORS1 locus, and it will be
interesting to see whether the genes can be separated with genetic methods.




                                          63
One hypothesis is that all three PSORS1 genes act together in the disease process. In
addition to the HLA-C, HCR and CDSN genes, several other genes expressed in skin
keratinocytes, such as SPR1, SEEK1 and STG, are located at the PSORS1 locus.
While the polymorphisms of these genes do not show as strong an association with
psoriasis, they may also be involved if the genes of PSORS1 form a functional
complex of genes affecting keratinocyte function. The genes of the minor
susceptibility loci might also interact with the PSORS1 genes or have a function
elswhere in the pathogenetic chain. In this study, a new minor locus for psoriasis 18p
was found which is believed to explain psoriasis risk in some patients without the
PSORS1 gene defect.


Functional studies are eventually needed to determine the actual disease-causing
effect of the PSORS1 gene/genes. Thus far, functional studies on the genes of
PSORS1 have been very limited. In this study, functional support for HCR as a
potential psoriasis gene was gained. In addition to disease association, the
HCR*WWCC allele was predicted to have an altered secondary structure compared
with the wild-type allele. This could lead to an altered antigenic response. The
expression of the HCR mRNA and protein also differed between lesional psoriatic
skin and non-lesional psoriatic and normal control skin. HCR is thus a tempting
candidate gene for psoriasis. However, at the moment, the function of the protein can
only be hypothesized, with more studies needed to determine the role of the HCR
protein in the pathogenesis of psoriasis.


Psoriasis is a complex disease and patients are likely to have different genetic
variations behind disease susceptibility. In the future, when the molecular genetic
background of psoriasis in each patient can be determined, treatments can be more
individually targeted. As the pathogenetic mechanisms of the disease become known,
novel therapies will likely emerge.




                                            64
ACKNOWLEDGEMENTS

This study was carried out at the Department of Medical Genetics, University of
Helsinki during the years 1997-2002. A large number of people have contributed to
this work in many different ways and I wish to express my sincere gratitude to all of
them, especially to:

My supervisor Juha Kere, for introducing me to the fascinating world of complex
disease genetics and the skillful guidance throughout this study. Without his never-
ending optimism and encouragement this study would never have been completed.

The former and present heads of the Department of Medical Genetics, Professors Juha
Kere, Pertti Aula, Anna-Elina Lehesjoki and Leena Palotie, for providing me with
excellent research facilities.

Docent Tarja Laitinen for her expertise in disease gene mapping and interest towards
my work in all parts of this study. Her contribution has been invaluable.

Docent Ulpu Saarialho-Kere for her expertise and enthusiasm, and for coordinating
the collection of new psoriasis families.

Dr. Sari Suomela for collecting psoriasis family material and for the
immunohistochemical studies of this thesis. Dr. Outi Elomaa for patiently teaching
me the basics of different protein techniques. Sari and Outi are also thanked for their
friendship and many valuable discussions.

The members of the Finnish Psoriasis Consortium: Dr. Raija Itkonen-Vatjus, Prof.
Christer Jansen, Prof. Jaakko Karvonen, Dr. Seija-Liisa Karvonen, Prof. Timo
Reunala, Docent Erna Snellman and Dr. Tutta Uurasmaa for examining and collecting
the psoriasis families. The importance of a good family material and correct diagnosis
can never be emphasized too much.

All psoriasis patients and their family members for participating this study.

Docent Marja-Liisa Lokki for helping with HLA typings and for a hint of new genes
at the MHC region. Dr. Päivi Lahermo and the personnel of the Finnish Genome
Center for performing the genome-wide scan.

All collaborators and co-authors of this study, especially Professors Richard Trembath
and Jonathan Barker, and the members of their research groups for pleasant and
fruitful collaboration.

Docent Irma Järvelä and Professor Aarne Oikarinen, the official reviewers of this
thesis, for their valuable comments and constructive critisism.

Carol Ann Pelli for revising the English language of this thesis.



                                           65
Riitta Lehtinen, Johanna Lahtinen, Siv Knaappila and Ranja Eklund for all their help
and skillful technical assistance in the laboratory.

Every present and past member of Juha’s group for their friendship and helpfulness.
Kata and Siru for their encouragement and valuable scientific advice. Sari, Hannele
and Jaana for understanding and sharing the sometimes desperate moments in
complex disease mapping. Hannu for assistance with all computer problems. Aino,
Anne, Hannes, Inkeri, Johanna V., Katja, Nina, Marja, Mikko, Minna, Paula and Ulla
for stimulating conversations and creating the lively atmosphere in the lab.

Ilpo Vilhunen, Pirjo Koljonen, Elina Lampainen, Minna Maunula, Minna Partanen
and Sinikka Lindh for their help in all practical matters.

Dr. Paula Kristo, Elvi Karila and Susanna Anjala for genotyping and sequencing
service.

All my wonderful friends, especially Sanna, Pirkko, Jonna, Anni and Raija, for their
friendship and for relaxing moments during the spare time.

My mother, my sister Minna and her family, for all their love and endless support.

My dear Tommi, for sharing everyday-life with me, for your love and encouragement
throughout these years.

This study was financially supported by the Academy of Finland and the Sigrid
Juselius Foundation. Personal support for my work was provided by the Helsinki
Biomedical Graduate School, the Finnish Cultural Foundation, the Finnish Medical
Foundation, the Paulo Foundation, and the Research and Science Foundation of
Farmos.


Helsinki March 2003,




                                          66
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