AKAP9 is a Genetic Modifier of Congenital Long-QT Syndrome Type 1circgenetics.ahajournals.org/content/circcvg/early/2014/... · AKAP9 is a Genetic Modifier of Congenital Long-QT Syndrome

DOI: 10.1161/CIRCGENETICS.113.000580

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AKAP9 is a Genetic Modifier of Congenital Long-QT Syndrome Type 1

Running title: de Villiers et al.; AKAP9: a LQTS modifier

Carin P de Villiers, PhD1; Lize van der Merwe, PhD1,2; Lia Crotti, MD, PhD3-5;

Althea Goosen, RHN6; Alfred L. George, Jr., MD7-9; Peter J. Schwartz, MD3;

Paul A. Brink, MD6; Johanna C. Moolman-Smook, PhD1; Valerie A. Corfield, PhD1

1Medical Research Council (MRC) Centre for Molecular and Cellular Biology, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, 6Department of Internal

Medicine, Stellenbosch University, Stellenbosch; 2Department of Statistics, University of Western Cape, Bellville, South Africa; 3IRCCS Istituto Auxologico Italiano, Center for Cardiac Arrhythmias of Genetic

Origin and Laboratory of Cardiovascular Genetics, Milan; 4Department of Molecular Medicine, University of Pavia, Pavia, Italy; 5Institute of Human Genetics, Helmholtz Zentrum München,

Neuherberg, Germany; 7Department of Medicine, 8Department of Pharmacology, 9Institute for Integrative Genomics, Vanderbilt University, Nashville, TN

Correspondence:

Carin de Villiers, PhD

Medical Research Council (MRC) Centre for Molecular and Cellular Biology

Division of Molecular Biology and Human Genetics

Faculty of Medicine and Health Sciences

Room 4036, Teaching Block, University of Stellenbosch

Tygerberg campus, Francie van Zyl Drive

PO Box 19063, Tygerberg 7505, South Africa

Tel: +27 72 456 7369

Fax: +27 21 938 9863

E-mail: [email protected]

Journal Subject Codes: [5] Arrhythmias, clinical electrophysiology, drugs; [89] Genetics of cardiovascular disease

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Abstract:

Background – Long-QT syndrome (LQTS), a cardiac arrhythmia disorder with variable

phenotype, often results in devastating outcomes including sudden cardiac death. Variable

expression, independently from the primary disease-causing mutation, can partly be explained by

genetic modifiers. This study investigates variants in a known LQTS-causative gene, AKAP9, for

potential LQTS-type 1 (LQT1) modifying effects.

Methods and Results – Members of a South African LQT1 founder population (181 non-

carriers and 168 mutation carriers) carrying the identical-by-descent KCNQ1 p.Ala341Val

(A341V) mutation were evaluated for modifying effects of AKAP9 variants on heart rate-

corrected QT interval (QTc), cardiac events and disease severity. Tag single nucleotide

polymorphisms in AKAP9 rs11772585, rs7808587, rs2282972 and rs2961024 (order: 5’-

3’positive strand) were genotyped. Associations between phenotypic traits and alleles, genotypes

and haplotypes were statistically assessed, adjusting for the degree of relatedness and

confounding variables. The rs2961024 GG genotype, always represented by CGCG haplotype

homozygotes, revealed an age-dependent QTc increase (1% per additional 10 years) irrespective

of A341V mutation status (P=0.006). The rs11772585 T allele, found uniquely in the TACT

haplotype, more than doubled (218%) the risk of cardiac events (P=0.002), in the presence of

A341V; additionally, it increased disease severity (P=0.025). The rs7808587 GG genotype was

associated with a 74% increase in cardiac event risk (P=0.046), while the rs2282972 T allele,

predominantly represented by the CATT haplotype, decreased risk by 53% (P=0.001).

Conclusions – AKAP9 has been identified as a LQT1-modifying gene. Variants investigated

altered QTc irrespective of mutation status, as well as cardiac event risk, and disease severity, in

mutation carriers.

Key words: arrhythmia, long QT syndrome, AKAP9, KCNQ1

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The long-QT syndrome (LQTS), a hereditary cardiac arrhythmia disorder resulting from

abnormal ventricular repolarization, is characterized by a prolonged QT interval on the surface

electrocardiogram (ECG) and the occurrence of cardiac events including syncope, cardiac arrest

and sudden death.1 This syndrome is genetically heterogeneous and hundreds of mutations have

been identified in different LQTS-susceptibility genes.2,3 The particular gene harboring the

disease-causing mutation influences the clinical course of the syndrome and may affect specific

triggers of cardiac events.4,5

However, despite much progress having been made in identifying LQTS disease-causing

genes and risk factors, the syndrome is associated with a high degree of unexplained phenotypic

variability, often in families with the same primary disease-causing mutation.6-8 This is seen in

the wide range of heart rate-corrected QT interval duration (QTc) , the incidence of cardiac

events (syncope, cardiac arrest and sudden death), age at which the first event occurs, as well as

the number and severity of the events experienced, with the most feared being sudden cardiac

death. Furthermore, reports of low penetrance in certain LQTS mutation carriers make it difficult

to predict clinical outcomes.7 This variability suggests that additional genetic and/or

environmental factors are at play. Consequently, recent focus has shifted towards identifying

genes, or more specifically genetic variants, that, although not directly disease-causative, may

contribute to the resulting phenotype.9-12

The current study investigates potential modifying effects of the AKAP9 gene. This gene

encodes, amongst other isoforms, the A-kinase anchor protein (AKAP), yotiao. Yotiao forms a

macromolecular complex with voltage- -subunits, Kv7.1 (also known

as KCNQ1), and -subunits (KCNE1), that are responsible for the slowly

activating delayed-rectifier K+ current, IKs.13-15 This AKAP isoform interacts with and enables

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the phosphorylation of KCNQ1.13 However, not only does it directly bind with and assist the

phosphorylation of KCNQ1, it is also phosphorylated itself and facilitates the conversion of

phosphorylation-induced changes into altered channel activity.16,17 It is therefore clear that yotiao

is crucial in the regulation of the KCNQ1-KCNE1 channel and it comes as no surprise that a

mutation (S1579L) discovered in this AKAP isoform results in a LQTS phenotype (LQT11).18

It is likely that AKAP9, in addition to its identified role in disease causation, acts as a

disease modifier. Here we test this hypothesis in members of a South African LQTS founder

population of Western European origin.6,19 Mutation carriers in these families harbor an

identical-by-descent KCNQ1 disease-causing mutation, p.Ala341Val

(NM_000218.2:c.1022C>T) hereafter referred to as A341V, yet their disease expression varies

considerably, providing an ideal population in which to evaluate additional modifying factors.

Materials and Method

Founder population subjects

The study population consisted of 23 South African LQTS founder families analyzed in the

present investigation, who carry the same KCNQ1 LQTS-causative mutation, A341V.6,19 All

subjects in the study provided written informed consent and the Stellenbosch University’s

Faculty of Medicine and Health Sciences Institutional Review Board approved this project

(2000/C077). Participants provided demographic information and history of disease (personal

and family). Further clinical data collected from clinical records included the timing and type of

symptoms experienced, treatment and ECG recordings. LQTS A341V-mutation status was

determined for all participants who provided blood. Exon screening of the KCNQ1, KCNH2,

SCN5A, KCNE1 and KCNE2 genes in the A341V founder family probands revealed a second

mutation/variant in only three of the probands (KCNE1: D91E and KCNH2: R328C 20 and

families harbor annnn

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KCNE1:D85N unpublished data) . No statistical correction for the presence of these variants was

made, as a small number of individuals carried them and/or there is a lack of functional and

clinical information on their effects in the study founder population and LQTS families in

general.20 Genomic DNA was extracted from peripheral blood leucocytes using a previously

described method.21 Clinical information available on the study subjects and the number of

individuals genotyped per category is summarized in Table 1. The number of individuals

included in different stages of the analyses is shown in Figure 1.

ECG analysis

Of the 349 participants for whom at least one single nucleotide polymorphism (SNP) was

successfully genotyped in this study, baseline resting ECGs recorded in the absence of beta-

blocker therapy were available for 273 individuals, of whom 137 were mutation carriers (Figure

1). The 12-lead ECG was analyzed by an investigator experienced with LQTS to ascertain

baseline heart rate (HR), RR intervals and QT interval duration. The QT interval, recorded in the

absence of beta-blocker therapy, was corrected for heart rate using the Bazett formula and ranged

between 397ms and 687ms for the mutation carrier group. 22

Cardiac events

Cardiac events recorded included syncope, aborted cardiac arrest (requiring resuscitation), or

sudden cardiac death. Mutation carriers were considered symptomatic if they had experienced at

least one of the above mentioned cardiac events irrespective of age. Mutation carriers were

classified as asymptomatic if they were older than 20 years and had never experienced one of

these events. This cut off value was chosen because a first cardiac event in this LQTS founder

population almost invariably occurred before the age of 20 years and very rarely after age 20.6

The time to first event analysis was performed on 114 mutation carriers of whom 88 were

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art rate (HR), RR intervals and QT interval duration. The QT interval, recorded

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symptomatic and 26 were asymptomatic. Mutation carriers who had never experienced cardiac

events but were on beta-blocker therapy prior to 20 years of age were excluded (n=17), as the

treatment could have prevented cardiac events from occurring. Furthermore, symptomatic

individuals who were on beta-blocker therapy prior to their first cardiac event were excluded

(n=7), as the age for their first cardiac event may have been different in the absence of beta-

blocker therapy. Finally, mutation carriers lacking required information for statistical modeling

(e.g. ECG and cardiac event information) did not form part of the analysis (n=30).

The severity of the disorder was evaluated using the score given in Table 2. This analysis

included 97 mutation carriers who had never been on beta-blocker therapy, or only started taking

beta-blocker treatment after the age of 20 years. In this “off” beta-blocker therapy group, all

individuals, except for two, were above the age of 20 years. One could already be classified in

the most severe category by the age of 9 years and the other, last followed up at the age of 16

years, was already symptomatic (had experienced one syncopal episode).The number of

individuals present in each severity category 0 to 3 was 27, 31, 25 and 14, respectively.

Individuals on beta-blocker therapy before the age of 20 years were separately investigated. That

group consisted of 43 individuals, with 26 below the age of 20 years (individuals per category 0

to 3: 13, 9, 7 and 14).

AKAP9 genotyping

TaqMan Validated SNP Genotyping Assays (Applied Biosystems, Foster City CA, USA) were

used to genotype all DNA samples for the intronic variants rs11772585, rs7808587, rs2282972

and rs2961024 (URL:http://www.ncbi.nlm.nih.gov/SNP). These SNPs were selected to ensure

entire gene coverage, including 2kb on either side, using Tagger analysis (URL:

http://www.broadinstitute.org/mpg/tagger), applying default settings with an r2 cut off value of

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0.8 and a minor allele frequency (MAF) of 0.2 (common SNPs).23 The HapMap Genome

Browser release#24 (Phase 1 & 2) full dataset was used and information pertaining to the CEU

HapMap population (Utah residents with ancestry from northern and western Europe) was

selected.

Polymerase chain reactions (PCR) were set up in 384-well plates, each reaction consisted

of 20ng of genomic DNA, 2.5

primer and probe dye mix, as well as 1.25 -free sterile water. Amplifications took place

using the following cycle: 2min at 50°C, 10min at 95°C, followed by 40 cycles of 15s at 92°C

and 1min 30s at 60°C. Allele discrimination was achieved by analyzing PCR amplified products

with the ABI Prism 7900HT Sequence Detection System SDS v. 2.3 software (autocaller

confidence level=95%). All allelic and genotype data given for the chosen SNPs corresponded to

the positive DNA strand relative to the reference sequence.

Statistical Analysis

SNP analysis

Pedstats v 6.11 (Wigginton and Abecasis, 2005) was used to check Mendelian inheritance, as

well as to perform exact tests Hardy-Weinberg Equilibrium (HWE) amongst selected maximally

informative but unrelated individuals from the founder population.24 Haploview v.4.2 (Barrett et

al. 2005) was used to estimate the MAFs, as well as the linkage disequilibrium (LD) measure

D’.25 As Haploview does not always select the same group of maximally informative but

unrelated individuals for estimating LD, the analysis was repeated 100 times, and median values

are reported . R, a language and environment for statistical computing, freely available from

www.R-project.org and R packages Coxme (Terry Therneau, 2012. coxme: Mixed Effects Cox

Models. R package version 2.2-3. http://CRAN.R-project.org/package=coxme) and kinship2

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(Terry Therneau, Elizabeth Atkinson, Jason Sinnwell, Martha Matsumoto, Daniel Schaid and

Shannon McDonnell, 2012. kinship2: Pedigree functions) were used for statistical analysis.

Traits

The traits investigated in this study were QTc duration, a severity score (Table 2) and the time

until the first cardiac event (age). As this founder population contains many affected individuals,

the QTc duration data was not normally distributed; it was positively skewed due to some very

large values. Because the mean and standard deviation are not appropriate statistical measures in

this case, terms of the median and interquantile range (IQR) were described. Furthermore, linear

statistical modeling required a log-transformation to approximate symmetry prior to analysis

ensuring the validity of results and conclusions. Kaplan-Meier curves are used to illustrate the

age at first cardiac event.

Linear mixed-effects models were used for QTc duration and the severity score, while

mixed-effects Cox regression models were used for the analysis of time to first cardiac event. All

models were adjusted for gender as a fixed effect, and for the degree of relationship between

each pair of individuals in the study (using a per individual random effect with kinship

coefficients), as well as a per family uncorrelated random effect. The linear models were further

adjusted for age and the QTc model also for the presence or absence of the A341V mutation as a

fixed effect. Therefore, significant QTc analysis associations were representative of the change

in QTc irrespective of mutation status. The mixed-effects Cox regression models for the time to

first cardiac event analysis were additionally adjusted for QTc. Effect estimates from the models

are reported as percentage change (not milliseconds) for QTc, due to the back-transformation

(exponentiation) of the logarithms, change in score for severity and hazard ratio (HR) for

experiencing a cardiac event.

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As an indication of precision, 95% confidence intervals (CI) are also provided.

Genetic association

For single SNPs, the genotype effect was tested by splitting it into an additive term (number of

minor alleles) and a heterozygote flag (yes/no) in all models. This is equivalent to testing a

genotype effect on two degrees of freedom. If either of the components was significant, the

corresponding genetic model, additive, dominant or recessive on the minor allele, was tested.

The result from the best fitting model is reported.

Simwalk v.2.91 (Sobel and Lange, 1996) was used to infer the most probable maternal

and paternal haplotype configuration for individuals with the necessary genotype and/or phase

information in the pedigree.26 Haplotypes with an estimated frequencies larger than 0.01 were

analysed. An additive (without heterozygote flag) model of association was fitted for individual

haplotypes, as described for SNP evaluations, thereby comparing each haplotype to all others in

each model. Additionally, the interaction of each possible confounder (age, gender and presence

or absence of A341V) was tested with all genetic variables (alleles and haplotypes) on QTc.

Furthermore, the interaction of gender was tested with each genetic variable on the risk of first

cardiac event.

In this study, no correction for multiple testing was performed. Correcting for multiple

testing can be overly stringent in family-based association studies. Furthermore, a Bonferroni

correction assumes independence between the different tests performed and is therefore not

appropriate when analyzing different SNPs in LD.27

Results corresponding to P-values lower than 5% are described as significant and

reported. A flow diagram of the statistical methods applied is provided in the supplementary

information.

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Results

Of the 350 individuals, for whom DNA was available, only one individual was not successfully

genotyped for at least one of the SNPs (Figure 1 and Table 1). Furthermore, all four SNPs

analyzed were in HWE for the selected subset of unrelated individuals. Minor allele frequencies

varied from 0.10 (T allele) for rs11772585 to 0.46 (G allele) for rs7808587. The number of

individuals successfully genotyped for the different SNPs and allele and haplotype frequencies

are given in Table 3. Due to strong linkage disequilibrium between the SNPs, and the fact that

this study was conducted with related individuals, haplotype frequencies varied from the

expected if assuming linkage equilibrium. The LD analysis, in the unrelated subgroups, revealed

a D’ value of 1 for all SNP pairs investigated, for all 100 runs. A D’ value of 1 indicates that the

two markers being tested are in complete (not perfect) linkage disequilibrium28, with at least one

two-marker haplotype having a frequency of 0, as was the case for all SNP pairs investigated in

the current study.

Each SNP was evaluated for association with all traits and the significant results are

tabulated in Table 4, as well as discussed briefly below.

QTc analysis

Initial investigation of the data revealed a highly significant correlation of gender (P<0.001) with

QTc. Females had an estimated 5.6% longer QTc than males. Additionally, the presence of

A341V was correlated with QTc (P<0.001). Mutation carriers had an estimated 21.4% longer

QTc than non-carriers. QTc heritability was estimated at 31% (55% unadjusted for A341V).

A significant interaction between age and rs2961024 on QTc (P=0.006), after adjusting

for gender and mutation status, was detected. Individuals with the TT genotype for this SNP

showed a 1% (95% CI, 0.2 to 1.3%) shorter QTc with every additional 10 years of life, while, for

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each minor G allele carried in genotypes GT and GG, this age effect increased by 1% (95% CI,

0.3 to 1.6%) over the same time period compared to the TT genotype. The net result was no

significant effect for GT, and a 1% increase for the GG genotype. Figure 2 indicates this increase

compounded over a number of years. The curves commenced at the highest QTc for a female

mutation carrier (Figure 2A) but dropped for male mutation carrier (Figure 2B) and non-carrier

estimates (Figure 2C and D) due to the difference in baseline QTc values. However, the relative

position of the genotype curves to each other remained the same.

As an example, a 10 year old individual with the GG genotype and a QTc of 450ms

would have an increase of 4.5ms over the next 10 years. The 1% increase over the following 10

years would then be calculated from a QTc of 454.5ms. Continuing this process, the effect of this

risk genotype from 10 to 40 years of age would be an increase of 13.6ms (QTc 40

years 463.6ms). However, as the millisecond change depends on the QTc of the individual, and

the QTc values for mutation carriers in this founder population ranged between 397ms and

687ms (mean age=34.9), the actual change in milliseconds, increase or decrease, between

mutation carriers is expected to be extremely variable.

Interestingly, all individuals in the founder population who had the GG genotype at

rs2961024 were also homozyotes for each of the other three SNPs investigated, that is they all

had two copies of the CGCG (order: rs11772585, rs7808587, rs2282972 and rs2961024;

frequency=0.282) haplotype. Consistent with this observation, and with the above results,

individuals with two copies of the CGCG haplotype had an average QTc 1% (95% CI, 0.2 to

1.4%) longer per 10 year increase in age (P=0.012), while carrying one copy of CGCG showed

no significant effect on QTc duration and in individuals without a copy of this haplotype there

was an average 1% (95% CI, 0.2 to 1.3%) shorter QTc duration per 10 year increase in age.

and a QTc of 450000msmmm

ase over tttthehhh ffff llollooowiwiwiwin

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p

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an age 34 9) the act al change in milliseconds increase or decrease bet een

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ppe fffrom 10 to 40 yyeaaars ofofofo ageee wwouuld bbe aan inccrereeeaasaa ee of 13.6m6m6ms (QQQQTcc 44440

6ms)))). HoHoHoHoweveerr, asas ttthhhehe milililliililissecondndnd chahahah ngngngee dededd pepepeendndndsss onnn tttthhhehe QQQQTcTTT oooofff f thththhe inini diddidiviviiidduduala

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15

However, given the small number of CATG observations it is impossible to discern whether the

effect of rs2961024 is restricted to the CGCG haplotype.

Interestingly, after adjusting for A341V and gender, rs2961024 was also significantly

associated with QTc (P=0.014) independent of age, with the GG genotype associated with longer

QTc relative to the TT and GT genotypes. Furthermore, including an adjustment for age yielded

the same result, with the presence of the GG genotype associated with a 5% longer QTc (95%

CI, 1.0 to 9.1%) compared to TT and GT. However, no statistically significant additive allelic or

haplotype associations with QTc were observed given these adjustments.

Therefore, after adjusting for age, gender and A341V, both rs2961024, and the

interaction between age and rs2961024, are significant predictors of QTc. The QTc increases

with age and this observation is progressively more pronounced with each additional G allele

compared to the TT genotype; even ignoring age, the average QTc is higher. Therefore, the age-

dependent effect can be considered the best description of the observed association between

rs2961024 and QTc.

Cardiac events analysis

The risk of experiencing a cardiac event was significantly greater in subjects with a longer QTc

duration for all models investigated (P=0.000035), consistent with previously reported

associations in this founder population.6 The significant effects described below are independent

of QTc.

The AKAP9 SNP, rs11772585, was associated with an increased risk of cardiac events

(P=0.002). For this SNP, no TT genotypes were observed, yet having the T allele within the CT

genotype more than doubled the risk of having a cardiac event (HR=2.18; 95% CI, 1.33 to 3.60).

Figure 3A illustrates the 118% greater probability of a cardiac event. Haplotype analysis

nts.

961000024242424, andd dd ththththe

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effect can be considered the best description of the observed association between

and QTc

betwtwtweeeeeenn n agagage ananand rs2961024, are significcccanaant predictors of QTQTQTQTc. The QTc increas

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16

supported this result (Table 4).

Another AKAP9 SNP, rs7808587, was associated with a recessive model (P=0.046), with

the GG genotype increasing the cardiac event risk by 74% (HR=1.74, 95% CI, 1.01 to 3.01)

relative to the AG and AA genotypes. Figure 3B illustrates this increased probability of a cardiac

event.

Finally, rs2282972 was associated with a dominant model (P=0.001) with the CT and TT

genotypes reducing the cardiac event risk by 53% (HR=0.47, 95% CI, 0.29 to 0.74) relative to

the CC genotype. Figure 3C illustrates this reduced probability of a cardiac event.

Consistent with the above result, CATT, the predominant haplotype containing the

rs2282972 T allele, was associated with a decrease of 35% (HR=0.65, 95% CI, 0.45 to 0.94) per

haplotype copy (P=0.022).

Severity score

The effect of any of these SNPs on the severity of the disease was investigated using the score

given in Table 2. In agreement with the other results, rs11772585 also affected disease severity

for the “off” beta-blocker therapy group. The T allele was significantly associated (P=0.025)

with a higher score by a value of 0.58 (95% CI, 0.07 to 1.08). Haplotype analysis supported this

result (Table 4). The separately investigated smaller group consisting of individuals on treatment

did not show any statistically significant results.

Discussion

The present study demonstrates that yotiao, the protein encoded by the AKAP9 gene, is a

modifier of the clinical phenotype of LQTS. This finding is important given that individuals with

LQTS present with symptoms that can vary considerably, irrespective of their carrying the same

disease-causal mutation, as demonstrated by the families in this study.6-8 In this founder

rdiac event.

otype co tttnt iiaiiniiiinggg tttthehehehe

T 4

c

c

of any of these SNPs on the severity of the disease was investigated using the sc

able 2 In agreement ith the other res lts rs11772585 also affected disease se e

T alalalallelelel lelelee, wawawas asasassociated with a decreaseee ooof f 35% (HR=0.65,55, 9995% CI, 0.45 to 0.94

coooopyyyy (P( =0.02222)...

core

of any yy of these SSSSNPNPNPs on thheh severitii y yy fofff thehhh ddddisiii ease was iiiinvestigagg ted usinggg the sc

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17

population, KCNQ1 A341V mutation carriers have a broad range of QT intervals and they may

be symptomatic or asymptomatic, depending on the occurrence of cardiac events.6 Worldwide,

the A341V mutation is associated with a very severe form of the disease, with the likelihood of

cardiac events occurring more frequently and earlier in affected individuals relative to other

KCNQ1 mutations.29 However, LQT1 individuals older than 20 years of age who, despite not

having treatment, are asymptomatic, seldom become symptomatic afterwards.5,29 Accordingly,

mutation carriers identified after this age rarely started treatment, explaining the low beta-

blocker treatment percentage for the study population in Table 1.

Despite the severe phenotype of the A341V mutation, it has been shown that it only has a

modest dominant negative effect on the wild-type channel.6 The channel subunits encoded by the

mutated allele, due to their association with other subunits to form the complete ion channel

complex, negatively influence the function of the wild-type subunits and thereby the

performance of the channel. Heijman et al. showed that the severity conferred by this mutation

can, in part, be explained by the reduced phosphorylation of KCNQ1.30 Yotiao plays an

important role in this phosphorylation process, as well as in downstream changes in channel

activity as a consequence of this phosphorylation.13,17 Therefore, it could be proposed that

variants in the AKAP9 gene that result in altered yotiao expression, structure and/or function are

likely to influence channel properties and could explain some of the severity and phenotypic

differences observed. As a number of KCNQ1 mutations, including A341V, suppress cyclic

adenosine monophosphate (cAMP) or protein kinase A (PKA) mediated channel regulation30,31,

it is possible that modifying effects by AKAP9 variants are acting on the wild-type channels.

This investigation strengthens the notion that yotiao is indeed involved in conferring

some of the severity and phenotypic variability. AKAP9 variants in this study were shown to alter

een shohhh wn tttthhhhat ttt iiit oooonlnlnlnly

m 6

e e

e

c t

be e plained b the red ced phosphor lation of KCNQ1 30 Yotiao pla s an

minananantntnt nnnegegegatata ivvve e e effect on the wild-type ccchahahh nnel.6 The channnnnneleee subunits encoded

ellel ,,,, due to theeirrr asssooociaaiatititit on wwwiiti hhh ootheeerr suubuuniiitsts ttttoo oo foff rmrrm thee cccompmm letetetee iiionnn chaaannne

egattiviivivelelelelyyy y iiinflflflueuencncee thhththe funcncncctititition ooofff ththththee wiwiiildldldd-typypypeeee sususubbbbunininiitttsts aa ddndnd theheheherererer bbbby tttheheh

ce of the channellll. HeHH ijiji man et al. shohh wedddd thahh t the severiiitytyty confffef rred by yy this mutatd

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18

the QTc interval, as well as the risk and severity of cardiac events, in the South African A341V

founder population. As these findings were observed in a population with a very specific genetic

background, further work is required to verify that the same effect is observed in other

population groups, as well as for different types of LQTS. Furthermore, as these variants are

located in the intronic regions of the gene, they themselves are unlikely to be the direct cause of

the modifying effects and variants in LD with them require further investigation. However, this

work clearly indicates involvement of the AKAP9 gene region as a LQTS modifier.

Most other genetic modifying effects reported have been associated with already known

LQTS genes. Variants associated with QTc and/or sudden cardiac death have been described for

KCNQ1, KCNH2, SCN5A, ANK2, KCNE1, KCNE2 and KCNJ2 (LQT1-7).11,32-35 Additionally,

recent genome-wide association studies have revealed a few novel QTc-associated loci,

including the nitric oxide synthase 1 adaptor protein gene (NOS1AP).34-36 NOS1AP has since

been repeatedly associated with QTc interval, LQTS disease severity and sudden cardiac death

risk37-40, with significant modifying effects reported for the same founder family described in the

current study.9

AKAP9 variant associated with QTc duration

The heritability of the QT interval is reported to range between 25% and 55% (latter value from

present study), with many common variants examined to date not explaining much more than 7%

of the total QTc variation.34,41 Furthermore, 31% of the estimated heritability for the South

African A341V founder population could not be explained by the A341V mutation, age or

gender (this study).

The current study identified an age-dependent additive allelic association between the

rs2961024 SNP and QTc (Table 4 and Figure 2). This SNP was identified as a modifier for QTc

iated with alreadydydyy kkkkn

th have bbbbeen deddd scccriririribbbbe

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ith significant modif ing effects reported for the same fo nder famil describedd

CNHNHNH222, SCSCSCSCN5NNN AAA,,, ANK2, KCNE1, KCNE2 22 anand KCNJ2 (LQTTT1111-7).11,32-35 Additiona

ommmmeeee-wide assoociiiationnn stuuuuddid es hhhavvvee revveealeded a fffewewww nnovvveel QTcTcTc-aasa sssociatatata ededede loooci,,,

he niiiitrtrtricicicic oooxideded ssynynynthththasase 111 addadadaptor rr pprprotototo ieieiinn gegeneee ((((NONONONOS1S1S1S APAPAPP((((( ).))) 343444-3-3-3-36 NONONONOS1S1S1S1APPP hhahah ss isisincn

tedly yy associatedddd wiiiti hhhh QTQTQTQ c iniii terv lall,,, LQLQLQL TSTSTSS ddddiiisi ease severiiiti y yy anddd d suddddd en cardiac de

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19

irrespective of mutation status in this specific founder population. Older individuals, with two

copies of the minor allele (GG genotype), displayed a longer QTc. The opposite was true for

individual carrying two copies of the major allele. Pfeufer et al. also report age-specific effects

on QT interval when looking at the gene encoding the RING finger protein 207, RNF207, as well

as that encoding the cardiac phospholamban protein, PLN.35 However, in their investigation,

each minor allele of the particular SNP investigated contributed towards a larger QT-prolonging

effect (rs846111) or a lower QT-shortening effect (rs12210810) in younger individuals.

Although, in this study, an association between rs2961024 and QTc was observed both

with and without considering the age-dependent effect, other investigations, not evaluating

interactions with specific confounding variables, could be missing specific variable-dependent

QTc associations. Additionally, studies using additive allelic models only could miss dominant

and recessive effects. This highlights the importance of evaluating different genetic inheritance

models, as well as interactions of genotypes with confounding variables on clinical outcomes.

Many previously reported common variants associated with QT interval only alter the variability

by small percentages, emphasizing the relevance of considering multiple models for detection.41

AKAP9 variants associated with modifying risk and severity of cardiac events

The AKAP9 gene not only alters QTc duration but also influences the risk and severity of cardiac

events, after controlling for QTc. However, different variants were associated with cardiac event

risk and severity than the one shown to alter QTc interval duration (Table 4). The extent of the

phenotypic variability in LQTS, as well as the existence of mutation carriers with normal QTc

intervals that experience cardiac events, suggests that there are at least one or more different

variants involved in modifying cardiac events and severity to QT interval duration.6-8,42

Furthermore, as yotiao-dependent regulation of the KCNQ1-KCNE1 channel is so versatile, in

QQTc was observeeeed d d d b

ations, n tttot evallull atatttininining

s d

a n

v a

well as interactions of genotypes with confounding variables on clinical outcom

io sl reported common ariants associated ith QT inter al onl alter the aria

s witititith h h spspspecececifififificcc ccconfounding variables, coooululuu d be missingg speeciccc fic variable-depend

attttiooons. Additiononnallylylyy, studududu ies uusinnngg addddditiveve allllllelelelicicic mmodoodels ononnlyyy cccouldlddld mmmisisiss dooommin

ve efeffffeefefectctctctsss. Thihihiss hihihih ghghhhlilillightstss tttthehehehe impmppooortatatat nccee fofoff eeevaavavalulluluattatatinnnggg diddidiffffffererentttt gegegegenetititicc iiininhehehh iririta

well as interactiiiions offf gggenotypypypes withh h co fnfffounding gg va iriii babbbles on clinical outcom

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20

targeting a number of proteins to the channel complex and in ensuring not only efficient KCNQ1

phosphorylation but also the subsequent channel response, it is not hard to imagine that different

variants in its encoding gene could result in slightly different phenotypic expression.13,17,43,44

Interestingly, Kao et al. also report a few NOS1AP variants affecting the QT interval but not

sudden cardiac death.45 Work dedicated to understanding the different mechanisms responsible

for these observations requires further attention.

Three AKAP9 SNPs were significantly associated with cardiac event risk, and one of

these was also associated with disease severity. The rs11772585 minor allele (T) was

significantly associated with both the risk of having a cardiac event and increasing the severity of

the disease. The presence of this allele more than doubled the risk of a cardiac event. These

results were supported by the haplotype analysis (Table 4). The rs7808587 GG genotype was

associated with a 74% increase in cardiac event risk, while the rs2282972 minor allele (T)

revealed a protective effect, reducing the risk of an event by 53%. A protective effect associated

with another LQTS-causative gene variant, KCNQ1 rs2074238, has also recently been reported,

with the relevant allele (T allele) modifying both the risk of symptoms and QTc duration.46

Conclusions

Variants in a number of arrhythmia related genes have been reviewed and their contributions

towards QTc interval and sudden cardiac death described in different populations.11,12,41

However, the LQTS gene, AKAP9, encoding a prominent protein in the regulation of the

KCNQ1-KCNE1 channel, has not previously been described as a potential modifier. Results

evaluated here clearly demonstrate that this gene contributes to LQTS phenotypic variability.

However, as these SNPs are located in the intronic regions of this gene, it remains to be seen

which functional and/or regulatory variants in LD with these SNPs are responsible for these

r allele (T)) was

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w

p c

er LQTS ca sati e gene ariant KCNQ1 rs2074238 has also recentl been repo

. Thehehehe ppprereresesesencnn e e ofooo this allele more than ddddoououblb ed the risk of aaa cardiac event. These

e suuupu ported byy ttthe hahahahapllotototo ype ee aanalaalysis (Taabble 4)4)4).. ThThThe rsrss780085888777 GGGG gggennnoootypppee w

with hhh aaa 74747474%%% inncrcreaeasese iiin n cardrdrddiaiaiaiac evvvenenent iririskskkk, hwhwhililileeee thththeee rs2s2s22828282829292977277 mimimiminononor alallllelelelel (((T)T)T)

prpp otective effect, ,, redddud ciiiinggg thhheh riiisi kk k offff an event byby 553%3%3%3%. AAA A prpp otectiiiive effect assoc

LQLQTSTS atii iiant KCKCNQNQ11 2s2070742423838 hha lal ltl bbe by guest on May 14, 2018


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21

modifying effects. Furthermore, it would be interesting to see if similar effects can be replicated

in other populations. Importantly, these findings provide insight into the role that AKAP9 plays,

not only as an LQTS-causal gene, but also as a phenotypic modifier.The identification of the

mutation-specific risk associated with a growing number of modifier genes epitomizes the

evolution in the understanding of LQTS, a disease which increasingly represents the clearest

example of how tightly connected the relationship between genotype and phenotype can be, and

how this is progressively impacting on clinical management.29,47

Acknowledgments: An affiliation with the MRC Biostatistics Unit is hereby acknowledged for part of the study. The authors thank Ina le Roux and Glenda Durrheim for technical assistance.

Funding Sources: Genotyping the selected AKAP9 SNPs was enabled by funding provided by the National Research Foundation grant FA2006040400017 (V.A.C.). The clinical work was supported by NIH grants HL068880 (A.L.G and P.J.S.), and by financial support from Telethon – Italy (Grant no. GGP07016). The financial assistance of the National Research Foundation (DAAD-NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to the DAAD-NRF.

Conflict of Interest Disclosures: None.

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Table 1. LQTS founder population data summary for individuals in whom at least one SNP was

genotyped

At least one SNP genotyped * N Non Carriers(n=181)

Mutation Carriers(n=168)

Gender, male (%) 349 95 (52) 75 (45)

BB, yes/total (%) 234 5/68 (7) 97/166 (58)

Age in years at which ECG was analyzed, mean (SD) 273 34 (20.2) 34.9 (22.9)

BB starting age in years, mean (SD) 95† 32.3 (22.5) 17.2 (17.7)

QTc, median (IQR) 273 401 (383-418) 483 (463-512)

Cardiac events, yes (%) 349 0 (0) 122 (73)

Age first cardiac event, median (range) 115 N/A 6 (2-22)

Age last followed up about CE, median (IQR) 161 N/A 40 (20-57)

BB indicates beta-blocker therapy; SD, standard deviation; IQR, interquantile range; CE, cardiac events*All SNPs were genotyped in 176 non carriers and 162 mutation carriers† There were two additional individuals for whom it was known that beta-blocker therapy was started after the age of 20 years. However, the age at which the treatment started was unknown.

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Table 2. LQTS severity score used to evaluate effects on disease severity

Score value Satisfying condition

0 No cardiac events

1 1-3 syncope episodes

2 More than 3 syncope episodes; no cardiac arrest or sudden cardiac death

3 Cardiac arrest and/or sudden cardiac death

Table 3. Minor allele and haplotype frequencies in the South African LQTS founder population

rs11772585 rs7808587 rs2282972 rs2961024

Number genotyped 346 345 345 342

Minor Allele T G T G

MAF 0.10 0.46 0.44 0.29

Haplotypes

HF=0.443 C A T T

HF=0.282 C G C G

HF=0.171 C G C T

HF=0.098 T A C T

MAF indicates minor allele frequency; LD, linkage disequilibrium; CI, confidence interval; HF, haplotype frequency

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Table 4. LQTS modifying effects of AKAP9 SNPs and haplotypes

Modifying effect SNP/Haplotype* P value Effect size Effect description

QTc

% change per 10 years

rs2961024 0.006 1% per G vs. TT

CGCG 0.012 1% per CGCG vs. 0:CGCG

CE % change in risk

rs11772585 0.002 118% CT CC

rs7808587 0.046 74%

rs2282972 0.001 53%

TACT 0.006 100%

CATT 0.022 35% per CATT

Severity change in score

rs11772585 0.025 0.58

TACT 0.032 0.54 ACT

haplotype absent; 1:, one copy of the haplotype present; CE, cardiac events* Haplotype order: rs11772585, rs7808587, rs2282972, rs2961024

Figure Legends:

Figure 1. Number of individuals entered into analyses based on available information. NC=non-

carriers; MC=mutation-carriers; CE=cardiac event; BB=beta-blocker treatment

Figure 2. Age-dependent change in QTc for rs2961024 genotypes by gender and A341V

mutation status. Modelled curves indicating the 1% per 10 year increase with each G allele

rn A

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relative to the TT genotype for a female mutation carrier (A), male mutation carrier (B), female

non-carrier (C) and male non-carrier (D). The net result was no significant effect for GT, and a

1% increase for the GG genotype. The curves were identical for the different categories;

however, they start at different baseline QTc values.

Figure 3. Genotype specific risk of cardiac events for AKAP9 . Kaplan-Meier cumulative event-

free survival of rs11772585, rs7808587 and rs2282972 genotypes.



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Schwartz, Paul A. Brink, Johanna C. Moolman-Smook and Valerie A. CorfieldCarin P. de Villiers, Lize van der Merwe, Lia Crotti, Althea Goosen, Alfred L. George, Jr., Peter J.

AKAP9 is a Genetic Modifier of Congenital Long-QT Syndrome Type 1

Print ISSN: 1942-325X. Online ISSN: 1942-3268 Copyright © 2014 American Heart Association, Inc. All rights reserved.

TX 75231is published by the American Heart Association, 7272 Greenville Avenue, Dallas,Circulation: Cardiovascular Genetics

published online August 2, 2014;Circ Cardiovasc Genet.

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SUPPLEMENTAL MATERIAL

Statistical analysis overview: Each outcome was modeled as a function of two genetic terms.

The first was additive allelic (counting the number of minor alleles) and the second was a

heterozygote flag. After detecting significant associations, we tested whether a dominant or

recessive model provided a better fit. We also tested genetic interactions with each of A341V

mutation, age and gender on QTc and with gender on cardiac events. Each model was adjusted

for appropriate confounders.

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