Identification of germline susceptibility loci in ETV6-RUNX1-rearranged childhood acute lymphoblastic leukemia.

Abstract

Acute lymphoblastic leukemia (ALL) is a malignant disease of the white blood cells. The etiology of ALL is believed to be multifactorial and likely to involve an interplay of environmental and genetic variables. We performed a genome-wide association study of 355 750 single-nucleotide polymorphisms (SNPs) in 474 controls and 419 childhood ALL cases characterized by a t(12;21)(p13;q22) - the most common chromosomal translocation observed in childhood ALL - which leads to an ETV6-RUNX1 gene fusion. The eight most strongly associated SNPs were followed-up in 951 ETV6-RUNX1-positive cases and 3061 controls from Germany/Austria and Italy, respectively. We identified a novel, genome-wide significant risk locus at 3q28 (TP63, rs17505102, P(CMH)=8.94 × 10(-9), OR=0.65). The separate analysis of the combined German/Austrian sample only, revealed additional genome-wide significant associations at 11q11 (OR8U8, rs1945213, P=9.14 × 10(-11), OR=0.69) and 8p21.3 (near INTS10, rs920590, P=6.12 × 10(-9), OR=1.36). These associations and another association at 11p11.2 (PTPRJ, rs3942852, P=4.95 × 10(-7), OR=0.72) remained significant in the German/Austrian replication panel after correction for multiple testing. Our findings demonstrate that germline genetic variation can specifically contribute to the risk of ETV6-RUNX1-positive childhood ALL. The identification of TP63 and PTPRJ as susceptibility genes emphasize the role of the TP53 gene family and the importance of proteins regulating cellular processes in connection with tumorigenesis.

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ORIGINAL ARTICLE
Identification of germline susceptibility loci in
ETV6-RUNX1-rearranged childhood acute lymphoblastic
leukemia
E Ellinghaus1,19, M Stanulla2,19, G Richter1, D Ellinghaus1, G te Kronnie3, G Cario2, G Cazzaniga4, M Horstmann5, R Panzer Gru ¨mayer6,
H Cave ´7, J Trka8, O Cinek9, A Teigler-Schlegel10, A ElSharawy1, R Ha ¨sler1, A Nebel1, B Meissner2, T Bartram2, F Lescai11, C Franceschi12,
M Giordan3, P Nu ¨rnberg13, B Heinzow14,15, M Zimmermann16, S Schreiber1,17,18,19, M Schrappe2,19and A Franke1,19
Acute lymphoblastic leukemia (ALL) is a malignant disease of the white blood cells. The etiology of ALL is believed to
be multifactorial and likely to involve an interplay of environmental and genetic variables. We performed a genome-wide
association study of 355750 single-nucleotide polymorphisms (SNPs) in 474 controls and 419 childhood ALL cases characterized
by a t(12;21)(p13;q22) --- the most common chromosomal translocation observed in childhood ALL --- which leads to an
ETV6--RUNX1 gene fusion. The eight most strongly associated SNPs were followed-up in 951 ETV6-RUNX1-positive cases and
3061 controls from Germany/Austria and Italy, respectively. We identified a novel, genome-wide significant risk locus at 3q28
(TP63, rs17505102, PCMH¼8.94?10?9, OR¼0.65). The separate analysis of the combined German/Austrian sample only,
revealed additional genome-wide significant associations at 11q11 (OR8U8, rs1945213, P¼9.14?10?11, OR¼0.69) and 8p21.3
(near INTS10, rs920590, P¼6.12?10?9, OR¼1.36). These associations and another association at 11p11.2 (PTPRJ, rs3942852,
P¼4.95?10?7, OR¼0.72) remained significant in the German/Austrian replication panel after correction for multiple testing.
Our findings demonstrate that germline genetic variation can specifically contribute to the risk of ETV6--RUNX1-positive
childhood ALL. The identification of TP63 and PTPRJ as susceptibility genes emphasize the role of the TP53 gene family and the
importance of proteins regulating cellular processes in connection with tumorigenesis.
Leukemia (2012) 26, 902--909; doi:10.1038/leu.2011.302; published online 11 November 2011
Keywords: genome-wide association study; childhood acute lymphoblastic leukemia; TP63
INTRODUCTION
Acute lymphoblastic leukemia (ALL) is characterized by the
malignant clonal proliferation of lymphoid cells that are blocked
at an early stage of differentiation. More than 60% of patients
diagnosed with ALL are children below the age of 15 years.1- -3The
annual incidence rates of childhood ALL vary worldwide between
one and four new cases per 100000 children younger than 15
years, with a peak incidence at about 2--5 years of age.3- -5
As for most other human malignancies not underlying a clear
hereditary genetic trait, the etiology of childhood ALL is believed
to be multifactorial, including environmental risk factors like
radiation and other toxins as well as genetic variables.6,7Different
subtypes of childhood ALL are classifiable according to either
immunophenotypic (for example, precursor B-cell vs T-cell lineage
ALL) or a constantly growing number of recurrent somatic genetic
aberrations that have been characterized during the last dec-
ades.1,2,8,9The two major genetic subtypes of childhood ALL are
characterized by either hyperdiploidy (450 chromosomes per
leukemic cell) or the chromosomal translocation t(12;21)(p13;q22)
leading to an ETV6--RUNX1 (TEL--AML1) gene fusion.10Hyperdiploid
and ETV6--RUNX1-rearranged childhood ALL account for B25%
and 22% of the entire childhood ALL population, respectively.
A minority of childhood ALL cases (o5%) is related to specific
genetic conditions such as Down’s syndrome or disorders
associated with impaired DNA repair capacities (for example,
ataxia teleangiectasia, Nijmegen breakage syndrome or Bloom’s
Received 9 June 2011; revised 3 September 2011; accepted 21 September 2011; published online 11 November 2011
1Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany;2Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Kiel,
Germany, on behalf of the German Berlin-Frankfurt-Mu ¨nster Study Group for Treatment of Childhood Acute Lymphoblastic Leukemia;3Department of Pediatrics, Laboratory of
Pediatric Hematology/Oncology, University of Padua, Padua, Italy;4M. Tettamanti Research Center, Children’s Hospital, University of Milan-Bicocca, Monza, Italy;5Clinic of
Pediatric Hematology and Oncology, University Medical Center, and Research Center Children’s Cancer Center, Hamburg, Germany;6St Anna Children’s Hospital and Children’s
Cancer Research Institute, Vienna, Austria;7Department of Genetics, Ho ˆpital Robert Debre ´, Paris, France;8Department of Pediatric Hematology/Oncology, Second Faculty of
Medicine, Charles University Prague, Prague, Czech Republic;
10Oncogenetic Laboratory, Department of Pediatric Hematology and Oncology, Justus-Liebig-University, Giessen, Germany;11Division of Research Strategy, University College
London, London, UK;12Department of Experimental Pathology, University of Bologna, Bologna, Italy;13Cologne Center for Genomics, University of Cologne, Cologne, Germany;
14State Social Services Agency Schleswig-Holstein, Kiel, Germany;15University of Notre Dame, Sydney Medical School, Sydney, Australia;16Pediatric Hematology and Oncology,
Hannover Medical School, Hannover, Germany;17Department of General Internal Medicine, University Hospital Schleswig-Holstein, Christian-Albrechts-University Kiel, Kiel,
Germany and18Popgen Biobank, University Hospital Schleswig-Holstein, Christian-Albrechts-University Kiel, Kiel, Germany. Correspondence: Professor Dr rer. nat. A Franke,
Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, Schittenhelmstr. 12, D-24105 Kiel, Germany.
E-mail: a.franke@mucosa.de
or Professor Dr med. M Stanulla, Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, D-24105 Kiel, Germany.
E-mail: martin.stanulla@uk-sh.de
19These authors contributed equally to this work.
9Department of Pediatrics, Second Faculty of Medicine, Charles University Prague, Prague, Czech Republic;
Leukemia (2012) 26, 902- -909
All rights reserved 0887-6924/12
& 2012 Macmillan Publishers Limited
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syndrome).6,7Recently, two genome-wide association studies
(GWAS) on childhood ALL performed in the UK and the US have
identified 13 susceptibility loci.11,12The UK study screened around
291000 single-nucleotide polymorphisms (SNPs) in 907 ALL cases
(a mixture of different subtypes) in a meta-analysis without
independent replication while the US study investigated nearly
308000 SNPs in 317 ALL cases (also a mixture) after quality
control. The two highest scoring loci were detected by both
studies and subsequently verified by others:13IKZF1, a transcrip-
tion regulator of lymphoid cell differentiation, and ARID5B a
member of the AT-rich interaction domain family of transcription
factors.
We report here the results of a genome-wide association study
conducted with patients displaying a subtype of childhood ALL
characterized by the chromosomal translocation which leads to an
ETV6--RUNX1 gene fusion. Thus, to the best of our knowledge, our
study is the first genome-wide approach to susceptibility loci
primarily addressing one of several well-defined and specific
subgroups of the heterogenous disease entity ‘childhood ALL’.
PATIENTS AND METHODS
Recruitment of patients and healthy controls
Samples were organized in panels that corresponded to the successive
steps of the present study. All individual panels (A--D, Supplementary
Table S1) were independent from each other.
All patients included in panel A, 206 patients from panel B,
and all patients from panels C and D were enrolled into the Austrian- -
German--Italian- -Swiss multicenter clinical trial AIEOP- -BFM ALL 2000 on
treatment of childhood ALL after informed consent had been obtained.
Patients were diagnosed and treated at one of 121 participating study
centers in Austria, Germany and Italy between 1999 and 2008. 200 ALL
patients from panel B were enrolled into two subsequent clinical trials
of the German COALL study group, COALL-06-97 and COALL-07-03
between 1997 and 2008 at 19 German treatment centers. To generate
the extended panel B with 664 patients, the 406 patients from panel B
were complemented with an additional 258 patients enrolled in the
years 2009 and 2010 into the Austrian- -German- -Italian- -Swiss multicenter
clinical trials AIEOP- -BFM ALL 2000 or its predecessor, the ongoing trial
AIEOP--BFM ALL 2009 after informed consent had been obtained.
Diagnosis was based on cytomorphology (FAB criteria) and cytochemistry
when X25% of lymphoblastic cells were present in the bone marrow,
or when lymphoblasts were present in the peripheral blood or
cerebrospinal fluid. Flow cytometric immunophenotyping was performed
according to consensus protocols based on the guidelines proposed by
the European Group for the immunological characterization of Leukemias.
The presence or absence of ETV6- -RUNX1, BCR- -ABL and MLL- -AF4 fusion
gene transcripts was analyzed by a multiplex PCR assay enabling the
detection of M-BCR- -ABL, m-BCR- -ABL, ETV6- -RUNX1 and MLL- -AF4 fusion
transcripts in a single PCR reaction. Positive results were confirmed by
interphase fluorescence in situ hybridization in the majority of the patients.
Written, informed consent was obtained from all study participants or their
legal guardians to use spare diagnostic specimens for research purposes
and all protocols were approved by the respective institutional ethical
review committees.
Bone marrow samples were obtained at initial diagnosis and at
consecutive follow-up time points during therapy, mainly after induction
and consolidation. Mononuclear cells were isolated by Ficoll-Paque
gradient centrifugation (Pharmacia, Freiburg, Germany) from bone marrow
samples followed by extraction of high-molecular weight DNA according
to standardized protocols. Quality and quantity of genomic DNA was
determined by spectrophotometry. In the present study only remission
DNA obtained at follow-up time points after induction or consolidation
was analyzed.
The German healthy control individuals of panel A and B were obtained
from the popgen biobank.14The healthy Italian controls of panel C were
recruited through the blood bank of the San Gerardo Hospital at Monza,
Italy. Written and informed consent was obtained from all study
participants and all protocols were approved by the institutional ethical
review committees of the participating centers. To account for their
heterogeneous sources, all DNA samples were first quality-controlled by
gel electrophoresis.
SNP genotyping for genome-wide screening
The genotyping for the GWAS samples (panel A), which was part of the
German NGFN GWAS initiative funded by the NGFN, was performed as a
service project by Affymetrix (South San Francisco, CA, USA) using the
genome-wide human SNP array 5.0. (500k) with 443816 markers. The array
is based on an assay termed whole-genome sampling analysis developed
for highly multiplexed SNP genotyping of complex DNA. This method
reproducibly amplifies a subset of the human genome through a single
primer amplification reaction using restriction enzyme digested and
adapter-ligated human genomic DNA.
In brief, 5ml of genomic DNA samples at 50ng/ml were aliquoted to the
corresponding wells of two 96-well plates. The first run of samples was
processed as an entire plate. In the lab, transfers were made with a
12-channel pipette, reducing the risk of sample tracking errors. One plate
was digested with Nsp I and the other plate was digested with Sty I.
The reaction was incubated at 371C for 2h and at 651C for 20min to
deactivate the enzyme. The digested DNA was then ligated to their
respective Nsp I adaptor and Sty I adaptor. The ligated product was then
PCR-amplified using a common primer. Both Nsp I PCR product and Sty I
PCR product were combined, and then purified by ethanol precipitation in
combination with membrane filter plate. Purified PCR product was further
fragmented with DNase I and then labeled with biotin. Labeled DNA was
combined with hybridization mix and then injected into array. Arrays were
hybridized for 18- -22h at 501C. DNA samples were recovered from arrays
and washed and stained by using Affymetrix FS450 fluidic stations. Stained
arrays were scanned using Affymetrix GeneChip Scanner 3000 7G.
Raw image files were converted into cel-files by Affymetrix genotyping
console. A preliminary QC call rate of 86% was used to pass arrays for
further data analysis. Passed arrays were clustered in the same batches
which were processed together in the lab. Genotypes were assigned using
the BRLMM-p algorithm. There were 12 positive control samples run in
each project. The positive controls were from a CEPH trio family: NA12740 - -
daughter (six repeats), NA12750 - - father (four repeats), and NA12751 - -
mother (four repeats). This set of controls allowed to calculate
experimental reproducibility, trio accuracy and HapMap concordance.
The average call rate was above 96%, and the average BRLMM-p call rate,
average reproducibility (controls), average HapMap concordance (controls)
and average trio accuracy were above 99%.
Samples with more than 5% missing genotypes, who showed excess
genetic dissimilarity to the other subjects or who showed evidence for
cryptic relatedness to other study participants (Supplementary Figure S1)
were removed. These quality control measures left 419 ETV6--RUNX1-
positive ALL patients and 474 healthy control samples for inclusion in
screening panel A. All gender assignments could be verified by reference
to the proportion of heterozygous SNPs on the X chromosome. Before
analysis, all SNPs that had a low genotyping rate (o95% in cases or
controls), were monomorphic or rare (minor allele frequency o2% in cases
or controls), or deviated from Hardy- -Weinberg equilibrium in the control
sample (PHWEp0.001) were excluded (n¼88066; 19.8% of all SNPs).
SNPlex, TaqMan and Sequenom genotyping
All downstream genotyping (panels B through D) was performed with
SNPlex and TaqMan technologies (Applied Biosystems, Foster City, CA,
USA) or the Sequenom platform using an automated laboratory setup and
all process data were written to and administered by a database-driven
laboratory information management system.
Of 100 selected SNPs 87 SNPs passed quality control in both replication
samples. These SNPs had a high call rate (495% in cases or controls), were
not monomorphic or rare (minor allele frequency 41% in cases or
controls) and did not deviate from Hardy--Weinberg equilibrium in the
control population (PHWE40.001).
Identification of risk loci in ALL subtype
E Ellinghaus et al
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Mutation detection
Amplicons were generated using the following touchdown PCR program:
951C for 12min, (951C for 30s, Tmfor 30s, 721C for 30s)?16 cycles
(td ?0.51C), (951C for 30s, Tmfor 30s, 721C for 30s)?19 cycles, 721C for
10min and 101C for N.
Sequencing of genomic DNA was performed using BigDye Terminator
v3.1 chemistry (Applied Biosystems) and an ABI3730 capillary sequencer
(Applied Biosystems) according to manufacturer protocols. Traces were
inspected for SNPs and InDels using novoSNP v2.03.
Statistical analyses
Our screening panel A had 80% power to detect a variant with an odds
ratio of 1.48 or higher at the 5% significance level, assuming a frequency of
the disease-associated allele of at least 30% in controls (calculated with PS
Power and Sample Size v3.0.1215). For subsequent statistical analyses, we
used a data set that passed stringent quality control filters (see section SNP
genotyping for genome-wide screening) resulting in 355750 SNPs
genotyped in 419 cases and 474 healthy controls. The total genotyping
rate across these samples was 99.8%. We found minimal evidence for an
overall inflation of the test statistics due to population stratification with a
moderate genomic control value of lGC¼1.14. Furthermore, a multi-
dimensional scaling analysis showed genuine European ancestry of panel
A and identity-by-state analysis revealed neither non-European ‘outliers’
nor cryptically related individuals after quality control (Supplementary
Figure S1). Genome-wide association analyses were conducted with
gPLINK v2.050 in combination with PLINK v1.07.16GWAS data were also
analyzed with R statistical environment version 2.10.0.
After the initial comparison of case- -control frequencies (Supplementary
Figure S2), we pruned the SNP list for redundancy due to linkage
disequilibrium (using the ‘clump’ command in PLINK) and visually inspected
the cluster plots of the obtained index SNPs with a P-value o2?10?4
(Supplementary Figure S3). The 100 most strongly associated SNPs were
subsequently selected for replication analysis and genotyped in the German
replication panel B (consisting of 406 cases and 1682 controls) and the
Italian replication panel C (comprising 287 cases and 579 controls). After
quality control, we excluded 12 follow-up SNPs with a call rate less than 95%
in cases or controls and one SNP with significant deviation from Hardy- -
Weinberg equilibrium in the controls (Po0.001). To take the different
geographic origin of the two replication panels into account, we used a
Cochran- -Mantel- -Haenszel test (PCMH) and a Breslow- -Day test for odds ratio
heterogeneity (PBD) in both the combined analysis of replication panels B
and C and in the overall study sample (panels A through C) (Supplementary
Table S2). The eight SNPs with a P-value o5?10?7in the combined
analysis of panels A and B or A through C were additionally genotyped in
258 ETV6- -RUNX1-positive ALL cases and 800 healthy controls from Germany
(extended replication panel B: 664 cases and 2482 controls).
Imputation
The software package BEAGLE v3.1.1(ref. 17)was used to impute the
genotypes of autosomal SNPs based on the 1000 Genomes data. As input
for imputation, we used only SNPs on the Affymetrix platform that passed
quality control. To take imputation uncertainty into account, association
analysis between the phenotype and the dosage data (expected allele
counts) was performed using PLINK’s logistic regression framework for
dosage data (with the ‘dosage’ command).
Plotting
Regional association plots were generated by using a modified version of
deBakker’s R script. SNPs are represented with their corresponding
P-values (on ?log10scale) on the vertical axis as a function of physical
position. Single-marker association analysis was performed using PLINK’s
standard case- -control allelic test (with the ‘assoc’ command) and study
SNPs as well as imputed SNPs were used as input. Physical positions on
chromosomes are based on NCBI build 36. Estimated recombination rates
from phased haplotypes in HapMap release 22 (build 36) were down-
loaded from the HapMap website, gene annotations were downloaded
from the UCSC genome browser (using build 36 coordinates).
eQTL analysis
Expression data of TP63 (TP73L), PTPRJ and INTS10 (FLJ10569) in EBV-
transformed lymphoblastoid cell lines from the HapMap samples was
available from the GENEVAR project,18and expression in the n¼60 CEU
parents was correlated to SNP genotypes, using linear regression in the
web-based tool SNPEXP v1.1. (http://app3.titan.uio.no/biotools/tool.php?
app¼snpexp).
RESULTS
Associations with ETV6--RUNX1 subtype of childhood ALL
Novel significant disease associations that withstood Bonferroni
correction (adjusted significance threshold a¼5.75?10?4(0.05/
87)) in the combined analysis of replication panels B and C
were obtained for rs17505102, an intronic SNP in the gene
encoding the tumor protein p63 isoform 3 (TP63; also known
astumor proteinp73-like:
TP73L)
(PCMH¼4.87?10?7, OR¼0.63, 95% CI¼0.52-0.75) and for the
intronic SNP rs3942852, located in the gene PTPRJ (protein
tyrosine phosphatase, receptor type, J) on chromosome 11p11.2
(PCMH¼2.54?10?4, OR¼0.77, 95% CI¼0.68-0.89) (Table 1 and
Figure 1). Although the associations of these loci with ETV6--
RUNX1-positive ALL did not achieve genome-wide significance
(conventional level of Po5.0?10?8) in the discovery panel,
weobtainedgenome-wide
(PCMH¼8.94?10?9) within the TP63 gene in the overall sample
of panels A through C comprising 1370 ETV6--RUNX1-positive ALL
cases and 3535 healthy controls. For rs3942852, the combined
analysis of the overall sample yielded PCMH¼1.00?10?6.
As the initial screening (discovery panel A) was performed with
German samples only, we additionally analyzed the follow-up
SNPs in the German/Austrian replication panel B exclusively to
identify potential loci that are more specific for northern
Europeans as compared with those that confer susceptibility in
the European population in general. Apart from the two novel
shared susceptibility loci (rs17505102 and rs3942852), we found
another two associated SNPs that remained significant after
correction for multiple testing: rs1945213 on chromosome 11q11
within the OR8U8 gene (olfactory receptor, family 8, subfamily U,
member 8; P¼1.63?10?7) and rs920590 on 8p21.3 24kb
upstream of
INTS10
(Integrator
P¼1.05?10?5).
(rs17505102, rs1945213 and rs920590) were genome-wide sig-
nificant in the combined analysis of the German/Austrian panels A
and B comprising 1083 cases and 2956 controls. For rs3942852 we
obtained a P-value of 4.95?10?7in the combined analysis
(Table1 and Figure 1). Of the newly identified susceptibility loci
especially the PTPRJ gene and the tumor protein 63 are very
interesting candidates, taking account of their functional role and
strong homology with the tumor suppressor gene TP53,
respectively. As the marker rs17505102 is located in the first
intron of the TP63 gene, we sequenced exons 1--3 in 47 ALL cases
to determine whether the lead SNP might tag one or more
potential causative coding polymorphisms via linkage disequili-
brium. However, we did not identify any coding SNP in these
regions. Additionally, we checked NCBI’s dbSNP19build 134 for
reported and validated, non-synonymous coding SNPs (X1%
frequency) within the TP63 gene, but found no SNP entries
satisfying these criteria.
Using a publicly available resource (web-based tool SNPexp
v1.120), we were not able to identify any significant correlation --
so-called eQTL effect --- between the genotypes at the lead SNPs
rs17505012, rs1945213, rs3942852 and rs920590 and the expres-
sion levels of the nearby located genes (TP63, PTPRJ and INTS10) in
EBV-transformed lymphoblastoid cell lines from the HapMap
samples (available from the GENEVAR project18). For OR8U8 no
expression data were available. However, it is noteworthy that the
SNP rs3942852, located in the first intron of the PTPRJ gene,
onchromosome3q28
significance forrs17505102
complex
of
subunit
associations
10;
Furthermore, threethese
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Table 1.
Summary of association results in combined replication panels B and C
Chr
position (bp)
dbSNP ID
Nearby genes
(relative position)
A1 A2
panel A - - GWAS
Germany
ETV6- -RUNX1-pos.
474 controls
419 cases
panel Bextended
Germany
ETV6- -RUNX1-pos.
2482 controls
664 cases
panel A + Bext.
Germany
ETV6- -RUNX1-pos.
2956 controls
1083 cases
panel C
Italy
ETV6- -RUNX1-pos.
579 controls
287 cases
panel Bext.+ C
Replication
ETV6- -RUNX1-pos.
3061 controls
951 cases
panel A + Bext.+ C
Combined analysis
ETV6- -RUNX1-pos.
3535 controls
1370 cases
AFA1contr.
AFA1case
PCCA
AFA1contr.
AFA1case
PCCA
AFA1contr.
AFA1case
PCCA
AFA1contr.
AFA1case
PCCA
PCMH
PBD
OR (95% CI)
PCMH
PBD
3
rs17505102
TP63
C
0.18
1.15?10?04
0.14
1.50?10?06
0.15
2.96?10?08
0.08
0.11
4.87?10?07
0.44
0.63 (0.52- -0.75)
8.94?10?09
0.57
190884470
(Within gene)
G
0.11
0.09
0.10
0.06
11
rs1945213
OR8U8
C
0.33
5.82?10?05
0.31
1.63?10?07
0.31
9.14?10?11
0.21
0.32
4.27?10?05
4.13?10?04
0.78 (0.69- -0.88)
3.89?10?08
2.21?10?04
55932247
(Within gene)
G
0.24
0.23
0.24
0.24
11
rs3942852
PTPRJ
C
0.24
1.58?10?04
0.21
1.67?10?04
0.22
4.95?10?07
0.18
0.43
2.54?10?04
0.21
0.77 (0.68- -0.89)
1.00?10?06
0.14
48071665
(Within gene)
T
0.17
0.17
0.17
0.17
14
rs7156960
C14orf118
G
0.52
9.16?10?06
0.47
2.54?10?03
0.48
3.74?10?06
0.41
0.10
6.05?10?04
0.89
0.83 (0.75- -0.92)
1.09?10?06
0.58
75773104
(±50kb)
C
0.41
0.42
0.42
0.37
15
rs207954a
SLCO3A1
T
0.29
1.24?10?04
0.28
0.09
0.28
6.58?10?05
0.26
5.60?10?03
2.45?10?03
0.22
0.80 (0.70- -0.93)
1.35?10?06
0.61
90458377
(Within gene)
C
0.21
0.25
0.23
0.20
8
rs920590
INTS10
C
0.29
5.19?10?05
0.31
1.05?10?05
0.31
6.12?10?09
0.45
0.36
1.32?10?03
1.87?10?03
1.19 (1.07- -1.33)
2.22?10?06
5.54?10?04
19695441
(±50kb)
T
0.38
0.38
0.38
0.43
4
rs282708a
- - -
A
0.39
3.91?10?05
0.41
0.03
0.40
5.41?10?06
0.46
0.42
0.03
0.51
1.15 (1.01- -1.30)
1.38?10?05
0.12
59198483
G
0.49
0.45
0.47
0.48
19
rs2910225a
CCDC130
C
0.42
1.02?10?05
0.36
0.16
0.37
6.97?10?04
0.30
0.01
9.83?10?03
0.23
0.84 (0.74- -0.96)
3.23?10?05
0.54
13727957
(Within gene)
T
0.32
0.33
0.32
0.24
15
rs11857366a
IGF1R
A
0.37
5.98?10?05
0.40
0.08
0.39
7.01?10?05
0.42
0.16
0.02
0.98
1.15 (1.02- -1.30)
3.41?10?05
0.45
97081324
(Within gene)
G
0.46
0.43
0.45
0.45
5
rs7734914a
- - -
A
0.40
1.32?10?05
0.44
0.42
0.43
6.09?10?04
0.46
0.03
4.71?10?02
0.21
1.13 (1.00- -1.28)
4.81?10?05
0.82
10085335
G
0.50
0.45
0.48
0.52
6
rs7738636
- - -
C
0.26
9.84?10?05
0.23
0.02
0.24
7.44?10?05
0.16
0.46
0.02
0.67
0.85 (0.75- -0.97)
8.54?10?05
0.35
77846527
A
0.18
0.20
0.20
0.15
1
rs343604a
KCNA3
T
0.07
8.39?10?05
0.09
0.03
0.08
1.19?10?06
0.08
0.67
0.14
0.08
0.84 (0.67- -1.06)
9.25?10?05
1.46?10?03
111060293
(±50kb)
C
0.03
0.06
0.05
0.09
8
rs630662a
RSPO2
A
0.30
3.61?10?05
0.28
4.46?10?03
0.29
2.02?10?06
0.23
0.45
0.06
0.02
0.87 (0.76- -1.01)
1.36?10?04
2.65?10?03
109041474
(Within gene)
G
0.22
0.23
0.22
0.25
13
rs7336133a
LHFP
A
0.38
3.53?10?05
0.32
0.42
0.34
6.01?10?03
0.34
0.01
0.03
0.13
0.86 (0.75- -0.98)
2.71?10?04
0.39
38845574
(Within gene)
G
0.29
0.31
0.30
0.28
1
rs17423910
PDE4B
G
0.14
2.18?10?06
0.17
0.01
0.17
6.37?10?06
0.23
0.43
0.11
0.05
1.11 (0.98- -1.27)
3.07?10?04
0.01
66461697
(Within gene)
A
0.23
0.20
0.21
0.21
6
rs6901152a
AIG1
T
0.46
3.19?10?05
0.42
4.63?10?03
0.43
4.71?10?06
0.44
0.37
0.09
0.01
0.90 (0.79- -1.02)
4.19?10?04
2.12?10?03
143700705
(Within gene)
C
0.36
0.36
0.36
0.47
4
rs936094a
RXFP1
C
0.15
9.42?10?06
0.17
0.01
0.17
1.43?10?06
0.25
0.28
0.24
0.02
1.09 (0.94- -1.27)
4.74?10?04
6.48?10?04
159663764
(Within gene)
T
0.23
0.21
0.22
0.23
3
rs6445754
ERC2
C
0.17
1.82?10?07
0.23
7.97?10?03
0.22
4.60?10?06
0.32
0.24
0.12
0.02
1.10 (0.98- -1.24)
5.08?10?04
1.62?10?03
55782295
(Within gene)
T
0.28
0.26
0.27
0.29
14
rs4901921a
DAAM1
T
0.52
1.48?10?04
0.47
0.29
0.48
5.41?10?03
0.40
0.04
0.04
0.32
0.88 (0.78- -0.99)
6.45?10?04
0.66
58908882
(±10kb)
C
0.43
0.45
0.44
0.35
6
rs6910780a
KCNQ5
T
0.16
1.47?10?04
0.19
0.01
0.18
3.89?10?05
0.22
0.64
0.09
0.06
1.13 (0.98- -1.31)
9.29?10?04
0.02
73500100
(Within gene)
C
0.23
0.23
0.23
0.21
Abbreviations: AF, allele frequency; Chr, chromosome; CI, confidence interval; GWAS, genome-wide association study; OR, odds ratio; SNP, single-nucleotide polymorphism.aThe 12 SNPs that were not
genotyped in the additional samples but only in the non-extended panel B: 1682 controls and 406 cases; panels A and B: 2156 controls and 825 cases; panels B and C: 2261 controls and 693 cases; panels A, B
and C: 2735 controls and 1112 cases. 100 SNPs were selected for genotyping in two independent replication panels of ETV6- -RUNX1-positive cases and healthy controls from Germany/Austria and Italy,
respectively, (panels B and C). Data are shown for the 20 SNPs that were nominal significant either in replication panel B (PCCAo0.05) or in the combined replication analysis of panels B and C (PCMHo0.05 and
PBD40.05). Additionally, these 20 SNPs were also genotyped in 326 German ETV6- -RUNX1-negative ALL cases (panel D) (Supplementary Table S3). The eight SNPs with a P-value o5?10?7in the combined
analysis of panels A and B or A through C were also genotyped in 258 additional ETV6- -RUNX1-positive ALL cases and 800 controls from Germany (panel Bextended664 cases and 2482 controls). Results of all 100
SNPs are shown in Supplementary Table S2. SNPs are ranked according to the P-value obtained in the combined analysis of panels A, B and C. Nucleotide positions (position (bp)) refer to NCBI’s build 36. A1
denotes the rare allele while A2 is the common allele. The respective allele frequencies are shown for allele A1 in controls and cases, respectively (AFA1contr./ AFA1case). P-values obtained in the case- -control
analysis using an allele based w2-test (one degree of freedom) are listed (PCCA). Odds ratios (OR) and 95% confidence intervals (95% CI) for carriership of the allele A1 are shown for the replication analysis (panels
B and C). Column PBDlists the asymptotic P-values of a Breslow- -Day test for heterogeneity. A significant P-value indicates a significant heterogeneity between replication panels in terms of the odds ratio of the
disease association. Combined P-values (PCMH) of the Cochran- -Mantel- -Haenszel test statistic (one degree of freedom) are shown. Significant P-values (PCCAo0.05 or PCMHo0.05 [only if PBD40.05]) of the
replication are highlighted in bold. SNP associations that remained significant after correction for multiple testing (PCMHof panels B and C) as well as genome-wide significantly associated SNPs in the combined
analysis (combined P-value of panels A and B or panels A- -C) are highlighted by grey shading.
Identification of risk loci in ALL subtype
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resides within a transcription factor binding site of BAF155
(SMARCC1), BAF170 (SMARCC2) and the proto-oncogene JunD
assayed by CHiPseq in HeLa-S3 cells (UCSC Browser hg18). Hence,
further experiments and analyses are required to elucidate the
mechanism by which the SNPs rs17505102, rs1945213, rs3942852
and rs920590 affect the risk of ALL.
Associations with childhood ALL independent of ETV6--RUNX1
subtype
In order to assess whether our findings were specifically associated
with the ETV6--RUNX1-rearranged subtype, we genotyped the 20 top
SNPs displayed in Table 1 in a second German replication cohort
(panel D) consisting of 326 ETV6- -RUNX1-negative childhood ALL
cases. We analyzed these cases together with the same 1682 controls
of panel B and obtained genotypes of 19 SNPs after quality control.
Of the 19 SNPs only rs7738636 on chromosome 6q14.1 was
significantly associated (P¼2.55?10?3). The combined analysis of
the replication panels B/Bextendedthrough D yielded only a marginally
stronger association with PCMH¼1.42?10?3for rs7738636 (Supple-
mentary Table S3). These results imply that rs7738636 might
represent a susceptibility locus for ETV6- -RUNX1-negative ALL
patients. However, due to the small sample of ETV6--RUNX1-negative
cases, our analysis does not have enough power to determine
whether the newly identified loci distinguish ETV6--RUNX1-positive
from -negative patients.
Associations of previously reported susceptibility-conferring SNPs
Additionally, wegenotyped
SNPs identified by Papaemmanuil et al.11and Trevin ˜o et al.,12
thesusceptibility-conferring
respectively, in panels A through D in order to confirm the
previously reported loci and as a positive control for our
experiment. We observed genome-wide significant association
(Po5?10?8) for IKZF1, DDC, ARID5B and CEBPE in the combined
sample (panels A--D) comprising 1438 cases and 2735 controls.
Suggestive evidence of association (Po0.05) was found for SIAT7C,
1q31.3 (rs6428370), KCNMB2, PARD3 and C12orf5 (Table 2).
DISCUSSION
In this study, we were able to identify attractive, novel risk loci for
ALL, even with a small screening sample -- compared with other
GWAS studies and diseases -- of only 419 patients. This success
might be due to the exclusive selection of patients with the
homogenous phenotype of ETV6--RUNX1-rearranged childhood
ALL. Given their interesting biological function, we below discuss
the relevance of TP63 and PTPRJ in ALL disease etiology.
The TP63 gene --- a member of the TP53 gene family --- codes for
p63 which has essential roles in embryonic development. TP63
contains two transcriptional start sites leading to p63 isoforms
either containing (TAp63) or lacking (WNp63) the trans-activation
domain.21TP63-null mice die cancer-free within a few hours of
birth, showing severe loss of limbs and a wide range of epithelial
structures including skin, prostate, breast and urothelia.22,23This
suggests that p63 acts as an important regulator of stemness in
developing epithelia.24,25In humans, TP63 mutations are asso-
ciated with ectodermal dysplasia, cleft lip or palate and limb
malformations, but not with a higher tumor incidence.21,26In line
with these observations, the TP63 coding region is found only
Figure 1.
logarithm of the P-values obtained in the GWAS (panel A) are shown. Panel A was imputed with CEU haplotypes generated by the 1000
Genomes Project (August 2010 release) as reference. For the central lead SNP of each plot, the combined P-value of panels A and B (rs1945213,
rs920590 and rs3942852) or panels A through C (rs17505102) is indicated. The magnitude of linkage disequilibrium (LD) with the central SNP
measured by r2is reflected by the color of each SNP symbol (for color coding, see upper right corner of each plot). Recombination activity
(in centimorgans (cM) per Mb) is depicted by a blue line. Positions are given as NCBI’s build 36 coordinates. For details, see Table 1.
Regional plots of the confirmed associations at TP63, OR8U8, on 8p21.3 and at PTPRJ. The regional plots of the negative decadic
Identification of risk loci in ALL subtype
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Table 2.
Association results for known loci
Chr
position (bp)
dbSNP ID
Gene name
A1A2
Panel A Germany
ETV6- -RUNX1 pos.
474 controls
419 cases
Panel B Germany
ETV6- -RUNX1 pos.
1682 controls
406 cases
Panel C Italy
ETV6- -RUNX1 pos.
579 controls
287 cases
Panel A + B + C
Combined analysis
ETV6- -RUNX1 pos.
2735 controls
1112 cases
Panel D Germany
ETV6- -RUNX1 neg.
?
326 cases
Panel A + B + C + D
Combined analysis
ETV6- -RUNX1 pos. + neg.
2735 controls
1438 cases
AFA1contr.
AFA1case
AFA1contr.
AFA1case
AFA1contr.
AFA1case
PCMH
PBD
OR (95% CI)
?
AFA1case
PCMH
PBD
OR (95% CI)
Papaemmanuil et al. (2009)
7
rs6964823
A
0.47
0.49
0.51
1.14?10?04
0.85
0.82 (0.74- -0.91)
- - -
5.56?10?05
0.93
0.83 (0.75- -0.91)
50427590
IKZF1
G
0.43
0.44
0.47
0.44
7
rs4132601
G
0.28
0.27
0.26
1.65?10?10
0.58
1.42 (1.27- -1.58)
- - -
8.26?10?13
0.65
1.43 (1.30- -1.58)
50438098
IKZF1
T
0.34
0.35
0.34
0.35
7
rs6944602
A
0.22
0.19
0.22
1.99?10?07
0.39
1.42 (1.25- -1.63)
- - -
3.10?10?09
0.42
1.42 (1.27- -1.60)
50441245
IKZF1
G
0.26
0.27
0.27
0.26
7
rs3779084
C
0.21
0.20
0.16
2.36?10?05
0.17
1.30 (1.15- -1.46)
- - -
2.44?10?06
0.19
1.30 (1.17- -1.46)
50536229
DDC
T
0.25
0.23
0.22
0.25
7
rs880028
C
0.21
0.20
0.16
2.91?10?05
0.09
1.29 (1.15- -1.46)
- - -
8.69?10?06
0.09
1.28 (1.15- -1.43)
50537630
DDC
T
0.25
0.23
0.22
0.24
7
rs7809758
G
0.35
0.34
0.32
2.04?10?05
0.27
1.29 (1.15- -1.45)
- - -
1.48?10?07
0.40
1.32 (1.19- -1.46)
50540827
DDC
A
0.40
0.39
0.40
0.41
10
rs7073837
A
0.40
0.37
0.43
7.76?10?07
0.85
1.29 (1.17- -1.42)
- - -
1.50?10?08
0.75
1.30 (1.19- -1.43)
63369901
ARID5B
C
0.44
0.44
0.49
0.46
10
rs10740055
A
0.51
0.53
0.49
8.75?10?07
0.73
0.78 (0.70- -0.86)
- - -
8.95?10?09
0.88
0.76 (0.70- -0.84)
63388485
ARID5B
C
0.47
0.46
0.42
0.44
10
rs7089424
G
0.33
0.32
0.38
9.05?10?10
0.30
1.38 (1.24- -1.53)
- - -
1.98?10?13
0.17
1.42 (1.30- -1.56)
63422165
ARID5B
T
0.38
0.42
0.44
0.43
14
rs2239633
T
0.48
0.50
0.43
1.53?10?08
0.51
0.75 (0.67- -0.83)
- - -
4.00?10?10
0.50
0.74 (0.68- -0.82)
22658897
CEBPE
C
0.42
0.41
0.38
0.41
Trevino et al. (2009)
1
rs10873876
T
0.17
0.14
0.18
8.14?10?03
0.74
1.20 (1.05- -1.36)
- - -
5.47?10?03
0.71
1.19 (1.05- -1.34)
76544916
SIAT7C
C
0.15
0.19
0.22
0.17
1
rs6428370
G
0.30
0.31
0.36
0.14
0.41
1.08 (0.97- -1.20)
- - -
0.03
0.63
1.12 (1.01- -1.23)
195111216
- - -
A
0.31
0.32
0.40
0.35
1
rs7554607
G
0.45
0.43
0.46
0.12
0.32
1.08 (0.98- -1.20)
- - -
0.34
0.18
1.05 (0.95- -1.15)
235333226
RYR2
A
0.48
0.41
0.51
0.41
1
rs1881797
C
0.17
0.19
0.17
0.71
0.42
1.03 (0.90- -1.17)
- - -
0.76
0.25
0.98 (0.87- -1.11)
245756155
OR2C3
T
0.17
0.19
0.19
0.16
2
rs12621643
T
0.29
0.28
0.42
0.14
2.16?10?03
1.08 (0.97- -1.20)
- - -
0.06
1.81?10?03
1.10 (1.00- -1.21)
223626227
KCNE4
G
0.31
0.34
0.37
0.32
3
rs9290663
T
0.15
0.14
0.23
0.02
0.70
1.18 (1.03- -1.34)
- - -
6.90?10?03
0.74
1.18 (1.05- -1.33)
179912633
KCNMB2
A
0.16
0.17
0.27
0.17
6
rs11155133
G
0.01
0.01
0.01
0.27
0.37
1.32 (0.81- -2.14)
- - -
0.28
0.41
1.28 (0.81- -2.03)
141211518
- - -
A
0.01
0.01
0.01
0.01
7
rs11978267
G
0.29
0.27
0.25
8.74?10?11
0.70
1.43 (1.28- -1.59)
- - -
1.07?10?12
0.73
1.44 (1.30- -1.59)
50433798
IKZF1
A
0.35
0.34
0.33
0.35
7
rs2242041
G
0.09
0.09
0.05
7.51?10?03
0.38
1.26 (1.06- -1.50)
- - -
8.47?10?04
0.48
1.30 (1.11- -1.52)
50496943
DDC
C
0.10
0.12
0.08
0.12
7
rs2167364
G
0.33
0.31
0.29
2.33?10?07
0.35
1.32 (1.19- -1.46)
- - -
1.73?10?08
0.36
1.32 (1.20- -1.45)
50533321
DDC
A
0.37
0.37
0.37
0.37
10
rs563507
A
0.04
0.04
0.07
0.047
0.65
1.25 (1.00- -1.55)
- - -
0.046
0.73
1.23 (1.00- -1.51)
34857994
PARD3
G
0.06
0.04
0.08
0.05
10
rs10994982
A
0.51
0.46
0.51
2.07?10?05
0.86
1.24 (1.12- -1.37)
- - -
1.39?10?06
0.94
1.25 (1.14- -1.37)
63380110
ARID5B
G
0.52
0.52
0.57
0.53
10
rs10821936
C
0.33
0.31
0.37
1.25?10?11
0.56
1.42 (1.29- -1.58)
- - -
4.06?10?15
0.42
1.46 (1.33- -1.60)
63393583
ARID5B
T
0.38
0.42
0.44
0.42
12
rs10849033
G
0.02
0.01
0.02
0.01
0.69
1.62 (1.11- -2.37)
- - -
0.046
0.91
1.43 (1.00- -2.02)
4295383
C12orf5
A
0.01
0.03
0.03
0.01
12
rs2089222
A
0.03
0.04
0.03
0.58
0.52
1.08 (0.83- -1.41)
- - -
0.87
0.40
1.02 (0.80- -1.31)
115487041
KRTHB5
G
0.04
0.04
0.03
0.03
Identification of risk loci in ALL subtype
E Ellinghaus et al
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rarely mutated in human cancers.27,28To date, recurrent somatic
TP63 mutations have only been described for non-small cell lung
cancer as well as chronic myeloid leukemia28,29and TP63 germline
genetic variation (rs710521) has only been associated with urinary
bladder cancer, so far.30However, mice heterozygous for TP63
mutations develop malignant lesions, demonstrating that TP63
can act as a tumor suppressor and mice heterozygous for
mutations in both TP53 and TP63 display higher tumor burden
and metastasis compared with mice only heterozygous for TP53
mutations.31Of particular interest to our study, TAp63 was
described to be involved in an antiapoptotic pathway regulating
normal B and chronic lymphocytic leukemia cell survival in a
CD74-dependant manner.32,33In chronic lymphocytic leukemia,
TAp63 expression upregulates VLA-4 integrin expression leading
to augmented migration and homing of chronic lymphocytic
leukemia cells to the bone marrow.33Also, it was found that
thymomas and certain B-cell lymphomas express high levels of
TP63, suggesting a role of TP63 in these cancers.34
PTPRJ is a receptor type protein tyrosine phosphatase involved
in the regulation of cellular processes including cell growth,
differentiation, mitotic cycle and oncogenic transformation.
Somatic mutations of PTPRJ have been described in a wide
spectrum of cancers like thyroid carcinomas, colon cancer, breast
and lung cancer.35- -38Furthermore, a specific PTPRJ haplotype was
shown to be associated with the risk of breast cancer
demonstrating that common PTPRJ variants may act as breast
cancer susceptibility alleles.39Interestingly, the PTPRJ gene is
expressed at high levels in hematopoietic cells.35,37,40Of particular
interest to the results reported in our study, mice with a
constitutively deleted transmembrane domain allele of PTPRJ
display a partial peripheral B-cell developmental block.41Hetero-
zygous mice do not show any notable phenotype.
Although the current knowledge on TP63 and PTPRJ may
provide some evidence for their role in tumorigenesis as well as
B-cell development and/or maintenance, more precise mechan-
isms how germline genetic variation at the TP63 and PTPRJ loci
may modulate susceptibility to precursour B-cell leukemia remain
speculative and underscore the need for functional studies.
Apart from our novel findings, our study could clearly confirm the
association of childhood ALL with the four previously published
susceptibility loci IKZF1, DDC, ARID5B and CEBPE. Nominal significant
P-values (Po0.05) were obtained for SIAT7C, 1q31.3, KCNMB2, PARD3
and C12orf5. Although our study panel had more than 80% power to
detect all of the SNPs/loci published by Papaemmanuil et al.11and
Trevino et al.,12respectively, we could not replicate the loci RYR2,
OR2C3, KCNE4, 6q24.1, KRTHB5, 18p11.32 and ZNF230, which
therefore require further investigations.
Overall, our findings demonstrate that germline genetic
variation can specifically contribute to the risk of specific ALL
subtypes, in this case ETV6--RUNX1-positive childhood ALL. Our
results also suggest that germline genetic variation can act as a
risk factor for childhood ALL in general or functions restricted to
specific subtypes, only.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
The authors wish to thank all ALL patients, families and physicians for their
cooperation. Also, we wish to thank T Wesse, T Henke, M Wittig, R Vogler, L Tittmann
and B Fedders for expert technical help. We are grateful to C Becker, R Niemiec and
C Kluck for genotyping the samples of GWAS panel A. Prof Dr T Wienker and
Dr M Steffens (IMBIE, University of Bonn) are acknowledged for performing the
quality control of the GWAS control data set. This study was supported by the
German Ministry of Education and Research (BMBF) through the National Genome
Research Network (NGFN), the popgen biobank, the Federal Radiation Protection
Agency (project no. 3609S30013), the Deutsche Krebshilfe and the Madeleine
Table 2 (Continued)
Chr
position (bp)
dbSNP ID
Gene name
A1A2
Panel A Germany
ETV6- -RUNX1 pos.
474 controls
419 cases
Panel B Germany
ETV6- -RUNX1 pos.
1682 controls
406 cases
Panel C Italy
ETV6- -RUNX1 pos.
579 controls
287 cases
Panel A + B + C
Combined analysis
ETV6- -RUNX1 pos.
2735 controls
1112 cases
Panel D Germany
ETV6- -RUNX1 neg.
?
326 cases
Panel A + B + C + D
Combined analysis
ETV6- -RUNX1 pos. + neg.
2735 controls
1438 cases
AFA1contr.
AFA1case
AFA1contr.
AFA1case
AFA1contr.
AFA1case
PCMH
PBD
OR (95% CI)
?
AFA1case
PCMH
PBD
OR (95% CI)
18
rs1879352
G
0.15
0.16
0.17
0.75
0.09
0.98 (0.85- -1.12)
- - -
0.83
0.11
0.99 (0.87- -1.12)
2488054
- - -
A
0.14
0.14
0.19
0.15
19
rs2191566
C
0.29
0.29
0.34
0.35
0.04
0.95 (0.85- -1.06)
- - -
0.88
0.01
0.99 (0.90- -1.10)
49203229
ZNF230
A
0.29
0.30
0.29
0.33
19
rs6509133
C
0.29
0.30
0.35
0.46
0.04
0.96 (0.86- -1.07)
- - -
0.98
0.02
1.00 (0.91- -1.10)
49214753
ZNF230
T
0.29
0.32
0.30
0.33
Abbreviations: AF, allele frequency; Chr, chromosome; CI, confidence interval; OR, odds ratio; SNP, single-nucleotide polymorphism. In order to confirm the associated SNPs reported by Papaemmanuil et al. and
Trevin ˜o et al., we genotyped the respective 28 SNPs in the panels A through D. SNPs are ranked according to their chromosomal position and to the respective study. Nucleotide positions (position (bp)) refer to
NCBI0s build 36. The rare allele is A1 while A2 denotes the common allele. The respective allele frequencies are listed for allele A1 in controls and cases, respectively, (AFA1contr./AFA1case ). Combined P-values
(PCMH) and combined odds ratios (OR) of the Cochran- -Mantel- -Haenszel test statistic (one degree of freedom) for the case-control analysis of panels A through C and panels A through D are listed. Column PBD
shows the asymptotic P-values of a Breslow- -Day test for heterogeneity. A significant PBDindicates a significant heterogeneity between replication panels in terms of the odds ratio of the disease association.
Significant P-values (PCMHo0.05 [only if PBD40.05]) are highlighted in bold. SNP associations that reached genome-wide significance in the combined panels A through C or A through D are highlighted by grey
shading.
Identification of risk loci in ALL subtype
E Ellinghaus et al
908
Leukemia (2012) 902- -909
& 2012 Macmillan Publishers Limited

Page 8

Schickedanz Kinderkrebs-Stiftung. The project received infrastructure support
through the DFG excellence cluster ‘Inflammation at Interfaces’. This study was
conducted within the frame of the International BFM Study Group.
AUTHOR CONTRIBUTIONS
EE, AF and GR performed the SNP selection, genotyping, data analysis and
resequencing. EE prepared figures and tables. DE prepared regional plots and
helped with data analysis and quality control. AE, RH, AN and MZ helped with data
analysis. PN coordinated the Affymetrix chip genotyping. M St, G Car and M Sch
coordinated the recruitment and collected the phenotype data. GK, G Caz, JT, OC, HC,
RPG, BM, TB, CF and MH recruited the additional case- -control panels. AT-S, GK, MG,
G Caz, and RPG were involved in phenotypic characterization of cases. AF, BH, G Car,
M Sch, M St and SS designed and supervised the experiment. EE, M St and AF wrote
the manuscript. All authors approved the final manuscript.
URLs
SNPexp20v1.1:
http://app3.titan.uio.no/biotools/tool.php?app¼snpexp
R script for Regional Association Plots:
http://www.broadinstitute.org/science/projects/diabetes-genetics-initiative/plotting-
genome-wide-association-results
1000 Genomes data:
http://www.sph.umich.edu/csg/abecasis/MACH/download/
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Supplementary Information accompanies the paper on the Leukemia website (http://www.nature.com/leu)
Identification of risk loci in ALL subtype
E Ellinghaus et al
909
Leukemia (2012) 902- -909
& 2012 Macmillan Publishers Limited