DOI: 10.1161/CIRCGENETICS.114.000785
1
Analysis for Genetic Modifiers of Disease Severity in Patients with
Long QT Syndrome Type 2
Running title: Kolder et al., Genetic modifiers in LQT2
Iris C.R.M. Kolder, PhD1,2*; Michael W.T. Tanck, PhD2*; Pieter G. Postema, MD, PhD1*;
Julien Barc, PhD1,3-6; Moritz F. Sinner, MD, MPH7; Sven Zumhagen, MD9,10; Anja Husemann
PhD9,10; Birgit Stallmeyer, PhD9,10; Tamara T. Koopmann, PhD1; Nynke Hofman, PhD11; Arne
Pfeufer, MD, MSc12-14; Peter Lichtner, PhD13; Thomas Meitinger, MD13,14; Britt M. Beckmann,
MD7; Robert J. Myerburg, MD15-17; Nanette H. Bishopric, MD15-17; Dan M. Roden, MD18; Stefan
Kääb, MD, PhD7,8; Arthur A.M. Wilde, MD, PhD1,3,19; Jean-Jacques Schott, PhD4-6**;
Eric Schulze-Bahr, MD 9,10**; Connie R. Bezzina, PhD1,3**
1Departments of Clinical & Experimental Cardiology, 2Clinical Epidemiology, Biostatistics
& Bioinformatics, 11Clinical Genetics, Academic Medical Center, Amsterdam; 3ICIN
(Netherlands Heart Institute), Utrecht, the Netherlands; 4Institut National de la Santé et de la
Recherche Médicale (INSERM) Unité Mixte de Recherche (UMR) 1087, L’Institut du
Thorax; 5Centre National de la Recherche Scientifique (CNRS) UMR 6291; 6Université de
Nantes, Nantes, France; 7Department of Medicine I, University Hospital Munich, Campus
Grosshadern & Innenstadt, Ludwig- Maximilians University; 8German Center for
Cardiovascular Research (DZHK), Munich Heart Alliance, Munich; 9Institute for Genetics of
Heart Diseases, Department of Cardiovascular Medicine, University Hospital Münster,
Münster; 10Interdisciplinary Centre for Clinical Research (IZKF) of the University of
Münster, Münster; 12Institute of Bioinformatics & Systems Biology, Helmholtz Zentrum
Munich, Neuherberg; 13Institute of Human Genetics, Helmholtz Zentrum München,
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MDDDD, MMSMM c12222--14; Peter Lichtner, PhD13; Thommas Meitinggggerere , MD13,14;;; Britt M. Beckm
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DOI: 10.1161/CIRCGENETICS.114.000785
2
Neuherberg; Institute of Human Genetics, Technische Universität München, Munich,
Germany; 14Departments of Medicine, 15Molecular & Cellular Pharmacology, 16Hussmann
Institute of Human Genomics, University of Miami Miller School of Medicine, Miami, FL;17Department of Medicine & Pharmacology, Vanderbilt University School of Medicine,
Nashville, TN; 18Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of
Hereditary Disorders, Jeddah, Kingdom of Saudi Arabia; 19Centre Hospitalier Universitaire
(CHU) Nantes, L’Institut du Thorax, Service de Cardiologie, Nantes, France
*/**contributed equally
Correspondence:
Connie R. Bezzina, PhD
Department of Clinical and Experimental Cardiology
Academic Medical Center
Meibergdreef 9, Room L2-108
1105 AZ Amsterdam
the Netherlands
Tel: +31 20 5665403
Fax: +31 20 6975458
E-mail: [email protected]
Journal Subject Codes: [106] Electrophysiology, [132] Arrhythmias - basic studies, [109] Clinical genetics, [157] Quantitative modeling
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DOI: 10.1161/CIRCGENETICS.114.000785
3
Abstract:
Background - Considerable interest exists in the identification of genetic modifiers of disease
severity in the Long QT Syndrome (LQTS) as their identification may contribute to refinement
of risk stratification.
Methods and Results – We searched for single nucleotide polymorphisms (SNPs) that modulate
the QTc-interval and the occurrence of cardiac events in 639 patients harboring different
mutations in KCNH2. We analyzed 1,201 SNPs in and around 18 candidate genes, and in another
approach investigated 22 independent SNPs previously identified as modulators of QTc-interval
in genome-wide association studies (GWAS) in the general population.
In an analysis for quantitative effects on the QTc-interval, 3 independent SNPs at NOS1AP
(rs10494366, p=9.5 10-8; rs12143842, p=4.8 10-7; rs2880058, p=8.6 10-7) were strongly
associated with the QTc-interval with marked effects (>12ms/allele). Analysis of patients versus
general population controls uncovered enrichment of QTc-prolonging alleles in patients for 2
SNPs, located respectively at NOS1AP (rs12029454, OR=1.85 [95% CI, 1.32-2.59], p=3 10-4)
and KCNQ1 (rs12576239; OR=1.84 [95% CI, 1.31-2.60], p=5 10-4). An analysis of the
cumulative effect of the 6 NOS1AP SNPs by means of a multi-locus genetic risk score
(GRSNOS1AP) uncovered a strong linear relationship between GRSNOS1AP and the QTc-interval
(p=4.2 10-7). Furthermore, patients with a GRSNOS1AP in the lowest quartile had a lower relative
risk of cardiac events compared to patients in the other quartiles combined (p=0.039).
Conclusions – We uncovered unexpectedly large effects of NOS1AP SNPs on the QTc-interval
and a trend for effects on risk of cardiac events. For the first time we linked common genetic
variation at KCNQ1 with risk for LQTS.
Key words: association study, polymorphism genetics, arrhythmia, ion channel, long QT syndrome, genetic risk score
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witttthhhh thththheee QTQTQTTc-inininnterval with marked effecccttsts (>12ms/alleeele). AAAAnalysis of patients v
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DOI: 10.1161/CIRCGENETICS.114.000785
4
Introduction
The congenital Long QT Syndrome (LQTS) is a heritable disorder associated with QTc-interval
prolongation on the electrocardiogram (ECG) and an increased risk of sudden cardiac death from
torsade de pointes polymorphic ventricular tachycardia. Mutations in multiple genes, primarily
encoding ion channel subunits have been identified in patients with the disorder. In around 75%
of cases, the disease is caused by the inheritance of a mutation in either KCNQ1 (LQT1),
KCNH2 (LQT2), or SCN5A (LQT3).1
Despite previous achievements in gene discovery, important issues in the clinical
management LQTS patients remain. As for most Mendelian disorders, patient management is
complicated by the variability in disease severity among mutation carriers.2 Variability is
observed both in the extent of the QTc-interval prolongation as well as in the occurrence of
arrhythmic events. While some mutation carriers display a severely prolonged QTc-interval, the
QTc-interval of others may be within the normal range. Similarly, not all patients suffer
arrhythmic events. Established modulators of disease severity include sex, age, heart rate, intake
of QTc-prolonging drugs, and affected gene and mutation location.3–6 Furthermore, in ~10% of
cases clinical disease severity can be explained by compound heterozygosity.7 However, while
additional genetic factors are also expected to play a role, these are largely unexplored.8–10
We here investigated the role of common genetic variants (minor-allele frequency, MAF >10%)
in the form of SNPs in patients with LQT2. In one approach we conducted a comprehensive
analysis of haplotype-tagging SNPs in 18 candidate genes. In a second approach we investigated
the effect of SNPs that have over the last years been associated with the QTc-interval in GWAS
conducted in the general population.11–15
sues in the clinicalalalal
, patieiii ntttt managgggemmmmeeenent
d 2
o f
events. While some mutation carriers display a severely prolonged QTc-interva
a
e ents Established mod lators of disease se erit incl de se age heart rate i
d bybybyby ttthehehehe vvararariabibibibility in disease severity amommong mutation carrrrrrieiii rs.2 Variability is
otttth in the extenttt offf ttthheheh QQQQTTcT -iiiinnteeervval prrolonongaatititionononon aas weww ll aas innnn ttthhheh oooccccccc urrrrrenccce of
eventntntsss.s WWWWhilelel ssomomee mum taaatititiioooon carrrriririerererersss dididiispspsplalall y aaaa seeevvvereeelyylyly pprorolololl nggggedeeded QQQQTcTcT iii-intntterervav
al of others mayyy bbbe wii hhthhinii thhheh normall l ranggge. SSSimilililarlyllyl , ,, not llalll papp tients suffer
nt EEst bablili hsh ded dod llat fof ddiis iit iin lcl dd hhe t te ii
DOI: 10.1161/CIRCGENETICS.114.000785
5
Methods
LQT2 patients
The study population consisted of 639 individuals from 254 families of European descent, all
harboring a mutation in KCNH2. Patients carrying >1 mutation in KCNH2 or carrying a second
mutation in another LQTS gene were excluded. These subjects were drawn from the LQTS
registries of four European clinical centers: Amsterdam (The Netherlands), Münster (Germany),
Munich (Germany), and Nantes (France). The Medical Ethical Committee at each center
approved the study. All subjects or their guardians provided informed consent for genetic and
clinical studies. Analyses were conducted in a set of 353 patients (Set 1), a non-overlapping set
of 286 patients (Set 2), and in Set 1 and Set 2 combined. Patient Set 1 and Set 2 were drawn a
few years apart of each other from the LQTS registries of the same four European academic
centers. Routine clinical and ECG parameters were acquired at the time of patient enrollment in
each of the registries (see Data Supplement for QTc-interval measurement). A first cardiac event
was defined as a first unexplained syncope, a first documented ventricular tachycardia or a first
aborted cardiac arrest. The observation period for cardiac events started at birth and lasted either
to the initiation of anti-adrenergic therapy ( -blockers) or the date of the last medical visit
(without anti-adrenergic therapy).
Selection of SNPs and genotyping
Candidate gene SNPs
Eighteen candidate genes (listed in Supplementary Table 1) were selected based on their
involvement in cardiac arrhythmia syndromes or their role as functionally important subunits of
these genes. Since at the time of assay design, the NOS1AP locus was already associated with the
QTc-interval in GWAS, this gene was also included in the candidate gene study. SNPs for
consent for genetiiiic c c c a
1), a non-overlllal ppppppininining
e n
apart of each other from the L S re stries of the same four European academi
utine clinical and ECG parameters were acquired at the time of patient enrollme
d as a first ne plained s ncope a first doc mented entric lar tach cardia or a
entsss (S(S(S(S tetetet 222),),),) andndndnd in Set 1 and Set 2 combbbbininii ede . Patient Set 111 aaand Set 2 were drawn
apppparrrrt of each otthhher frrrrom ttthheh LLLLQTQTQTSS regiistriiees ooff f thththee e samemm foouur EuEuEuuropeeeeaaan acccaddded mmi
utine e clclclclininininiiicalll aa ddndnd EEECGCCGCG ppparararamamamameterrrsss wewewew ree aacqcquiuiuireereredddd attatat thehehee timimii ee ofof ppppatatata iiiienttt eenrnrololllllmlme
regig stries ((see DaDD ta SSSupppplplement foff r QTQTQTQTc-iniii tervallll measurement))t)). AA A first cardiac
ddd ffiirst llaiin ded fifi t ddo nt ded triic ll t hh drdiia
DOI: 10.1161/CIRCGENETICS.114.000785
6
genotyping were selected from all HapMap SNPs available for the CEU population within the
genes and the 50 kb flanking regions. Tag-SNPs were selected using Tagger16 employing the
following criteria: pairwise only tagging with r2
10%. A total of 1424 SNPs were derived in this way for genotype analysis using an Illumina
GoldenGate custom assay (Data Supplement).
The systematic analysis of haplotype-tagging SNPs in the 18 candidate genes was
conducted in LQT2 patient Set 1 (n=353). SNPs found to be significantly associated with the
QTc-interval in this analysis were subsequently investigated in LQT2 patent Set 2 (n=286).
SNPs from QTc-interval GWAS
We also investigated SNPs previously associated with the QTc-interval in GWAS conducted in
the general population. Twenty-two independent SNPs were identified from the literature.11–15
SNPs were pruned based on their extent of linkage disequilibrium (LD, R2<0.5). SNPs thus
selected were genotyped in patient Sets 1 and 2 combined (n=639; Set 1+ Set 2) using iPLEX
Gold chemistry (Data Supplement).
Calculation of the Genotype Risk Scores
The genotypes from the 22 SNPs from GWAS studies were used to calculate an un-weighted
multi-locus genetic risk score (GRS22). In addition, a GRS based on the six NOS1AP SNPs was
also generated (GRSNOS1AP). The directionality of the effect of each SNP was based on the
original publication11,12,15,17,18. For each QT-shortening allele, one point was subtracted from the
score, whereas one point was added for each QT-prolonging allele. A negative GRS indicates an
excess of QT-shortening alleles, whereas a positive GRS indicates an excess of QT-prolonging
alleles.
pap tent Set 2 (n=288886)6)66 .
vestigated SNPs previously associated with the QTc-interval in GWAS conducte
s
ere genotyped in patient Sets 1 and 2 combined (n=639; Set 1+ Set 2) using iPLE
istr (Data S pplement)
vestttigigigi atattatededede SSSSNPPPPsss previously associated wiwiwiwithth the QTc-intervavaval lll in GWAS conducte
ppop pppup lation. TwwTwentytyy-twowowowo inddddeepepeeenddennnt SNPNPs wewerererere iiidentnntified fromomom thehehehe llllitererratuuuree.1
prunneddeded bbbbaseddd oon n ththth ieieir r extetetentntntnt of lililil nkknknkagagaga e dididdiseseququuililililibiibibriiririummm ((((LDLDLDL , RRRR2222<0<0<0<0.5555))). SSSNPNPNPNPs s thththusu
ere gegg notyyypepp d in pppatieiii nt SSSets 111 and dd 222 combbbiiined ((n=636363639;9;9 SSSSet 1111+ SSSeS t 2)) usinggg iPLE
ist ((DData SS lpl )t)
DOI: 10.1161/CIRCGENETICS.114.000785
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Statistical analyses
QTc-interval data were normally distributed (Shapiro-Wilk statistic W>0.90) and are reported as
mean ± standard deviation.
Effects of KCNH2 mutation type and/or location, effects of covariates and effects of
SNPs and the GRS on the QTc-interval were estimated using the linear mixed effect model
function (lmekin). The model fit of the GRSNOS1AP was compared to the GRS22 using Akaike's a
information criterion (AIC). The effect of SNPs and the GRS on the secondary endpoint ‘age at
first cardiac event’, were estimated using the Cox proportional hazards function (coxme). Both
lmekin and coxme functions from the coxme package in R19 and are correlated random effects
models. The models allow for a per-patient random effect that are correlated based on a matrix
containing the kinship coefficients for each pair of individuals. This way, dependency between
some of the study subjects due to familial relatedness is taken into account. For each SNP-
phenotype relationship, an additive genetic model was assumed.
The effects of the SNPs and the GRS on QTc-interval were adjusted for center, sex, age
at ECG, proband status, -blocker use at the time of the ECG, and mutation type and location,
whereas the effects of the SNPs on age at first cardiac event were adjusted for center, sex and
mutation type and location only. With respect to mutation type and location, mutations were
classified into 5 different classes: (1) nonsense, frameshift (small indels or splice site mutations),
large deletions and insertions, independent of location, (2) missense, N-terminal, (3) missense,
transmembrane S1-S4, (4) missense, transmembrane S5-loop-S6, and (5) missense, C-terminal.
The classes were treated in the models as an unordered factor. The annotation of mutation
location was based on the Uniprot database (http://www.uniprot.org/uniprot/Q12809).
For the 22 SNPs from QTc-interval GWAS we also compared genotype counts between
s function ((coxme)e)e)e). . B
orrelll tttat ddedd randddod m efefefefffffe
a
the kinsh coefficients for each pair of individuals. This way, dependency betw
e
r
effects of the SNPs and the GRS on QTc inter al ere adj sted for center se
e momomodededeelslslsls aaalllll owww for a per-patient randommm efff ect that are corrrererelated based on a ma
thheh kinship coefffficcieeeents fofofof r eaeeachchch ppairrr oof indndividididuauauaualss. ThTThis wwaaay,, , ddded pendndndndennncccy bbbbeetw
e stududdudyyy y susususubbbjeccttsts ddddueue tttoo famimimiililililial relelelattatatededededneessss iiiiss tttakkakakennn iiiintttooo acaccocouunt. FFFFooor eacachhhh SNSNNSNPPP-
relationshipp,,, an addddddditiii ive gegg netic modeddd lll was assumed.ddd
ffff ts ff hth SNSNPPs dnd thhe GGRSRS QTQTc iinte lal dadjj st ded ff t
DOI: 10.1161/CIRCGENETICS.114.000785
8
the probands from Set 1 and Set 2 combined (n=278), with those of 498 general population
controls drawn from the Genome of the Netherlands project (GoNL).20 For 20 of the 22 GWAS
SNPs investigated, genotypes were available in all 498 GoNL controls. For the remaining two
SNPs genotype information was available in 497 (rs4725982) and 472 (rs2074238) individuals,
respectively. Genotype counts were compared using logistic regression assuming an additive
genetic model. (No covariates were included due to lack of access to individual participant data
for GoNL).
The SNAP tool (http://www.broadinstitute.org/mpg/snap/) was used to assess LD
between SNPs using the CEU reference population. The significance thresholds applied and a
statement on statistical power can be found in the Data Supplement.
Results
Study populations
The characteristics of the LQT2 patients studied are presented in Table 1. Patient Set 1 was
comparable to patient Set 2 and only differed in the occurrence of cardiac events (p=0.039).
Considering Sets 1 and 2 together, QTc-intervals differed significantly between probands (479 ±
50 ms) and relatives (460 ± 40 ms, p=8 10-7). Males and females had similar QTc-intervals (462
± 44, males; 468 ± 43 ms, females; p=0.06). Beta-blocker use at the time of the ECG did not
affect the QTc-interval (464 ± 43 in non-users; 469 ± 49 in users; p=0.32). The relatively low
beta-blocker use at the time of ECG (~16%) most likely reflects the fact that the ECGs used in
this study were ECGs acquired at enrollment.
Effects of KCNH2 mutation type and location
Since the type and location of the KCNH2 mutation may affect the extent of QTc-interval
prolongation6 we evaluated such effects in the patients in the current study. Considering Sets 1
s used to assess LDDDD
threshhhh lollldddds apppplilliliedddd aaaand
o
u
teristics of the LQT2 patients studied are presented in Table 1. Patient Set 1 was
on ssstatatatititisttststicicici alalalal powowower can be found in the DDDatatata Supplement.
ulations
tetteririststicicss ofof ttthehhe LLLQTQTQT222 papatitienenttsts sstuttudidiedded aarere ppreresesentntteded iiinn TaTablblb ee 1.11. PPPatatieientnt SSetet 11 wwasas
DOI: 10.1161/CIRCGENETICS.114.000785
9
and 2 combined, a total of 197 different KCNH2 mutations were present among the 639 patients
who originated from 254 different families. The number of patients per family ranged from 1 to
20. We grouped the nonsense, frameshift (small indels or splice site mutations) and large
duplications/deletions as one category since these mutation types are all expected to have a
drastic effect on the protein structure and likely lead to haploinsufficiency. We detected no
difference in extent of QTc-interval prolongation when this category of mutations was compared
to missense mutations (p=0.13). We then classified the missense mutations according to the
channel sub-domain in which they occurred (locations of missense mutations in the channel are
represented in Supplementary Figure 1); we found that carriers of a missense mutation in the
transmembrane non-pore region (S1-S4) had on average a longer QTc-interval compared to
individuals carrying a missense mutation in any of the other 3 locations, i.e. transmembrane pore
region (S5-pore-S6), N-terminus or C-terminus (p=2.0×10-4, Table 2). When the 25 patients
with S1-S4 region missense mutations were excluded (8 different mutations), patients with a
missense mutation in the pore region (transmembrane S5-loop-S6) displayed a longer QTc-
interval compared to patients with a non-pore missense mutation (N- or C-terminal, p=0.046)
Missense mutations were primarily located at the N- and C-termini and the S5-pore-S6 region
(Supplementary Figure 1).
Individual SNP effects
Candidate gene study
Following quality control, a total of 1,201 SNPs across the 18 candidate genes were left for
analysis for effects on the QTc-interval as a quantitative variable in the 353 LQT2 patients of Set
1 (all association results are listed in the Data Set file/Supplementary Table 2). Three SNPs
passed the pre-set Bonferroni-corrected p-value threshold for association of p<4.2×10-5
utations in the channnnnennn
issense m ttuttattttioiii n inininin tth
rane non-pore region (S1-S4) had on average a longer QTc-interval compared to
carr ng a missense mutation in any of the other 3 locations, i.e. transmembrane
p
m tation in the pore region (transmembrane S5 loop S6) displa ed a longer QTc
rannne e e nononon-nn-popoporee rrrege ion (S1-S4) had on aveveverarrage a longer QTc-c-c-interval compared to
ccac rrrrr ying a missssensseee muuuutatat tiononon innn aanyyy oof thehe othththerererer 333 looocaationsnss, ii.i eee. traaaansnsnsn mememembmbmbbraane
pore-S6S6S6S6),))) N-ttterer imiminunus s or CCC ttt-termiiiinununus (((p===222.0×0×0×0 1001010-4-4-4-4, TTTTablblbleeee 2)2)2)2 . WhWhWhWhenenenen tttthhhhe 222555 papatititienen
regggion missense muta iitions were excllul ddded d dd (8(8(8(8 difffefff rent mutatiiioi ns),),)) pppatients with
tatiio iin thhe igi ((tr bbr S5S5 ll S6S6)) didi lla dd llo QQTTc
DOI: 10.1161/CIRCGENETICS.114.000785
10
(0.05/1202); these included a SNP at the NOS1AP locus (rs16847548), a SNP at KCNH2
(rs956642) and a SNP at CASQ2 (rs1935778; Table 3). The minor allele at both the NOS1AP and
CASQ2 loci was associated with a longer QTc-interval, while that at KCNH2 was associated with
a shorter QTc-interval. The absolute effect sizes per minor allele were >12 ms in all three cases.
The 3 SNPs that were significantly associated with the QTc-interval in Set 1 were
subsequently tested in Set 2 (Table 3). In Set 2, only the NOS1AP SNP (rs16847548) displayed a
significant association at the Bonferroni-corrected p-value threshold of p<0.016 (0.05/3). The
direction of the effect was consistent with that found in Set 1, with the minor allele being
associated with a longer QTc-interval. The other two SNPs (rs1935778, rs956642) showed a
non-significant effect on QTc-interval in Set 2 (effect <0.5 ms). Combining the results improved
the accuracy for all estimates, but the effects of rs1935778 and rs956642 were reduced with 40-
50% (Table 3).
SNPs from QTc-interval GWAS
Analysis of SNP effects on the QTc-interval analyzed as a quantitative variable: Twenty-two
SNPs previously found to associate with the QTc-interval in the general population were
analyzed for modulatory effects on the QTc-interval as a quantitative variable in Sets 1 and 2
combined (Table 4). Three SNPs (rs10494366, rs12143842, rs2880058), all from the NOS1AP
locus were found to associate with the QTc-interval at the pre-set Bonferroni-corrected p-value
threshold of 2.27×10-3 (0.05/22). In all cases the minor allele was associated with a longer QTc-
interval and the effect size per minor allele was >12 ms. Of note, rs16847548 which was found
to associate with the QTc-interval in the candidate gene study (Table 3), is in LD with
rs12143842 (R2=0.88). Four SNPs displayed nominal statistical significance; these were
rs12029454 and rs16857031 at NOS1AP, rs2074238 at KCNQ1, and rs17779747 at KCNJ2.
minor allele beingngngg
8, rs959595956666666642424242))) ) shhhhowwwwedededed
c
y for all estimates, but the effects of rs1935778 and rs956642 were reduced with
e
m
f SNP effects on the QTc inter al anal ed as a q antitati e ariable: T ent t
cannnttt efefefffefefefectct ooon QTQQQ c-interval in Set 2 (effefefefectctct <0.5 ms). Commbibibibining the results imp
yyyy foooro all estimmatttes,, bbbub t ththththe efffffefefectttss off rss199357787878 aaaandndnd rrrs99566464642 weww re rrrreeede ucucuced wwith
e 3).
m QTQQ c-interval GWGWGWASASAS
fff SNSNPP fefffect thhe QQTTc iinte lal ll dd ntiitatii iri bablle TT t t
DOI: 10.1161/CIRCGENETICS.114.000785
11
Case-control analysis: The 22 SNPs from QTc-interval GWAS were also investigated for
association with LQTS status using a case-control design employing independent probands from
Set 1 and 2 combined as cases, and general population individuals from the GoNL project as
controls (Table 5).20 In this analysis, 2 SNPs were significantly (p<2.27×10-3) associated with
LQTS status and displayed the expected directionality of effect, that is, the allele associated with
a longer QTc-interval in the general population was the risk allele. The SNPs were located at
NOS1AP (rs12029454) and KCNQ1 (rs12576239), respectively. Another 7 SNPs displayed a
nominal association.
Individual SNP effects on cardiac events
SNPs that displayed a significant or nominal association in any of the above analyses were LD-
pruned (R2<0.5) and assessed for association with cardiac events in Sets 1 and 2 combined
(n=639). Since it has been previously suggested that SNP effects on risk of cardiac events might
be more pronounced in patients with QTc<500 ms,9 we also tested for association with cardiac
events in this sub-group alone. Of the 12 SNPs tested (Table 6), none were associated with
cardiac events after correction for multiple testing (0.05/12; p<4.2×10-3). However, three SNPs,
two at NOS1AP and one at KCNE1, were nominally associated with cardiac events. In all three
cases, the allele associated with a longer QTc-interval increased risk. The results differed
somewhat when LQT2 patients with QTc-interval <500 ms were analyzed separately, with the
effect of the KCNE1 SNP no longer remaining (nominally) significant (Table 6). Re-analysis of
the 3 SNPs by adding QTc-interval as an additional covariate in the model, resulted in lower and
non-significant relative risks for all three (rs10494366, 1.15 [0.92 – 1.44]; rs12029454, 1.27
[1.00 – 1.62]; rs1805128, 1.26 [0.95 – 1.67]).
displayed a significant or nominal association in any of the above analyses were
<
ince it has been previously suggested that SNP effects on risk of cardiac events m
o d
his s b gro p alone Of the 12 SNPs tested (Table 6) none ere associated ith
disppplalalal yeyeyed ddd a aa sis gngngnificant or nominal associaiaiaiatition in any of theee aaabove analyses were
<0000.555) and asseesssseddd ffffoor aaaasssssociaiaiai ttit onoon wiiithh caarrdiaaacc eveveveveentsss iin Settts 111 and 2222 comomombiinenned
ince iiittt hahahahasss beenen ppprere iiviviouo sllly yy susususuggessstetetedddd thththatatt SSSNPNPNPN eeeefffffffececects oooonn ririi kksksk of fff cacaccardrdrddiaacc evevenenttsts m
onounced in papp tiiiients wiiiti h hh QTQTQTQ c<50505000 0 ms,,,999 we also testeddd ffffor associiaii tion with card
hisi bb llo OfOf thhe 1122 SNSNPPs t tedd (T(T bablle 66)) iiatedd ii hth
DOI: 10.1161/CIRCGENETICS.114.000785
12
Genetic risk score
We finally tested the effect of the 22 SNPs from GWAS, and a subset of six NOS1AP SNPs
thereof, in aggregate by first generating two multi-locus genetic risk scores (GRS22 and
GRSNOS1AP) per individual and then testing these GRS for association with QTc-interval and
occurrence of cardiac events. This analysis was conducted in patients from Sets 1 and 2
combined. The GRS22, that varied from -8 to 14 with a mean (± SD) of 3.0 ± 3.8, was strongly
associated with the QTc-interval with an increase of 2.3 (S.E.: 0.50) ms per point increase in
GRS22 (p=4.3 10-6; Figure 1A). There was a linear increase in QTc-interval with increasing
GRS22; patients with GRS22 in the 2nd, 3rd or 4th quartile had mean QTc-intervals that were,
respectively, 7 (S.E. 5), 13 (S.E. 6) and 19 (S.E. 5) ms longer than individuals in the lowest
GRS22 quartile. When the 6 NOS1AP SNPs were not included in the GRS calculation, the
correlation between the GRS and the QTc-interval was no longer significant (p=0.15). The
GRSNOS1AP, consisting of the six NOS1AP SNPs only, showed a similar/better fit than the GRS22
(AICGRS22: 5199.2, AICGRSNOS1AP: 5194.9). The GRSNOS1AP, varying from 0 to 11, was strongly
associated with the QTc-interval with an increase of 3.5 (S.E.: 0.69) ms per point increase in
GRSNOS1AP (p=4.2 10-7; Figure 1B). Patients with GRSNOS1AP in the 2nd, 3rd or 4th quartile had
mean QTc-intervals that were, respectively, 14 (S.E. 5), 15 (S.E. 5) and 23 (S.E. 5) ms longer
than individuals in the lowest GRSNOS1AP quartile.
No associations were found between GRS22 or GRSNOS1AP quartiles and the occurrence of
a cardiac event, neither in the entire LQT2 patient sample (GRS22: p=0.192; GRSNOS1AP:
p=0.119; Figure 1C, D, Supplemental Figure 2A, B), nor in the subset of patients with a QTc-
interval <500 ms (data not shown). The results did not differ when only patients with
documented VT or aborted cardiac arrest/VF were considered (data not shown). While risk of a
terval with increasisisisingnnn
c-intervallls hhthatat werererere,
y t
r
, G
5199 2 AIC 5194 9) Th GRS i f 0 11
y, 7 7 7 (S(S(S( EE.E. 5)5)5)5), 1313133 (S.E. 6) and 19 (S.E. 5) msmsm longer thann indddivivivividuals in the lowest
rtiili eee.e When thee 666 NONNOS1APAPAPA SNPNN sss wwerrre nott iincluluuudededededd innn tthe GGRSRSRS cccalcuuuulaaatiiionn, tththeeP
between the GRGRGRS S S andd d the QQQQTc-inttterval was no llllonger iisiignifififiicicant ttt (((p=000 11.15)5)5). ThThThe
, cococonsnsnsisistitingngng oooff ththee e sisixx x NONOS1S1APAP SSNPNPss s onononlyly,, , shshowowoweded aaa sssimimililPP ararar/b/betetetteteterr r fifitt t ththananan ttthehe GG
5199 2 AIC 5194 9) Th GRS i f 0 11
DOI: 10.1161/CIRCGENETICS.114.000785
13
cardiac event did not increase linearly between quartiles, inspection of the data in Figure 1C, D
and Supplemental Figure 2 suggested that individuals in the quartile with the lowest GRS (Q1)
might be protected as opposed to individuals in any of the other 3 quartiles (Q2-4). A statistical
comparison of the cumulative event-free survival in these two groups, that is Q1 versus Q2-Q4
uncovered a protective effect for patients in Q1 (GRS22 RR 0.67, 95% CI 0.46 – 0.98, p=0.041;
GRSNOS1AP RR 0.69, 95% CI 0.48 – 0.98, p=0.039; Supplementary Figure 3).
The QTc-interval was a strong predictor of cardiac events in patients with a QTc-interval in the
highest quartile with a RR of 2.11 (95% C.I. 1.35-3.30) as compared to patients in the lowest
QTc-interval quartile (p=7.9×10-7, Supplementary Figure 4).
Discussion
Considerable interest exists in the identification of genetic factors that modulate disease severity
in the LQTS as the identification of such factors is expected to contribute to the refinement of
risk stratification in the individual patient. However, studies aimed at the identification of these
genetic factors are scarce.8-10 In this study we undertook two approaches to identify common
genetic variants that modulate the QTc-interval and the occurrence of cardiac events in a large
set of patients with LQT2. In one approach we conducted an exploratory analysis of SNPs
tagging common haplotypes within and around 18 candidate genes. In a second approach we
investigated the role of 22 independent SNPs from 14 chromosomal loci that were previously
identified as modulators of the QTc-interval in GWAS studies conducted in the general
population. Our analysis confirms and extends on previous observations that common genetic
variants at the NOS1AP locus modulate disease severity in the LQTS. We identified multiple
SNPs at this locus displaying markedly large effects on the QTc-interval among LQT2 patients
and/or enrichment of the QTc-prolonging allele in LQT2 patients versus general population
o patients in the lowowowowe
b v
S as the identification of such factors is expected to contribute to the refinement
cation in the individual patient. However, studies aimed at the identification of t
le iiiintntntn erererresesese t t t exexexe isisistssss iiinn n ththththe idididdenenenntitititififf caaaatititit onononon ooof f f gegegeg nenenen tititit c cc fafafafactctcttorororo s ththththaaat momomom dudududulalalatetetete dddiiisi eaeaeae sesesese sssevee
S as the identifffficicicicatatatioioioionn ofofofof sssucucucuch hh fafafafactctctororororssss isisiss eeexpxpxppeecece ted ddd tototoo ccccononontrtrtrtribibibutututu e ee totototo ttthe refinement
cccatatioionn inin ttheheh iindnddiviividdiduaualll papatitiienent.t. HHHowoweveverer,, ststtududieiess aiaiimemedd atat ttthhehe iideddentntififificicatatioionn ofof tt
DOI: 10.1161/CIRCGENETICS.114.000785
14
controls. Additionally, two NOS1AP SNPs also appeared to impact on the risk of cardiac events.
Similar effects on the QTc-interval and risk of cardiac events were observed when the NOS1AP
SNPs were considered in aggregate as a GRS. Our data also implicates for the first time common
genetic variation at KCNQ1 as a risk factor for LQTS.
NOS1AP
Genome-wide association studies conducted in the general population have consistently shown
that SNPs at the NOS1AP locus exert the strongest influence of any of the common genetic
variation known to influence the QTc-interval.11,12,15,17 In this study, SNPs at NOS1AP have
similarly emerged as the strongest modifiers of the QTc-interval and possible modifiers of
cardiac events among LQT2 patients, both when considered as single variants, as well as when
considered cumulatively as a GRS. Of the six independent signals (R2<0.4) that we tested at this
locus, three (rs10494366, rs12143842 and rs2880058) displayed highly significant associations
with the QTc-interval; one of these (rs10494366) also displayed a suggestive association with the
occurrence of cardiac events. Besides these, rs12029454 was significantly enriched in LQT2
probands versus controls and displayed a suggestive association with both the QTc-interval and
cardiac events.
Three studies have previously investigated the role of NOS1AP SNPs as modulators of
disease severity in LQTS. One study investigated NOS1AP SNPs in 135 carriers of the founder
mutation KCNQ1-A341V and identified rs4657139 (in high LD with our rs2880058) and
rs16847548 (in high LD with our rs12143842) as modifiers of the QTc-interval and risk of
cardiac events.8 A second study analyzed NOS1AP SNPs in 901 LQTS patients of different
genetic subtypes (primarily LQT1-3).9 This study also identified rs4657139 and rs16847548 as
modifiers of the QTc-interval, and detected effects on cardiac events for rs4657139 and
NPs at NOS1AP hahahahavvevv
possibbbblllel m dddodififififiiiei rsss ooooffff
nts among LQT2 patients, both when considered as single variants, as well as w
a
e (rs10494366, rs12143842 and rs2880058) displayed highly significant associat
T w
of cardiac e ents Besides these rs12029454 as significantl enriched in LQT
nts aaamomomongngng LLLQTQTQTT2 patients, both when cooonsnnsidered as singleee vvvariants, as well as w
cccummmmulatively asss a GGGGRSRSR . OOfO tttheheheh sssixx indnndepenendeeennt ssssigigignaaalss (R2222<<<0.4.44)))) thatatatat wwweee tttestttedddd a
e (rs1010110494949494343436666, rsrs121212144141438424242 aaaand rs2s2s2888888880000005858558)) ) dididisppspsplallalayeyeyed hiihihi hghghllyly ssigninininififificantntt aassssocociiaiat
Tc-interval;; one of ff thhhhese (r(r( s1111040404949494943363 6)6)6)6 alllso dididid sppplayeyy ddd d a suggggggestiiiive association w
ff drdiia ts BBe isidde hth 1s120202929454544 isi ififiic ltl iri hch ded ii LQLQTT
DOI: 10.1161/CIRCGENETICS.114.000785
15
rs10494366. A third study tested NOS1AP SNPs in 112 phenotypically discordant (one clinically
affected and one not) patient duos carrying the same mutation in either KCNQ1 or KCNH2 and
identified a suggestive association between rs12029454 and the QTc-interval.10
Keeping in mind that these three studies and ours are for several reasons not directly
comparable (e.g. different sizes of the patient study sample which impacts on the statistical
power, different study design, patients studied harbor mutation in different LQTS gene, and the
fact that not all studies investigated every independent signal linked thus far to the QTc-interval
in the general population), in aggregate their findings allow us to start drawing some conclusions
concerning the role of NOS1AP SNPs in modulation of disease severity in the LQTS. It is
obviously clear that common genetic variation at this locus also modulates the QTc-interval in
patients with the LQTS, with some individual SNPs (such as rs12143842 and rs2880058) now
displaying highly convincing associations with the QTc-interval in the majority of the studies.
Another observation emerging from these studies is that the effect of NOS1AP SNPs on the QTc-
interval is larger in LQTS patients as compared to that observed in the general population in
previous GWAS. In our analysis for example, each T-allele at rs12143842 increased the QTc-
interval by an average of 13.2 ms while its effect in a large sample of the general population was
of 3.15 ms.11 We also detected similarly large effects for rs10494366 and rs2880058 (see Table
4). Effect sizes of 7 and 8 ms were observed for rs4657139 and rs16847548 respectively, in the
study of Tomas and co-workers.9 The larger effect sizes among LQTS patients are likely due to
the sensitized genetic background of these patients: they are all carriers of a rare genetic variant
with a putatively large deleterious effect on repolarization reserve which in turn may make the
repolarization process more permissive to the effect of common genetic variation. This
observation brings forward the possibility that further genetic studies in LQTS patients may
drawing some connnnclccc u
ty in ththththe LQLQLQLQTSTSTSTS. ItIttt iiiissss
c a
th the TS, with some individual SNPs uch as rs12143842 and rs288005 n
h i
e
arger in LQTS patients as compared to that obser ed in the general pop lation i
cleaaarrr thththhattatat ccomoo momm n genetic variation at thihihihis locus also modululullataa es the QTc-interva
thh h thhhe LQTS, wiwiwith sooomeee e iini diiiiviviviv duuuaal SSSNNPs (ssucchh h asasasas rrs1214384442 2 aaand rsrsrss22282 8080800055558))) n
highlhlhhly yy cocococonvinnciciingngng aassssociaiaiatititiioooons wiwiwithhthth tttthhehe QQQQTTcTcT -iiintntntnterrrvvval inninin tttthehehh mmajjjororororititity offf ttthhhehe ssttutudidd
servation emergiigiinggg ffffrom thhhhese studddiei s iiisi thahhh t the efffefff ct off ff NONONON S1S1S11AAPAA SNPs on theP
a ii LQLQTSTS atiient dd to thhat bob ded ii hth ll lla iti ii
DOI: 10.1161/CIRCGENETICS.114.000785
16
uncover QTc-modulating genetic variants that would otherwise remain unidentified in GWAS
conducted in the general population due to the small effect size in the latter.
However, while strong associations have been laid in LQTS patients between NOS1AP
SNPs and the QTc-interval, this cannot be said of the effect of the same SNPs on the risk of
cardiac events. In our study, while 2 out of the 6 NOS1AP SNPs we tested were nominally
associated with risk of cardiac events, none, even those displaying very robust effects on the
QTc-interval, displayed association p-values for cardiac events that exceeded the Bonferroni-
corrected threshold for multiple testing. Notwithstanding, considering the fact that QTc-
modulating NOS1AP SNPs have already been implicated in modulation of risk for cardiac events
in two studies8,9, one could argue that the Bonferroni correction we applied is too harsh. Of the 2
SNPs that showed a nominal association with cardiac events in our study, rs10494366 was
previously associated with risk of cardiac events by Tomas and co-workers.9
NOS1AP encodes a nitric oxide synthase adapter protein. Functional studies have
suggested that it regulates action potential duration of cardiomyocytes via calcium and potassium
currents.21 The NOS1AP SNPs that impact on the QTc-interval are located in the non-coding
regions of the gene and if their effect on the QTc-interval indeed occurs through NOS1AP, it is
then likely that this occurs through modulation of the level of NOS1AP transcript abundance and
consequently protein levels.
SNPs at other loci
In our analysis of the 22 SNPs from GWAS for quantitative effects on the QTc-interval, besides
the SNPs at NOS1AP discussed above, no additional SNPs passed the Bonferroni-corrected
significance threshold. Two SNPs (rs2074238 at KCNQ1, and, rs17779747 at KCNJ2) however
displayed a nominal association with a direction of effect consistent with that found previously in
the fact that QTc--
on offff risiii kkkk fofff r carddddiaiaiaiacccc e
i 8 9 f
s
a
S
hat it reg lates action potential d ration of cardiom oc tes ia calci m and pota
ies8,9,,, onononee cocc ulululd ddd argue that the Bonferronononii correction we appplppp ied is too harsh. Of
shhhhowwwwed a nominininal aassssocccciaiaiai tionnn wiithh caaarrdiaac evvenenntstststs iiin ouoour studududy,y,y, rrrs104044499949 36363666 wawwas
assoccciaaiaiateteteteddd wiwiiththth rrisisi kkk ofo cararrdidididiac evevevennnts sss bybyby TTTomomomassasas aaanddndnd ccooo-wowo kkrkrkers.9999
S1AP encodes a niiit iiriic oxididide syyynthhah se adddad ptptp er ppprotein. FFunctiiiion llall studies have P
hhhat iit llate iti ot iti lal dd atiio fof didi te ii llcii dd ta
DOI: 10.1161/CIRCGENETICS.114.000785
17
the general population.11,12 The T-allele at rs2074238 is associated with a shorter QTc-interval.
Of note, this SNP was recently reported to be associated with a shorter QTc-interval and
decreased risk of symptoms in the study of Duchatelet et al.10 We detected no effect of this SNP
on cardiac events in the LQT2 patients studied here. The study by Duchatelet et al. however
detected larger effects for this SNP, both on the QTc-interval as well as cardiac events, as
compared to our study, and while our study was sufficiently powered to detect those effects, it
was underpowered to uncover an association with the small effects we detected.
Besides our quantitative trait analysis of the 22 SNPs from GWAS with the QTc-interval,
we additionally investigated their association with LQTS syndrome status in a case-control
association analysis of the LQT2 probands versus individuals from the general population (Table
5). This additional analysis uncovered two significant associations with the expected direction of
effect (i.e. the QTc-interval prolonging allele being enriched among the cases versus the
controls) highlighting the potential utility of this approach as recently also demonstrated by us
for the Brugada Syndrome.22 Our current analysis, for the first time, linked rs12576239 at
KCNQ1 with susceptibility to the LQTS.
Genetic Risk scores
We considered for the first time the combined effect of all 22 SNPs linked to the QTc-interval by
constructing a genetic risk score for each individual (GRS22) and relating it to the QTc-interval
and occurrence of cardiac events. We demonstrated a significant positive linear relationship
between GRS22 and the QTc-interval. The correlation between GRS22 and the QTc-interval
however appears to be largely driven by the effect of the 6 NOS1AP SNPs as the association
between the GRS and the QTc-interval did not remain significant when these SNPs were
removed from the GRS calculation. A GRS based on the 6 NOS1AP SNPs only (GRSNOS1AP),
WAS with the QTccc----inininint
atus iniii a case-connntrtrtrtrooool
analysis of the LQT2 probands versus individuals from the general population (
ditional analysis uncovered two s nificant associations with the expected direct
t
ighlighting the potential utility of this approach as recently also demonstrated by
gada S ndrome 22 O r c rrent anal sis for the first time linked rs12576239 at
analalalalysysysisisiss ooof f f f thhhe e e LQT2 probands versus iiiindndnn ividuals from thhheee general population (
diiti iooonal analyssisss uncnccovererere eede tttwoww siigniiifificantnt assssococcciaiaiattit oonss wiiithhh thehehee exppppecececteeeddd d diiiireect
the QQQQTcTcTcTc-ininintervrv llalal ppprorolllolongggininingggg allelelelel bbbbeieieie ngng eenrnriccheehehedddd amamamonononng thththee caseseseesss verssusus ttthehehh
ighg lighg tinggg the pppotentiallll utiiiilllil tytyy off f hthhisiii apppppproach as recen ltllly yy llallso ddddemonstrated byy
dad SS dnd 2222 OO nt ll isi ff thhe ffiirst tiim lliinkkedd 1212575762623939 t
DOI: 10.1161/CIRCGENETICS.114.000785
18
showed a similar predictive value for QTc to that of the GRS22. No significant (linear) relation
was found between either GRS22 or GRSNOS1AP and the risk for cardiac events, but patients with
scores in the first quartile had significantly less events than the patients in the other three
quartiles combined. The latter observation will require further investigation in additional
patients.
Effect on cardiac events
Our single SNP analysis did not uncover significant associations between any of the investigated
SNPs and the occurrence of cardiac events. Furthermore, our GRS analyses did not reveal a
linear relationship between the GRS and risk of cardiac events. On the one hand, when one
considers the fact that the SNPs tested are candidates with a strong a priori probability of being
involved, one could argue that in the single SNP analysis, our correction for multiple testing
might be too conservative. On the other hand, one can posit that while the QTc-interval is
governed by an appreciable genetic component, the precipitation of arrhythmias in the LQTS
may be heavily influenced by other factors such as environmental triggers that vary largely
across patients. In any case, the low relative risk associated with these variants currently
precludes their immediate clinical utility for arrhythmia risk stratification.
Many SNPs previously shown to affect the QTc-interval in the general population were
silent with respect to their effect on the QTc-interval in our analysis. Some investigators have
argued that the effect of SNPs in LQTS patients is dwarfed by the large effect of the primary
mutation (so called ‘ceiling effect’).8 While this seems a plausible explanation, it is unclear why
NOS1AP SNPs are not affected by this phenomenon. One possibility could be the larger effect
size among LQTS patients of NOS1AP SNPs as compared to the others, which would argue for
investigation of the non-associating SNPs in larger patient sets.
alyses did not reveaeaeaeal lll a
e one hhhhandddd, whehhh n onononone
h b
ne could argue that in the single SNP ana sis, our correction for mult le testin
o
y T
a il infl enced b other factors s ch as en ironmental triggers that ar largel
he fafafafacttct ttthahahah ttt thttt e e SNSSS Ps tested are candidattteeses with a strongg a prprpriori probability of b
nnnne cccoc uld arguee tthaat iiiin hththhee e singnngleee SSNPPP anaalyysis,s ooooururuu coroorrrectioioion fofofoor muuuultltltl ipppleee teeestttit n
o connnseseseservrvrvr ativvee. OOOOnn thhththe otttheheheerr handndnd, onnnne cacann popoposiisisitttt thththat wwwhihihihilelel ttthehhh QQQQTcTcTcTc-inntetet rvrvalalll iiiss
y yy an appppppreciable gggenetiiici compopp nent,,, hthhhe prpp ecipipitation offf f arrhhhhytytythhhmh ias in the LQTQ
iill ii fnfll dd bb hth ff to hh iir nt lal t iri hthat lla lel
DOI: 10.1161/CIRCGENETICS.114.000785
19
Candidate gene study
Besides SNPs from QTc-interval GWAS we also systematically investigated the effect of
haplotype-tagging SNPs in 18 candidate genes in LQT2 patient Set 1 (Table 3). Besides
rs16847548 in NOS1AP this analysis uncovered two associations, at CASQ2 (rs1935778) and
KCNH2 (rs956642), respectively. Neither of the latter two SNPs was however validated in
patient Set 2. Although these two SNPs may merit further investigation in additional samples,
these signals may represent a false positive association. One could argue that our correction for
multiple testing in Set 1 may be too stringent and that true associations may exist above the
Bonferroni-corrected p-value threshold we employed as the 18 genes were selected based on
their high a priori probability for modulating the QTc-interval. Nevertheless, we preferred to
apply stringent criteria for the most reliable findings with the current data.
Study limitations
In the current study, we limited genetic heterogeneity by considering only LQTS patients with a
KCNH2 genetic defect. Nevertheless, although we accounted for this in the statistical analysis,
some confounding may remain as a consequence of the variability in the severity of the
haploinsufficient defect and/or the biophysical defect associated with the different KCNH2
mutations among the patients. Considering the fact that the LQTS is a rare disorder we have here
studied a substantial number of patients. However, the patient set may yet be considered modest
for the study of common genetic variants with small effects. The effect of SNPs that we describe
here may be different in the setting of other LQTS genetic subtypes. Furthermore, SNP effects
may be allele-dependent as we previously demonstrated for SNPs in the 3’ UTR of the KCNQ1
gene.23 The design of the current study precludes the analysis for such effects.
s may y exist above ththththe
were sellllectttted ddd bbbbaseeeedd dd o
a
g
t
ent study, we limited genetic heterogeneity by considering only LQTS patients w
netic defect Ne ertheless altho gh e acco nted for this in the statistical anal
a prrrioioioioriririi ppprororobabbb bibibiililll ty for modulating the QTQTQTcc-interval. Nevertrtrtthehhh less, we preferred
geenenttt t criteria foor thee mmmossssttt t reliabababa leee ffindddinngss wwithhh tthehehe currrrrent daaataa.a
tationnnssss
ent studyy,,, we limiiti ddedd gggeneticiii hhheterogggen iieiitytyty bbbby yy considddderinii g gg onlyllyl LLLQTQTQQ S papp tients w
iti ddeffect NNe rthhelle lal hth hgh tedd ffo hthiis ii hth statiis iti ll lal
DOI: 10.1161/CIRCGENETICS.114.000785
20
Conclusions
Our comprehensive analysis demonstrates that among SNPs previously linked to the QTc-
interval in the general population, NOS1AP SNPs are the strongest modulators of the QTc-
interval in patients with LQT2. The effect of these SNPs in LQT2 patients is markedly larger
then that observed in the general population. Our study also uncovered common genetic variation
at KCNQ1 as a risk factor for LQTS.
Funding Sources: This study was supported by a grant from Fondation Leducq (Alliance Against Sudden Cardiac Death, 05 CVD 01). We acknowledge the support from the Netherlands CardioVascular Research Initiative (CVON-PREDICT project): the Dutch Heart Foundation, Dutch Federation of University Medical Centres, the Netherlands Organisation for Health Research and Development and the Royal Netherlands Academy of Sciences. Prof. E. Schulze-Bahr is supported by an IZKF grant. Dr. Barc was supported by the Netherlands Heart Institute (ICIN). This study makes use of data generated by the GoNL Funding for GoNL was provided by the Netherlands Organization for Scientific Research under award number 184021007, dated July 9, 2009 and made available as a Rainbow Project of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL). Samples where contributed by LifeLines (http://lifelines.nl/lifelines-research/general), The Leiden Longevity Study (http://www.healthy-ageing.nl; http://www.langleven.net), The Netherlands Twin Registry (NTR: http://www.tweelingenregister.org), The Rotterdam studies, (http://www.erasmus-epidemiology.nl/rotterdamstudy) and the Genetic Research in Isolated Populations program (http://www.epib.nl/research/geneticepi/research.html#gip). The sequencing was carried out in collaboration with the Beijing Institute for Genomics.
Conflict of Interest Disclosures: None
References:
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on Leducq (Alliancceeeepppppopopoportrtrtrt ffffrorororom mm m ththththeeee NeNeNNeththththerrrrutch HHHHeaeaeartrtrt FFFouououundndndndataa io
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NOSOSOSOS1A1A1A1APPP P iiiis a gggenenenneeeeti
M, Napolitano C, De Giuli L, Bloise R, Subirana I, Malovini A, et al. Polymorph1AP gene modulate interval duration and risk of arr thmias in the long TJ
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M,,, NNNaN politaaanononon CCCC, DeDeDeDe GGGiuiuiui liii LLLL,,, BlBlBlloioioio sesesee RRRR,, Suubbirananaana IIII, , , MaMaMaMalololovivivininiini A, etetetet aaal.l.l.l. Pololololymymyymororororph1AAPPP gene moddduuulatte QTTQTT iiinterrrvvalll dduraaatiionn aanddd rrissskk k oof arrrhyythththmimimiaaas in thhhhe looonggg QQQQTJ AAAAmmm Coll CCCarrdddiol. 222010000;5;5;55:2727274545454 –2757757 2.
elet S, Crotti LLLL, ,, PePeeatatatat RRRA,A,A,A DDDDenenenenjojojooyyy y I,I,I,, IIIItototot h h h H,H,H, BBBBeere thhhhetetet MMMM, etetetet aaall.l. IIIdededeentntntntifii ication of a olyymorppphism actiinii g gg as a ppprotectiiivi e modididid fififif er agagg inst arrhyhyhh hthhmiiiic iiriisk in longgg-QTQ CCCirc Cardidiidiovovovovasassasc cc c GeGeGeGenenenetttt... 2020202013131313;6;6;6:3:3:3:35454454–3–3–361616161..
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net. 2014;22:221–222222227
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DOI: 10.1161/CIRCGENETICS.114.000785
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Table 1: Characteristics of the LQT2 patients studied
LQT2 patients
Set 1n = 353
LQT2 patients
Set 2n = 286
LQT2 patients
Set 1 + Set 2n = 639
Female 208 (59%) 157 (55%) 365 (57%)
Proband 86 (24%) 88 (31%) 174 (27%)
Median (IQR) age at ECG (years) 30 (28) 27 (31) 29 (30)
-blocker use at time of ECG 60 (17%) 44 (15%) 104 (16%)
Mean (±SD) QTc-interval (ms) 467 ± 43 463 ± 44 465 ± 44
Cardiac event 126 (36%) 76 (27%) 202 (32%)
Median (IQR) follow-up (years) 26 (30) 27 (33) 26 (32)
Table 2: Effect of KCNH2 mutation type and location on the QTc-interval
Mutation type and location
Patient Set 1
n=353
QTc-interval (ms)
Patient Set 1 + Set 2
n=639
QTc-interval (ms)
nonsense, frameshift, large deletions and insertions, all locations 150 (42%) 466 ± 40 277 (43%) 463 ± 40
missense, N-terminus 77 (22%) 460 ± 49 150 (23%) 458 ± 47
missense, transmembrane S1-S4 11 (3%) 522 ± 48 25 (4%) 496 ± 55
missense, transmembrane S5-loop-S6 86 (24%) 474 ± 40 132 (20%) 474 ± 40
missense, C-terminus 29 (8%) 455 ± 32 55 (9%) 462 ± 46
%)%)%)%) 101010104 444 (1(1(1(16%6%6%6%))))
44 464646465555 ±±±± 4444444
e
Q
ennnnttt 1212126 6 6 (3(3(3( 6%%) 76767676 (((2722 %)%%% 202 2 2 2 (3(3(32%222 )
QR)R)R)) ffffollow-upuu (yyyeaarsss) 226 (330) 27227 (333) 262626 (3222))
DOI: 10.1161/CIRCGENETICS.114.000785
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Table 3: SNPs from the candidate gene study that were associated with the QTc-interval
SNP ChrCandidate
geneMajor allele
Minor allele MAF
Effect on QTc-interval in Set 1*(n=353)
Effect on QTc-interval in Set 2*(n=286)
Effect on QTc-interval in Set 1 + Set 2*
(n=639)
±SE (ms) †P-value ±SE (ms) ‡P-value ±SE (ms) P-value
rs16847548 1 NOS1AP A G 0.255 16.9±3.5 1.0 10-6 10.1±3.8 0.007 13.2 ± 2.6 4.8 10-7
rs1935778 1 CASQ2 A G 0.419 12.4±2.9 2.1 10-5 0.5±3.6 0.894 7.6 ± 2.4 0.001
rs956642 7 KCNH2 A G 0.405 -14.8±3.1 1.3 10-6 0.3±3.7 0.942 -7.1±2.4 0.003
Chr: chromosome, MAF: minor allele frequency. * The coded allele is the minor allele in all cases. † SNPs passing the discovery-phase Bonferroni corrected p-value threshold (p<4.2 10-5) are listed.‡ The P-value for the SNP passing the replication-phase Bonferroni corrected p-value threshold (p<0.016) is depicted in bold.
10101010.1.111±3±3±3±3.8.8.8.8
quency
GGG 0.419 12.4±2.9 2.2.2..1 1010101 -5 0.0 5±3.6
G 0.000.404040405555 -11114.4.4..8±8±8±8 3.33.11 1.1.1.3333 1010100-6 0.00 3±3.7
quququenenencycycy
DOI: 10.1161/CIRCGENETICS.114.000785
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Table 4: Effects of SNPs previously associated with the QTc-interval in the general population, in LQT2 Sets 1 and 2 combined
SNP from GWAS(n=22)
Chromosome Closestgene
Major allele
*Minor allele
*Effect on QTc-interval (n=639)
±SE (ms) †P value
rs10494366 1q23.3 NOS1AP T G ) 14.1±2.6 9.5 10-8
rs12029454 1q23.3 NOS1AP G A ) 8.4±3.1 0.007‡rs12143842 1q23.3 NOS1AP C T ) 13.2±2.6 4.8 10-7
rs16857031 1q23.3 NOS1AP C G ) 6.8±3.3 0.043
rs2880058 1q23.3 NOS1AP A G ) 12.2±2.5 8.6 10-7
rs4657178 1q23.3 NOS1AP C T ) 1.5±2.7 0.595
rs10919071 1q24.2 ATP1B1 A G ( ) 3.4±3.5 0.335
rs37062 16q21 CNOT1 A G ( ) -1.8±2.7 0.518
rs1805128 21q22.12 KCNE1 G A ( ) 4.7±4.7 0.309
rs2968863 7q36.1 KCNH2 G A ( ) 4.0±3.0 0.175
rs4725982 7q36.1 KCNH2 C T ) 1.8±3.1 0.552
rs12576239 11p15.5 KCNQ1 C T ) 3.3±3.3 0.318
rs2074238 11p15.5 KCNQ1 C T ( ) -10.0±5.1 0.049
rs2074518 17q11.2-q12 LIG3 G A ( ) -0.7±2.4 0.765
rs8049607 16p13.13 LITAF C T ) 0.9±2.4 0.702
rs846111 1p36.31 RNF207 C G ( ) -0.2±2.9 0.954
rs12053903 3p22.2 SCN5A T C ( ) -2.8±2.7 0.297
rs3825214 12q24.21 TBX5 A G ) 0.5±3.1 0.865
rs17779747 17q24.3 KCNJ2 G T ( ) -7.0±2.6 0.007
rs2478333 13q13 SUCLA2 C A ) -1.4±2.6 0.586
rs11970286 6q22 PLN C T ) -0.3±2.5 0.900
rs12210810 6q22 PLN G C ( ) 0.9±6.7 0.894
*The coded allele is the minor allele in all cases. The direction of effect found in genome-wide associationstudies conducted in the general population, are denoted in parenthesis; -in QTc-interval.†P-values for SNPs passing the Bonferroni corrected p-value threshold (p<2.3 10-3) are depicted in bold. ‡In LD with rs16847548 from the candidate gene study (R2=0.88); see Table 3. GWAS, genome-wide association study.
.2±2.5 8.6 10101010 7
5±2.7 7 7 7 0.000 5959595 5555
4±3 5555 0000 333333335555
9
11p15 5 KCNQ1 C T ( ) 10 0±5 1 0 049
16161616qqq2q 1111 CNOT1 A G ( ) -1111 88.88±2.7 0.518
21q22.1222 KCKCKCK NEEE11 GGG A A A ((( ))) 4.777±44.4 777 00.3000999
7q7q7q7q3636363 ..1. KCKCKCCNHNHNHNH222 GGGG A A AA (((( )))) 444.4 0±0±0±0±3.3 0 0 0 0 00.00 1717177555
7q36.1111 KCKCKCK NHNHNHNH222 CCCC T ))) 1.1.1.1 8±8±8±8 3.3.3.1111 0.552
999 111111p1p1p1p1555.555 KCKCKCNQNQNQQ111 CCC T T T T ))) 333.3±3±3±333.333 000.313131888
1111 1p155 55 KCKCNQNQ11 CC TT (( )) 1100 0±0±55 11 00 040499
DOI: 10.1161/CIRCGENETICS.114.000785
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Table 5: Case-control analysis of SNPs previously associated with the QTc-interval in the general population, in probands from LQT2 Sets 1 and 2 combined
SNP from GWAS
Closestgene
Coded allele
Frequency coded allele(cases /controls) OR (95% CI) P-value Concordance with SNP
effect on QTc-interval
rs10494366 NOS1AP G 0.43/0.34 1.35 [1.03-1.76] 0.028 yesrs12029454 NOS1AP A 0.21/0.13 1.85 [1.32-2.59] 0.0003 yesrs12143842 NOS1AP T 0.31/0.23 1.49 [1.13-1.96] 0.005 yesrs16857031 NOS1AP G 0.17/0.14 1.25 [0.89-1.76] 0.20rs2880058 NOS1AP G 0.40/0.32 1.39 [1.08-1.80] 0.014 yesrs4657178 NOS1AP T 0.31/0.23 1.45 [1.10-1.93] 0.009 yesrs10919071 ATP1B1 G 0.12/0.11 1.20 [0.82–1.77] 0.35rs37062 CNOT1 G 0.25/0.24 1.04 [0.79-1.37] 0.77rs1805128 KCNE1 A 0.04/0.02 1.95 [1.06–3.57] 0.03 yesrs2968863 KCNH2 A 0.22/0.24 0.89 [0.65-1.21] 0.45rs4725982 KCNH2 T 0.20/0.20 1.00 [0.74-1.34] 0.98rs12576239 KCNQ1 T 0.20/0.12 1.84 [1.31–2.60] 0.0005 yesrs2074238 KCNQ1 T 0.06/0.08 0.77 [0.49-1.21] 0.26rs2074518 LIG3 A 0.46/0.46 0.97 [0.75-1.25] 0.82rs8049607 LITAF T 0.46/0.53 0.77 [0.60-0.98] 0.03 yesrs846111 RNF207 G 0.28/0.31 0.86 [0.65–1.13] 0.27rs12053903 SCN5A C 0.32/0.35 0.88 [0.68-1.14] 0.31rs3825214 TBX5 G 0.22/0.21 1.09 [0.81-1.47] 0.58rs17779747 KCNJ2 T 0.36/0.33 1.14 [0.87-1.50] 0.34rs2478333 Intergenic A 0.41/0.33 1.43 [1.11-1.86] 0.007 yesrs11970286 PLN T 0.46/0.45 1.08 [0.83-1.40] 0.56rs12210810 PLN C 0.03/0.06 0.56 [0.29-1.10] 0.09
0.00 200.000 0101010144440000 000000009999[ ]
0 20/0 12 1 84 [1 31 2 60] 0 0005
[ ]0.0.0.12121212//0/0/ .11 1.2000 [[[0.82–1.77777777]] 0.350.2555/000.224444 1.1..044 [0.79797979---11.1 3737] 00.00 777770.0.0.0.04040404/00/00.0.0.002222 1.1.1.1.95959595 [[[[1.1.1 06060606–––333.3 57575757]]]] 00.0.0 030303030.22/0/0/0/0.2.2224444 0.0.0.0.8989898 [[[[0.0.0.0 655655--1.1.1.1 21212121]]]] 0.450.0.0.0 202020/0/0/00.2.2.22000 1.1.1.1.00000000 [[[[0.0.0.0 74747474-1.1..1.3334]4]4]4] 0.0.0.0.989800 2020/0/0 1122 11 8484 [[11 3131 22 6060]] 000 00000000505
DOI: 10.1161/CIRCGENETICS.114.000785
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Table 6: Effect of SNPs on event-free survival in Sets 1 and 2 combined
*The coded allele is the minor allele. † P values for nominally-associating SNPs are displayed in italics.
SNP Closestgene
Major allele
Minor allele
*Effect on event-free survival *Effect on event-free survival in patients with QTc<500ms
RR [95% CI] †P value RR [95% CI] †P value
rs10494366 NOS1AP T G 1.30 [1.04 - 1.61] 0.020 1.35 [1.04 - 1.77] 0.027
rs12029454 NOS1AP G A 1.37 [1.08 - 1.74] 0.011 1.45 [1.07 - 1.95] 0.015
rs12143842 NOS1AP C T 1.14 [0.91 - 1.42] 0.246 1.13 [0.86 - 1.48] 0.373
rs16857031 NOS1AP C G 1.03 [0.78 - 1.35] 0.855 0.93 [0.66 - 1.32] 0.694
rs4657178 NOS1AP C T 1.22 [0.97-1.53] 0.08 1.32 [1.00-1.74] 0.06
rs2880058 NOS1AP A G 1.16 [0.94 - 1.44] 0.167 1.22 [0.95 - 1.57] 0.118
rs1805128 KCNE1 G A 1.33 [1.01 – 1.76] 0.044 1.29 [0.89 - 1.85] 0.174
rs2074238 KCNQ1 C T 0.83 [0.53 - 1.31] 0.422 0.78 [0.46 - 1.32] 0.356
rs12576239 KCNQ1 C T 1.13 [0.86-1.48] 0.38 1.02 [0.74-1.42] 0.89
rs17779747 KCNJ2 G T 1.14 [0.92 - 1.42] 0.221 1.21 [0.94 - 1.55] 0.137
rs8049607 LITAF C T 0.91 [0.74-1.14] 0.42 0.93 [0.73-1.20] 0.59
rs2478333 intergenic C A 1.04 [0.83-1.31] 0.73 1.05 [0.82-1.34] 0.70
0.0.0..010101011111 1.1.1.1.4444
0.246666 1.1.1.1 1
3
9
T 0 83 [0 53 1 31] 0 422 0 7
GGGG 1.03 [0.78 -- 111.35]]] 0.000 855 0.9
TTT 1.222 [0..9997-1-11.53]3]3]3] 000.080808 111.3
G 111.16 [[[000.949 --- 1.44]4]4]] 0.0 16111 7 1.2
AAA 111.333333 [[[111.010101 – 111 77.76]6]6] 000.040404444 111.2922
T 0 83 [0 53 1 31] 0 422 0 7
DOI: 10.1161/CIRCGENETICS.114.000785
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Figure Legend:
Figure 1: (A, B) Association between the Genetic Risk Scores GRS22 and GRSNOS1AP and the
QTc-interval in LQT2 patient Set 1 and Set 2 combined (n=639; GRS22: p=4.3 10-6; GRSNOS1AP:
p=4.2 10-7). (C, D) Analysis of the relation between the Genetic Risk Scores GRS22 and
GRSNOS1AP and event-free survival in LQT2 patient Set 1 and Set 2 combined (n=639; GRS22:
p=0.192, GRSNOS1AP: p=0.119). Q1 is the quartile with the lowest genetic risk score.
A B
C D DDDD