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TRANSCRIPT
Genetic Variants in ST2 Previously Associated with
Asthma are Associated with Asthma and sST2
Levels in a Brazilian Population Living in an Area
Endemic for Schistosoma mansoni
Bergljót Rafnar Karlsdóttir
Thesis for BSc-degree in Medicine
University of Iceland
School of Health Sciences
Genetic Variants in ST2 Previously Associated with Asthma are Associated
with Asthma and sST2 Levels in a Brazilian Population Living in an Area
Endemic for Schistosoma mansoni
Bergljót Rafnar Karlsdóttir1
Thesis for the degree of B.Sc. in Medicine
Supervisors: Dr. Kathleen C. Barnes2, Dr. Candelaria Vergara
2, Dr. Li Gao
2,
Dr. Rasika Mathias2
1Department of Medicine, University of Iceland
2 Division of Allergy and Clinical
Immunology, Department of Medicine, The Johns Hopkins University
Department of Medicine
School of Health Sciences
June 2013
© Bergljót Rafnar Karlsdóttir, 2013
Printing: Háskólaprent
Reykjavík, Iceland, 2013
i
Abstract
Introduction. Asthma is a common, heterogenous syndrome of the respiratory system
estimated to affect as many as 300 million people worldwide. A strong overlap between
immune response mechanisms responsible for both asthma and resistance to schistosomiasis,
a helminth infection, in addition to genetic linkage and association data, supports the
hypothesis that genetic determinants conferring resistance to schistosomiasis are also
associated with greater risk of asthma. Genome-wide association studies (GWAS) have
identified IL1RL1, also known as ST2, as one of the most consistently associated candidate
genes for asthma. The aims of this study are to determine whether genetic variants of ST2,
previously associated with asthma and serum ST2 levels in asthmatics, are co-associated with
resistance to schistosomiasis and serum ST2 levels in participants exposed to schistosomiasis.
Materials and methods. 697 Brazilian DNA samples stored in -80°C were selected and
prepared for genotyping. Four ST2 SNPs associated with asthma and soluble serum ST2
(sST2) levels in an African-American population from Baltimore/Washington were genotyped
using TaqMan ABI 7900. Hardy-Weinberg testing and tests for Mendelian inconsistencies
were performed using PLINK, and pairwise linkage disequilibrium estimations using
Haploview. Tests for genetic association were performed using a generalized estimating
equation (GEE) under a dominant model on the soluble adult worm antigen (SWAP)-specific
IgE/IgG4 ratio (a measure of resistance to Schistosoma mansoni infection) and sST2 levels.
Variables were log-transformed adjusting for age and gender as needed.
Results. Three ST2 markers (rs1420101, rs12712135 and rs6543119) previously associated
with asthma in African Americans are co-associated with changes in sST2 levels in a study
population of Brazilians. One marker positively associated with asthma in African-Americans
(rs76930359) is negatively associated with wheeze in the study population. The four genetic
variants in ST2 are not associated with prevalence or severity of infection by S. mansoni,
IgE/IgG4 ratio, nor tIgE levels in this population.
Conclusion. The coded allele C in the SNP rs76930359 is negatively associated with wheeze
and thus could be linked with protection against asthma in this population. The SNPs
rs1420101, rs12712135 and rs6543119 are associated with changes in sST2 levels in both
Brazilian and African American populations and may be an interesting area of study, for
example, in the research on sST2 as a biomarker for disease. Further analyses are needed to
better understand the role of variants in ST2 in asthma, expression of sST2, and infection by
Schistosoma mansoni.
ii
Table of contents
Abstract ....................................................................................................................................... i
Table of contents ........................................................................................................................ ii
List of figures ............................................................................................................................ iv
List of tables and graphs ............................................................................................................. v
List of abbreviations .................................................................................................................. vi
Acknowledgements ................................................................................................................. viii
1 Introduction ......................................................................................................................... 1
1.1 Atopy and allergy ........................................................................................................ 1
1.1.1 Definitions of atopy and allergy ....................................................................... 1
1.2 Asthma ......................................................................................................................... 1
1.2.1 Prevalence and definition of asthma ................................................................. 1
1.2.2 Modifiers of risk for asthma ............................................................................. 2
1.2.3 Asthma and the immune system ....................................................................... 2
1.3 Race and the genetics of allergic disease ..................................................................... 2
1.3.1 Linkage Disequilibrium (LD) and analyses of genetic association .................. 3
1.4 Genome-wide association studies (GWAS) of asthma ................................................ 3
1.4.1 Advantages of GWAS ...................................................................................... 3
1.4.2 Limitations to GWAS ....................................................................................... 4
1.4.3 Meta-analyses of GWAS – GABRIEL and EVE ............................................. 4
1.5 IL-33 and ST2 .............................................................................................................. 5
1.5.1 IL-33 ................................................................................................................. 5
1.5.2 ST2 .................................................................................................................... 7
1.5.3 sST2 levels in disease ....................................................................................... 8
1.6 Schistosomiasis ............................................................................................................ 9
1.6.1 Prevalence of schistosomiasis .......................................................................... 9
1.6.2 Schistosoma species and their life cycles ......................................................... 9
1.6.3 Schistosomiasis and the immune system ........................................................ 10
1.7 Asthma and schistosomiasis ...................................................................................... 10
1.7.1 Interaction between asthma and infection with S. mansoni ............................ 10
iii
1.7.2 Co-associations in genetics of asthma and schistosomiasis ........................... 11
2 Aim of study ..................................................................................................................... 11
3 Materials and methods ...................................................................................................... 12
3.1 Previously existing data - location and study population .......................................... 12
3.1.1 Demographic information and sample collection ........................................... 12
3.1.2 Evidence of asthma and schistosomiasis ........................................................ 12
3.1.3 Assessment of water contact. .......................................................................... 13
3.2 Genotyping ................................................................................................................ 13
3.2.1 The Taqman method ....................................................................................... 13
3.2.2 Genotyping Quality Control. .......................................................................... 14
3.3 Measurements of sST2, tIgE and the IgE/IgG4 ratio ................................................ 14
3.3.1 sST2 Levels .................................................................................................... 14
3.3.2 Total IgE measurements ................................................................................. 14
3.3.3 IgE/IgG4 ......................................................................................................... 14
3.4 Statistical Methods. ................................................................................................... 14
3.4.1 Data testing and analyses ................................................................................ 14
3.4.2 Population stratification .................................................................................. 15
4 Results ............................................................................................................................... 16
4.1 Clinical characteristics of the analyzed subjects ....................................................... 16
4.2 sST2 levels compared to other traits in the total population ..................................... 18
4.3 Analyzed SNPs – a closer look .................................................................................. 19
4.4 GEE analyses of ST2 markers and other traits .......................................................... 21
5 Discussion ......................................................................................................................... 24
5.1 Clinical characteristics compared to other populations. ............................................ 24
5.2 Associations between sST2 levels and other traits .................................................... 26
5.3 Associations between genetic variations in ST2 and other traits ............................... 26
5.4 Conclusions ............................................................................................................... 29
References ................................................................................................................................ 30
iv
List of figures
Figure 1 IL-33/ST2 signaling .............................................................................................. 6
Figure 2 Human ST2 ............................................................................................................ 7
Figure 3 Transmission cycle of schistosomes ...................................................................... 9
Figure 4 Plots of the first principal components from analysis on Brazilians founders ..... 15
v
List of tables and graphs
Table 1 Clinical characteristics of the analyzed subjects .................................................... 16
Table 2 Clinical characteristics of the analyzed subjects distributed by gender ................. 17
Table 3 Pairwise GEE analysis of sST2 and other traits in the total population ................. 18
Table 4 HWE and MAFs of sST2 markers in a Brazilian population. ................................ 19
Table 5 GEE analysis of ST2 markers and wheeze in a Brazilian population..................... 21
Table 6 GEE analysis of ST2 markers and sST2 levels in a Brazilian population .............. 22
Table 7 GEE analysis of ST2 markers and SWAP, total IgE and egg count in a Brazilian
population. ................................................................................................................................ 23
Graph 1 Gene structure of ST2 and LD pattern of analyzed markers in a Brazilian
population ................................................................................................................................. 20
vi
List of abbreviations
AFR: African
ASN: Asian
CEU: CEPH (Utah residents with ancestry from northern and western Europe)
CHB: Han Chinese in Beijing, China
Chr.: Chromosome
CI: Confidence Interval
COPD: Chronic Obstructive Pulmonary Disease
DNA: Deoxyribonucleic Acid
ELISA: Enzyme-Linked Immunosorbent Assay
EUR: European
GEE: Generalized Estimating Equation
GWAS: Genome-Wide Association Study
HWE: Hardy Weinberg Equilibrium
IFNγ: Interferon gamma
Ig: Immunoglobulin
IL-: Interleukin
IL1RAcP: IL-1 Receptor Accessory Protein
IL1RL1: Interleukin-1 Receptor-Like 1
IRAK: IL-1R-Associated Kinase
JNK: JUN N-terminal Kinase
kb: kilobases
LD: Linkage disequilibrium
vii
MAF: Minor allele frequency
MyD88: Myeloid Differentiation primary response protein 88
NF-κB: Nuclear Factor kappa-B
No.: Number
ORMDL3: Orosomucoid like 3
PC: Principal Component
PCA: Principal Component Analysis
PCR: Polymerase chain reaction
SNP: Single nucleotide polymorphism
ST2: suppressor of tumorigenicity 2
SWAP: Soluble adult worm antigen preparation
Th: T-helper
tIgE: total Immunoglobulin E
TIR: Toll-like/Interleukin-1 receptor
TNFα: Tumor Necrosis Factor alpha
TR: Transmembrane
TRAF-6: TNF Receptor-Associated Factor 6
USA: United States of America
YRI: Yoruba in Ibadan, Nigeria
viii
Acknowledgements
I would first and foremost like to thank my supervisor, Kathleen Barnes, for allowing me the
opportunity to work with her and her team at Johns Hopkins University. Thank you to
everybody at the Division of Allergy and Clinical Immunology who helped me with this
project, in particular; Monica Campbell for teaching me the Taqman method for genotyping,
Nicholas Rafaels for helping with statistical analyses, Candelaria Vergara for helping with
interpretation of data and Romina Ortiz for her assistance and friendship.
I would also like to thank my parents, Þórunn Rafnar and Karl Ólafsson, for their love, help
and support.
1
1 Introduction
1.1 Atopy and allergy
1.1.1 Definitions of atopy and allergy
Atopy refers to the immune system producing specific IgE (Immunoglobulin E) in response to
otherwise harmless environmental substances known as allergens. This is reflected in either a
positive skin prick test or the presence of specific IgE to one or more known allergens in
serum. Elevated total IgE is also sometimes used as an equivalent to atopy.
Allergy is a detrimental immune response to allergens which results in allergic diseases such
as asthma, atopic dermatitis, eczema, food allergy and allergic rhinitis. Atopy and allergic
diseases are strongly associated with each other, however, not everybody with clinical
manifestations of an allergic disease can be shown to be atopic nor do all atopic persons
display signs of an allergic disease.1,2
1.2 Asthma
1.2.1 Prevalence and definition of asthma
Asthma is a common, heterogeneous syndrome of the respiratory system with a global
prevalence ranging from 1% to 18% of the population in different countries.3-7
Asthma
prevalence is increasing in most countries, especially among children.3 It is estimated to affect
as many as 300 million people worldwide and is expected to increase to 400 million by the
year 2025.3,4
Asthma is believed to account for 1 in every 250 deaths worldwide, resulting in 250,000
deaths each year.3,4
Many of the deaths are preventable, being due to suboptimal long-term
medical care and delay in obtaining assistance during the final acute attack.8,9
Asthma is a chronic, obstructive lung disease and is characterized by the presence of chronic
inflammation in the lower airways with variable and reversible airway obstruction and
bronchial hyperresponsiveness. Clinical symptoms include recurrent episodes of coughing,
wheezing, breathlessness, and chest tightness.2,3
A hallmark of other obstructive pulmonary diseases, such as emphysema and chronic
bronchitis, is change in patient spirometry. These changes are not always present in asthma
and as there are no known biomarkers or tests that can definitively diagnose asthma, diagnosis
is based on the presence of clinical features and exclusion of diseases that might mimic the
symptoms of asthma.3,9
2
1.2.2 Modifiers of risk for asthma
Asthma has many different phenotypes that depend on both genetics, with heritability
estimates varying between 35% and 95%,10-24
and environmental factors as well as the
interaction between these factors.
Several associations have been introduced by epidemiologic studies between environmental
exposures at critical times in development and the subsequent risk for asthma and other
allergic diseases. Modifiers of risk that have been reported in the literature are for example
race/ethnicity,25
sex,26
passive27-31
and active32-34
tobacco smoke exposure and many more.
Further research is needed to clarify the importance of each and to determine the mechanisms
through which each of these factors modifies risk for asthma and allergy.
1.2.3 Asthma and the immune system
Asthma is predominantly a disease of the Th2 (T-helper 2) immune response with production
of inflammatory cytokines such as IL-4 (Interleukin 4), IL-5 and IL-13 and subsequent
production of IgE.35
Recent studies have demonstrated a role played by the Th1 immune
response as well as other cytokines, such as IL-25 and IL-33, in the pathogenesis of asthma.36
1.3 Race and the genetics of allergic disease
The population of this study was ascertained from five communities (Buri, Camarao,
Genipapo, Sempre Viva and Cobo) in the district of Conde, Bahia, Brazil. Brazilians represent
a trihybrid population as the result of the admixture of West African, European, and
Amerindian ancestral populations, with a substantial African component in Bahia.37
This is important to note because of racial differences in both expression of asthma and
genetics. Phenotypic expression of the disease may vary between racial groups and can also
be confounded by environmental differences. The frequency of genetic variation varies
between races so an important disease susceptibility allele common to one race and
uncommon in another may not be detected due to lack of statistical power and sample size
required to identify the rarer variant. In addition, for some genetic variants, the most common
form (allele) is the less frequent form in a different race.38
3
1.3.1 Linkage Disequilibrium (LD) and analyses of genetic association
Another point to note is that the correlation between genetic variants in a gene may differ
based on historical geographical ancestry. A SNP (single nucleotide polymorphism) can be
strongly associated with one or more SNPs in the same gene or even across neighboring
genes, termed linkage disequilibrium (LD). When SNPs are in full LD they are always
inherited together and thus represent a single genetic signal. However, LD between SNPs can
vary between populations. In order to locate genetic variants which influence disease, such as
asthma, several different approaches have been explored. Linkage analyses of families with a
high prevalence of disease could only identify large regions containing many co-segregated
genes and candidate gene studies tended to be underpowered and hard to replicate. Thus
association studies of asthma have, until recently, been largely unsuccessful.
1.4 Genome-wide association studies (GWAS) of asthma
The number of identified asthma susceptibility genes has increased rapidly over the last few
years, especially with the application of the GWAS approach. In an asthma GWAS 300.000 to
more than a million DNA (deoxyribonucleic acid) polymorphisms covering the genome are
investigated for association with asthma in large samples of cases and control subjects.39
1.4.1 Advantages of GWAS
The greatest advantage of genome-wide approaches is that they can discover novel genes and
pathways involved in disease pathogenesis. Other, older methods (the candidate gene studies,
for example) focus primarily on polymorphisms in genes with a known function. In candidate
gene studies of asthma bias lay heavily on genes with immunologic functions though genes
with other functions have also been identified. GWASs do not require the studying of
families40
and should, in theory, be able to detect associations between disease and common
variation in the genome.
4
1.4.2 Limitations to GWAS
A limitation of GWAS approaches is the statistical burden that results from the large number
of tests performed and the resulting requirement for very large sample sizes to achieve
genome-wide statistical significance.
The correction for multiple tests will recommend a typical P-value requirement of less than
.2 Thus, real existing associations which do not reach this limit will not be recognized by
a GWAS analysis.
Another limitation of the GWAS is that it does not detect less common risk variants, both
because the genotyping platforms include mostly common variants and because of the
reduced power to detect associations with SNPs with low (less than 1%) minor allele
frequencies (MAFs). In fact, the GWASs to date, across many diseases including asthma,
suggest that the risk alleles identified by this approach account for a very small proportion of
the genetic risk thus indicating that rarer variants may have a larger effect on disease risk.2
For example, the seven most associated SNPs in the GABRIEL meta-analysis (discussed
below) could accurately classify individuals with asthma with only 35% sensitivity.41
Therefore, although the variants show significant associations with asthma, they are in general
poor predictors of disease status, reflecting their small effect size and common allele
frequencies in the population.
Correlation found between SNPs in GWASs can also make it difficult to determine which
SNPs should be investigated further when they are in high LD with each other. For example,
ORMDL3 (Orosomucoid like 3) was identified as a novel asthma susceptibility locus on
chromosome 17p21 in the first GWAS of asthma, published in 2007.42
Association was
observed between SNPs in multiple genes in this region on chromosome 17 leading to the
article “Guilt by Association” which raised the question as to whether ORMDL3 is the
relevant gene.43
In this example, it will be necessary to identify all associated variants,
common and rare, and test them separately to reach a final conclusion.
1.4.3 Meta-analyses of GWAS – GABRIEL and EVE
The need for large sample sizes to achieve statistical significance has encouraged
collaborations and meta-analyses of GWASs where smaller studies are combined to increase
power. Two meta-analyses of asthma GWAS have recently been completed, one by the
GABRIEL Consortium of European Investigators including subjects of European ancestry41
5
and one by the EVE Consortium of U.S. Investigators including racially and ethnically
diverse subjects from the USA and Mexico.44,45
Both large meta-analyses of asthma GWASs produced similar results.2,41,44,45
SNPs in or near seven loci were associated with asthma in both studies, and SNPs in or near
four of these loci had P-values at or near genome-wide levels of significance in both studies
with contributions from ethnically diverse samples. Two of these loci were IL1RL1
(Interleukin 1 Receptor-Like 1), also known as and from now on referred to as ST2
(Suppressor of Tumorigenicity 2), and IL-33. Both can be considered robustly associated
asthma susceptibility genes.
1.5 IL-33 and ST2
IL-33 and ST2 are two of the most consistently associated candidate genes for asthma.41,46
1.5.1 IL-33
The IL-33 gene, located on chromosome 9, spans approximately 42.2 kilobases (kb),
harboring 8 exons.39
The gene encodes a protein, also named IL-33, which is a member of the
IL-1 cytokine family and was first discovered as a nuclear factor in endothelial cells of high
endothelial venules in 2005.47
IL-33 has two main functions; as an intracellular transcription factor, influencing gene
transcription independent of its receptor, and as a cytokine, signaling through its receptor
complex.
IL-33 works as a nuclear factor by binding directly to the chromatin48,49
as well as to the
NF-κB (Nuclear Factor kappa-B) proteins p50 and p65.50
In doing so, IL-33 is capable of
regulating the expression of pro-inflammatory genes, such as IL-6, IL-8, and the p65 subunit
of NF-κB51,52
and thus might be a direct regulator of the inflammatory response.
Cellular necrosis and tissue damage can lead to the release of the full-length functional IL-33
protein from leaking cells, promoting inflammation.53,54
In contrast, during apoptosis, IL-33 is
cleaved by caspases and released in its biologically inactive form.53,55,56
(Figure 1)
This mechanism was probably retained through evolution to limit the release of bioactive
full-length IL-33 during programmed cell death.54
6
Because IL-33 is released on injury or damage, it has been described as an “alarmin,”
translating damage into activation of the inflammatory response.
Once released, biologically active IL-33 activates a heterodimeric receptor complex
containing an isoform of ST2 and IL1RAcP (IL-1 Receptor Accessory Protein).47,57
(Figure 1)
Binding of IL-33 to the ST2/IL1RAcP receptor complex recruits signaling adaptor proteins.
These proteins activate pathways that induce gene expression leading to, for example,
cytokine and chemokine synthesis.54
A large number of cell types relevant to asthma
pathogenesis have been shown to express ST2 and to be responsive to IL-33,58
including Th2
cells,59
mast cells,60,61
invariant natural killer T cells,62
eosinophilic and basophilic
granulocytes63,64
and epithelial cells.65
By activating these cells, IL-33 has been
shown to mediate a wide range of
responses, including Th17- mediated
airway inflammation66
and neutrophil
influx.67
However, the best described
activity of IL-33 is the activation of
innate and adaptive immune responses
characterized by the production of IL-4,
IL-5, and IL-13.39
Through its effects on both the innate
and adaptive immune responses, IL-33
can promote the pathogenesis of asthma
and exacerbate various other allergic
diseases as well as being host-
protective against helminthic
infection.54,68
Figure 1 IL-33/ST2 signaling. ST2L: transmembrane
isoform of ST2; sST2: soluble isoform of ST2;
IRAK: IL-1R-Associated Kinase; JNK: JUN N-
terminal Kinase; MyD88: Myeloid
Differentiation primary response protein 88;
TRAF-6: TNF Receptor-Associated Factor 6.
(From Mueller, T. and B. Dieplinger 2013)
7
1.5.2 ST2
The ST2 gene, which is located on chromosome 2q12, spans approximately 40.5 kb,
harboring 11 exons and a distal and a proximal promoter.39
It was first identified in oncogene-
or serum-stimulated fibroblasts54,69,70
and encodes proteins with an extracellular region
carrying three immunoglobulin-like domains, a transmembrane (TM) domain, and an
intracellular region harboring a Toll-like/IL-1 receptor (TIR) domain.40
(Figure 2)
Three transcripts of the ST2 protein are expressed through alternative splicing; a short isoform
encoding the soluble protein sST2,69
a long isoform encoding the full transmembrane receptor
ST2L,71
and a less well-known variant that encodes a truncated protein with two
immunoglobulin-like domains and a hydrophobic tail called ST2V.39,72
Binding of ST2L with its ligand on the surface of basophils, eosinophils and mast cells
promotes their activation,63
increased adhesion and survival73
and degranulation,74
respectively. Functionally, ST2L acts to transduce the IL-33 signal (discussed above) to the
intracellular compartment while the soluble sST2 functions as a decoy receptor, capturing IL-
33 and inhibiting its function.39
(Figure 1) sST2 corresponds to the extra-cellular domain of
ST2L except for nine amino-acids in the C-terminal region.75
sST2 has a relatively ubiquitous
tissue distribution showing the highest levels of the secreted form in the lung followed by the
heart and the brain.76
Figure 2 Human ST2. A schematic representation of the ST2 gene with its intron/exon structure and a
depiction of the protein structure of sST2 and ST2L. Also depicted are the immunoglobulin (Ig)
domains common to sST2 and ST2L and the transmembrane (TM) and the class-identifying
Toll-like/Interleukin-1 receptor (TIR) domain unique to ST2L.
(From Kakkar, R. and R.T. Lee 2009.)
8
1.5.3 sST2 levels in disease
sST2 levels in serum have been studied and used as a biomarker for disease severity and
outcome, for example in sepsis,77
dengue,78
chronic, obstructive pulmonary disease
(COPD),79
acute myocardial infarction80
and heart failure76,80
as well as atopic asthma.81
Because the elevated sST2 levels are not specific for a certain disease group they cannot be
used as a definitive diagnostic test. However, sST2 has emerged as a powerful prognostic
marker in many clinical settings.82
9
1.6 Schistosomiasis
1.6.1 Prevalence of schistosomiasis
Schistosomiasis is an often neglegted tropical disease endemic in 74 countries in regions of
Africa, Asia, and South America.83
It is a major public health concern, and according to recent
estimates approximately 200 million individuals are infected, with a further 700 million
people living at risk of infection.84,85
1.6.2 Schistosoma species and their life cycles
Schistosomiasis is caused mainly by the blood flukes Schistosoma mansoni, S. haematobium,
and S. japonicum.83
S. japonicum (China and Southeast Asia) and S. mansoni (Africa, Arabia,
and South America) dwell in peri-intestinal venula, and cause intestinal and hepatosplenic
schistosomiasis. S. haematobium (Africa and Arabia) live in the perivesical plexus and causes
urinary schistosomiasis of the bladder, ureters, and kidneys. Infection occurs where the human
host comes into contact with water that harbors the intermediate snail host for Schistosoma
species. Schistosomes have a life cycle that runs
from the primary host to water, to the secondary
host of a freshwater snail, back to water and
then penetrating the skin through water contact
the helminth returns to the human.86
(Figure 3).
Figure 3 Transmission cycle of schistosomes. (A) Adult
schistosomes in small blood vessels around the
intestines or bladder. (B) Schistosome eggs
leave the body with excreta. Left-to right: S.
haematobium, S mansoni, and S. japonicum.
(C) Miracidium hatches and swims freely in
water. (D) Snail intermediate hosts are infected
by miracidium. Left-to-right: Oncomelania,
Biomphalaria, and Biomphalaria. (E) Cercariae
hatch from snails 4 to 6 weeks later.
(F) Cercariae infect humans through the skin.
(G) Cercariae develop into schistosomula,
which migrate with the bloodstream to the
portal vein and mature into adult schistosomes,
which mate and migrate to the destination.
(From Gryseels B, Polman K, Clerinx J, et al.
2006)
10
The species of Schistosoma that causes most infections in Brazil is S. mansoni83
and will
therefore be the main focus of this paper.
1.6.3 Schistosomiasis and the immune system
Soon after infection and before the egg-laying phase, the immune response is predominantly
the Th1 inflammatory immune response, with high levels of TNFα (tumor necrosis factor
alpha), IL-1, IL-6 and IFNγ (interferon gamma) characterizing the acute phase of
schistosomiasis.87
The natural progress of the disease leads to liver and intestinal injury
caused mainly by the immune response against S. mansoni eggs deposited in these sites. In
the chronic phase of the disease, there is a predominance of the Th2 immune response to egg
antigens, with low production of IFNγ and high levels of IL-4, IL-5, IL-13 and IL-10 as well
as IgE.88-91
Resistance to reinfection with schistosomes has been associated with a Th2 immunity and
consequent high level of IgE.83,92
High levels of IgE against SWAP (soluble adult worm
antigen preparation) have been associated with resistance to reinfection,93,94
whereas high
levels of IgG4 (Immunoglobulin G4) against adult worm and egg antigens have been
associated with susceptibility to reinfection.92,95
The balance between IgE and IgG4 might
determine resistance or susceptibility to S. mansoni infection, thus the IgE/IgG4 ratio has
become a biomarker of Schistosoma resistance.95,96
1.7 Asthma and schistosomiasis
1.7.1 Interaction between asthma and infection with S. mansoni
Exposure to allergens in an individual with asthma and infection with S. mansoni each elicit a
Th2-mediated immune response and stimulate production of IgE.91
In regions endemic for
extracellular parasitic diseases, such as schistosomiasis, there is a lower prevalence of atopy
overall and, among patients with asthma, less severe disease.97-101
This indicates that helminth
infections protect against allergic disease. The reverse is also true: asthma and history of
atopy appear to confer resistance against helminthic parasites such as S. mansoni.97-101
Additionally, persons with asthma who also have schistosomiasis have been shown to
experience worsening in asthma severity after antihelminthic treatments.102
11
1.7.2 Co-associations in genetics of asthma and schistosomiasis
There is extensive evidence for a genetic basis for schistosomiasis and of particular interest is
how many of the associated loci overlap with asthma susceptibility.103
For example, the
linkage to schistosomiasis in chromosome 5q31–q33 is in the same locus where some of the
most compelling evidence for linkage to asthma and atopy have been reported104-108
and
multiple polymorphisms in genes within the Th2, IgE-mediated pathway are co-associated
with both traits.103
2 Aim of study
Asthma and schistosomiasis both have a strong genetic component that often overlaps. One of
the most commonly associated candidate genes for asthma is ST2, encoding the receptor for
IL-33. The aim of this study is to determine whether genetic variants in the ST2 gene, which
have previously been positively associated with asthma and sST2 levels in an African-
American population,109
are also associated with asthma, schistosomiasis, sST2 levels and
other related factors in a study population from Brazil.
12
3 Materials and methods
3.1 Previously existing data - location and study population
This study was performed using samples and data acquired for previous studies of this
Brazilian population.
Asthmatic subjects and their families were recruited in five communities (Buri, Camarao,
Genipapo, Sempre Viva and Cobo of the district of Conde, Bahia, Brazil) between July 23
and September 2, 2004.110 Those who had an available DNA sample for genotyping and
complete phenotype data were included in this analysis.
3.1.1 Demographic information and sample collection
Basic demographic information and data on environment exposure was collected as well as
information on family relationships for all first-degree relatives. Willing participants were
asked to give blood samples for storage and future genotyping and serum for measuring total
IgE levels. All adults provided written consent (or verbal consent recorded by a witness), and
children gave verbal assent and written consent was obtained from a parent or guardian.
Children under the age of 6 were excluded.
Blood was collected through venipuncture and serum was separated for measuring total-IgE
(tIgE) levels.
The research protocol was approved by the Institutional Review Boards of the Johns Hopkins
University School of Medicine and of the Federal University of Bahia and was endorsed by
the National Commission for Ethics in Human Research in Brazil.110
3.1.2 Evidence of asthma and schistosomiasis
In this population, asthmatic individuals were identified using a modified ISAAC
questionnaire.111,112
Previous investigations in this region verified the endemicity of schistosomiasis.98,99,101
Regular, publicly administered mass-treatment campaigns against helminthic infection last
occurred during 2001.110
13
Two fecal samples were collected from subjects at an interval of 2–40 days. Stool samples
were tested by using the Kato-Katz method113,114
to estimate the number of S. mansoni eggs
per gram of fecal matter from three subsamples taken from the two independent samples per
individual. Then the arithmetic mean was calculated for each subject. This test has been
shown to give reproducible results when 3–5 exams are performed.114
3.1.3 Assessment of water contact.
A previously developed environmental exposures questionnaire115
was administered to
quantitate the intensity of exposure to S. mansoni.
3.2 Genotyping
DNA was extracted from whole blood using standard protocols from 697 participants from
the Brazil study. Four ST2 SNPs were selected for genotyping based on prior association with
asthma and sST2 levels in an African-American population from Baltimore/Washington.109
Of the four SNPs; rs12712135, rs1420101, rs6543119 and rs76930359, only one had prior
published associations. SNP rs1420101 is significantly associated with asthma and eosinophil
count in European and East-Asian populations.46
3.2.1 The Taqman method
Genotyping of the polymorphisms was performed by using TaqMan probe-based, 5´nuclease
allelic discrimination assay on the 7900HT Sequence Detection System (Applied Biosystems,
Foster City, California, USA). TaqMan-validated assays and Master mix were manufactured
by Applied Biosystems. PCR was conducted in a 5-mL volume by using a universal Master
mix and four predesigned and validated TaqMan assays for the SNPs. The thermal cycling
conditions were as follows: 95°C for 10 minutes, followed by 40 cycles of 95°C for 15
seconds/60°C for 1 minute and an extension step of 60°C for 5 minutes.
14
3.2.2 Genotyping Quality Control.
Nontemplate negative and genotyping-positive controls were included in each genotyping
plate. Automatic calling was performed with a quality value of greater than 99%. 10% of the
samples were genotyped in duplicate with 99% reproducibility.
3.3 Measurements of sST2, tIgE and the IgE/IgG4 ratio
3.3.1 sST2 Levels
sST2 levels were measured using the Presage ST2 Assay82
(Critical Diagnostics, California,
USA) by ELISA (Enzyme-Linked Immunosorbent Assay) in serum samples using the
protocol suggested by the manufacturer.
3.3.2 Total IgE measurements
Total-IgE levels were measured using chemiluminescence (ADVIA Centaur Bayer
Corporation, Salvador, Brazil) and a ratio of intensity of response was obtained using a
confirmed non-allergic reference sample.116
3.3.3 IgE/IgG4
IgG4 specific for SWAP, soluble egg antigen, IL-4-inducing principle from S. mansoni eggs
and Sm22.6 was measured by using an indirect enzyme-linked immunosorbent assay as
described elsewhere.83,117,118
3.4 Statistical Methods.
3.4.1 Data testing and analyses
Hardy-Weinberg testing and tests for Mendelian inconsistencies were performed using
PLINK, and pairwise linkage disequilibrium estimations using Haploview.
All analyses were performed using generalized estimating equations (GEE) to adjust for
relatedness in the Brazil population and adjusting for the first 2 principal components (PCs).
15
In addition, before analysis, sST2, SWAP, and tIgE were log10-transformed and adjusted by
age and gender. Egg count was log10-transformed and adjusted by age, gender and water
contact index. Along with the first two PCs, wheeze was adjusted by egg count (yes/no) and
gender.
3.4.2 Population stratification
Average population admixture and individual admixture estimates were determined using a
panel of 237 SNPs119
A model-based clustering method was used by grouping data for the
total sample in three ancestral populations (K = 3) to reflect the admixture history in South
America with the software STRUCTURE (version 2.3.3). Assessment of stratification was
done by using principal component analysis (PCA) using the smartpca program from the
software package eigenstrat120
including the three putative ancestral populations available in
HapMap (CEU: Utah residents with ancestry from northern and western Europe, CHB: Han
Chinese in Beijing, China and YRI: Yoruba in Ibadan, Nigeria) as reference. (Figure 4)
Principal components were determined for each individual in this population included in the
current analysis.
Figure 4 Plots of the first principal components (PC1, PC2) from analysis on Brazilians founders.
Individuals are color plotted by asthma status (Cases/Controls) in each sample. Included are the putative
parental populations available in HapMap (CEU, CHB and YRI).
16
4 Results
4.1 Clinical characteristics of the analyzed subjects
Table 1 shows the characteristics of the study population which consisted of 697 Brazilians.
Of those, 196 were founders, defined as unrelated individuals; namely the parents in the
oldest generation, and in-laws in subsequent generations. This group is older than the total
population.
Of note, Table 1 shows that the reported prevalence of wheeze is 30% ( ) in the total
analyzed population and 31.6% ( ) in founders. A higher percentage of males reported
wheeze compared to females.
Mean sST2 levels are similar between all research groups with mean serum levels between 28
and 30 ng/mL. Mean total IgE levels are highest in the group with wheeze and lowest in
founders.
S. mansoni infection is most prevalent and most severe in the wheeze group but least
prevalent and least severe in the founders.
Table 1 Clinical characteristics of the analyzed subjects
Characteristic Total Founders Wheeze(+)
No. 697 196 212
Males; N (%) 307 (44.1%) 82 (41.8%) 100 (47.2%)
Age; Mean (SD) 27.3 (19.0) 42.6 (19.2) 27.4 (19.0)
Wheeze(+); N (%) 212 (30.4%) 62 (31.6%) 212 (100%)
sST2*^; Mean (95% CI) 28.4 (27.6-29.2) 30.1 (28.3-32.0) 28.2 (26.8-29.6)
Total IgE*^;
Mean (95% CI)
2773
(2533-3036)
2635
(2211-3141)
3123
(2669-3654)
IgE/IgG4; Mean (95% CI) 1.11 (0.93-1.34) 0.74 (0.55-0.98) 0.92 (0.66-1.29)
S. mansoni(+); N (%) 228 (43.4%) 58 (39.2%) 74 (44.3%)
Egg count*; Mean(95%CI) 54.9 (46.8-64.4) 44.7 (31.2-64.1) 56.6 (43.8-73.0)
*Geometric mean. ^[ng/mL]. No: Number, CI: Confidence Interval
17
Table 2 shows the clinical characteristics of the study population distributed by gender.
The main information Table 2 adds is that males have higher prevalence of wheeze and higher
mean sST2 and tIgE levels compared to females in both total population and founder groups.
Males also have a higher prevalence and severity of Schistosoma mansoni infection and
correspondingly lower IgE/IgG4 ratios.
For these reasons, further analyses were adjusted by gender and age as described in the
Statistical methods section.
Table 2 Clinical characteristics of the analyzed subjects distributed by gender
Characteristic Total males Total
females
Founder
males
Founder
females
No. 307 390 82 114
Age; Mean (SD) 26.1 (18.8) 28.2 (19.2) 42.2 (19.6) 42.9 (19.0)
Wheeze(+); N (%) 100 (32.6%) 112 (28.7%) 28 (34.2%) 34 (29.8%)
sST2*^;
Mean (95% CI)
31.3
(30.0-32.7)
26.2
(25.3-27.1)
35.1
(32.0-38.5)
26.8
(24.8-28.9)
Total IgE*^;
Mean (95% CI)
3578
(3196-4006)
2669
(1987-2589)
3568
(2856-4456)
2114
(1665-2718)
IgE/IgG4; Mean
(95% CI)
0.87
(0.69-1.10)
1.35
(1.03-1.75)
0.54
(0.37-0.79)
0.93
(0.61-1.41)
S. mansoni(+); N (%) 115 (51.1%) 113 (37.5%) 30 (50.9%) 28 (31.5%)
Egg count*;
Mean (95% CI)
64.9
(51.5-81.8)
46.3
(37.2-57.6)
62.6
(37.6-104)
31.2
(18.8-51.6)
*Geometric mean. ^[ng/mL]
18
4.2 sST2 levels compared to other traits in the total population
Table 3 shows the pairwise GEE analysis between sST2 levels and other covariates in the
total study population.
sST2 levels are observed to be positively associated with infection with Schistosoma mansoni
and total IgE levels with a P-value of less than 0.05, but not significantly associated with
wheeze or Schistosoma egg count.
sST2 levels are associated with the male gender and are negatively associated with the
IgE/IgG4 ratio.
There is a trend of higher age – higher sST2 levels, however, the P-value is borderline
significant.
Table 3 Pairwise GEE analysis of sST2 and other traits in the total population.
Comparing unadjusted log(sST2) to all other covariates.
Variable Estimation Standard
error Log(P) P-value
Sex -0,0966 0,0098 22,2301 5,89*10-23
Age 5,00*10-4
3,00*10-4
1,3019 0,0499
Wheeze 0,0192 0,0121 0,943 0,114
Log(egg count) 0,0264 0,0162 0,9866 0,103
S. mansoni infection
(Yes/No) 0,0613 0,0108 7,8441 1,43*10
-8
Log((IgE/IgG4)+1) -0,0423 0,0129 2,9812 0,001
Log(tIgE) 0,0436 0,0082 6,9973 1,01*10-7
19
4.3 Analyzed SNPs – a closer look
Table 4 summarizes the SNPs genotyped, their chromosomal positions, alleles and minor
allele frequencies (MAF) in the total population.
The coded allele represents the allele analyzed as reference to conferring risk or protection of
any of the traits.
All four markers; rs12712135, rs1420101, rs6543119 and rs76930359 were in Hardy
Weinberg Equilibrium (HWE) so they were all included in the analysis.
Table 4 HWE and MAFs of sST2 markers in a Brazilian population.
Marker Chr. Position Allele1 Allele2 Coded
allele MAF HWE
rs12712135 2 102930948 A G G 0,437 0,878
rs1420101 2 102957716 T C C 0,308 0,463
rs6543119 2 102963072 T A A 0,299 0,858
rs76930359 2 102924684 T C C 0,077 0,265
Chr.: Chromosome
20
Graph 1 shows the location of the four genotyped SNPs in the ST2 gene and their LD pattern.
SNP rs76930359 is in the promoter region.
SNPs rs12712135, rs1420101 and rs6543119 are located in intronic regions; introns 1, 5 and 8
respectively.
SNPs rs1420101 and rs6543119 are contained in a 5 kb haplotype block and are in high LD
with a value of 0.93 in this population.
Graph 1 Gene structure of ST2 and LD pattern of analyzed markers in a Brazilian
population
21
4.4 GEE analyses of ST2 markers and other traits
Table 5 shows associations between the genotyped SNPs and wheeze in a Brazilian
population. Before analysis wheeze was adjusted by Schistosoma mansoni egg count (yes/no)
and gender.
One of the four markers is significantly associated with wheeze in the Brazilian population.
SNP rs76930359 has a coded allele C that is negatively associated with wheeze in the study
population.
No association was found between the other three markers and wheeze.
Table 5 GEE analysis of ST2 markers and wheeze in a Brazilian population
Marker – coded allele Estimation Standard
error Log(P) P-value
rs12712135 – G 0,0353 0,0317 0,5776 0,264
rs1420101 – C -0,0575 0,0352 0,9911 0,102
rs6543119 – A -0,0582 0,0318 1,1753 0,0668
rs76930359 – C -0,1021 0,044 1,6901 0,0204
22
Table 6 shows associations between genotyped SNPs and sST2 levels in a Brazilian
population. Before analysis sST2 levels were log10-transformed and adjusted by age and
gender.
Three markers are significantly associated with sST2 levels in the study population.
SNP rs12712135 has a coded allele G that is positively associated with sST2 levels in a
Brazilian population.
SNP rs1420101 with a coded allele C and SNP rs6543119 with the coded allele A are both
negatively associated with sST2 levels in the study population.
No association was found with SNP rs76930359.
Table 6 GEE analysis of ST2 markers and sST2 levels in a Brazilian population
Marker – coded allele Estimation Standard error Log(P) P-value
rs12712135 – G 0,0585 0,0122 5,8137 1,53*10-6
rs1420101 – C -0,0593 0,012 6,0758 8,40*10-7
rs6543119 – A -0,0604 0,0106 7,8668 1,35*10-8
rs76930359 – C 0,0367 0,021 1,0902 0,0812
23
Table 7 shows associations between the genotyped SNPs and SWAP-specific IgE/IgG4, tIgE
levels and Schistosoma mansoni egg count.
Before analysis, SWAP and tIgE were log10-transformed and adjusted by age and gender,
egg count was log10-transformed and adjusted by age, gender and water contact index
None of the four SNPs genotyped in the Brazilian study population showed significant
association with any of these traits.
Table 7 GEE analysis of ST2 markers and SWAP, total IgE and egg count in a Brazilian
population.
24
5 Discussion
5.1 Clinical characteristics compared to other populations.
The Brazilian study population has a 30% prevalence of wheeze. (Table 1) This is quite high
compared to the global prevalence of asthma; ranging from 1% to 18% of the population in
different countries.3-7
One of the reasons behind this difference could be that this study
population has a substantial African admixture.37
African ancestry is a known risk factor for
asthma and high total IgE levels in African admixed populations.119
Environmental factors
can also play a role in the differences in prevalence among different populations.
The mean total-IgE levels are also very high in all groups of the study population, especially
in the wheeze group that displays mean tIgE levels of more than 3000 ng/mL. (Table 1) This
population has a high prevalence of parasitic infections.98,99,101
Total IgE includes the
quantification of IgE against parasites and this factor can account for the high total IgE levels.
The measurements of specific IgE levels against relevant allergens can help us to disentangle
the influence of parasites in the high values observed in this population.
The males have higher tIgE levels compared to the females. (Table 2) This corresponds to
previous findings that at all ages, males have higher total IgE concentrations than females.1
The mean tIgE levels in all groups are much higher than the reference value of 4-274 ng/mL
(1.5-114 kU/L with one kU/L being equal to 2.4 ng/mL121
) established in 1981122
and even
higher than the clinically relevant threshold for predicting allergy which is 480 ng/mL.122,123
Other studies have shown that after 14 years of age, total serum IgE levels over 800 ng/mL
are considered abnormally elevated and strongly associated with atopic disorders, such as
allergic rhinitis, extrinsic asthma, and atopic dermatitis.123,124
This corresponds to the high
prevalence of wheeze in this Brazilian population. However, IgE levels can vary greatly
depending on birthplace and background123,125,126
and also seem to have a genetic
component.116
Therefore if diagnosis is based on tIgE it is important to study the reference
values compatible with that patient’s particular population.
A higher percentage of males presents with wheeze than females (Tables 1 and 2) in the study
population. In general, prior to the age of 14, asthma prevalence is nearly twice as high in
boys as in girls127
but by adulthood the prevalence of asthma is greater in women.3
25
The reason for this gender-related difference is not clear although it is speculated this may be
due to lung size, which is smaller in males than in females at birth but larger in adulthood.128
The founder group is, as expected, older than the total population and has both the lowest
prevalence and severity of Schistosoma infection. (Table 1) This indicates that there is an age-
related resistance to reinfection that has also been demonstrated in other studies.110,129
The Presage sST2 assay showed mean sST2 levels to be relatively similar between the total
population, founder- and wheeze groups around 28-30 ng/mL. (Table 1) When distributed by
gender there was a higher level of sST2 in males than in females, (Table 2) corresponding
with sST2 levels being significantly associated with the male gender. (Table 3)
Therefore it is clear, both in this study and others130,131
(discussed below), that sST2 levels are
higher in males compared to females. The reasons and possible consequences of this
difference are unknown. Perhaps it has to do with hormonal differences or other gender-
specific traits. In any case it would be interesting to study whether sST2 levels as a biomarker
predict different outcomes between the sexes.
The mean sST2 levels are in general in the higher range compared to other populations; for
example compared to an Austrian study which identified gender-specific reference intervals of
4–31 and 2–21 ng/mL for males and females, respectively, using samples obtained from
blood donors in Austria.130
A study performed in the United States established combined reference intervals for both
males (8.6–49.3 ng/mL) and females (7.2–33.5 ng/mL)131
that were significantly higher than
the Austrian levels and more in keeping with the sST2 levels measured in this Brazilian study
population. These variations in sST2 levels between studies may be accredited to genetics
and/or demographic differences as well as variation in screening and exclusion criteria.
Contrary to studies showing inverse association between asthma and schistosomiasis, the
wheeze group in this population shows both higher prevalence and severity of S. mansoni
infection. (Table 1) An unknown factor is whether the population with wheeze would have an
even higher prevalence of asthma and/or more severe symptoms if introduced to anti-
helminthic treatments as has been shown in other studies.102
Asthma has also been shown to
be linked to various environmental factors which are not included in this study and could thus
confound the results.
26
The IgE/IgG4 ratio is lowest in the wheeze group, (Table 1) further indicating its role as a
biomarker for resistance to Schistosoma spp. infection.95,96
The total IgE levels are, as
expected, also highest in the wheeze group as asthma is closely associated with serum tIgE
levels.41,132
5.2 Associations between sST2 levels and other traits
As discussed in the Introduction, elevated sST2 levels have been linked to various diseases
such as acute asthma, heart failure, acute myocardial infarction and more. In the pairwise
GEE analysis comparing sST2 levels to all other covariates the sST2 levels are, however, not
significantly associated with wheeze in this population. (Table 3)
In this study, wheeze was determined by the ISAAC questionnaire and used as an equivalent
of asthma. Studies have various different definitions of asthma, with wheeze being one
criterion. However, it is only one of multiple expressions of this heterogeneous disease which
could explain the varying results between studies.
Both Schistosoma mansoni infection and mean tIgE levels are significantly and positively
associated with sST2 levels in this study population. (Table 3) It is possible that the parasitic
infection could mask the symptoms of wheeze in some of the non-wheeze controls in the
population through dampening of the asthma immune response discussed in the Introduction.
Correspondingly, there is a significant negative association between sST2 levels and the
SWAP-specific IgE/IgG4 ratio, previously described as a biomarker for Schistosoma
resistance.95,96
5.3 Associations between genetic variations in ST2 and other traits
All four genotyped SNPs; rs12712135, rs1420101, rs6543119 and rs76930359, had
previously been positively associated with asthma and sST2 levels in a study population of
African-Americans from the Baltimore/Washington area in the USA.109
These data are
unpublished but are the basis for this study and the selection of SNPs for genotyping.
27
The GEE analyses showed that, in contrast to these prior association results, only one of the
genotyped SNPs (rs76930359) was significantly associated with wheeze in this study
population of Brazilians. (Table 5)
The other three; rs12712135, rs1420101, rs6543119, were significantly associated with sST2
levels. (Table 6) It is possible that we found more of a signal in the study of sST2 levels than
in other components, such as wheeze, because the sST2 levels are more standardized and
objective in terms of measurement.
None of the genotyped SNPs showed significant association with tIgE, Schistosoma mansoni
egg count nor SWAP-specific IgE/IgG4. (Table 7)
The SNP rs76930359 was negatively associated with wheeze (Table 5) in the Brazilian study
population as opposed to the prior positive association in the African American population.
This indicates that the coded allele C in rs76930359 confers “protection” against wheeze in
Brazilians but increases risk for asthma in a different population.
A possible explanation of the discrepancies between study results is the difference in allelic
frequencies between study populations. Allele frequencies of three reference populations
(AFR – Africans, ASN – Asians and EUR – Europeans) were therefore located on Ensembl
Genome Browser133
in order to compare the allele frequencies with those of the Brazilian
study population.
The negative association of rs76930359 with wheeze is not very robust with a P-value of
0.0204 (Table 5) however, the minor allele frequency is 0.077 (Table 4) which means only
7.7% of chromosomes in the study population display the coded allele. In the reference
populations, the less frequent allele of the Brazilians, C, is the more frequent one with allele
frequencies of 97%, 100% and 80% in African, Asian and European populations
respectively.133
Thus it is clear that a much larger study population is needed to detect a signal of this SNP in
the Brazilian study population than in the reference populations. A solution to this problem
would be simply to increase the number of subjects in the study population in order to
increase the power of the study.
28
The three genotyped SNPs associated with sST2 levels in the Brazilian population did not all
show the same association.
SNP rs12712135 indicated a positive association with sST2 levels in this study population
corresponding to prior findings in an African-American population.
Conversely, both SNPs rs1420101 and rs6543119 were negatively associated with sST2
levels in the study population. They had very similar results (Table 6) that are likely the result
of the same signal as they are in high LD with each other with an value of 0.93. (Graph 1)
The most likely explanation for the different association findings between populations is that
the SNPs in and of themselves do not convey the effect but are rather in varying LD with
causal and/or protective genetic variants for asthma in different populations.
Distribution of allelic frequencies, as seen in the SNP rs76930359, can also vary between
populations and thus increase or decrease the power needed to detect the causal variant.
Various environmental factors could also be influencing phenotypic expression.
Additionally, as mentioned above, the traits studied, such as asthma/wheeze and sST2 levels,
can be different between studies depending on definition, inclusion criteria, age of population
and many other factors.
These same reasons can explain why the SNP rs1420101, previously associated with asthma
in European and East-Asian populations,46
showed no association with wheeze in the
Brazilians.
The minor allele frequency of the coded allele C in the Brazilian study population is 30.8%.
(Table 4) This is lower than the corresponding allele frequencies in all reference populations;
AFR: 59%, ASN: 57% and EUR: 66%.133
In order to detect a signal from this marker in the study population, the sample size must be
larger to reach significance than in studies of the reference populations. A logical direction of
research now would be to study larger sample sizes of several different populations to
determine and possibly replicate association between rs1420101 and asthma.
29
5.4 Conclusions
In conclusion, it is clear that heterogeneity exists between populations. Thus, even though this
study did not replicate the findings of the previous study in African-Americans, it does not
negate those results. However, further analyses are needed to better understand the role of
variants in ST2 in asthma, expression of ST2 and infection by Schistosoma mansoni.
Interesting areas of study in Brazilians lie in larger sample sizes due to varying allele
frequencies between populations.
SNP rs76930359 and genetic variations in high LD with this SNP are of note in studying
asthma resistance in this population. The other three SNPs may warrant future study, for
example, in the research on sST2 as a biomarker for disease.
It is evident that the asthma-associated genetic variants discovered to date are only the tip of
the iceberg. The common variants discovered by the GWAS approach (and meta-analyses of
GWAS) do not seem to have a large effect on the risk of asthma and are poor predictors of
disease status. This common disease may therefore be the result of an additive affect of many
variants or of rarer variants that have a larger effect size and play a bigger part in the
heritability of asthma. A future way of detecting these markers is through whole-genome or
exome sequencing in order to discover causal variants.
30
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