Download Fetal hemoglobin in sickle cell anemia: genetic

The Pharmacogenomics Journal (2007) 7, 386–394
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ORIGINAL ARTICLE
Fetal hemoglobin in sickle cell anemia: genetic
determinants of response to hydroxyurea
Q Ma1, DF Wyszynski1,
JJ Farrell1, A Kutlar2, LA Farrer1,
CT Baldwin3,1 and
MH Steinberg1
1
Department of Medicine, Boston University
School of Medicine, Boston, MA, USA;
2
Department of Medicine, Medical College of
Georgia, Augusta, GA, USA and 3Center for
Human Genetics, Boston University School of
Medicine, Boston, MA, USA
Correspondence:
Dr MH Steinberg, Center of Excellence in Sickle
Cell Disease, E248, Boston Medical Center, 88
E. Newton Street, Boston, MA 02118, USA.
E-mail: [email protected]
The increase in fetal hemoglobin (HbF) in response to hydroxyurea (HU)
varies among patients with sickle cell anemia. Twenty-nine candidate genes
within loci previously reported to be linked to HbF level (6q22.3–q23.2,
8q11–q12 and Xp22.2–p22.3), involved in metabolism of HU and related to
erythroid progenitor proliferation were studied in 137 sickle cell anemia
patients treated with HU. Three-hundred and twenty tagging single
nucleotide polymorphisms (SNPs) for genotyping were selected based on
HapMap data. Multiple linear regression and the nonlinear regression
Random Forest method were used to investigate the association between
SNPs and the change in HbF level after 2 years of treatment with HU. Both
methods revealed that SNPs in genes within the 6q22.3–23.2 and 8q11–q12
linkage peaks, and also the ARG2, FLT1, HAO2 and NOS1 genes were
associated with the HbF response to HU. Polymorphisms in genes regulating
HbF expression, HU metabolism and erythroid progenitor proliferation might
modulate the patient response to HU.
The Pharmacogenomics Journal (2007) 7, 386–394; doi:10.1038/sj.tpj.6500433;
published online 13 February 2007
Keywords: SNPs; association analysis; sickle cell; fetal hemoglobin; hydroxyurea
Introduction
Received 6 June 2006; revised 19 September
2006; accepted 6 November 2006; published
online 13 February 2007
Fetal hemoglobin (HbF) inhibits the polymerization of sickle hemoglobin (HbS).1
As many of the complications of sickle cell anemia (homozygosity for HBB,
glu6val), like osteonecrosis, acute chest syndrome and painful episode, are
associated with the level of HbF, and, HbF is inversely associated with mortality,
investigators have assiduously sought pharmacological means of increasing HbF
production.2–6
Hydroxyurea (HU), a ribonucleotide reductase inhibitor, is one drug that
increases HbF concentration in patients with sickle cell anemia7–10 and it is the
sole FDA-approved agent for treating sickle cell anemia. Most, but not all patients
respond to HU treatment with an increase in HbF, but as with the baseline HbF
concentration, which varies widely among patients, the magnitude of the HbF
response to HU is also variable.10–13
The regulation of HbF level might be a complex genetic trait governed by
genetic elements linked to the b-globin gene-like cluster and quantitative trait
loci (QTL) present on chromosomes 6, 8 and on the X-chromosome; other
regulatory loci are also likely to exist and epigenetic and cellular factors could
also have regulatory roles.14–27 It is possible that these and other regulatory
elements also modulate the HbF response to HU.
Accordingly, we hypothesized that single nucleotide polymorphisms (SNPs)
in candidate genes or QTL with putative roles in the regulation of HbF
SNPs, hydroxyurea and HbF in sickle cell anemia
Q Ma et al
387
production might modulate the HbF response to treatment
with HU. We therefore studied the association of SNPs in
these loci with response to HU in patients who participated
in the Multicenter Study of Hydroxyurea (MSH), a trial
designed to evaluate the efficacy of this drug in sickle cell
anemia.
Results
The distribution of HbF for 137 patients enrolled in this
study is shown as Figure 1. The change of HbF after HU
treatment did not follow a normal distribution and
resembled a bimodal distribution with a large portion of
people with minor or no change (mean ¼ 0) and a small
portion of people with extreme change (mean 440). This
distribution suggested that categorizing these data, like
dividing subjects into quartiles by the HbF change, and
comparing patients in lowest quartile group vs those
patients in the highest quartile of change, might be an
alternative approach for analyzing these data. However,
considering the relatively small sample size in our study, this
approach provides very limited power for detecting genetic
associations.
Three-hundred and twenty tagging SNPs in 29 candidate
genes (Table 1) were examined in 137 sickle cell anemia
patients treated with HU. We considered an SNP to have a
significant association with response to HU treatment when
the P-value was p0.01, unless there was more than one
Figure 1 Distributions of HbF change (a) in percentage (%) and (b) in
grams (g/dl).
SNP in a gene showing an association when the P-value for
significance was set at 0.05. Tables 2 and 3 present the
statistically significant results of the quantitative trait
analysis for single SNP association with the change of HbF
level after a 2-year treatment, expressed as percentage of
total hemoglobin and as the absolute HbF level, expressed as
g/dl, respectively. An analysis was also performed expressing
the increment in HbF after HU treatment as F-cells.
Seventeen SNPs were significantly associated with the
change in percent HbF (Table 2). They included two in
MAP3K5, five in TOX, two in NOS1, three in FLT1, two in
ARG2 and two in NOS2A. The most significant association
was observed with SNP rs2182008 (P ¼ 0.003) in FLT1
(Fms-related tyrosine kinase 1), a vascular endothelial growth
factor, which is involved in cell proliferation and differentiation. Twenty SNPs were significantly associated with the
change of absolute HbF (Table 3); a similar pattern of
association was observed and the most significant association
was in SNP rs10483801 (P ¼ 0.0013) in ARG2 (arginase type II,
involved in drug metabolism of HU). Using F-cells as the
outcome measure gave similar results (data not shown).
For candidate genes with significant association in multiple SNPs, haplotype associations were explored using
Haplo.stats (version 1.2.1) as given in the R library (available
at http://cran.us.r-project.org).28 However, as these SNPs
studied here are tagging SNPs and most of them are not in
linkage disequilibrium (LD) with each other, we did not find
improved association by haplotype analysis in any genes
(data not shown).
The results of joint analysis of all the SNPs and covariates
(age, sex and the a- and b-globin gene cluster haplotypes)
using Random Forest analysis are shown in Figures 2 and 3.
The relative importance of one independent variable
(a SNP) is measured by %IncMSE (see Materials and methods
for details), and the larger the value of %IncMSE, the higher
importance that variable has for correct prediction of
HbF response to HU. This analysis revealed that the most
important variables for predicting the change of HbF
level matched most of the SNPs identified by SNP
association analysis. Interestingly, SNPs within ASS (argininosuccinate synthetase) and ARG1 (arginase, liver) were
observed to have strong effect on the change of HbF level,
which was not detected by single SNP association analysis.
This suggests that these two genes might be involved in
interaction with other genes to regulate the response to HU
treatment.
SNP rs2182008 in FLT1 showed a strong effect on response
to HU treatment. This SNP was significantly associated with
the change in HbF under a dominant model (P-value ¼ 0.003
for HbF in percentage and 0.002 for HbF in g/dl) and it was
also a highly ranked predictor for response to HU from the
Random Forest analysis (second for HbF in percentage and
third for HbF in g/dl). The A allele of this SNP was associated
with increased HbF level after HU treatment; there is no
difference between AA and AG genotypes and the increase
in HbF in subjects with these genotypes was on average 5.9
times higher than that in subjects with the GG genotype
(Figure 4).
The Pharmacogenomics Journal
SNPs, hydroxyurea and HbF in sickle cell anemia
Q Ma et al
388
Table 1
Candidate genes selected
Gene
Chromosomes Function
Cytochrome P450, family 4, subfamily A, polypeptide 11 (CYP4A11)
Hydroxyacid oxidase 2 (long chain) (HAO2)
Kinase insert domain receptor (a type III receptor tyrosine kinase) (KDR)
Arginase, liver (ARG1)
Phosphodiesterase 7B (PDE7B)
Microtubule-associated protein 7 (MAP7)
Mitogen-activated protein kinase kinase kinase 5 (MAP3K5)
Peroxisomal biogenesis factor 7 (PEX7)
NADPH oxidase 3 (NOX3)
Met proto-oncogene (hepatocyte growth factor receptor) (MET)
Nitric oxide synthase 3 (endothelial cell) (NOS3)
Glutathione reductase (GSR)
CCAAT/enhancer binding protein (C/EBP), delta (CEBPD)
1
1
4
6
6
6
6
6
6
7
7
8
8
Transcription elongation factor A (SII), 1 (TCEA1)
8
SRY (sex determining region Y)-box 17 (SOX17)
8
Thymus high mobility group box protein TOX (TOX)
8
Argininosuccinate synthetase (ASS)
Cytochrome P450, family 2, subfamily C, polypeptide 9 (CYP2C9)
Beta-globin gene cluster
Nitric oxide synthase 1 (neuronal) (NOS1)
Fms-related tyrosine kinase 1 (vascular endothelial growth factor/vascular
permeability factor receptor) (FLT1)
Arginase, type II (ARG2)
Aquaporin 9 (AQP9)
NADPH oxidase, EF-hand calcium binding domain 5 (NOX5)
Nitric oxide synthase 2A (inducible, hepatocytes) (NOS2A)
EGF-like-domain, multiple 6 (EGFL6)
Glycoprotein M6B (GPM6B)
C-fos induced growth factor (vascular endothelial growth factor D) (FIGF)
Pirin (iron-binding nuclear protein) (PIR)
9
10
11
12
13
14
15
15
17
X
X
X
X
Drug metabolism
Drug metabolism
Cell differentiation
Drug metabolism
Chr6 QTL
Chr6 QTL, cell differentiation
Chr6 QTL
Chr6 QTL
Drug metabolism
Cell proliferation
NO production
Drug metabolism
Regulation of DNA
transcription
Chr8 QTL, regulation of
DNA transcription
Chr8 QTL, regulation of
DNA transcription
Chr8 QTL, regulation of
DNA transcription
NO production
Drug metabolism
Chr11 QTL
NO production
Cell proliferation and
differentiation
NO production
Drug metabolism
Drug metabolism
NO production
Chr X QTL
Chr X QTL
Chr X QTL
Chr X QTL
Tagging
SNPs
4
5
17
3
17
3
10
6
17
5
4
6
2
3
3
57
24
4
8
23
32
7
8
6
6
9
21
6
4
Abbreviations: NO, nitric oxide; QTL, quantitative trait loci; SNPs, single nucleotide polymorphisms.
Discussion
HU can increase HbF concentration in most individuals with
sickle cell anemia but some patients who take the drug
exactly as directed by experienced physicians either fail to
respond or have an HbF response that might not be
clinically significant. Even among patients with an increase
in HbF, the magnitude of this increase varies.10–13 The cause
of this variability is poorly understood.11,12,29,30
HbF concentration in blood is determined by interactions
among chromosome remodeling activities, transcription
factors, genes modulating erythropoiesis, genetic elements
linked to the b-globin gene cluster, the kinetics of erythroid
cell differentiation and differential red cell survival (for
reviews see Bank31 and Stamatoyannopoulos32). This
complex regulatory environment provides ample opportunity
for genetic modulation of HbF production. Because of
evidence suggesting that heterogeneity in genetic elements
modulate HbF baseline concentration, we reasoned that
The Pharmacogenomics Journal
similar heterogeneity might account for the varied response
in HbF among sickle cell anemia patients treated with HU.
We found statistically significant associations of the HbF
response to HU with multiple SNPs in several genes. TOX
(thymus high-mobility group box protein) is located within
the 8q11–q12 linkage peak that Garner et al.,16,33 found to
interact with the 158 CT 5’ Gg-globin gene SNP and that
might also effect the g- to b-globin gene switch. A member
of the high-mobility group (HMG) box protein family,
TOX contains a single HMG box motif and binds DNA in a
sequence-specific manner. All HMG box proteins are able to
induce a sharp bend in DNA. Multiple SNPs in PDE7B
(phosphodiesterase 7B) within 6q22–q23 QTL were also
associated with the HbF response to HU. SNPs in this gene
were previously reported to be associated with baseline HbF
level in patients with sickle cell anemia.21
Nitric oxide (NO) binds and activates sGC, which
increases cGMP production. cGMP interacts with transcription factors increasing the expression of the g-globin
SNPs, hydroxyurea and HbF in sickle cell anemia
Q Ma et al
389
Table 2
SNPs associated with a significant change in HbF%
Genetic model
SNP
rs10494225
rs9376230
rs9483947
rs826729
rs765587
rs9693712
rs172652
rs380620
rs816361
rs7977109
rs9319428
rs2182008
rs8002446
rs10483801
rs10483802
rs1137933
rs944725
Chromosomes
Gene a
1
6
6
8
8
8
8
8
12
12
13
13
13
14
14
17
17
HAO2
MAP3K5
MAP3K5
TOX
TOX
TOX
TOX
TOX
NOS1
NOS1
FLT1
FLT1
FLT1
ARG2
ARG2
NOS2A
NOS2A
Function
Untranslated
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Synonymous
Intron
Codominant
Dominant
Recessive
0.015
NS
NS
NS
NS
0.019
0.049
NS
NS
NS
NS
0.012
0.033
0.026
0.015
NS
NS
NS
NS
NS
0.045
0.031
0.0098
NS
0.047
NS
NS
NS
0.003
NS
NS
NS
NS
0.02
0.0039
0.036
0.034
NS
NS
NS
NS
NS
0.045
0.029
0.047
NS
0.011
0.0075
0.0038
0.031
NS
Abbreviations: HbF, fetal hemoglobin; NS, nonsignificant; SNPs, single-nucleotide polymorphisms.
a
See Table 1 for full names.
NS: P-value 40.05.
Bold and italics: SNP with the most significant P-value.
Table 3
SNPs associated with a significant change in HbF (g/dl)
Genetic model
SNP
rs10494225
rs2327669
rs11154849
rs9376173
rs1480642
rs487278
rs2693430
rs765587
rs12155519
rs9693712
rs380620
rs7309163
rs7977109
rs3751395
rs9319428
rs2182008
rs2387634
rs8002446
rs10483801
rs10483802
Chromosomes
Gene a
Function
1
6
6
6
6
6
8
8
8
8
8
12
12
13
13
13
13
13
14
14
HAO2
PDE7B
PDE7B
PDE7B
PDE7B
PDE7B
TOX
TOX
TOX
TOX
TOX
NOS1
NOS1
FLT1
FLT1
FLT1
FLT1
FLT1
ARG2
ARG2
Untranslated
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Intron
Codominant
Dominant
Recessive
0.0078
NS
NS
NS
NS
NS
NS
NS
NS
0.048
0.038
NS
NS
NS
NS
0.0085
NS
0.029
0.0052
0.014
NS
NS
0.05
NS
NS
NS
0.049
0.044
0.037
0.02
0.016
NS
NS
NS
NS
0.0021
NS
NS
NS
NS
0.0018
0.041
NS
0.049
0.044
0.017
NS
NS
NS
NS
NS
0.038
0.023
0.039
0.044
NS
0.037
0.01
0.0013
0.0037
Abbreviations: HbF, fetal hemoglobin; NS, nonsignificant; SNPs, single nucleotide polymorphisms.
a
See Table 1 for full names.
NS: P-value 40.05.
Bold and italics: SNP with the most significant p-value.
genes.34 ASS (argininosuccinate synthetase) is an enzyme
that catalyzes the penultimate step of the arginine biosynthetic pathway. Arginine is the substrate for the NO
synthases. SNPs in ASS gene were strong predictors for HbF
response to HU. Significant associations were also found
with SNPs in ARG2 and ARG1, genes coding for enzymes
The Pharmacogenomics Journal
SNPs, hydroxyurea and HbF in sickle cell anemia
Q Ma et al
390
Figure 2 Joint analysis of multiple SNPs with change in HbF in percentage (%): only predictors that have relative importance value (%IncMSE)
43.0 are shown here; *: SNPs in x-QTL.
that hydrolyze arginine to ornithine. SNPs in NOS1
(12q24.2-q24.31, neuronal NO synthase) and NOS2A, an
NO synthase expressed in liver, were also associated with
HbF response to HU.
FLT1 (vascular endothelial growth factor (VEGF)/vascular
permeability factor receptor) is a receptor for VEGF. SNP
rs2182008 in this gene was found to be strongly associated
with the HbF response to HU. This gene has tyrosine protein
kinase activity that is important for the control of cell
proliferation and differentiation.35
Most SNPs associated with HbF response to HU treatment
were in either untranslated portions of the genes or in
introns. Most likely, the associations we found are in LD
with the actual functionally important elements. Although
most associations have biologically plausible mechanisms
by which they might influence the expression of the
g-globin genes or the concentration of HbF, our studies
are hypothesis generating rather than mechanistic, and
we were unable to do functional studies because of the
The Pharmacogenomics Journal
nature of our study samples. Nevertheless, these SNPs
were associated with HbF concentration, for example, the
increase of HbF in subjects carrying A allele of SNP
rs2182008 was almost six times higher than that in subjects
with GG genotype.
To identify genetic factors regulating the expression of
HbF in sickle cell anemia at baseline and in the absence of
treatment with HU, we studied a sample of 327 subjects and
an independent group of 987 subjects. We replicated in part
our previous work21 using this expanded sample of patients
and denser SNP coverage, by finding multiple tagging SNPs
in genes abutting the 6q22.3–q23.2 QTL. We also found
a significant association of HbF with SNPs in TOX and
within EGFL6, GPM6B and FIGF in the Xp22.2–p22.3 QTL
(unpublished data).
According to the common disease–common variant
hypothesis, many of the genetic variants causing complex
diseases or phenotypes are expected to have only a
small effect on disease outcome.36 The power to obtain
SNPs, hydroxyurea and HbF in sickle cell anemia
Q Ma et al
391
Figure 3 Joint analysis of multiple SNPs with change in HbF in grams (g/dl): only predictors that have relative importance value (%IncMSE) 43.0
are shown here; *: SNPs in x-QTL.
typical thresholds of P-value significance after applying
multiple testing corrections is limited for such markers,
because significance is a function of sample size, allele
frequency and effect size. Considering our relatively
small sample size, which nevertheless constitutes the
largest number of patients with sickle cell anemia taking
HU under controlled conditions, we validated our findings
by using a different analytical method but did not
apply multiple testing corrections for reporting our results.
Also, as we selected haplotype tagging SNPs that tag
different LD blocks and almost all our associations are
with more than a single SNP in a gene, it is unlikely that the
result with one SNP merely reflects the same association
signal with other SNPs. Therefore, out of the 320 SNPs
tested, it is less likely that multiple hits with SNPs in the
same gene is a chance event. Further replication with fewer
candidate genes and denser SNP coverage may be a more
practical way to reduce false discovery and confirm our
findings.
Materials and methods
Patients
DNA samples and laboratory data from adult AfricanAmericans with sickle cell anemia who participated in the
MSH were analyzed. This was a randomized, placebocontrolled, double-blind trial designed to test whether HU
could reduce the number of vasoocclusive events in adults
with moderate to severe sickle cell anemia.37 HU was given
daily, in a single dose starting at 15 mg/kg, and increased by
5 mg/kg every 12 weeks up to a maximum-tolerated dose
of 35 mg/kg, unless toxicity developed. When toxicity
occurred, treatment was stopped until blood counts recovered. HU was then resumed at a dose 2.5 mg/kg lower than
the toxic dose. The MSH enrolled 299 patients, of which 152
were randomly assigned to HU. The minimum length of
follow-up evaluation for patients with HbF measured at the
end of the study was 21 months (maximum, 38 months;
mean, 28). DNA samples were available and HbF was
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SNPs, hydroxyurea and HbF in sickle cell anemia
Q Ma et al
392
study population have sickle cell disease.43–48 SNPs that had
more than 25% missing genotypes or less than 5% minor
allele frequency were not considered in the analysis. This
resulted in 280 SNPs being tested for association.
Figure 4 HbF change for SNP rs2182008 (a) in percentage (%) and (b)
in grams (g/dl). median; D: mean.
measured in 137 of the 152 patients randomized to receive
HU. Although pill counts and measurements of serum HU
levels were used to assess compliance with treatment, the
rapid clearance of HU from the circulation prevented us
from knowing with certainty if a patient took all of the
medication prescribed. The studies reported here were
approved by the IRB of Boston Medical Center.
Laboratory studies
HbF was measured by alkali denaturation at least twice
pretreatment and twice post-treatment.10 The laboratory
methods used to define the hemoglobin phenotype and the
haplotypes of the b-like and a -globin gene cluster were
described previously.11,38,39
SNP selection and genotyping
The selection of genes was based on previously reported
linkage peaks (6q22–q23, 8q11–q12 and Xp22.2–p22.
3)16,19,40 genes involved in metabolism of HU and genes
related to erythroid progenitor proliferation. Thirty-three
candidate genes were chosen in our study, and 320 tagging
SNPs were selected based on genotype data from the
HapMap project (Phase I, Yoruba sample)41 using Haploview
(v3.2)42 (Table 1). Genotyping was carried out using a
custom 384 multiplex design using an Illumina platform.
For quality control purposes, about 3% of the DNA samples
were re-genotyped, and Hardy–Weinberg equilibrium was
assessed for each SNP. Hardy–Weinberg equilibrium was
determined before analysis and was performed for quality
control purposes rather than to evaluate if the genotypes
met Mendelian expectation because all members of our
The Pharmacogenomics Journal
Statistical analysis
Single SNP association was investigated via multiple linear
regression analysis (SAS v9.1) with simultaneous adjustment
for age, sex and the a- and b-globin gene cluster haplotypes.
Three genetic models (codominant, dominant and recessive) governing modulation of response to HU treatment
were tested. In the codominant system, an allele causes the
homozygous form to look different from the wild-type and
the heterozygous form (all three look different from each
other). In dominant transmission, an allele causes the
homozygous and the heterozygous forms to look the same
as each other, but different from the wild type. In a recessive
model, an allele affects the phenotype if it is present in the
homozygous state only. One subject exceeding the top third
standard deviation of all the values of the change in HbF
levels was omitted from the analysis to prevent the extreme
values from biasing the results.
We used the nonlinear regression Random Forest method
to identify additional predictors of response to HU treatment and to validate our results from single SNP association.49 Random Forest consists of a collection of regression
trees, each regression tree itself being a regression function.
Each of these trees predicts a real value by querying a set
number of variables and instances within the regression
model. Each regression tree is thus trained on a different
bootstrap sample of both training instances and features.
The Random Forest then averages the predictions made by
the trees in the forest to produce the final output. Random
Forest is a variance reduction technique and has provable
properties with regard to resisting over fitting. Additionally,
Random Forest is very efficient to train and test, and has
built-in mechanisms for estimating test error and confidence in each prediction made. This procedure is nonparametric, not model based, and identifies those
independent variables that best segregate subgroups as
important predictors and identifies interactions among
independent variables. The relative importance of the
independent variables (e.g. SNPs in our case) is defined as
follows. The mean of squared residuals (MSE) is computed
on the out-of-bag data for each tree, and then the same is
computed after permuting a variable. The differences are
averaged over all trees and normalized by the standard error,
and this calculation is repeated for each independent
variable. A large difference in MSE (%IncMSE) indicates that
the independent variable is important for correct prediction,
whereas a small difference indicates that the independent
variable is less important for correct prediction. Relative
importance provides a measure by which predictors can be
ranked with respect to each other. Our analyses were performed using the Random Forest (v 4.5–16) package as given
in the R library (available at http://cran.us.r-project.org),
except that, as the number of trees to be grown in the
SNPs, hydroxyurea and HbF in sickle cell anemia
Q Ma et al
393
forest was set at 2500, we used default parameters of this
program.
16
Acknowledgments
17
We thank the investigators of the MSH who obtained blood samples
for DNA-based studies and analyzed data from these studies for the
study publications cited in the text of this paper. This study was
supported by NHLBI Grant HL R01 70735 (MHS).
18
Duality of Interest
The study sponsor had no involvement in study design, data
collection, analysis or interpretation, writing of the paper or the
decision to submit the paper for publication.
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