Genetic Risk Factors for Systemic Lupus Erythematosus

Genetic Risk Factors for Systemic Lupus Erythematosus
Digital Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Medicine 395
Genetic Risk Factors for Systemic
Lupus Erythematosus
From Candidate Genes to Functional Variants
ISSN 1651-6206
ISBN 978-91-554-7336-5
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List of papers
This thesis is based on the following papers, which are referred to in the text
by their roman numerals.
Abelson AK, Johansson CM, Kozyrev SV, Kristjansdottir H, Gunnarsson I, Svenungsson E, Jönsen A, Lima G, Scherbarth HR, Gamron S,
Allievi A, Palatnik SA, Alvarellos A, Paira S, Graf C, Guillerón C, Catoggio LJ, Prigione C, Battagliotti CG, Berbotto GA, García MA, Perandones CE, Truedsson L, Steinsson K, Sturfelt G, Pons-Estel B; Argentinean Collaborative Group, Alarcón-Riquelme ME.
No evidence of association between genetic variants of the PDCD1
ligands and SLE
Genes and Immunity 2007 Jan;8(1):69-74.
Sánchez E, Abelson AK, Sabio JM, González-Gay MA, OrtegoCenteno N, Jiménez-Alonso J, de Ramón E, Sánchez-Román J, LópezNevot MA, Gunnarsson I, Svenungsson E, Sturfelt G, Truedsson L,
Jönsen A, González-Escribano MF, Witte T; The German SLE Study
Group, Alarcón-Riquelme ME, Martín J.
Association of a CD24 gene polymorphism with susceptibility to systemic lupus erythematosus.
Arthritis and Rheumatism 2007 Sep;56(9):3080-6.
Kozyrev SV*, Abelson AK*, Wojcik J, Zaghlool A, Linga Reddy MV,
Sanchez E, Gunnarsson I, Svenungsson E, Sturfelt G, Jönsen A,
Truedsson L, Pons-Estel BA, Witte T, D'Alfonso S, Barizzone N,
Danieli MG, Gutierrez C, Suarez A, Junker P, Laustrup H, GonzálezEscribano MF, Martín J, Abderrahim H, Alarcón-Riquelme ME.
Functional variants in the B-cell gene BANK1 are associated with
systemic lupus erythematosus.
Nature Genetics 2008 Feb;40(2):211-6.
Abelson AK*, Delgado-Vega AM*, Kozyrev SV*, Sánchez E*,
Velázquez-Cruz R, Eriksson N, Wojcik J, Linga Reddy MV, Lima G,
D’Alfonso S, Migliaresi S, Baca V, Orozco L, Witte T, Ortego-Centeno
N and the AADEA group, Abderrahim H, Pons-Estel BA, Gutiérrez C,
Suárez A, González-Escribano MF, Martín J and Alarcón-Riquelme
STAT4 associates with SLE through two independent effects that
correlate with gene expression and act additively with IRF5 to increase risk
Annals of the Rheumatic Diseases, in press.
* These authors contributed equally to the work
The articles were reprinted with permission of the publisher:
Nature Publishing group
Copyright¤ 2007 Wiley-Liss, Inc., a subsidiary of John Wiley
& Sons Inc.
Genetic variation ......................................................................................11
Medical genetics.......................................................................................13
Mendelian disorders.............................................................................13
Complex disorders ...............................................................................15
Identification of susceptibility genes........................................................16
Linkage analysis ..................................................................................16
Association studies ..............................................................................17
Genome-wide association studies........................................................19
Candidate-gene approach.....................................................................20
Animal models.....................................................................................21
Complicating factors............................................................................21
Functional mutations ...........................................................................22
Systemic Lupus Erythematosus................................................................22
Pathology .............................................................................................22
Aetiology and pathogenesis.................................................................24
Genetic studies of SLE ........................................................................26
Present investigation .....................................................................................31
Material and methods ...............................................................................31
Patients and controls ............................................................................31
Genotyping ..........................................................................................32
Statistical analysis................................................................................33
Paper I: No evidence of association between genetic variants of the
PDCD1 ligands and SLE..........................................................................34
Results and discussion .........................................................................34
Paper II: Association of a CD24 gene polymorphism with susceptibility to
systemic lupus erythematosus...................................................................36
Results and discussion .........................................................................36
Paper III: Functional variants in the B-cell gene BANK1 are associated
with systemic lupus erythematosus...........................................................38
Results and discussion .........................................................................39
Paper IV: STAT4 associates with SLE through two independent effects
that correlate with gene expression and act additively with IRF5 to
increase risk .............................................................................................41
Results and discussion .........................................................................42
Concluding remarks ......................................................................................46
NK cells
American College of Rheumatology
B-cell scaffold protein with Ankyrin repeats 1
Base pairs
B-Lymphocyte tyrosine Kinase
Copy Number Polymorphism
Copy Number Variation
Deoxyribonucleic Acid
Epstein-Barr Virus
Family-Based Association Test
Fc Gamma (γ) Receptor
Genome-Wide Association Study
Human Leukocyte Antigen
Haplotype-based Haplotype Relative Risk
Hardy-Weinberg Equilibrium
Interferon Regulatory Factor 5
Integrin Alpha M
Inositol 1,4,5-triphosphate Receptor
Linkage Disequilibrium
Logarithm of Odds
Multiple Sclerosis
Natural Killer cells
Polymerase Chain Reaction
Protein Tyrosine Phosphatase Non-receptor 22
Restriction Fragment Length Polymorphism
Systemic Lupus Erythematosus
Single Nucleotide Polymorphism
Signal Transducer and Activator of Transcription 4
Transmission Disequilibrium Test
Tumour Necrosis Factor
Since prehistoric times it has been known that offspring resemble their parents and that some traits are inherited. This knowledge was used for refining
plants and animals by breeding long before the underlying mechanisms were
known. In the mid 19th century, important steps towards the understanding of
these genetic mechanisms were taken by the Augustinian monk Gregor
Mendel. His now famous experiments with pea plant crossings revealed that
the inheritance of certain traits, such as petal colour, is a discrete process
following distinct laws1,2. These laws are now known as Mendel’s laws of
inheritance. A series of discoveries during the early 20th century then lead to
the identification of DNA as the carrier of genetic information, and in 1953,
the structure of DNA as a double helix was determined by James Watson
and Francis Crick3. Since then, the increase of genetic knowledge has been
explosive. A recent milestone in genetic history was the publication of the
first draft of the entire human sequence in the year 20014,5. It is now known
that the human genome consists of more than 3.25 billion base pairs (bp)
organised into 23 chromosome pairs, comprising approximately 20,00025,000 protein-coding genes. Furthermore, large international efforts have
contributed to the field by mapping a great proportion of the genetic variation that exists6. The knowledge of the human genetic sequence, in combination with the continuous development of new, increasingly efficient and
cost-effective tools for sequencing and genotyping, has provided valuable
tools for geneticists of today. With the help of these tools, we are continuing
to discover genetic causes to diseases and to unravel the mysteries of human
The aim of this thesis has been to identify genetic variants that increase
the susceptibility for Systemic Lupus Erythematosus (SLE), an autoimmune
disease caused by a complex interplay between various genetic and environmental factors. Five different candidate genes are selected through different strategies, and are analysed for association with SLE in an attempt to
distinguish some of the underlying mechanisms of this disease.
Genetic variation
The genetic composition is roughly 99.9% identical between humans, but
that still leaves millions of base pairs that differ7. These genetic variations
vary in type and size, ranging from differences in single nucleotides to duplications of large segments. Most sequence differences have no effect, but
some contribute to variation in appearance, risk of disease and response to
the environment.
Single Nucleotide Polymorphisms
The most prevalent type of genetic variation is the Single Nucleotide Polymorphism (SNP). Almost 15 million SNPs are currently registered in
NCBI’s SNP database, which gives an average of one SNP every 220 bp in
the genome (; build 128). As understood by the
name, SNPs are differences in single nucleotides, and usually a minor allele
frequency of at least 1% is used as a definition. Those with lower frequencies are generally regarded as ‘rare mutations’.
SNPs may have functional importance, e.g. by altering amino acid sequence of a protein. They are relatively cheap and easy to genotype, and
have therefore been extensively studied in medical genetics.
Insertion/deletions, or indels, comprising one or a few bp are the second
most common type of variation. More than 2 million are listed in the NCBI
database (; build 128). However, since relatively
few efforts have been made to identify new indels, it is believed that many
more are yet to be found. It has been estimated that indels represent around
16-25% of all human variation8. Similar to SNPs, indels can affect the phenotype by altering important genetic sequences.
Repetitive sequences
A substantial part of the genome consists of repetitive sequences of varying
lengths, with varying numbers of repeat units. There are several classes of
repetitive sequences, which can be tandemly repeated or interspersed. Microsatellites, and perhaps also minisatellites, are the types most extensively
studied in human genetics.
Microsatellites, also known as Short Tandem Repeats, are tandemly repeated sequences of one to five, sometimes six, bp per repeat unit. Their
high degree of polymorphism, in combination with high abundance dispersed across the whole genome, has made microsatellites useful markers in
forensics as well as medical genetics.
Minisatellites are slightly longer tandem repeats of about 10-100 bp,
which are found dispersed in the genome and clustered at the telomeres.
Their qualities resemble those of microsatellites, and they are therefore used
in similar types of analyses, although to a lesser degree. Tandem repeats of
longer sequences are called satellite DNA or megasatellites, and can be several kb long.
Copy-Number Variations
Copy-Number Variations (CNVs) are usually defined as DNA segments
longer than 1 kb that are present at variable copy numbers in comparison
with a reference genome9,10. The more common CNVs, with frequencies
>1% are also referred to as Copy-Number Polymorphisms (CNPs). CNVs
include insertions, deletions, duplications and complex multi-site variants,
and are widely distributed throughout the human genome. CNVs have received relatively little attention compared with SNPs and smaller insertions/deletions. However, several studies have recently been published, revealing a high proportion of copy-number variable regions in the human
genome10-12. Notably, the CNV regions have been shown to have higher
nucleotide content per genome than SNPs10. The importance of CNVs is
further emphasised by their colocalisation with known genes and other functional elements. Currently (October 2008), 7332 genes are overlapped by
CNVs according to the Database of Genomic Variants11,13. These variants
may disrupt genes or alter gene dosage and thereby influence gene expression and phenotype. Several diseases have recently been associated with
variations in copy-number of genes14-18.
Medical genetics
Medical genetics is the science of inherited diseases. It is a genetic subdiscipline that involves analysis of the connection between inherited variations
and human disorders.
Mendelian disorders
Gregor Mendel, mentioned earlier in the introduction, studied pea plants in
the 19th century and discovered that some traits follow distinct rules of inheritance. For example, he discovered that each individual has two ‘factors’
for each trait, one from each parent, which may or may not contain the same
information. The ‘factor’ variants are called alleles, and an individual with
two identical alleles is called homozygous, whereas one carrying two different alleles is called heterozygous. Mendel also observed that for many traits,
there is one dominant and one recessive allele. For example, pink petal colour is dominant over white, so that a pea plant that is heterozygous for that
trait will express the pink colour. Similarly, there are human diseases that
follow Mendel’s laws of inheritance.
In dominant disorders (Figure 1a), the disease allele is dominant over
the healthy allele, so that heterozygous individuals will express the disease.
If the causative mutation is located on an autosomal (i.e. non-sex determining) chromosome, the disease is termed autosomal dominant. Achondropla13
sia (a common form of dwarfism) and Huntington’s disease are examples of
autosomal dominant disorders.
In recessive disorders (Figure 1b), the healthy allele is dominant and the
disease allele is recessive. Individuals that are homozygous for the healthy
allele as well as those who are heterozygous will then be healthy, and only
those who are homozygous for the disease allele will express the disease.
Two examples of autosomal recessive disorders are cystic fibrosis and albinism.
If a disease allele is located on the X-chromosome, it is termed X-linked.
Recessive X-linked diseases (Figure 1c), such as haemophilia or colour
blindness, mainly affect men. Since men only have one X-chromosome, they
will always express the disease if they have the disease allele. Women, on
the other hand, have two X-chromosomes and thus require two copies of the
disease allele to develop the disease.
These diseases that follow Mendel’s laws of inheritance are often referred
to as Mendelian diseases. Most of them are due to the mutation of a single
gene, resulting in disruption or an altered functionality of the protein. A certain degree of allelic heterogeneity is usually present, where several different
mutations within the same gene may cause the disease. There can also be
locus heterogeneity, where mutations of different genes give rise to the same
disease. However, the common feature of these genetic variants is their high
penetrance. The term genetic penetrance denotes the degree to which a genetic variant is displayed in the phenotype. For example, a disease polymorphism with 95% penetrance will lead to disease in 95% of the cases.
Mitochondrial disorders
Genetic diseases can also be caused by mutations in the mitochondrial
genome. Mutations within this genome can give rise to various disorders,
such as Leber Hereditary Optic Neuropathy (LHON) and Maternally Inherited Diabetes and Deafness (MIDD)19. Since the mitochondria are maternally
inherited in humans, the diseases are transmitted from mother to child.
Figure 1. Genetic disorders. a) Autosomal dominant disorder: individual with
healthy alleles (H) on both chromosomes is healthy, individual with both H and
disease (D) alleles is affected, individual with two D alleles is also affected.
b) Autosomal recessive disorder: individuals with two H alleles or with both H and
D alleles are healthy, individual with two D alleles is affected. c) X-linked recessive
disorder: male with H allele is healthy, male with D allele is affected, females are
affected as by an autosomal recessive disorder. d) Complex disease: a combination
of various genetic risk factors ( R) on different chromosome pairs and environmental
factor causes the disease.
Complex disorders
A complex disorder can be defined as a disease with an unclear aetiology, or
one that displays familial aggregation, but the pattern of inheritance is not
consistent with Mendelian disease models. Instead, the disease is caused by a
combination of several genetic and environmental risk factors (Figure 1d),
each only making a small contribution to the overall heritability. The conferred increase in risk for a genetic variant is often only two-fold or less. In
other words, the penetrance is very low and may be affected by interactions
with other genetic factors or require a certain environment to be expressed.
Diabetes mellitus, bipolar disorder, schizophrenia and many types of cancer
belong to this category of diseases, as well as SLE.
The proportion of genetic versus environmental causes varies between different complex disorders. The genetic component of a disease can be measured by the level of familial aggregation, denoted λ (lambda). This value
specifies the difference in disease prevalence for relatives of an affected
individual compared with the general population. The higher the λ value, the
higher the degree of familial aggregation. To some extent, the λ value also
reflects the degree of genetic causes to the disease. However, in addition to
sharing genetic background, family members usually also share many environmental factors, which may also affect the λ value. An alternative measure
of the genetic component of a disease is the comparison of concordance rates
in monozygotic and dizygotic twins. Twin pairs usually share the same environment, but whereas monozygotic twins are genetically identical, dizygotic
twins only share around half of their genetic composition. Therefore, if
monozygotic twins have a significantly higher concordance (i.e. both twins
are affected) than dizygotic, it is an indication that the disease has a strong
genetic component.
Identification of susceptibility genes
There are many different approaches to identifying genetic predisposing
factors behind a disease. The methods can be broadly divided into two main
categories: linkage studies and association studies. The basic difference between the two methods is that linkage analysis studies how the inheritance of
a disease follows certain chromosomal regions in a family, whereas association analysis compares allele frequencies of affected individuals with a set of
controls. A brief overview of different strategies for identification of risk
genes is given here below.
Linkage analysis
Adjacent genetic features tend to be inherited together more frequently than
those located further apart on the chromosome. The distance between two
genetic loci correlates with the probability of them being separated by meiotic recombination. This coinheritance of closely located loci is the basis for
linkage analysis. Genetic linkage is measured by the fraction of recombination between loci, where unlinked genes show 50% recombination.
A genome-wide scan for linkage can be performed, studying how chromosomal segments co-segregate with a disease in one or several families. In
such studies, a large amount of genetic markers, usually microsatellites,
spread across the whole genome are genotyped in the families. Linkage is
then calculated for each marker based on the degree of concurrence between
alleles and disease within each family.
Linkage can be measured by a LOD score, which is the logarithm of the
odds that two loci are linked with a recombination fraction less than 0.5,
compared with the likelihood of independent assortment. There are different
opinions on the threshold for significant linkage, but LOD scores above 3.3
are often considered significant in a genome-wide analysis20. This is equivalent to a p value of 5×10-5, which corresponds to a 5% probability of association by chance in a genome-wide scan.
Linkage analysis is a method with high power for finding rare variants
with high penetrance21. It has therefore been very successful for mapping
Mendelian diseases, which, by definition, are caused by such genetic variants. Several factors for complex diseases have also been found through
linkage analysis, although it is in general a less powerful method for detecting common variants with low penetrance21,22.
Association studies
Genetic association describes the co-occurrence of a phenotypic trait, e.g. a
disease, with a genetic trait, often an allele of a certain SNP. In an association study, one may for example investigate if the frequency of an allele is
higher in a set of patients compared with healthy individuals. When association between an allele and a disease is found, the association may reflect a
functional effect of the associated allele. However, the associated allele often
has no function, but is in linkage disequilibrium (described below) with the
causative mutation.
There are two main categories of association studies: case/control and
family-based studies. Case/control studies simply compare the frequencies of
the allele of interest in a group of patients (cases) and a group of healthy
individuals (controls). Family-based studies usually analyse which alleles are
inherited from healthy parents to an affected child. The chromosomes that
are not inherited are then used as the ‘healthy’ control set, and are compared
with the allele frequencies in the affected children.
Linkage disequilibrium
There is often a certain degree of correlation between alleles of different
polymorphisms located within the same chromosomal region. This correlation is referred to as Linkage Disequilibrium (LD). For example, two adjacent SNPs may have the alleles C/T and A/G, respectively. If they are completely independent of one another with random segregation of the alleles,
the frequency (f) of the TA haplotype will be: f (TA) = f (T) × f (A). Any
deviation from this formula indicates LD between the SNPs (Figure 2). The
extreme case would be if only two haplotypes are observed, for example TA
and CG. This means that the two SNPs are perfect proxies of each other, and
are said to be in complete LD.
Figure 2. Two closely located SNPs are said to be in linkage disequilibrium if the
observed haplotype frequencies deviate from the expected, which are calculated
from allele frequencies as shown here.
There is a variety of measures for pairwise LD between markers. The most
frequently used in the literature are Lewontin’s D’ and the correlation coefficient r2 (described e.g. by Mueller23). They both range between 0 and 1,
where 0 denotes random segregation and 1 means complete (or almost complete) LD.
Although the probability of LD is higher for adjacent polymorphisms, the
degree of LD is not directly proportional to genetic distance. Factors such as
recombination events, population bottlenecks and genetic drift affect the
genetic architecture of a population and the degree of LD across a genomic
region. Consequently, the pattern of LD often varies between different populations. It has been suggested that the genome can be divided into blocks of
high LD, separated by regions where recombination events have been frequent referred to as ‘recombination hotspots’. The LD blocks, also called
haplotype blocks or ‘recombination coldspots’, show less variation and a
limited number of haplotypes24. However, the size of the haplotype blocks
will be affected by which markers that are included, as well as the migratory
and admixture history of a population.
Linkage disequilibrium in association studies
LD is a feature that may be of great use in association studies. Depending on
the degree of LD in the region, association may be detected with markers
located near or sometimes several kb from the functional variant.
In 2002, an extensive international effort was initiated with the aim to investigate the patterns of LD and create a map of human genetic variation by
genotyping a large amount of SNPs in four different populations. This is
referred to as the ‘HapMap project’, and a public database
( provides tools for exploring LD patterns that can be very
useful in association studies6. An important application of HapMap data is
the possibility to select ‘tag SNPs’, which represent several haplotypes
within a region of interest. Since many of the adjacent SNPs will be correlated, only a certain number of them need to be genotyped to gain information regarding the whole region. In many cases, a long haplotype block can
be tagged by a single SNP, thus capturing all variation within that block by a
single genotyping experiment. However, sceptics have raised concerns that
too wide assumptions are made based on the limited dataset of the HapMap
project. There are several potential confounding factors, such as allelic heterogeneity, ethnic admixture, and the correlation between a small sample set
and over-estimation of the degree of LD. If not fully evaluated, such factors
may give rise to incorrect conclusions25.
Genome-wide association studies
The recent advances in genotyping techniques, which enable fast genotyping
of large amounts of markers at a low cost, in combination with the deposition of millions of SNPs into databases and the mapping of LD patterns by
the HapMap project6, have laid the foundation for genome-wide association
studies (GWAS). This large-scale approach, where a dense set of markers
(usually SNPs) across the genome is genotyped, is a promising new tool for
detecting genetic risk factors for complex diseases.
Similar to genome-wide scans for linkage, GWAS have no a priori hypothesis of the location of susceptibility variants. However, whereas linkage
analysis has been successful in the location of rare variants with strong effects, GWAS has a higher power to detect common variants with moderate
effects on disease risk21. Since it has been argued that common complex
diseases are caused mainly by common variants with moderate effects26, a
powerful association study seems to be an adequate method in the search for
such risk variants. Indeed, in the past few years, numerous diseasesusceptibility loci have been identified through GWAS27-30.
Since only a fraction of all genomic variation is genotyped in a GWA
scan, risk variants will only be detected if they are among the genotyped
markers or if they are in LD with these markers. There are many indications
that the genome contains long segments of strong LD, which enables detection of a large proportion of genetic variants with this approach. However,
the power to descry an association decreases with the degree of LD, and
those variants that lie outside LD block structures (approximately 1% of all
SNPs31) will not be detected unless they are directly genotyped.
In a GWA study, a substantial number of tests are performed, which consequently leads to a great number of markers associated by chance. It is
therefore necessary to perform some type of correction for multiple testing in
order to separate the wheat from the chaff. Risch and Merikangas21 suggested a conservative p-value threshold of 5×10-8, which is equivalent to
p=0.05 after Bonferroni correction for 1 million independent tests. However,
the Bonferroni method is perhaps not completely relevant since many of the
markers are in LD and therefore not independent. Other, less stringent methods for correction have also been suggested, such as permutation testing, or a
multi-stage approach, with a relatively liberal threshold in the first step (to
minimise the loss of true signals), followed by thorough studies in additional
As mentioned earlier, the power of GWAS to detect common susceptibility variants is high. This is of course on condition that the number of patients
and controls is sufficiently high. Numbers in the thousands are often required for true signals of moderate size to be distinguished in the noise of
spurious signals27,33. Considering the large amount of data generated in a
GWAS, careful thought must be put into both study design and data analysis.
Biases and errors can be created in every step of the procedure, including
sample collection, genotyping and statistical evaluation. Even if careful precautions are taken and the results are closely scrutinised, a great number of
false positive results are likely to emerge. It is therefore essential to replicate
each finding in other cohorts.
Candidate-gene approach
A candidate-gene study is a hypothesis-based search, in which a gene is selected based on its function and biological context. A candidate gene is simply a gene for which there is indication of a role in the studied disease or
The analysis of a candidate gene often starts with an association study of
common variants, usually SNPs, that represent as much as possible of the
variation within the gene. By genotyping these ‘tag SNPs’, clues can be
given to where in the gene a putative association lies. The association study
can also incorporate polymorphisms based on a potential functional effect,
such as missense mutations or alterations of putative regulatory sites for the
gene. If it is suspected that the candidate gene contains functional variants
that are previously unknown, it may be necessary to resequence the gene in a
selected number of patients and controls. This resequencing may include the
entire gene and its promoter region, or may be focused on selected regions,
such as exons, splice sites and sequences that are conserved between species
and thus may have regulatory importance.
When comparing the candidate-gene approach and the genome-wide approach, both have their pros and cons. Genome-wide scanning for linkage or
association has the advantage of being hypothesis-free. This means that information of the disease pathogenesis is not required before study initiation,
and completely new pathways may be discovered. The candidate-gene approach, on the other hand, has a major economic advantage, and allows a
dense study of the gene in question with fewer requirements for multipletesting corrections.
Animal models
An animal that has a disease or symptoms similar to a human condition is
called an animal model. These animals can be valuable tools in identifying
genetic risk factors behind the human disease in question. It is common to
study inbred strains of mice or rats that spontaneously develop a disease, or
strains that are susceptible to induction of the disease, e.g. by immunisation
with an auto-antigen. Similar methods can be used as in human genetics:
susceptibility regions can be identified by linkage analysis, and association
studies of candidate genes can be performed. In animals, the reversed approach is also possible, where a genetic sequence is modified and the phenotypic consequences are studied.
Studying animal models may have many advantages over human subjects.
Much of the variability that exists in humans with complex diseases can be
eliminated by the use of inbred strains with a common genetic background,
which are housed in a controlled environment. The breeding possibilities in
animals are also major benefits, enabling crossings of interesting specimen
and creating large amounts of offspring, which increases the statistical power
and the genetic resolution. There are also experiments that cannot be performed on human test subjects for ethical reasons, such as gene knock-out.
Although these animals are just models of the human condition, this type of
studies can yield valuable complementary information.
Complicating factors
In all types of genetic studies, there are factors that complicate the matter
and that may give rise to inaccurate results. For complex diseases, there is
often substantial heterogeneity on several levels, including the phenotype,
the risk genes involved, and even different risk variants within the same
gene. There may also be epistatic (gene-gene interaction) effects, where the
risk of an allele only is expressed when combined with another genetic factor. An additional aggravating factor in the study of complex traits is that
most risk variants seem to convey only a modest effect, which demands
large datasets to reach statistical significance.
There are numerous factors and events that can skew the statistics of a
genetic study and give rise to false positive (or false negative) results. For
example, an imprecise phenotype or differences in diagnosis of a disease
may create a heterogeneous patient group. Population stratification, with
different ethnic backgrounds of the studied individuals, can also be a major
cause for spurious signals in case/control studies. However, family-based
studies are generally protected from the effects of ethnic admixture owing to
their use of internal controls. Bias can also be caused by inaccurate sample
handling or technical errors, such as incorrect genotyping.
In other words, biases and errors can be created in every step of a study.
Especially in large-scale studies such as GWAS, precaution is required to
prevent the true associations from drowning in a sea of spurious signals.
Functional mutations
A major reason for studying genetic risk factors for complex diseases is to
achieve a better insight into the pathological processes of the disease. However, proceeding from an associated marker to a deeper understanding of
functional, causative variants and their means of action is complicated. A
wide range of possible processes that affect the gene product may be involved. There could be polymorphisms that affect protein structure, such as
altered amino acid sequences or splicing variants. There is also the possibility of changes in gene expression levels or localisation. Such changes could
be caused by alterations in various types of regulatory sequences, such as
binding sites for transcription factors or other regulatory elements, or affect
epigenetic factors such as DNA methylation sites.
Bioinformatic tools that may assist in the interpretation of association
data are available, where information from several databases is combined,
including LD, gene expression, gene structure and other features34. However,
there will most likely be a significant development of such tools in the future, as well as improvement of experimental approaches.
Systemic Lupus Erythematosus
SLE is a chronic autoimmune disease associated with a multitude of symptoms and a vast array of autoantibodies. The clinical manifestations range
from rashes, fatigue, fever, arthritis and lack of various blood cells, to
thrombosis, serositis, nephritis, seizures and psychosis. The autoantibodies
are mainly directed against various nuclear components, such as doublestranded DNA or histones, but antibodies against cytoplasmic, cellmembrane or extracellular molecules are also frequently observed35. The
disease severity can also differ significantly between the patients. Some have
relatively mild symptoms and require little or no medical treatment, whereas
others are severely affected by chronic inflammation of multiple internal
organs and require aggressive treatment with high-dose corticosteroids and
cytostatic drugs. Due to the heterogeneity of this disease, the American College of Rheumatology (ACR) has established 11 criteria for classification36,37
(Table 1). Fulfilment of at least four criteria is sometimes a requirement for
diagnosis, although the ACR criteria were constructed mainly for classification purposes. A large study of European patients identified the most common clinical manifestations as arthritis (48.1% of patients), the characteristic
malar rash (31.1%) and renal disorder (27.9%)38.
Table 1. The American College of Rheumatology’s criteria for classification of SLE,
revised in 1982 and 1997.
Malar rash
Fixed erythema, flat or raised, over the malar eminences, tending to
spare the nasolabial folds
Erythematosus raised patches with adherent keratotic scaling and follicular plugging; atrophic scarring may occur in older lesions
Skin rash as a result of unusual reaction to sunlight, by patient history or
physician observation
Oral or nasopharyngeal ulceration, usually painless, observed by a
Nonerosive arthritis involving 2 or more peripheral joints, characterised
by tenderness, swelling or effusion
Pleuritis (convincing history of pleuritic pain or rub heard by a physician or evidence of pleural effusion)
Pericarditis (documented by ECG or rub or evidence of pericardial
Persistent proteinurea > 0.5 g/day, or greater than 3+ if quantification
not performed
Cellular casts: red cell, hemoglobin, granular, tubular or mixed
Seizures (in the absence of offending drugs or known metabolic derangements)
Psychosis (in the absence of offending drugs or known metabolic derangements)
Haemolytic anaemia with reticulocytosis
Leukopenia (<4000/mm3 on 2 or more occasions)
Lymphopenia (<1500/mm3 on 2 or more occasions)
Thrombocytopenia (<100,000/mm3 in the absence of offending drugs)
Antibody to native DNA in abnormal titre
Antibody to Sm nuclear antigen
Antiphospholipid antibodies - based on 1) abnormal serum level of IgG
or IgM anticardiolipin antibodies, 2) positive test result for lupus anticoagulant using a standard method, or 3) a false-positive serologic test for
syphilis for at least 6 months confirmed by Treponema pallidum immobilisation or fluorescent treponemal antibody absorption test
Abnormal titre of antinuclear antibody by immunofluorescence or
equivalent assay, in the absence of drugs known to be associated with
‘drug-induced lupus’ syndrome
Discoid rash
Oral ulcers
Renal disorder
SLE affects about 0.03% of populations with European ancestry, but the
prevalence varies with several factors, such as gender, age, ethnicity and
time period studied. About 90% of the SLE patients are women, and the
peak age of onset is during childbearing years. Populations of African and
Asian decent have a significantly higher prevalence than those with European ancestry, and the general prevalence has increased in the recent years,
probably due to improved diagnosis and better survival rates for SLE patients39-41.
Aetiology and pathogenesis
SLE is classified as a complex disease, where the cause is believed to be a
complicated interaction between various genetic and environmental factors.
There are, however, a few examples of rare monogenic forms of SLE, with
complete deficiencies of early complement components, such as C1q42, or
defects in the TREX1 gene43.
The genetic component of SLE
The disease has a clear genetic component, as shown by an increased risk to
develop the disease for relatives of SLE patients44. The risk of disease has
been estimated to be 20 times higher if you have a sibling with SLE45. Twin
studies also indicate a significant genetic contribution: the concordance rate
is 24-69% in monozygotic twins, but only 2-3% in dizygotic twins46-48.
A number of genetic risk factors for SLE have been identified, and research within this field has been successful especially in the last few years.
The genes associated with SLE have various functions important for regulation of the immune system. In combination, these genetic variants will lead
to exaggerated and prolonged immune reactions and sub-optimal suppression of reactions to self, which results in increased susceptibility for the disease. Genetic studies of SLE are further discussed in the next section.
Epigenetics, which refers to inheritable modifications not affecting the
DNA sequence, have also been suggested in SLE pathogenesis, especially
hypomethylation of DNA in T cells, leading to dysregulation49.
Environmental factors
Although twin studies clearly imply a genetic component in SLE aetiology,
the concordance rates are still relatively low, indicating involvement of environmental factors. Various infectious agents, especially the Epstein-Barr
Virus (EBV) causing mononucleosis, have been connected with the disease.
Possible mechanisms behind this association are antibody cross-reactivity
between viral antigens and self antigens, and immortalisation of self-reactive
B cells by EBV 50-53.
UV radiation also appears to be a risk factor. SLE patients are often extremely photosensitive, but UV light could also trigger disease onset54. There
is also a long list of medications inducing lupus-like symptoms. Druginduced lupus erythematosus often has a long latency period, where symptoms emerge months or sometimes years after starting on the medication55.
Other examples of environmental factors implicated with risk of SLE are
exposure to high levels of silica dust56, diet (especially alfalfa sprouts, which
contain high amounts of L-canavanine57) and smoking58. There are also some
indications that heavy metals, solvents, pollutants, pesticides and hair dyes
could be risk factors54, although this may need confirmation in larger studies.
Gender bias and hormonal effects
The female prevalence of the disease, with post-puberty onset and disease
activity sometimes affected by menstrual cycle or pregnancy, suggest that
there may be hormonal effects involved. Indeed, the female sex hormone
oestrogen has been implicated with SLE in several ways. It has been shown
to repress tolerance of self, and to have various effects on B and T cells, as
well as other leukocytes59-63. However, many studies of oestrogen effect on
SLE have had inconclusive results. Some studies have found association of
oral contraceptives with slight increase in risk of SLE64-66. During pregnancy, some studies67,68 but not all69,70 have observed an increase in flares
and disease activity. In men with SLE, lower testosterone levels and higher
oestrogen levels have been noted71. Interestingly, men with Klinefelter’s
syndrome (XXY instead of the normal XY) are more prone to SLE, which
could suggest a gene dosage effect72.
In conclusion, there is no simple explanation of the gender bias observed
in SLE, although several sex hormones in combination with X chromosome
genes are likely to be involved.
Generalised SLE pathogenesis
A likely course of events in the development of SLE could be the following: An immune response is triggered by an environmental factor, such as an
EBV infection53, or possibly by a self-antigen73. Antigens are taken up by
antigen-presenting cells, which present peptides to T cells. Activated T cells
stimulate B cells to produce autoantibodies, and further contribution to Band T-cell stimulation is provided by various accessory molecules and cytokines. An inadequate tolerance system fails to repress the autoreactivity.
Increasing amounts of immune complexes are formed, and an impaired
clearing of these complexes causes deposition in tissues, where they give
rise to inflammation via complement activation74. In some cases, autoantibodies may cause symptoms without involvement of the complement system, for example by binding of different types of blood cells, and thereby
cause haematologic disorder. This model is illustrated in Figure 3.
Figure 3. A plausible model of the course of events leading to SLE.
Genetic studies of SLE
In the last few years, we have witnessed a general shift of focus regarding
approaches to complex disease genetics. Advances in genotyping techniques
and increased knowledge of human genetic variation have drawn the interest
towards large-scale association studies. Both extensive hypothesis-based
association studies and hypothesis-free GWAS have provided highly convincing evidence for new risk genes, as well as confirmation of previously
identified loci.
Linkage studies
Several genome-wide linkage scans of SLE have been performed75-85 identifying a number of susceptibility loci for SLE or a subphenotype, such as
nephritis. A selection of genetic loci displaying linkage to SLE, with LOD
scores higher than 3.0 (equivalent to p<0.001), are listed in Table 2.
Table 2. Susceptibility regions with LOD scores >3.0 (or p<0.001) identified in
genome-wide scans for linkage with SLE.
Chromosomal region
LOD or p
3.37; 4.41
4.24; 4.1
3.84; 3.28
3.85; 3.06
Moser82; Olson84
Lindqvist81; Olson84
Gray-McGuire78; Olson84
Gaffney77, Nath83
Lee86 (meta-analysis)
LOD scores or p values as given in the original articles. Different methods were used, and
the values are therefore only roughly comparable between studies.
Although several of the susceptibility loci have been replicated, none of
them has been reproduced in all studies. Two meta-analyses both confirm
the HLA region on chromosome 6p21 as the strongest region, and report
linkage to one additional region each, namely 16p12.3-16q12.286 (significant
linkage) and 20p11-q13.1387 (suggestive linkage), respectively.
There are several mouse models for SLE, such as MRL, BXSB and the F1
hybrid of NZW/NZW88. A number of murine linkage regions have been
identified, for example Sle1, Sle2, and Sle3, which are syntenic with the human linkage loci 1q23, 9p22, and 19q13, respectively81,89.
Several of the findings from linkage studies have lead to the identification
of interesting susceptibility genes. For example, the human 1q22-23 region
contains genes encoding receptors for IgG, which have been associated with
SLE90, chromosome 2q37 harbours the associated PDCD1 gene91, and recently, the strong association of ITGAM was identified through fine-mapping
of a linkage region on chromosome 16p1192.
Associated genes
A great number of association studies have been performed for SLE, some
with more convincing results than others. In general, associations detected in
larger cohorts that have been replicated in independent samples and/or supported by functional studies, can be considered as more convincing.
Thus far, three large GWA studies of SLE have been published28-30. A
handful of genes have reached genome-wide significance in all three studies,
which must be regarded as extremely convincing of a role in SLE aetiology.
The combined result of the three GWAS confirms the already wellestablished association of the HLA region, as well as the recently identified
strong association of IRF5 and STAT4. In addition, the novel risk genes BLK
and ITGAM are identified.
The HLA region on the short arm of chromosome 6 is a genetic locus that
is distinguished in most contexts. The extended HLA region contains at least
252 expressed genes and is unique for its level of polymorphism, LD and
cluster of genes that are important in the immune system93. The region has
shown highly convincing association with numerous diseases, including
SLE. This association has been known for over 30 years94, and it continues
to be replicated. Recent GWAS and meta-analyses of linkage screens indicate that the HLA region contains the greatest genetic risk factors in SLE
susceptibility28-30,86,87,95. However, the nature of this locus, with its high level
of LD and great variability, makes it difficult to identify the causative gene.
The most consistent associations seem to be with MHC class II genes, and
for example, haplotypes including the alleles DRB1*1501/DQB1*0602,
DRB1*0801/DQB1*0402, and DRB1*0301/DQB1*0201 have shown convincing association96,97. In addition to the genes of the MHC complex, the
region also contains several highly interesting genes that have been implicated with SLE, such as Tumour Necrosis Factor α and β (TNFα and β)98,99
and complement components, such as C4A, C4B and C2100,101. It is not
unlikely that several independent effects exist within this region, which is
indicated by results of the SLEGEN GWA study29.
The association of the gene encoding the IRF5 (Interferon Regulatory
Factor 5) transcription factor was first identified in an association screen of
genes related to type I interferon (IFN)102. An independent study then identified a strongly associated haplotype containing functional SNPs affecting
expression levels and splicing of IRF5103. High expression of type I IFN and
IFN-inducible genes, the so-called ‘IFN signature’, is commonly observed in
SLE patients104, suggesting an important role in the pathogenesis. There are
also many reports of SLE as a consequence of IFN-α treatment in cancer
The gene encoding the STAT4 transcription factor was first identified as
a risk factor for rheumatoid arthritis in an association study of a linkage region. The same study also found association to SLE with the same risk haplotype106. In addition to the three GWA studies, this association was recently
confirmed by two independent studies, including paper IV in this thesis2830,107
. STAT4 plays an important role in the differentiation of Th1 cells and
IFN-γ production, and it has also been reported to mediate type I IFN signalling108. The identification of IRF5 and STAT4 as SLE risk genes supports the
hypothesis that the type I interferon pathway is central to the disease pathogenesis.
The ITGAM gene (Integrin Alpha M, also called CD11b) is localised in a
linkage region for SLE (16p11), and association was identified in a finemapping study of the region92. Association was also detected in the three
GWAS studies, which were published simultaneously with the linkageregion fine-mapping or shortly after28-30. ITGAM encodes a subunit of the
complement receptor 3 (CR3, also Mac-1), which is expressed mainly on
neutrophils, macrophages and dendritic cells. The receptor is important in
the regulation of many immunologic functions, including iC3b-mediated
phagocytosis and leukocyte adhesion and emigration from the bloodstream
via interactions with ICAM-1 and ICAM-2109.
Association with the BLK gene (B lymphoid tyrosine kinase) was detected as one of the most significant associations with SLE in all three GWA
studies28-30. A risk allele localised between BLK and the gene C8orf13 was
associated with reduced expression of the BLK, but also with increased expression of C8orf1330. It is thereby implied that both genes may constitute
risk factors for SLE. BLK is a B-cell specific member of the Src family of
tyrosine kinases, and may thus influence proliferation and differentiation of
B cells110. The function of C8orf13 is still unknown.
In addition to the five genes with triple genome-wide significance described above, there are several other genes displaying convincing association with SLE in several datasets. PTPN22 (protein tyrosine phosphatase
non-receptor 22), which was first identified as a risk gene for type I diabetes111 and shortly after for Rheumatoid Arthritis and Grave’s disease112,113,
has been associated with SLE in several large association studies, including
one of the GWAS29,114,115. PTPN22 encodes a protein involved in downregulation of T-cell activation, and the risk allele results in an amino acid
substitution interfering with the interactions of this protein with the protein
kinase CSK112.
The Fcγ receptor genes (FcγR), which encode low-affinity receptors for
IgG antibodies, have been extensively studied in connection with autoimmune diseases. A cluster of five FcγR genes is located on chromosome 1q23,
a region with reported linkage to SLE82,84. High degree of sequence similarity between the genes has made analysis of this region difficult. Early studies
have sometimes reported conflicting results for these genes, however, the
most consistent association seems to be with a missense mutation in
FCGR2A90. This allele was also significantly associated in a GWA study29.
Interestingly, copy-number variation of the FCGR3B gene has also been
associated with SLE14,15.
Protective and risk haplotypes within the gene encoding TNFSF4 (TNF
Super-Family 4, also OX40 ligand) were recently identified in several large
case-control and family-based cohorts116. TNFSF4 is expressed on antigenpresenting cells, and binding of the OX40 receptor mediates T-cell proliferation and differentiation into memory T cells117.
Other genes that have been associated in several cohorts and supported by
functional data are for example PDCD191 and CTLA-4118, both inhibitory
receptors present on T cells, and the recently identified association with
BANK1 encoding a B-cell scaffold protein (paper III). Genetic and functional data have also indicated roles for the tyrosine kinase LYN29,119 (which
interacts with BANK1), the MBL-2 gene encoding the complement activator
MBL120, and the cytokine IL-10121,122.
It will also be very interesting to follow further studies of genes that were
identified as potential risk factors for SLE by reaching genome-wide significance in only one of the GWA studies. These genes include XKR, ATG5,
ICA1, SCUBE129 and TNFAIP328.
Table 3. A selection of genes associated with SLE.
HLA region
BLK, C8orf13
Reviewed by Fernando95
Sigurdsson102, Graham103
Nath92, GWAS28-30
Reviewed by Brown90
Paper III
Reviewed by Monticielo120
Present investigation
The studies presented here aim to identify genetic risk factors for the autoimmune disease Systemic Lupus Erythematosus.
Material and methods
Patients and controls
Several different sets of patients, families and healthy controls have been
studied in the present investigation. The same samples have often been included in several studies. However, due to variation in access of samples, the
number in each set may differ between studies. An overview of the casecontrol datasets is presented in Table 4. In addition, 149 Swedish and 90
Mexican trios, and 10 Icelandic multicase families were studied in Paper I.
All patients fulfil at least four of the ACR classification criteria36,37, and have
given their informed consent to participate in the studies. The population
controls were matched for ethnicity, and individuals with parents or grandparents with other ancestry were excluded.
Table 4. Number of cases and controls analysed in the four papers of this thesis,
indicated by their roman numbers.
No of patients
No of controls
There is a broad range of methods available for identifying an individual’s
genotype at a certain genetic position. In the papers on which this thesis is
based, four different methods were used for SNP genotyping: sequencing,
restriction fragment length polymorphism (RFLP), TaqMan SNP genotyping, and a high-density oligonucleotide SNP array.
Since direct sequencing yields the complete genetic sequence of the fragment you wish to analyse, it can be used to identify new genetic variations as
well as obtain genotypes of known SNPs. It is also easy to assess the quality
of each assay, so that uncertain genotypes can be excluded. Sometimes this
method is referred to as resequencing, since the general sequence already is
known, and the aim is to find any deviations from this sequence in your
sample of interest.
RFLP genotyping, which uses a restriction enzyme that will cleave one
allele but not the other, is a simpler method that was used for genotyping one
of the SNPs in paper I. Depending on the enzyme and the sequence analysed,
the reliability of this assay will vary. Therefore, the accuracy of the assay
must be verified by another genotyping method, such as sequencing.
The TaqMan SNP genotyping assay123 provided by Applied Biosystems
was used for genotyping most of the SNPs in all four papers. The method is
based on hybridisation of allele-specific fluorescent probes to your sample
during a Polymerase Chain Reaction (PCR). The probe contains a reporter
dye at the 5’ end and a quencher dye at the 3’ end. During the PCR reaction,
the probe anneals to the DNA. As elongation proceeds, the probe is cleaved
whereupon reporter and quencher are separated, resulting in increased fluorescence of the reporter. Since each assay contains two allele-specific probes
with different reporter dyes, the genotype of each sample can be calculated
as the relative fluorescence of these two dyes. The genotypes are determined
automatically by computer software. This genotyping assay is significantly
faster than the two methods described above, due to a single-step setup in the
lab and the automated genotype calling.
In papers III and IV, genotypes of some of the SNPs were obtained from a
collaboration where a GWAS was performed using the Affymetrix 100k
GeneChip®. This type of high-density oligonucleotide SNP array contains
probes for almost 100,000 SNPs, allowing them to be genotyped simultaneously. The procedure includes restriction enzyme digestion of the DNA,
PCR amplification, fragmentation, biotin labelling, hybridisation to the array
with allele-specific probes, addition of streptavidin-conjugated fluorophores,
detection of fluorescence, and automated allele calling. SNP array is a highthroughput genotyping method, although the specificity and sensitivity may
be lower compared with other methods.
Statistical analysis
There is a wide range of statistical tests within the field of association studies, each with strengths and weaknesses. The two main categories of association analysis are case-control and family-based studies. Comparing cases
with healthy controls using a χ2 (chi square) test or a similar statistic is perhaps the most commonly used approach. As mentioned previously, it is important to use ethnically homogenous cohorts in case-control studies, to
avoid spurious association due to population stratification.
Family-based association studies were developed in order to circumvent
the problem of population stratification. Many of the family-based methods
are variations of the Transmission-Disequilibrium Test (TDT)124, which
studies cases with unaffected parents, and compares allele frequencies in the
group of transmitted versus non-transmitted alleles. TDT is not affected by
population stratification, but can be biased by inclusion of incomplete families or by reconstruction of parental genotypes from their offspring125. The
Haplotype-Based Haplotype Relative Risk test (HHRR)126 is based on the
same principle, but is considered more powerful than TDT, which only uses
information from heterozygous parents. HHRR includes both homozygous
and heterozygous parents, which increases the power, especially when a rare
allele is analysed. There are also tests that can incorporate all types of pedigrees, such as the Family-Based Association Test (FBAT), which compares
the observed and expected allele frequencies of affected individuals127.
In the present study, case-control cohorts were primarily analysed using
χ2 of 2×2 contingency tables. In paper I, the trios were analysed by HHRR,
since many of the SNPs had low minor allele frequencies and the statistical
power of TDT would not be sufficient. We also applied the FBAT test to the
same material.
There are several different methods for studying genotypic effects. In papers II-IV in this thesis, we have used the Unphased software128, where homozygosity for the non-risk allele was set as a reference with odds ratio=1.
Testing for Hardy-Weinberg Equilibrium (HWE) can serve as a rough
quality control. The HWE test analyses the relation between allelic and
genotypic frequencies, which should be correlated under certain conditions,
such as unlimited population sizes, random mating and no selection or migration. Although these criteria will never be fully met, an approximate
HWE should be expected unless genotyping errors or other confounding
factors have caused skewed distributions in genotype frequencies. However,
if association is strong it may create deviations from HWE in the patient
cohort. Thus, deviations from HWE in the patient group may reflect true
association, whereas in controls, it could indicate a bias129,130.
A meta-analysis is an approach that combines the results of several different datasets. In case-control studies, this is a chance to analyse an overall
effect across several populations, but still keep the cohorts as separate units.
Different statistical methods exist, where some analyse fixed effects and
assume homogeneity between the strata, whereas other methods analyse
random effects and can be applied to heterogeneous cohorts. An initial
analysis of homogeneity between the cohorts can therefore be necessary. The
studies in this thesis have applied the Breslow-Day test for homogeneity,
Mantel-Haenszel meta-analysis of homogeneous strata, and DerSimonianLaird test for heterogeneous strata, all of which were implicated in the
StatsDirect software.
Paper I: No evidence of association between genetic
variants of the PDCD1 ligands and SLE
In this study, we analyse SNPs in the genes of the PDCD1 ligands, PD-L1
(CD274) and PD-L2 (CD273), for association with SLE. The importance of
the PDCD1 pathway in peripheral tolerance has been demonstrated by several studies (reviewed e.g. by Keir et al131). Furthermore, a regulatory polymorphism in the PDCD1 receptor gene had previously been identified as a
risk factor for SLE91, and suggestive linkage had been found to a marker
located close to the genes of the two PDCD1 ligands81. We therefore considered the PD-L1 and PD-L2 genes as interesting candidates for susceptibility
to SLE.
Results and discussion
When this study was initiated, very few SNPs were known in these genes.
We therefore sequenced samples from patients and controls in order to identify polymorphisms in the exons and in regions of potential regulatory importance. Later, as more SNPs became available in the databases, we also
selected a SNP that altered the binding site of an important transcription
factor. In total, 23 SNPs were then analysed in Swedish trios using two different family-based tests for association: HHRR and FBAT. The HHRR test
found eight of these SNPs to be associated to SLE (p<0.05), whereas the
FBAT test did not find any association.
Due to the conflicting results of the two statistical tests, we analysed the
eight ambiguously associated SNPs in three additional cohorts: Mexican
trios and case-control cohorts from Sweden and Argentina. No association
was found in either of the replication sets, with the exception of one allele
that was found in 2.0% of patients and 4.5% in controls in the Argentinean
cohort (p=0.0157). However, for several reasons we believe that this is very
likely to be a false positive result. First, the risk of type I error is greater for a
rare allele than for a common132. Second, since several SNPs now have been
analysed in four different cohorts, the risk of spurious association has increased. If correction for multiple testing is applied, the association disappears. Third, this Argentinean cohort contains some elements of admixture
between European and Amerindian ancestry, which further increases the risk
of false positive results133.
Allele frequencies in the Swedish trios and case-control cohort are shown
in Table 5. Comparison of allele frequencies (transmitted with patient alleles, and non-transmitted with control alleles) indicates that bias may have
been introduced.
Table 5. Allele frequencies in the PD-L1 and PD-L2 genes. SNPs with rs- or
ENSSNP-numbers can be found in databases. Minor allele frequencies are shown.
Nt= not tested. Transm=transmitted. Number of non-transmitted alleles varies with the number of informative trios for the SNP, and is therefore given as a range.
The lack of association in the replication cohorts, including the Swedish
cases and controls, indicates that the association seen in the HHRR analysis
was a false positive result. The allele frequencies differ markedly between
the non-transmitted alleles from the HHRR analysis and the healthy popula35
tion controls. This indicates that bias of some sort has been introduced in the
HHRR analysis of the Swedish trios, and the FBAT result is more likely to
reflect the true picture.
Power estimations show that the investigated cohorts should have enough
power to detect association of alleles with relative risks above 1.5. We analysed 23 SNPs spread across the PD-L1 and PD-L2 genes, and do not find
cogent evidence of association, which suggests that these genes are not risk
factors for SLE. The possibility of a risk factor that is not in linkage disequilibrium with any of the SNPs analysed can, however, not be excluded.
A study published shortly after paper I reported association to SLE with
the SNP rs7854303 in PD-L2 in 164 patients and 160 controls from Taiwan134. This SNP is not polymorphic in any of our Swedish or Icelandic
cohorts as verified by sequencing, and may thus be an Asian-specific risk
Paper II: Association of a CD24 gene polymorphism
with susceptibility to systemic lupus erythematosus
CD24 (or Heat-Stable Antigen) is a glycosyl phosphatidylinositol (GPI)–
linked protein which is expressed on the surface of various cell types, such
as activated T cells, B cells, mature granulocytes, macrophages, and dendritic cells135-138. The biologic function of CD24 is unclear, although a role in
the activation and differentiation of B cells has been indicated139, as well as
in activation of both CD4+ and CD8+ T cells through a CD28-independent
costimulatory pathway135,137,140. CD24 has also been shown to modulate the
interaction between Very Late Activation antigen 4 (VLA-4) and Vascular
Cell Adhesion Molecule 1 (VCAM-1)141. These adhesion molecules are important in lymphocyte costimulation in specific tissues and sites of inflammation in SLE patients142.
The CD24 gene maps to a linkage region for SLE and other autoimmune
diseases on chromosome 6q21–2581,82. Association with Multiple Sclerosis
(MS) was detected for the SNP rs8734 encoding an amino acid substitution
from alanine to valine (A57V). The same study also reported a higher expression of CD24-57V compared with CD24-57A on T cells, as determined
by flow cytometry143.
Results and discussion
In this study, we analyse if the A57V polymorphism in CD24 is associated
with SLE in three independent cohorts from Spain, Germany and Sweden. In
the Spanish cohort, the CD24-57V allele was associated with increased risk
for SLE. There was also an increased risk associated with the V/V homozygous genotype when compared with the A/A genotype. In the German cohort, a similar trend was observed, with higher frequencies of the V-allele
and the V/V-genotype in the patients than in the controls. This difference,
however, did not reach statistical significance. In the Swedish cohort, no
differences between patients and controls were observed. Data from these
analyses are shown in Table 6.
Table 6. Allelic and genotypic association of the CD24 A57V polymorphism
(rs8734) in three sets of patients and controls, with meta-analysis of homogenous
strata (Spain+Germany) using Mantel-Haenszel.
Allele /genotype
P value
Odds ratio (95% CI)
V allele
V/V genotype
V/A genotype
V allele
V/V genotype
V/A genotype
V allele
V/V genotype
3.6 (2.13-6.16)
3.7 (2.16-6.34)
1.05 (0.83-1.32)
1.45 (0.67-3.13)
1.54 (0.70-3.40)
1.15 (0.70-1.70)
1.03 (0.58-1.76)
1.03 (0.58-1.81)
V/A genotype
1.01 (0.72-1.41)
V allele
1.20 (1.05-1.36)
2.19 (1.50-3.22)
V/V vs. V/A+A/A 9.9%
Homogeneity analysis showed combinability of all three cohorts at the allelic level. However, at the genotypic level, frequencies of the Swedish cohort
were significantly different from the other two. Thus, a meta-analysis of
odds ratios assuming fixed effects can be performed with all three cohorts at
the allelic level, but only with the Spanish and German sets at the genotypic
level. Meta-analysis using random effects can be applied to a joint analysis
of the genotypic association in all three cohorts. However, it is perhaps more
relevant to assess each population separately in such cases.
An interesting observation is that the allele frequencies are very similar
among patients in these three cohorts, whereas the frequencies in controls
differ markedly from each other. This polymorphism has also been analysed
for association with MS in several cohorts of European origin144,145. When
control frequencies of these studies are included, it appears that there may be
a north-south gradient in European populations. The CD24-57V allele had
the lowest frequency among healthy individuals from Southern Spain
(23.8%), with gradual increases in Northern Spain (25.3%), Germany
(27.4%) Belgium (30%), Sweden (30.8%), and Great Britain (34%). The
original study reported a frequency of 26.8% in healthy individuals from
Ohio with self-reported ‘European’ ancestry143. The frequency among pa37
tients has been relatively constant, regarding both MS (30.1-33.2%) and SLE
(29.4-31.6%). This population heterogeneity correlates with the inconsistent
results. Association with MS has been detected in cohorts with Southern
European145 and admixed European143 ancestry, and lack of association was
reported for Belgian and British cohorts144. A similar observation has been
made regarding association of PDCD1 with SLE146.
The lack of replication could also have other explanations. There may be
differences in LD patterns between the populations, and the CD24-57V allele
could be a marker for another causative allele, which is in stronger LD in
Southern European populations. There could also be epistatic effects with a
risk allele that is more frequent in those populations. Alternatively, failure to
replicate in different populations may be reflective of a false negative result,
although this is less probable if association is detected in multiple independent studies.
In conclusion, we find that the CD24-57V allele is associated with increased risk of SLE in a Spanish set of patients and controls. This association is supported by a trend for association with the same allele in a German
cohort, whereas a Swedish cohort shows no differences between patients and
The associated allele encodes an amino acid substitution, which has been
shown to correlate with cell-surface expression of the CD24 antigen143. Recently, protective association for both MS and SLE was also found with a
dinucleotide deletion in the 3’UTR of CD24 mRNA147. This deletion was
reported to decrease the stability of the CD24 mRNA. LD between this deletion and the A57V polymorphism was low, which indicates that they represent independent effects.
Paper III: Functional variants in the B-cell gene BANK1
are associated with systemic lupus erythematosus
The B-cell scaffold protein with ankyrin repeats (BANK1) is an adaptor
protein involved in B-cell responses to antigen stimulation. BANK1 has
been shown to associate with the tyrosine kinase Lyn and the inositol 1,4,5triphosphate receptor (IP3R). It is believed to regulate signals from the B-cell
receptor by connecting protein tyrosine kinases to the IP3R, which subsequently leads to calcium mobilisation and B-cell activation148.
B cells are generally considered to have a key role in SLE pathogenesis
by autoantibody production and regulation of innate and adaptive immune
responses by antigen presentation and cytokine signalling. The importance
of this cell type is confirmed by the success of novel therapies for SLE,
which are aimed at depleting the B-cell population149.
Results and discussion
In a GWA study of Swedish SLE patients and controls, we identified association with the SNP rs10516487, encoding a non-synonymous substitution
(R61H) in exon 2 of the BANK1 gene. This association was replicated in
SLE cases and controls from Germany, Italy and Spain, and a trend for association was observed in samples from Argentina. A small set of patients
from Denmark was also included, and was analysed together with the Swedish samples in a joint Scandinavian cohort. Association with SLE was found
in all five cohorts, except the borderline-significant p value in Argentina
(Table 7).
Fine-mapping of the genetic region in the Swedish cohort, using 30 tag
SNPs in the BANK1 genetic region, revealed association with nine SNPs
located between introns 1 and 7 of the gene.
The B-cell specificity of BANK1 was confirmed by expression analysis,
where only very weak expression in other cell types was detected, possibly
due to contamination with other cell types. Surprisingly, when full-length
BANK1 cDNA was amplified, and PCR products were verified by gel electrophoresis, two main bands were detected. Sequencing of these bands revealed that the larger size PCR product represented a full-length cDNA, and
the smaller was a novel isoform with an in-frame deletion of exon 2 (2
isoform). This 2 isoform lacks the putative IP3R-binding domain, and one
may therefore speculate that the encoded protein has different functions than
does the full-length isoform. When cDNA from 83 healthy individuals and
30 SLE patients were analysed, the 2 isoform was present in every sample.
Moreover, cDNA from chimpanzee and mouse spleen also contained this
isoform, suggesting conservation across species.
Evaluation of known polymorphisms in the gene identified a SNP located
in a potential branch-point site, which could affect the splicing of exon 2.
This SNP, rs17266594, was in strong (although not complete) LD with the
previously associated rs10516487. Association between rs17266594 and
SLE was detected in all five cohorts (Table 7).
Table 7. Allelic association of rs10516487 (R61H) and rs17266594 in five sets of
SLE cases and controls and pooled analysis with Mantel-Haenszel.
Population SLE G
Scandinavia 76.3%
Odds ratio
Ctrl G P-Value (95% CI)
69.9% 7.27E-04 1.39 (1.14-1.68)
1.31 (0.98-1.74)
74.3% 0.0564
68.8% 8.13E-04 1.52 (1,18-1,95)
1.46 (1.09-1.94)
70.2% 0.0078
1.30 (1.07-1.58)
71.2% 0.0065
70.8% 3.74E-10 1.38 (1.25-1.53)
SLE T Ctrl T
76.4% 70.4%
82.7% 73.4%
75.1% 67.9%
75.1% 65.5%
76.6% 71.8%
77.0% 70.3%
Odds ratio
P Value (95% CI)
1.36 (1.10-1.68)
1.06E-04 1.73 (1.30-2.31)
1.43 (1.09-1.87)
1.59 (1.18-2.14)
1.29 (1.06-1.56)
4.74E-11 1.42 (1.28-1.58)
When expression levels of the full-length and 2 isoforms were analysed,
we observed clear differences owing to genotype of rs17266594. Those who
were homozygous for the T allele with the classical sequence of the branchpoint site150 displayed higher expression of the full-length isoform compared
with individuals who were homozygous for the C allele. For the 2 isoform,
reversed expression patterns were observed (Figure 4).
Figure 4. Relative mRNA levels of full-length (FL) and 2 isoforms correlates with
genotypes of the branch-point SNP rs17266594. (This figure is adapted from Fig 1C
in the published article: Kozyrev et al. Nat Genet. 2008 Feb;40(2):211-6.)
Both associated markers are affecting the protein sequence at the IP3Rbinding domain. The risk-associated allele of rs17266594, as previously
mentioned, increases the probability that the whole domain is excluded, and
rs10516487 encodes an amino acid substitution (R61H) within this domain.
This amino acid substitution may have an affect on the binding ability, owing to the different properties of the arginine ( R) and histidine (H). One may
speculate that the arginine, which is highly protonated under conditions of
physiological pH, would cause a stronger binding. The alleles of these two
SNPs are highly correlated, and the associated haplotype contains both al-
leles that are hypothesised to generate a BANK1 protein with higher capacity to interact with the IP3R.
Association with the SNP rs3733197, causing an alanine to threonine substitution (A383T) in the ankyrin domain, was also detected in our datasets.
The potential effect of alterations in ankyrin repeats were recently highlighted151, which makes this an additional potentially functional variant.
However, the association with this SNP was distinctively weaker than with
the other two in our datasets, which indicates that such an effect would be a
minor contributor to the association of this gene.
In conclusion, these findings implicate BANK1 as a susceptibility gene for
SLE, with variants affecting functional domains. The disease-associated
variants could contribute to hyperactive B cells with sustained reactions,
which is characteristic in SLE. Previously published studies have reported
that over-expression of BANK1 increases IP3R signalling148. A seemingly
disparate observation was that a knock-out mouse for the BANK1 gene
showed increased antibody secretion and germinal centre formation152. The
contradictions of these results may be owing to the differences in experimental design and model systems used. Furthermore and importantly, the
BANK1 gene was knocked-out by interruption of exon 2, which leaves the
possibility that the Δ2-isoform was still expressed. If the full-length and 2
isoforms have distinct functions, this could explain the apparent contradictions in the previous studies. Unpublished data indicates that the two isoforms may indeed have different roles in B cell activation153.
Paper IV: STAT4 associates with SLE through two
independent effects that correlate with gene expression
and act additively with IRF5 to increase risk
The Signal Transducer and Activator of Transcription 4 (STAT4) is a cytoplasmic transcription factor involved in cytokine signal transduction. It is
primarily activated by interleukin-12 (IL-12), and is an important factor in
both innate and adaptive immune reactions. In the initial stages of an immune response, STAT4 causes natural killer (NK) cells and antigenpresenting cells to produce interferon-γ (IFN-γ), cytokines and chemokines
that are critical in generating an inflammatory reaction108,154. It has been
demonstrated that Stat4 knock-out mice have impaired IL-12 mediated functions, including Th1 response, IFN-γ production, cellular proliferation and
NK cell cytotoxic activity155,156. Furthermore, in mouse models for several
autoimmune diseases, mice that are deficient for STAT4 exhibit less in-
flammation and milder disease symptoms than wild-type mice157-160. Other
studies observe that increased expression of IL-12 and IFN- γ in the kidneys
of MRL-lpr/lpr mice precede the development of glomerulonephritis161,162.
We found association to SLE with SNPs in this gene when analysing a
GWA scan of Argentinean patients and controls, and considered it a strong
candidate due to its function in regulation of immune functions. This gene
appears to have been discovered in parallel by several independent research
groups, and yielded strong signals in all three GWAS for SLE published thus
far, which speaks for a significant role in the risk of SLE28-30,106,107. The association of STAT4 has also been found to be particularly strong with severe
manifestations of SLE, especially nephritis163.
Results and discussion
Fine-mapping and replication
After detecting association with STAT4 in our Argentinean cohort, and verifying this association in other populations, we proceeded with fine-mapping
of the genetic region in a set of Spanish SLE cases and controls. This cohort
is considerably larger than the Argentinean and therefore has a higher power
to detect association. Since the STAT4 gene is located proximal to the STAT1
gene, which also has important functions in the immune system, we decided
to include both genes as well as the intergenic region in the fine-mapping.
We selected 29 tag SNPs in the STAT1-STAT4 region, and during the analysis of these SNPs, the article by Remmers et al was published, announcing
strong evidence for association with rs7574865 in their datasets. We then
chose to include this SNP in our fine-mapping.
The strongest association in our Spanish cohort was observed with SNPs
rs3821236 and rs3024866, with rs7574865 in third place, followed by
rs1467199 (Table 8). These four SNPs are spread across the major part of
the STAT4 gene, with rs1467199 in the intergenic region between STAT1 and
STAT4, rs7574865 in intron 3, and rs3024866 and rs3821236 (both in the
same LD block) in introns 12 and 16, respectively. Of the six LD blocks in
the STAT1-STAT4 region, we observe association in the four blocks covering
STAT4 and the intergenic region, but find no association in STAT1 blocks.
These results differ from the original study, where the association mainly
was restricted to intron 3106. A second, recently published fine-mapping
study, also detected association throughout most of the STAT4 gene, although it was concluded that the association could be explained by the intron
3 SNPs alone107.
The four strongest SNPs from our fine-mapping study were then genotyped in independent sets of cases and controls from Germany, Italy, Argentina and two sets from Mexico (adult and paediatric). In the German cohort,
only a weak borderline association with rs7574865 was observed (Table 8).
In the Italian and Mexican adult cohorts, we observed strong association
with rs7574865, as well as association with rs3821236 and rs3024866. In the
Argentinean set, the strongest association was observed with rs3821236, but
there was also strong association with rs7574865 and rs3024866, which is
similar to the results from the Spanish cohort (Table 8). These results indicate that the STAT4 gene may contain other effects independent of the previously reported rs7574865.
Two independent effects
Rs3821236 and rs3024866 are located in the same LD block, although not
in complete LD (r2=0.64). They are both poorly correlated with rs7574865
(r2 is 0.42 and 0.29, respectively). The correlation of rs1467199 with
rs7574865 is also low (r2=0.30). Conditional logistic regression analysis
indicated that there are two independent risk effects in the STAT4 gene, represented by rs7574865 and rs3821236: when conditioning on rs7574865,
rs3821236 remains significant, and vice versa. Analysis by PLINK164 also
showed that rs7574865 and the haplotype tagged by rs3821236 represent
two independent associations. A joint conclusion of these analyses would be
that there are at least two independent risk variants in the STAT4 gene, represented by rs7574865 and rs3821236 (or rs3024866). Association with
rs1467199, which was found in the Spanish and the Mexican paediatric cohorts, could possibly represent a third risk factor, however, less convincing
than the other two.
Population differences and meta-analysis
The results from our replication studies indicate that there may be population differences in susceptibility alleles for SLE in the STAT4 gene. It is not
unlikely that a certain degree of allelic heterogeneity exists, where various
risk alleles have different effects in different populations. Two other studies
of STAT4 and SLE have found association with SNPs in intron 3 but no
strong signals from other genetic regions in individuals of Northern European descent106,107. This is similar to what we observe in our German cohort.
However, the results from our study indicate that additional risk factors may
be involved in populations from Southern Europe and Latin America.
Homogeneity analysis revealed borderline heterogeneity between the
strata (p values ranging between 0.013 and 0.138). Thus, for a meta-analysis,
it is uncertain if a fixed effects model (for homogeneous strata) or random
effects (for heterogeneous strata) should be used. We therefore apply both
methods for meta-analysis for all for markers (Table 8). The Mexican paediatric set only showed association for rs1467199, and was significantly heterogeneous from the other cohorts for all four SNPs. We therefore excluded
this set from the analysis. It is not unlikely that child-onset SLE has, at least
partly, different mechanisms than adult-onset SLE.
Table 8. Population-specific replication of the main associated SNPs from the finemapping study, and meta-analysis using fixed and random effects (Mantel-Haenszel
and DerSimonian-Laird methods, respectively).
Mexico adult
Meta (fixed)
p value
p value
p value
OR p value
1.09E-07 1.7 2.07E-07 1.72
0.807 0.96
0.051 1.35
7.70E-04 1.66 2.98E-10 2.7
1.38E-02 1.47 9.11E-03 1.52
1.47E-04 1.66 3.01E-08 2.1
2.31E-12 1.51 4.44E-23 1.82
Meta (random) 1.41E-02 1.24 8.98E-11
OR = odds ratio
1.30E-04 1.48 7.81E-08 1.82
Analysis of interaction with IRF5
Several genetic risk factors for SLE have now been established, and it is
therefore of interest to investigate the relationships between these genetic
effects. Recently, association between SLE and variants within the IRF5
gene were reported102,103. Since both STAT4 and IRF5 are implicated in regulation of type-I IFNs, which have a well-documented role in SLE pathogenesis104,105,165, we wanted to examine the possibility of genetic interaction between the risk alleles. The four SNPs from STAT4 are analysed in combination with three associated SNPs in IRF5 using multiple regression analysis
and the c-statistic. No significant interaction between the risk alleles was
observed, which appeared to have additional effects. This conclusion was
also drawn by another group just recently107.
Functional analyses
To investigate the possibility of splicing variants underlying the association between STAT4 and SLE we sequenced cDNA from various human
tissues. Two isoforms were previously known (α and β), and we did not
detect any new variants with differences in amino acid sequence. However, a
wide variety of different 5’UTR sequences was detected. These previously
unpublished variants appeared to be tissue-specific and contained various
combinations of exons 1α and 1β with occurrence of intronic elements, as
well as two novel exons located upstream of the gene. The variations in the
5’UTR sequence may reflect the presence of tissue-specific promoters,
which could affect gene expression in different tissues. The possibility of
tissue-specific effects of STAT4 is also supported by functional observations. Increased expression of IL-12 and IFN-γ (both closely implicated with
STAT4) in the kidneys of MRL-lpr/lpr mice has been shown to precede development of glomerulonephritis. Furthermore, STAT4 polymorphisms were
recently reported to be particularly associated with kidney disease163.
We also performed expression analysis in human mononuclear cells from
73 individuals using quantitative real-time PCR (TaqMan). This analysis
indicated that homozygosity for the risk allele of rs3024866 was associated
with a modestly elevated increase in expression levels of the α isoform.
Similar correlations were also observed for rs3821236 and rs7574865, but
not for rs1467199. The β isoform followed the same pattern as α isoform,
although with much lower levels.
This study confirms the association between STAT4 and SLE. In addition
to the previously reported association of rs7574865 in intron 3106, we identify at least one independent risk factor represented by SNPs rs3024866 and
rs3821236 located in introns 12 and 16, respectively. This effect appears to
be the strongest in populations of southern European or Latin American origin. Correlation is also observed between moderately elevated expression of
STAT4 and risk alleles of rs3024866, rs3821236 and rs7574865. However,
the best correlation is not found with the most strongly associated SNP,
which indicates that the matter will need further investigations. Furthermore,
if two independent genetic effects are observed, these are likely to reflect the
presence of more than one functional variant.
Identifying the functional variants responsible for the association of
STAT4 may prove difficult considering the size of the gene and the potential
presence of several effects. However, the overwhelming evidence for a role
in SLE from several large association studies indicates that it may be worth
the extra effort.
Concluding remarks
SLE is a complex disease with substantial heterogeneity regarding both
symptoms and aetiology. Until quite recently, there were relatively few
breakthroughs in the genetics of SLE and other complex diseases. However,
the last few years have brought highly convincing evidence for a number of
risk genes, much thanks to the combination of technical advancements and
large collaborations, enabling comprehensive association studies of large
cohorts. If the launch of GWA studies brought a sudden boost, the speed of
detecting new disease variants will most probably decelerate in the future.
On the other hand, the future may bring new technical inventions, which
create opportunities to detect similar amounts of information regarding the
underlying mechanisms of SLE.
In this thesis, five candidate genes for SLE are analysed. Two of these
genes, PD-L1 and PD-L2, appear not to contain any major risk factors for
SLE in the analysed European and South American populations. In two other
genes, CD24 and STAT4, there appears to be population-specific effects. The
A57V amino acid substitution in the CD24 antigen, previously implicated
with MS, was associated in a Spanish cohort, with a weak trend in German
samples, and no association in Swedish. The previously reported and highly
convincing association of the STAT4 transcription factor was confirmed in
all our cohorts. Interestingly, the results indicate the presence of at least two
independent risk variants: the first, represented by a previously reported
SNP, was the strongest in individuals of Northern European ancestry, and
the second was more pronounced in individuals from Southern Europe and
Latin America. We also report the identification of a novel susceptibility
gene, BANK1. This gene encodes a scaffold protein involved in B-cell activation, and contains functional variants affecting important domains, which
are associated in all investigated cohorts from Europe and Latin America.
The results of these studies confirm the existence of replicable associations
between genetic variants and SLE, which are common and present in many
populations. The results also illustrate a certain degree of heterogeneity,
where some risk factors could have variable effect in different populations.
The identification of genetic variants associated with SLE is a first step in
obtaining knowledge regarding the causes of the disease. It will now be of
high interest to search for functional explanations to the increase in susceptibility. For several associated genes, some clues have been identified to how
the genetic risk variants affect protein function and expression. However,
much remains to be unravelled regarding these and other associated risk
variants and the effect in their pathways. The identification of causative
variants and their downstream consequences will be a major challenge in the
field of complex disease genetics. Nevertheless, such knowledge may be of
great value for developing new tools for diagnosis and treatment.
I would like to express my sincere gratitude to the following people, without
whom this thesis would not have seen the light of day. I would especially
like to thank:
My supervisor, Marta Alarcón-Riquelme, for convincing me to start my
graduate studies, for your admirable level of enthusiasm and energy, and for
always being available for discussions, local or trans-Atlantic, and regarding
small or big issues.
My co-supervisor, Ulf Gyllensten, for valuable discussions and for creating a stimulating research environment in the ‘PCR group’ and the Rudbeck
All the past and present members of the SLE group: Cissi, who almost
deserves an acknowledgement page of her own for all her valuable input,
especially regarding the exhausting story of the ligands, and for being a great
desk neighbour and fika friend. Veronica, for introducing me to the field of
SLE genetics back in my undergraduate days. Bobo and Ludmila, our NIH
stars, for your inspiring passion for science. Sergey, for excellent work on
‘functional stuff’ on both paper III and IV. Casimiro, for asking all those
tricky questions and for entertaining the office with exotic Spanish expressions. Hong, for all the work with DNA samples, and for keeping me company during odd hours at work and at the gym. Prasad, for giving me the
BANK1 project when you were too busy with IRF5, for excellent Indian
cooking, and for being such a nice guy and true gentleman. Angélica, my
fellow TaqWoman, for great work on paper IV, for good comments on this
thesis, and for all the discussions regarding statistical methods, Colombian
practical jokes etc. Ammar, for discovering the 2 isoform, and for all the
nice talks at lunch and fika. Juan, for keeping an eye on the lab during the
night shift and for your great smile. Sara, for organising the weekly group
fika. Helga, Eduardo and Bryndis, for founding a strong Icelandic branch
of the group. Serena, for taking good care of my desk when I was away, and
for your great smile and Italian determination. And finally, all the other nice
and enthusiastic students through the years, especially Madhu, Sandra,
Susanna, Jonas and David.
The rest of the ‘PCR group’, for making meetings and lunch breaks fun
and/or interesting: Marie Allen’s forensic girls Hanna x2, Martina (CSI
Stockholm), Anna-Maria, and all their nice students. Ulf G’s group, including Emma, Malin, Anna W, Jessica, Inger G, Max, Åsa, Anna B and
Veronika V. The former members Lucia and Marie S, and all the new
members like Tobias et al.
Genomcentret, especially Inger J, Jenny, Charlotte, Ann-Sofi and
Katarina for help with sequencing, for letting me borrow your machines etc
and for fun lunch and coffee breaks.
All the people that make things work at work. Especially Gunilla Å,
Frida, Ulla, Lena, and the other administrators, and the computer guys PerIvan (PIW) and Viktor.
All other people on the 2nd and 3rd floor of the Rudbeck Laboratory for
making it such a nice working place. Ulf P’s group: Lioudmila, Sara,
Mårten and the rest. The Lannfelt group, especially Anna, Hille, and Charlotte. Niklas Dahl’s group, especially Anne-Sophie, Miriam, Malin, Larry
and Johanna, and all the other nice people with whom I’ve shared many
‘close’ moments in the not-so-spacious kitchen… Maja, Mehdi, my låtsastvilling Jenny (thanks for help with spiking-issues), my teaching colleagues
Caroline and Chatarina…
Also, my sincere thanks to all collaborators at research centres and clinics, and all participating patients.
Jag vill också rikta ett stort och hjärtligt tack till alla för mig viktiga personer i världen utanför Rudbeck:
Världens bästa mamma och pappa, tack för att ni trott på mig och stöttat
mig i allt! Världens bästa och bussigaste syster Stina, som till och med kom
och städade mina badrum när jag inte hann med! Och alla trevliga och roliga
släktingar och ingifta familjemedlemmar, som sällan missar ett stort kalas,
även om det är på andra sidan Sverige.
Stort tack också till alla de vänner som bidragit till att göra studietiden så
rolig. Världens bästa Sofie & Liv som hängt med hela livet, känns det som.
Alla trevliga kursare på biologprogrammet, särskilt Liv J. Världens kanske
näst bästa studentorkester Glasblåsarna, med Lotten, Petter, Sanna,
Lise… och alla andra roliga och svårt musikaliska personer. Äsch, ni vet
vilka ni är allihop. Och tack till Jenny, Linda och Karin i Barnvagnsmaffian för att ni sett till att jag fått både schysst motion och en jämn koffeinnivå
hela våren.
Sist och såklart störst tack till min familj. Världens bästa Klas, som varit
världens bästa hemmafru hemmaman under den här intensiva avhandlingsskrivar-, husköpar- och hussäljarhösten. Tack för allt ditt stöd, för alla cashewnötter och vaknätter, och för allt kul vi haft. Och tack mina älskade
Alva och Stig för att ni alltid påminner mig om vad som är viktigt i livet.
Om jag utelämnat någon viktigt person ber jag allra ödmjukast om ursäkt,
och ber denne fylla i nödvändig kompletterande information här nedan.
I especially wish to thank ………………………………………………
(your name here)
(please select)
‡ all your help
‡ giving me inspiration
‡ being such a nice person
‡ all the fun times we’ve had
‡ …………………………..
‡ all of the above
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Acta Universitatis Upsaliensis
Digital Comprehensive Summaries of Uppsala Dissertations
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