Circulation ORIGINAL RESEARCH ARTICLE Transethnic Genome-Wide Association Study Provides Insights in the Genetic Architecture and Heritability of Long QT Syndrome Editorial, see p 339 Najim Lahrouchi, MD* Rafik Tadros, MD, PhD* BACKGROUND: Long QT syndrome (LQTS) is a rare genetic disorder and a major ⁝ preventable cause of sudden cardiac death in the young. A causal rare genetic Connie R. Bezzina , PhD variant with large effect size is identified in up to 80% of probands (genotype positive) and cascade family screening shows incomplete penetrance of genetic variants. Furthermore, a proportion of cases meeting diagnostic criteria for LQTS remain genetically elusive despite genetic testing of established genes (genotype negative). These observations raise the possibility that common genetic variants with small effect size contribute to the clinical picture of LQTS. This study aimed to characterize and quantify the contribution of common genetic variation to LQTS disease susceptibility. METHODS: We conducted genome-wide association studies followed by transethnic meta-analysis in 1656 unrelated patients with LQTS of European or Japanese ancestry and 9890 controls to identify susceptibility single nucleotide polymorphisms. We estimated the common variant heritability of LQTS and tested the genetic correlation between LQTS susceptibility and other cardiac traits. Furthermore, we tested the aggregate effect of the 68 single nucleotide polymorphisms previously associated with the QT-interval in the general population using a polygenic risk score. *Drs Lahrouchi and Tadros contributed RESULTS: Genome-wide association analysis identified 3 loci associated with equally to the study and share first LQTS at genome-wide statistical significance (P<5×10−8) near NOS1AP, KCNQ1, authorship. and KLF12, and 1 missense variant in KCNE1 (p.Asp85Asn) at the suggestive The full author list is available on page threshold (P<10−6). Heritability analyses showed that ≈15% of variance in overall 334. LQTS susceptibility was attributable to common genetic variation (h2SNP 0.148; Key Words: genome-wide association standard error 0.019). LQTS susceptibility showed a strong genome-wide genetic study ◼ inheritance patterns ◼ long QT syndrome correlation with the QT-interval in the general population (rg=0.40; P=3.2×10−3). The polygenic risk score comprising common variants previously associated with Sources of Funding, see page 336 the QT-interval in the general population was greater in LQTS cases compared © 2020 The Authors. Circulation is with controls (P<10−13), and it is notable that, among patients with LQTS, published on behalf of the American Heart Association, Inc., by Wolters this polygenic risk score was greater in patients who were genotype negative Kluwer Health, Inc. This is an open compared with those who were genotype positive (P<0.005). access article under the terms of the Creative Commons Attribution CONCLUSIONS: This work establishes an important role for common genetic Non-Commercial-NoDerivs License, variation in susceptibility to LQTS. We demonstrate overlap between genetic which permits use, distribution, and reproduction in any medium, provided control of the QT-interval in the general population and genetic factors that the original work is properly cited, contributing to LQTS susceptibility. Using polygenic risk score analyses aggregating the use is noncommercial, and no modifications or adaptations are made. common genetic variants that modulate the QT-interval in the general population, we provide evidence for a polygenic architecture in genotype negative LQTS. https://www.ahajournals.org/journal/circ 324 July 28, 2020 Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome of genetic and nongenetic modifying factors underlies Clinical Perspective ORIGINAL RESEARCH this clinical variability. A comprehensive knowledge of such risk factors that affect penetrance and expressiv- What Is New? ARTICLE ity of disease-causing variants in LQTS will improve the • A genome-wide association study in long QT syn- predictive accuracy of genetic testing in the individual drome (LQTS) patients establishes and quantifies patient and enable personalized clinical interventions. the role of common genetic variation in suscepti- While many clinical risk factors such as sex, hypoka- bility to LQTS. lemia, or bradyarrhythmia, have been implicated as • Genetic overlap exists between control of QT-inter- modulators of the clinical manifestations of LQTS,9 val in the general population and susceptibility to modulatory genetic factors remain largely unexplored LQTS. with the exception of a few proof-of-concept studies • Polygenic risk score analyses based on common using a candidate gene approach.10–14 genetic variants that modulate the QT-interval in Besides variability in disease manifestations among the general population provide evidence for a poly- carriers of pathogenic variants, an outstanding issue in genic architecture in LQTS patients that remains genetically elusive despite genetic testing of estab- LQTS is the fact that in ≈20% of patients, an underlying lished genes (ie, genotype negative). causal rare genetic variant remains unidentified after ex- tensive panel-based genetic testing.15 This complicates What Are the Clinical Applications? cascade screening in families and the presymptomatic identification of affected relatives. Although a small pro- • These findings enhance the understanding of the portion of such patients with genotype negative LQTS genetic basis of LQTS and underscore the genetic relationship between QT-interval in the general could have a yet unknown Mendelian defect, another population and susceptibility to LQTS. possibility is that a more complex inheritance pattern • Increasing burden of QT-prolonging common vari- underlies the disorder in a subset of these patients. ants is associated with higher susceptibility for LQTS. Previous work has shown that a genome-wide as- • Polygenicity in genotype negative LQTS patients sociation study (GWAS) comparing cases of a rare ar- implies that risk is not primarily attributable to 1 rhythmia syndrome with unaffected controls can de- genetic factor inherited from 1 of the biological par- fine modulators of disease susceptibility and suggest a ents as is the case for autosomal dominant LQTS. polygenic etiology.16 We report here a GWAS in ≈1700 • Future clinical utility of genetic testing based on poly- unrelated patients with LQTS, of European or Japanese genic inheritance necessitates the availability of poly- ancestry, identifying common genetic variants impli- genic risk scores with high discriminative capacity. cated in LQTS disease susceptibility, and providing a quantification of the contribution of common genetic L ong QT syndrome (LQTS) is a rare inherited disorder variants to LQTS predisposition. Using polygenic risk of ventricular repolarization characterized by pro- score analyses aggregating common genetic variants longation of the QT interval on the ECG.1,2 LQTS that modulate the QT-interval in the general popula- has a prevalence of approximately 1 in 2500, and is tion, we provide evidence for a polygenic architecture a major and often preventable cause of sudden car- in genotype negative LQTS. diac death in the young.3,4 Multiple genes have been implicated in LQTS and clinical genetic testing is now METHODS performed to identify causative rare genetic variants.5 The summary statistics generated in this study are available from Disease-causing variants (ie, mutations) in the 3 major the corresponding author on request or on the Cardiovascular LQTS genes (ie, KCNQ1 [LQT1], KCNH2 [LQT2], and Disease Knowledge Portal (http://www.broadcvdi.org/). SCN5A [LQT3]), account for up to 80% of LQTS cases overall and >95% of genotype positive LQTS.2 Study Population Studies in families with multiple mutation carriers We established an international consortium allowing recruit- have shown that disease penetrance (proportion of ment of 1781 unrelated patients with LQTS: 1344 cases of carriers that manifest with a prolonged QT-interval) can European ancestry from 23 referral centers in Europe, New be low,6–8 and that among those with disease mani- Zealand, and North America, as well as 437 patients of East festations, there can be broad variability in the types Asian ancestry from 4 referral centers in Japan (Table I in the Data Supplement). Included unrelated individuals were pro- of symptoms and severity thereof (variable expres- bands (97%) except when DNA was not available, in which sion).2,6–8 These observations suggest that, like other case 1 other affected family member was included instead. Mendelian disorders, allocating the disease in the in- Included patients had a clinical diagnosis of LQTS5 and were dividual patient exclusively to a rare variant at a single classified as “genotype positive” if they carried a single rare locus (ie, monogenetic) might be an oversimplification variant in 1 of the 3 established major LQTS genes (KCNQ1 of biological phenomena. It is likely that a combination [LQT1], KCNH2 [LQT2] and SCN5A [LQT3]), or “genotype Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 July 28, 2020 325 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome negative” if no rare variant was identified in genes unequivo- [LQT3], or genotype negative), sex, and principal components 1 ORIGINAL RESEARCH cally associated with nonsyndromic LQTS (KCNQ1, KCNH2, to 10. Genome-wide association analyses were carried out sep- SCN5A, CALM1-3, and TRDN).17–19 A rare variant was defined arately for the European and Japanese LQTS cohorts, followed as a protein sequence altering (ie, missense, nonsense, by meta-analysis using an inverse variance weighted fixed ARTICLE frameshift deletion, in-frame deletion, large deletion, and effect model, implemented in METAL (version 2011-03-25).27 duplication) or splice-site variant with an allele frequency Genome-wide statistical significance and suggestive thresholds <1×10−4 in the Genome Aggregation Database.20–22 Genetic were set to P<5×10−8 and P<1×10−6, respectively. Summary testing and variant curation as per the American College of statistics were uploaded to FUMA (Functional Mapping and Medical Genetics and Genomics and Association of Molecular Annotation of GWAS) for generation of Manhattan, quantile- Pathology guidelines23 was conducted as described in the quantile, and regional association plots for risk loci.28 Methods section in the Data Supplement. All subjects or their guardians provided informed consent, and the study was approved by the appropriate ethical review boards. Survival Analyses Time to life-threatening arrhythmic events (LAE) survival analyses were performed in the LQTS cases. Follow-up started Phenotypic Characterization and at birth and stopped at the date of a documented LAE, the Measurement of the QT-Interval last visit, or the 41st birthday, whichever came first. LAE Clinical data were collected at each of the participating cen- were defined as out of hospital cardiac arrest, hemodynami- ters. We collected a baseline ECG for each patient, preferably cally unstable ventricular tachycardia/ventricular fibrillation, not during β-blocker use. The QT-interval duration was mea- or appropriate implantable cardioverter-defibrillator therapy. sured as previously described (Figure I in the Data Supplement, The effect of genotype positive versus genotype negative and Methods in the Data Supplement).24 In genotype negative status was estimated using Cox proportional hazards regres- patients, a LQTS diagnosis was additionally curated by 2 clini- sion with/without adjustment for classic risk factors (ie, sex cians (NL, RT) and in case of uncertainty, 2 senior LQTS experts and QTc≥500 ms). To examine possible differences in effect (AAW, PJS) were consulted. As per international guidelines,5 of these well-recognized risk factors in genotype positive and we only included genotype negative patients with a LQTS risk genotype negative LQTS cases, interactions between these score≥3.5 or with a resting QTc≥500ms in repeated 12-lead risk factors and genotype status were included in the model. ECGs, in the absence of a secondary cause for QT prolongation. In addition, puberty and a sex × puberty interaction were included to model the modifying effect of puberty on the effect of sex. Puberty was included as time-varying covariate Genome-Wide Array Genotyping, Quality and the age of puberty was set at 16 years in both sexes (ie, Control, and Imputation during the follow-up period before the age of 16, puberty We performed genome-wide genotyping for all European was coded as 0, whereas puberty was coded as 1 during the cases on the Illumina HumanOmniExpress array and for all remainder of the follow-up period). Kaplan Meier curves were Japanese cases on the Illumina Global Screening Array. created to illustrate the cumulative event free survival and log Genotypic data of 8219 control individuals of European ances- rank tests were used to compare the survival curves. try and 1671 individuals of Japanese ancestry were obtained from different cohorts (Table II in the Data Supplement). Polygenic Risk Scores Quality control,25 imputation and association analysis were For all cases and controls, we calculated a weighted QT poly- performed separately in the European and Japanese datas- genic risk score (PRSQT) comprising 68 SNPs that had been ets. All genetic variants were mapped to and reported using associated with the QT-interval in the general population Genome Reference Consortium Human genome build 37. at genome-wide statistical significance, in a study primar- After quality control (see Methods in the Data Supplement ily including Europeans.29 All 68 SNPs were included in the for details), we performed genome-wide imputation using European dataset analyses whereas only 60/68 SNPs were Eagle2 phasing, Minimac3 and the Haplotype reference well-imputed and included in the Japanese dataset analyses consortium (HRCr1.1) panel implemented on the Michigan (Table III in the Data Supplement). PRSQT was calculated by Imputation Server for both the European and Japanese datas- multiplying the alternate allele dosage by the associated effect ets.26 After imputation, only single nucleotide polymorphisms size (β) in the published QT GWAS for each of the 68 SNPs. (SNPs) with minor allele frequency >0.01 and a Minimac3 Then, the PRSQT was normalized to a mean of 0 and standard imputation score of R2>0.3 were included in further analyses. deviation of 1. We used logistic regression to test for associa- tion of PRSQT with case–control status, correcting for principal Genome-Wide Association Analysis components 1 to 10. We also used P value thresholding and We performed genome-wide association analyses to assess the R2 pruning with P values of 5×10−8, 1×10−5, 1×10−4, 1×10−3, role of common variants in LQTS susceptibility (case–control) and 1×10−2 and R2 of 0.2 and 0.1 on summary statistics and severity (QTc within the cases). Case–control association of from a European29 and Japanese30 descent general popula- alternate allele dosage with LQTS was performed using logis- tion QT-interval GWAS. The resulting 10 models were used to tic regression correcting for genotypic principal components calculate a European and Japanese PRSQT. The association of 1 to 10. Quantitative trait analyses for QTc were conducted PRSQT with LQTS was assessed using a logistic regression for using a linear regression model correcting for age, β-blocker the European and Japanese cases separately. The best model use at ECG, LQTS type (KCNQ1 [LQT1], KCNH2 [LQT2], SCN5A was selected based on the maximal C-statistic, as recently 326 July 28, 2020 Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome performed.31 No other covariate was used to avoid model Table 1. Clinical Characteristics of All Unrelated LQTS Cases ORIGINAL RESEARCH overfitting. Genotype Genotype The odds ratios (ORs) associated with quartile 2, 3, and 4 Parameter Positive (n=1584) Negative (n=197) were calculated using the first PRSQT quartile as the reference. ARTICLE Male, n (%) 584/1584 (37) 76/197 (39) The association of PRSQT and known QT predictors with QTc QTc mean±SD, ms 482±47 500±52 was performed using a univariable linear regression followed by multivariable analysis, including in the final model only Genotype, n (%) those variables with a P<0.05 in the univariable analyses. The  KCNQ1 800/1584 (50) — association of PRSQT quartiles with time to LAEs was assessed  KCNH2 661/1584 (42) — using Cox proportional hazards regression with/without adjustment for classic risk factors. Association analyses of  SCN5A 123/1584 (7.8) — PRSQT with case–control status, QTc, and time to LAE were Syncope, n (%) 722/1584 (46) 75/197 (38) performed separately in the European and Japanese datasets, LAE (OHCA or VF/VT) before age 262/1578 (17) 33/196 (17) followed by a fixed-effects model meta-analysis. 41, n (%)  Age at first LAE, median [IQR] 21 [13–29] 26 [16–35] Common Variant Heritability Treatment during follow-up, n (%) We used the generalized restricted maximum likelihood  Beta-blocker 1169/1487 (79) 124/168 (74) (GREML) approach of GCTA (GCTA-GREML)32 to estimate how  ICD 277/1562 (18) 38/172 (22) much of the variance in LQTS susceptibility could be attributed to common genetic variants (SNP-based heritability, h2SNP).  PM 50/1565 (3.2) 11/171 (6.4) Before heritability estimation, we conducted additional strin-  LCSD 29/1583 (1.8) 1/171 (0.6) gent genetic quality control, as previously suggested (Methods Family history of SCD <50 yr of 323/1409 (23) 24/189 (13) in the Data Supplement).33 We estimated the SNP-heritability on age, n (%) the liability scale assuming a 0.04% prevalence with principal ICD indicates implantable cardioverter-defibrillator; IQR, interquartile components 1-10 as covariates.1 We assessed the robustness of range; LAE, life-threatening arrhythmic event; LCSD, left cardiac sympathetic heritability estimates from GCTA-GREML using the GREML and denervation; OHCA, out-of-hospital cardiac arrest; PM, pacemaker; SCD, phenotype-correlation genotype-correlation regression34 analy- sudden cardiac death, SD, standard deviation; and VF/VT, hemodynamically ses implemented in LDAK.35 We estimated h2SNP in the overall unstable ventricular fibrillation/tachycardia LQTS and genotype positive LQTS dataset in the both European and Japanese ancestries. Because of small sample size we were no disease causing variant was identified (ie, genotype not able to estimate h2SNP in genotype negative patients with negative) despite extensive genetic testing. LQTS using the approaches implemented in GCTA or LDAK. The mean QTc interval in genotype negative cases was higher in comparison with genotype positive ones Genetic Correlation With Other Traits (500±52ms vs 482±47ms, P=2×10−5) and in genotype We used bivariate linkage disequilibrium score regression36 to negative cases a family history of sudden cardiac death evaluate the genetic correlation between LQTS susceptibility at <50 years of age in 1st and 2nd degree relatives was less (as obtained in the European descent case–control GWAS) and frequent compared with genotype positive ones (12.7% other cardiac electric traits,2 namely PR, QRS, QT, heart rate (HR) vs 22.9%, P=0.001). Of the 1584 genotype positive cas- at rest, HR in response to exercise and recovery, and atrial fibrilla- es, 1333 (84%) carried a pathogenic or likely-pathogenic tion (see Methods in the Data Supplement for origin of summary variant according to American College of Medical Genet- statistics). We used Bonferroni correction to account for multiple ics and Genomics and Association of Molecular Pathology testing (P=0.05/7=0.0071). We did not constrain the bivariate guidelines, and the remainder had a variant of unknown regression intercepts in any of these analyses given the potential significance. The QTc did not significantly differ between for (modest) sample overlap and population stratification. carriers of variants of unknown significance and those with a pathogenic or likely-pathogenic variant (P=0.9). RESULTS In total, 429 cases (24%) had an LAE at a median age of 28 years (interquartile range, 17 to 46 years), with Clinical Characteristics of the Case Cohort 295 cases (17%) having such an event by age 40. LAE- Demographic and clinical characteristics of the unre- free survival did not significantly differ between geno- lated LQTS cases are presented in Table IV in the Data type negative and positive cases (P=0.8) or between Supplement separately for the European and Japanese European and Japanese cases (P=0.053; Figure 1). In a datasets and in Table 1 for the combined cohort. We multivariable Cox proportional hazard model, male sex included a total of 1781 unrelated patients with LQTS (OR 1.9; P=0.004), QTc>500ms (OR 1.8; P=4×10−6) and of European (n=1344, mean QTc±SD: 484±48ms) and Japanese ancestry (OR 1.4; P=0.03) were independent Japanese descent (n=437, QTc: 485±49ms). A total of risk factors for LAE (Table V in the Data Supplement). We 1584 cases (89%) were genotype positive, carrying a found a significant sex-puberty interaction (P=1×10−6), rare variant in KCNQ1 (LQT1, n=800), KCNH2 (LQT2, where males were at higher risk of LAE in the prepuber- n=661), or SCN5A (LQT3, n=123), while in 197 (11%) tal years but lower risk thereafter (Figure II in the Data Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 July 28, 2020 327 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome 1.23–1.54]; P=1.92×10−8; Figure VIIIB in the Data Sup- ORIGINAL RESEARCH plement). At the third locus, the lead SNP, rs17061696 (OR=1.25 [95% CI, 1.15–1.35]; P=4.33×10−8), was lo- ARTICLE cated in an intron of KLF12 (Figure VIIIC in the Data Supplement). All 3 loci had been previously associated with the QT-interval duration, a measure of myocar- dial repolarization on the ECG, in the general popu- lation (Table 1).29 The low-frequency missense variant in KCNE1, p.Asp85Asn (rs1805128, OR=2.78 [95% CI, 1.67–3.90]; P=5.31×10−7; Figure VIIID in the Data Sup- plement) reached the suggestive statistical significance threshold in the European case–control analysis. This variant, which is rare and not well imputed in the Japa- nese dataset (minor allele frequency=0.001; R2<0.3), has the largest reported effect size among the 68 inde- pendent SNPs (hereafter referred to as QT-SNPs) previ- ously associated with QT-interval in the general popula- tion (7.4ms increase per minor allele).29 Of note, The KCNE1-p.Asp85Asn variant had a more pronounced Figure 1. Kaplan-Meier life-threatening arrhythmic event–free survival effect in genotype negative (OR=7.64 [95% CI, 3.66– curves stratified by ancestry. 15.95]; P=5.99×10−8) than in genotype positive LQTS EU indicates European LQTS cases; Geno-, genotype negative LQTS cases; Geno+, genotype positive LQTS cases; JP, Japanese LQTS cases; LAE, life- (OR=2.28 [95% CI, 1.46–3.54]; P=2.59×10−4). threatening arrhythmic event (defined as the composite of out of hospital cardiac arrest or hemodynamically unstable ventricular tachycardia/arrhythmia; and LQTS, long QT syndrome. Log-rank test P=0.3. Genetic Overlap Between LQTS and QT- Interval in the General Population Supplement). The effect of the conventional risk factors The identification of SNPs previously associated with QT- sex (Pinteraction=0.3) and QTc≥500ms (Pinteraction=0.7) did not interval in the general population is in line with the fact differ between genotype positive and genotype negative that QT-interval prolongation on the ECG (representing cases. Genotype (KCNQ1, KCNH2, SCN5A, or negative) prolonged cardiac repolarization) is the central intermedi- significantly affected time to LAE (log-rank test P<0.001; ate phenotype underlying LQTS. In fact, 23 of the 68 QT- Figure III in the Data Supplement). Cases with a rare vari- SNPs previously associated with QT-interval in the general ant in KCNQ1 had a lower risk of LAE compared with population, were associated with LQTS at nominal signifi- KCNH2, SCN5A, and genotype negative ones (P<0.01 cance (ie, P<0.05), while only 4 would be expected under for all comparisons). None of the other post hoc pairwise the null hypothesis (Table VI in the Data Supplement). We comparisons reached statistical significance. Time to LAE observed a strong positive correlation between the effect did not differ between cases with a variant of unknown that each of the 68 QT-SNPs had on the QT-interval in the significance and those with pathogenic or likely-patho- general population29 and the risk they conferred for LQTS genic variant (Figure IV in the Data Supplement). in the current study. This effect was consistent across both the European (Figure 3A; R2=0.67; P=2.04×10−17) and the Japanese (Figure 3B; R2=0.52; P=1.43×10−10) da- Case–Control GWAS tasets. Overlap between genetic risk for LQTS and genetic We conducted a case–control GWAS separately in Eu- determinants of the QT-interval in the general popula- ropean (1238 cases vs 8219 controls, genomic test tion29 was further demonstrated by genome-wide bivari- inflation (λ)=1.024) and Japanese (418 cases vs 1617 ate linkage disequilibrium score regression,36 which de- controls, λ=1.034) cases using ancestry-matched con- tected a significant positive genetic correlation (rg=0.40, trols (Figures V and VI in the Data Supplement), fol- SE=0.14; P=3.2×10−3) between these phenotypes. No lowed by transethnic meta-analysis (λ=1.028). This significant correlation was found for other cardiac electric uncovered 3 loci reaching the threshold for genome- traits (Figure IX in the Data Supplement). wide statistical significance (Table 2, Figure 2, and Fig- ures VII and VIII in the Data Supplement). The most significant association was obtained for rs12143842 Analysis of PRSQT in LQTS Disease (OR=1.32 [95% CI, 1.21–1.42]; P=1.09×10−11) located Susceptibility upstream of NOS1AP (Figure VIIIA in the Data Supple- We then tested the aggregate effect of the 68 QT- ment). The lead SNP at the second locus was located SNPs (PRSQT) on susceptibility to LQTS by means of PRS in an intron of KCNQ1 (rs179405, OR=1.38 [95% CI, analysis (Table III in the Data Supplement). PRSQT was 328 July 28, 2020 Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome Table 2. Significant Loci in LQTS Case–Control GWAS ORIGINAL RESEARCH Meta-Analysis European Japanese AAF AAF Effect ARTICLE Alternative Reference Closest (Controls (Controls on QT Lead SNP GRCh37 Allele Allele Gene OR 95%CI P /Cases) OR P /Cases) OR P (ms)* rs12143842 1:162033890 T C NOS1AP 1.31 1.21–1.42 1.09E−11 0.26/0.32 1.29 7.34E−08 0.38/0.47 1.41 2.13E−05 3.5 rs179405 11:2525395 A G KCNQ1 1.38 1.23–1.54 1.92E−08 0.14/0.17 1.34 4.03E−06 0.10/0.14 1.63 5.42E−04 1.9† rs17061696 13:74511991 C G KLF12 1.25 1.15–1.35 4.33E−08 0.37/0.43 1.27 8.91E−08 0.19/0.21 1.16 1.43E−01 0.58 AAF indicates alternative allele frequency; GRCh37, genomic position on build GRCh37; and OR, odds ratio per alternative allele. *QT increase (in ms) per alternative allele in the general population. †The lead SNP at the KCNQ1 locus (rs179405) is in linkage disequilibrium with rs7122937 (R2=0.497) which had been previously associated with QT-interval in the general population (1.9 ms increase per risk allele). significantly associated with a diagnosis of LQTS in Supplement). Ten different PRS derived by the pruning the European set, the Japanese set, and in the meta- and thresholding method on summary statistics from analysis of both datasets (Figure 4A and 4C; Table 3; the European descent general population QT-interval meta-analysis β=0.34, SE=0.03; P=1.1×10−38, hetero- GWAS did not significantly outperform the PRSQT in dis- geneity P=0.15). Similar results were obtained when criminating case–control status (Table VIIIA in the Data we excluded common variants located at the known Supplement). Similarly, Japanese ancestry-specific PRS Mendelian LQTS loci from the PRS (Table VII in the Data derived from summary statistics of a small Japanese Figure 2. Manhattan plot of long QT syndrome case–control meta-analysis. Manhattan plot displaying the base-pair position of each of the tested single nucleotide polymorphisms (SNPs; each dot represents an individual SNP) along the chromosomes on the x axis and the corresponding −log10 transformed association P value on the y axis. The association P values from the meta-analysis of the 2 genome-wide association studies conducted separately in European and Japanese cases and controls, respectively, are displayed. The upper and lower dashed lines indicate the genome-wide significance (P<5×10–8) and suggestive significance (P<1×10­–6) thresholds, respectively. SNPs at genomic regions that reached the genome-wide or suggestive significance thresholds, are marked in red, whereas SNPs from other regions are marked in black or grey. The association for variant rs1805128 (KCNE1:p.Asp85Asn) is solely driven by the European analysis because it is not well imputed and rare (R2<0.3. minor allele frequency=0.001) in the Japanese dataset. Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 July 28, 2020 329 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome ORIGINAL RESEARCH ARTICLE Figure 3. Correlation of effect size of QT-associated single nucleotide polymorphisms with their effect size in long QT syndrome genome-wide as- sociation study. The x axis represents the effect estimates from the QT-interval genome-wide association study (GWAS) conducted in the general population (milliseconds per alternative allele) and the y axis the effect of each of these QT-interval associated alleles on disease risk of long QT syndrome (LQTS; [Ln(OR)]) in the European (A) and Japanese (B) datasets. All 68 SNPs associated with QT in the general population were assessed in Europeans, whereas 60 SNPs were properly imputed in the Japanese dataset. In the LQTS-GWAS meta-analysis, 23/68 SNPs previously associated with the QT in the general population reached nominal significance (see Table VI in the Data Supplement). Loci that reached genome-wide significance in the LQTS case–control transethnic meta-analysis NOS1AP-rs12143842, KCNQ1- rs179405, KLF12-rs728926, and KCNE1-rs1805128 are identified with text. QT-interval GWAS30 had less discriminative accuracy in above the threshold compared with those below have the Japanese case–control dataset compared with the an OR (95%CI) of 2.9 (2.2–4.0), 4.1 (2.9–5.8), and 5.7 European-derived PRSQT, likely because of the small size (3.9–8.4), respectively, for genotype negative LQTS. Of of the Japanese QT-interval GWAS (Table VIIIB in the interest, the higher PRSQT in genotype negative patients Data Supplement). compared with genotype positive patients was reflect- We next explored whether the genetic architecture ed by the larger difference in PRSQT between genotype of genotype negative patients (ie, those lacking a rare negative patients versus controls (Table 3; meta-analy- variant after extensive genetic testing of the estab- sis β=0.735, SE=0.074; P=2.24×10−23) compared with lished LQTS disease genes) differed from that of gen- genotype positive versus controls (Table 3; meta-analy- otype positive patients. This was done by comparing sis β=0.294, SE=0.028; P=1.09×10−25). PRSQT between both groups, uncovering a significantly higher PRSQT in genotype negative patients, pointing to a more prominent role for common variants in disease Common Variant Heritability of LQTS susceptibility in these patients. This effect was con- To evaluate the proportion of variance in LQTS suscep- sistently observed in both the European (P=5.1×10−6, tibility explained by common genetic variants (h2SNP) we Figure 4B) and the Japanese (P=2.0×10−3, Figure 4D) used GCTA-GREML.32,37 Assuming a disease prevalence datasets (Table 3). Similar results were obtained in a of 0.04%,1 the SNP heritability estimate on the liabil- sensitivity analysis correcting for QT-interval, ensuring ity scale was h2SNP=0.148 (SE=0.019 [95% CI, 0.111– that enrichment of QT prolonging alleles in the geno- 0.185]; P=5.0×10−18) in the overall European LQTS da- type negative patients was not driven by differences in taset. h2SNP was similar when the analysis was restricted QT-interval (P=7.4×10−5 in Europeans; P=2.6×10−3 in to genotype positive patients with LQTS. Similar results Japanese, Table 3). These associations remained sta- were also observed in the Japanese dataset and when tistically significant when we restricted the analysis to using the phenotype-correlation genotype-correlation patients with a pathogenic or likely-pathogenic variant regression34 and the GREML estimation implemented according to American College of Medical Genetics in LDAK,35 as well as when we restricted h2SNP analyses and Genomics and Association of Molecular Pathology to only patients with a pathogenic or likely-pathogenic guidelines (ie, excluding cases with a rare variant of un- variant (Table XII in the Data Supplement). known significance; Methods in the Data Supplement and Tables IX and X in the Data Supplement). Increas- ing PRSQT quartiles were associated with a significantly Association Analyses of Single SNPs and higher disease susceptibility for genotype negative PRSQT With LQTS Severity LQTS compared with the lowest quartile (Figure 5; Table To identify genetic modifiers of disease severity we con- XI in the Data Supplement). It is notable that, using a ducted a GWAS for QT-interval within the LQTS cases PRSQT percentile threshold of 80, 90, and 95, individuals which did not uncover any genome-wide significant 330 July 28, 2020 Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome ORIGINAL RESEARCH ARTICLE Figure 4. Distribution of QT polygenic score in controls, long QT syndrome, and genotype positive and negative subgroups. The x axis represents the QT polygenic score (PRSQT) in the European (A and B; blue) and Japanese (C and D; red) long QT syndrome (LQTS) case–control datasets. In A and C, all LQTS cases are grouped regardless of whether they are genotype positive or negative, whereas in B and C, cases have been stratified in genotype positive and negative LQTS subgroups. PRSQT was normalized to a mean of 0 and standard deviation of 1. Reported P values refer to the effect of PRSQT in a logistic regression correcting for the first 10 principal components. *Refers to case–control association. Comparison of PRSQT between genotype negative versus genotype positive LQTS uncovered a significantly higher PRSQT in genotype negative patients. This effect was consistently observed in both the European (P=5.1×10−6) and the Japanese (P=2.0×10−3) patients (Table 3). loci (Figure X in the Data Supplement). None of the 68 SNPs previously associated with QTc in the gen- DISCUSSION eral population29 showed association with QTc after Our findings establish an important role for common Bonferroni correction. PRSQT showed a weak positive genetic variation in LQTS susceptibility and support a correlation with QTc in the European cases (correla- complex (polygenic) architecture in genotype negative tion coefficient [r]=0.06; P=0.042; Figure XI in the LQTS. Case–control GWAS identified 3 genome-wide Data Supplement). In a multivariable linear regression significant risk loci near NOS1AP, KCNQ1, and KLF12. model including clinical covariates associated with QTc Heritability analysis demonstrated that ≈15% of LQTS (age at ECG recording, LQTS type, and sex), PRSQT was disease liability is attributable to common genetic varia- not significantly associated with QTc (Table XIII in the tion. PRS analysis testing the aggregate effect of SNPs Data Supplement). Similarly, in a subanalysis restricted previously associated with QT-interval in the general to probands (comprising 97% of the total of unrelated population (PRSQT) identified a higher PRSQT in LQTS LQTS cases) using the multivariable linear regression cases compared with controls and higher PRSQT in gen- model, PRSQT was not significantly associated with otype negative versus genotype positive LQTS. QTc (data not shown). In exploratory subgroup analy- ses, PRSQT was independently associated with QTc in Shared Genetics of LQTS and QT-Interval KCNH2 rare variant carriers but not in KCNQ1 rare variant carriers (Table XIII in the Data Supplement). in the General Population This result was not replicated in the Japanese LQTS The case–control GWAS uncovered 3 genetic LQTS sus- dataset. PRSQT was not significantly associated with ceptibility loci at genome-wide statistical significance time to LAE in neither Europeans nor Japanese cases near NOS1AP, KCNQ1, and KLF12, and 1 missense (Figure XII in the Data Supplement). variant in KCNE1 at the suggestive threshold (Figure 2). Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 July 28, 2020 331 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome Table 3. Association of QT Polygenic Score With Long QT Syndrome ORIGINAL RESEARCH European Japanese Meta-analysis Association Analysis of PRSQT n β SE P n β SE P n β SE P ARTICLE All LQTS vs Controls 1238/ 8219 0.322 0.030 4.93E−26 418/ 1671 0.412 0.055 6.16E−14 1656/9890 0.343 0.0263 1.08E−38 Genotype positive LQTS 1115/ 8219 0.277 0.032 3.47E−18 356/ 1671 0.348 0.058 2.52E−09 1471/ 9890 0.294 0.028 1.09E−25 vs Controls Genotype negative 123/ 8219 0.733 0.090 3.74E−16 62/ 1671 0.740 0.129 1.19E−08 185/9890 0.735 0.0738 2.24E−23 LQTS vs Controls Genotype negative vs 123/ 1115 0.447 0.098 5.05E−06 62/356 0.401 0.129 2.01E−03 185/1471 0.430 0.078 3.54E−08 Genotype positive LQTS Genotype negative vs 123/ 1115 0.409 0.103 7.36E−05 62/356 0.393 0.130 2.62E−03 185/1471 0.403 0.0807 6.05E−07 Genotype positive LQTS* β indicates regression coefficient; n, sample size (cases/controls); P, P-value; PRSQT, QT polygenic score; and SE, standard error. *Correcting for QTc. The association of SNPs at KCNQ1 points to the involve- contribution of common variants to disease susceptibil- ment of common variants acting alongside rare variants ity may also contribute to variable disease penetrance. in these genes in mediating disease susceptibility, akin It has been well established that LQTS probands have a to what was previously reported for common and rare longer QT-interval and greater arrhythmic risk compared variation in and around the SCN5A gene in Brugada with family members carrying the same variant.7,13,39 syndrome.16 All 4 risk loci had been previously impli- This observed increased penetrance in probands may cated in genetic control of the QT-interval by GWAS in result from a greater burden of common QT-prolonging the general population.29 For the 68 SNPs associated variants compared with other, less-severely affected, or with QT-interval in the general population, we noted unaffected mutation-carriers. However, because this a strong positive correlation between their effect on study comprised only unrelated patients, this remains QT-interval (obtained in the general population) and to be determined. Whether the PRSQT could discrimi- their OR for LQTS susceptibility, indicating, as expected, nate between affected versus unaffected mutation car- that the larger the effect a SNP has on the QT-interval, rier family members is intuitively appealing but remains the more it increases LQTS susceptibility (Figure 3). The to be formally demonstrated. strong genetic correlation between LQTS susceptibil- ity and QT-interval in the general population provides quantitative support for genetic overlap (Figure IX in Genotype Negative LQTS, A Polygenic the Data Supplement). Subtype of LQTS? The association with the highest effect in the case– PRS analysis, testing the aggregate effect of SNPs previ- control GWAS was found for the p.Asp85Asn missense ously associated with QT-interval in the general popula- variant in KCNE1 (rs1805128). This variant increased tion (PRSQT), identified a higher PRSQT in genotype nega- susceptibility for LQTS in the overall cohort but had a tive versus genotype positive patients. This observation more prominent effect in genotype negative LQTS with points to genotype negative LQTS, comprising ≈10% of an OR of ≈7 versus an OR of 2 in genotype positive pa- patients with LQTS, as a polygenic subtype of the dis- tients. This variant has an allele frequency of ≈1.2% in order where the underlying etiology involves, at least in non-Finnish Europeans and ≈0.5% in East Asians, and part, a high burden of common QT prolonging alleles. has the largest effect size on the QT in the general (Eu- As such, genetic susceptibility in genotype negative ropean descent) population (7.42 ms per minor [Asn] patients may not be determined to a large extent by allele).29 It has been shown to be enriched in patients 1 strong genetic factor as occurs in genotype positive with drug-induced torsades de pointes.38 patients but results from the accumulation of multiple variants (polygenic inheritance). The lower rate of fam- ily history of sudden cardiac death in genotype negative Genetic Architecture of Genotype patients with LQTS is in line with polygenic inheritance. Positive LQTS Our observations corroborate findings in other herita- LQTS has traditionally been viewed as a monogenic dis- ble phenotypes, such as familial hypercholesterolemia, order mostly attributed to a rare variant with a drastic where patients without a disease-causing variant in the effect on ion channel function. We now demonstrate LDLR, APOB, and PCSK9 genes have a higher PRS based that a considerable extent (≈15%) of disease liability is on low-density lipoprotein modulating variants in com- attributable to common genetic variation. In genotype parison with those with rare familial hypercholesterol- positive LQTS families, where the penetrance of patho- emia causing genetic variants.40 As such, the accumula- genic variants may be low for certain variants,8 the tion of multiple discrete common variants may confer 332 July 28, 2020 Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome survival as their genotype positive counterparts (Fig- ORIGINAL RESEARCH ure 1). The effect of established clinical risk factors, for example sex and QTc-duration, did not significantly ARTICLE differ between genotype positive and negative (no in- teraction effect) suggesting they may also be used to stratify risk of events in genotype negative LQTS. Common Variants Do Not Contribute to LQTS Severity Within Probands We sought to identify genetic modifiers of LQTS. In contrast to the case–control GWAS, GWAS for QTc and arrhythmic events within the unrelated LQTS cases did not uncover any genome-wide significant locus. PRSQT was also not significantly associated with QTc nor with the occurrence of events. At first glance, this may seem contradictory to previous studies in LQTS that demon- strated a modulatory effect of SNPs at NOS1AP on the QTc and arrhythmic events,10,11,13 as well as a study in the general population that showed a modulatory ef- fect of PRS derived from previous GWAS on QT-inter- val.41 For example, a study we previously conducted in patients with LQT2 uncovered strong associations with large effect sizes (>12 ms/allele) for SNPs at NOS1AP.13 An important difference however, is that the current study did not include family members but only 1 pa- tient per family (97% probands), whereas the previous studies considered both probands and genotype posi- tive relatives. Conceptually, inclusion of both probands Figure 5. Increasing long QT syndrome risk with increasing QT poly- and relatives results in greater variation in QTc and is genic score quartiles. thus expected to increase statistical power for detection Odds ratio (OR) for genotype negative long QT syndrome (LQTS; filled circles) and 95% CI (vertical bars) associated with each QT polygenic score (PRSQT) of modulatory effects. Moreover, the different rare vari- quartile taking the first PRSQT quartile as the reference. Data shown correspond ants in the patients we studied here are associated with to a meta-analysis of effects computed separately in the European and Japanese biophysical defects of varying severity. As such, they are datasets. P values refer to comparison of each quartile against the first quartile. also expected to contribute to interindividual variability risk similar to a monogenic mutation. This was recently which is difficult to account for. For instance, patients with LQT2 with pore-region variants are known to be demonstrated for common disorders such as coronary more severely affected than other patients with LQT2.42 artery disease and atrial fibrillation, where individuals Indeed, in a subanalysis, restricted to European patients at the upper extreme of the PRS distribution had a risk with LQT2, where we accounted for the mutation loca- of developing the disease reportedly comparable with tion (pore versus nonpore), we detected an association carriers of a monogenic mutation.31 The overlap in the of PRSQT with QTc. In sum, our data show that com- PRSQT distributions among genotype negative LQTS cas- mon variants do not affect disease severity across all es and controls (Figure 4) suggests that other factors probands studied. Further studies are needed to explore are involved, possibly including low-frequency genetic their predictive role in family members. variants with intermediate effect sizes as well as other common variants with smaller effect sizes. In addition to providing insight into the genetic ar- Potential Clinical Implications chitecture of genotype negative LQTS, we here also de- In genotype negative LQTS, disease susceptibility esti- scribe for the first time the natural history in these pa- mation for relatives does not follow a Mendelian pat- tients. All ≈200 genotype negative patients with LQTS tern. In our cohort, a positive family history of sudden met diagnostic criteria for definite LQTS (ie, QTc>500ms cardiac death was less often observed in genotype or LQTS score≥3.5) and underwent sequencing of the negative individuals compared with genotype posi- unequivocal nonsyndromic LQTS genes. Genotype neg- tive ones, suggesting that risk for family members in ative patients with LQTS had a higher QTc in compari- genotype negative patients may be lower. Polygenicity son with patients with LQT1–3 but similar event-free in genotype negative individuals implies that risk is not Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 July 28, 2020 333 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome primarily attributable to 1 genetic factor inherited from genetic variation affecting the QT-interval in the general ORIGINAL RESEARCH 1 of the biological parents as is the case for autosomal population contributes to disease susceptibility in both dominant LQTS. In such cases, cascade screening may genotype positive and genotype negative LQTS. The ARTICLE necessitate clinical evaluation of both maternal and pa- role of common variants is predominant in genotype ternal family members. Future clinical utility of genetic negative LQTS, suggesting that the latter may consti- testing based on polygenic inheritance necessitates the tute a polygenic form of LQTS. Increasing burden of QT- availability of polygenic risk scores with high discrimi- prolonging common variants (eg, PRSQT) is associated native capacity. The discriminative capacity of a PRS with higher susceptibility for LQTS but is not associated based on QT modulating SNPs is expected to improve with disease severity within LQTS probands. Further as knowledge concerning variants that modulate the studies are needed to assess the role of polygenic risk QT-interval become known, for example through larg- within LQTS families. er GWAS studies, or by combining it with nongenetic modifiers. In a recent study, a PRS based on 61 QT-SNPs (a subset of the 68 QT modulating SNPs included in ARTICLE INFORMATION Received January 23, 2020; accepted June 22, 2020. the PRSQT used herein) explained a substantial propor- The Data Supplement, podcast, and transcript are available with this article tion of QT-interval response to QT-prolonging drugs in at https://www.ahajournals.org/doi/suppl/10.1161/circulationaha.120.045956. a trial of 3 QT-prolonging drugs conducted in healthy individuals, as well as risk of torsade de pointes in a Authors case–control study.43 This provides further support to a Najim Lahrouchi, MD; Rafik Tadros, MD, PhD; Lia Crotti, MD, PhD; Yuka liability threshold model whereby multiple factors, ge- Mizusawa, MD, PhD; Pieter G. Postema, MD, PhD; Leander Beekman, BS; Roddy Walsh, PhD; Kanae Hasegawa, MD, PhD; Julien Barc, PhD; Marko netic and nongenetic, impact on cardiac repolarization Ernsting, PhD; Kari L. Turkowski, BS; Andrea Mazzanti, MD, PhD; Britt M. and determine arrhythmic risk. In this respect, calcula- Beckmann, MD; Keiko Shimamoto, MD; Ulla-Britt Diamant, MD, PhD; Yanushi tion of PRSQT for the purpose of preventive avoidance D. Wijeyeratne, MD; Yu Kucho, MD; Tomas Robyns, MD, PhD; Taisuke Ishikawa, PhD; Elena Arbelo, MD, PhD; Michael Christiansen, MD; Annika Winbo, MD, of QT-prolonging drugs may be desirable for relatives of PhD; Reza Jabbari, MD, PhD; Steven A. Lubitz, MD, MPH; Johannes Steinfurt, genotype negative LQTS. It is clear that further studies MD; Boris Rudic, MD; Bart Loeys, MD, PhD; M. Ben Shoemaker, MD; Peter E. are needed to address how testing for polygenic sus- Weeke, MD, PhD; Ryan Pfeiffer, BS; Brianna Davies, MS; Antoine Andorin, MD; Nynke Hofman, PhD; Federica Dagradi, MD; Matteo Pedrazzini, PhD; David ceptibility may become clinically useful. J. Tester, BS; J. Martijn Bos, MD, PhD; Georgia Sarquella-Brugada, MD, PhD; Óscar Campuzano, PhD; Pyotr G. Platonov, MD, PhD; Birgit Stallmeyer, MD; Sven Zumhagen, MD; Eline A. Nannenberg, MD, PhD; Jan H. Veldink, MD, PhD; Study Limitations Leonard H. van den Berg, MD, PhD; Ammar Al-Chalabi, MD, PhD; Christopher E. Shaw, MD, PhD; Pamela J. Shaw, MD; Karen E. Morrison, MD, PhD; Peter Although in genotype negative patients with LQTS we M. Andersen, MD; Martina Müller-Nurasyid, PhD; Daniele Cusi, PhD; Cristina performed sequencing of the coding region of nonsyn- Barlassina, PhD; Pilar Galan, MD, PhD; Mark Lathrop, PhD; Markus Munter, dromic LQTS genes, this may have missed copy number PhD; Thomas Werge, PhD; Marta Ribasés, PhD; Tin Aung, MD, PhD; Chiea C. Khor, MD, PhD; Mineo Ozaki, MD, PhD; Peter Lichtner, PhD; Thomas Meitinger, variation or disease-causing variants in the noncoding MD; J. Peter van Tintelen, MD, PhD; Yvonne Hoedemaekers, MD, PhD; Isabelle region44 of established genes as well as mutations in Denjoy, MD; Antoine Leenhardt, MD; Carlo Napolitano, MD, PhD; Wataru yet unknown disease genes. This may have blunted the Shimizu, MD, PhD; Jean-Jacques Schott, PhD; Jean-Baptiste Gourraud, MD, PhD; Takeru Makiyama, MD, PhD; Seiko Ohno, MD, PhD; Hideki Itoh, MD, PhD; differences between genotype negative and genotype Andrew D. Krahn, MD; Charles Antzelevitch, PhD; Dan M. Roden, MD, PhD; positive patients and thus would not affect the study Johan Saenen, MD, PhD; Martin Borggrefe, MD; Katja E. Odening, MD; Patrick conclusions. Despite being the largest international da- T. Ellinor, MD, PhD; Jacob Tfelt-Hansen, MD; Jonathan R. Skinner, MD; Maarten P. van den Berg, MD, PhD; Morten Salling Olesen, PhD; Josep Brugada, MD, taset of unrelated patients with LQTS published to date, PhD; Ramón Brugada, MD, PhD; Naomasa Makita, MD, PhD; Jeroen Breckpot, the study had limited statistical power to detect lower MD, PhD; Masao Yoshinaga, MD, PhD; Elijah R. Behr, MD; Annika Rydberg, MD, PhD; Takeshi Aiba, MD, PhD; Stefan Kääb, MD, PhD; Silvia G. Priori, MD, effect associations at GWAS significance threshold. PhD; Pascale Guicheney, PhD; Hanno L. Tan, MD, PhD; Christopher Newton- The prespecified design of meta-analyzing European Cheh, MD; Michael J. Ackerman, MD, PhD; Peter J. Schwartz, MD; Eric Schulze- and Japanese GWAS may also miss disease loci with Bahr, MD; Vincent Probst, MD, PhD; Minoru Horie, MD, PhD; Arthur A. Wilde, MD, PhD; Michael W.T. Tanck, PhD; Connie R. Bezzina, PhD differences in haplotype structure among European and East Asian chromosomes. Nonetheless, GWAS in Correspondence separate ancestries did not detect any association at Connie R. Bezzina, PhD, Amsterdam UMC, AMC Heart Center, Department of GWAS threshold. Last, studies in larger patient sets are Clinical and Experimental Cardiology, Meibergdreef 9, 1105 AZ Amsterdam, required to further refine our understanding of the ge- The Netherlands. Email c.r.bezzina@amsterdamumc.nl netic architecture underlying LQTS in genotype nega- tive patients. Affiliations Amsterdam UMC, University of Amsterdam, Heart Center; Department of Clini- cal and Experimental Cardiology, Amsterdam Cardiovascular Sciences, The Neth- Conclusions erlands (N.L., R.T., Y.M., P.G.P., L.B., R.W., N.H., H.L.T., A.A.W., C.R.B.). Member of the European Reference Network for Rare, Low Prevalence, and Complex Dis- This work establishes an important role for common eases of the Heart - ERN GUARD-Heart (N.L., L.C., Y.M., P.G.P., L.B., R.W., J.B., genetic variation in susceptibility to LQTS. Common M.E., A.M., U.-B.D., Y.D.W., T.R., R.J., N.H., F.D., G.S.-B., I.D., A.L., C.N., J.-J.S., 334 July 28, 2020 Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome J.-B.G., J.T.-H., J.B., E.R.B., A.R., S.G.P., H.L.T., P.J.S., E.S.-B., V.P., A.A.W., C.R.B.). Hospital Southampton, United Kingdom (K.E.M.). Department of Neurology, Ulm Cardiovascular Genetics Center, Montreal Heart Institute and Faculty of Medicine, University, Germany (P.M.A.). Department of Pharmacology and Clinical Neurosci- ORIGINAL RESEARCH Université de Montréal, Canada (R.T.). Center for Cardiac Arrhythmias of Genetic ence, Umeå University, Sweden (P.M.A.). Institute of Genetic Epidemiology, Helm- Origin (L.C., F.D., P.J.S.), Laboratory of Cardiovascular Genetics (L.C., M.P., P.J.S.), holtz Zentrum München–German Research Center for Environmental Health, ARTICLE and Department of Cardiovascular, Neural and Metabolic Sciences, San Luca Hos- Neuherberg, Germany (M.M.-N.). Chair of Genetic Epidemiology, IBE, Faculty of pital (L.C.), Istituto Auxologico Italiano, IRCCS, Milan, Italy. Department of Medi- Medicine, LMU Munich, Germany (M.M.-N.). Department of Health Sciences, cine and Surgery, University of Milano-Bicocca, Milan, Italy (L.C.). Department of University of Milan, Italy (D.C., C.B.). Bio4Dreams - Business Nursery for Life Sci- Cardiovascular Medicine, Shiga University of Medical Science, Otsu, Japan (K.H., ences, Milan, Italy (D.C., C.B.). Equipe de Recherche en Epidémiologie Nutrition- S.O., H.I., M.H.). Department of Cardiovascular Medicine, Faculty of Medical Sci- nelle, Centre d’Epidémiologie et Statistiques Paris Cité, Université Paris 13, Inserm ences, University of Fukui, Japan (K.H.). L’Institut du Thorax, INSERM, CNRS, UNIV (U1153), Inra (U1125), COMUE Sorbonne-Paris-Cité, Bobigny, France (P.G.). Mc- Nantes, France (J.B., J.-J.S., J.-B.G., V.P.). Institute for Genetics of Heart Diseases, Gill University and Génome Québec Innovation Centre, Montréal, Canada (M.L., Department of Cardiovascular Medicine, University Hospital Muenster, Germany M.M.). The Lundbeck Foundation Initiative for Integrative Psychiatric Research, (M.E., B.S., S.Z., E.S.-B.). Departments of Cardiovascular Medicine (Division of iPSYCH, Copenhagen, Denmark (T.W.). Institute of Biological Psychiatry, Mental Heart Rhythm Services and the Windland Smith Rice Genetic Heart Rhythm Clin- Health Centre Sct Hans, Copenhagen University Hospital, Roskilde, Denmark ic), Pediatric and Adolescent Medicine (Division of Pediatric Cardiology), and Mo- (T.W.). Department of Clinical Medicine, University of Copenhagen, Denmark lecular Pharmacology & Experimental Therapeutics (Windland Smith Rice Sudden (T.W.). Psychiatric Genetics Unit, Institute Vall d’Hebron Research (VHIR), Universi- Death Genomics Laboratory), Mayo Clinic, Rochester, MN (K.L.T., D.J.T., J.M.B., tat Autònoma de Barcelona, Spain (M.R.). Singapore Eye Research Institute (T.A.). M.J.A.). Molecular Cardiology, ICS Maugeri, IRCCS and Department of Molecular Genome Institute of Singapore (C.C.K.). Ozaki Eye Hospital, Miyazaki, Japan Medicine, University of Pavia, Italy (A.M., C.N., S.G.P.). Department of Internal (M.O.). Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Medicine I, University Hospital of the Ludwig Maximilians University, Munich, Germany (P.L., T.M.). Department of Clinical Genetics, University Medical Centre Germany (B.M.B., M.M.-N., S.K.). Department of Cardiovascular Medicine, Na- Groningen, The Netherlands (J.P.v.T., Y.H.). Department of Clinical Genetics, Uni- tional Cerebral and Cardiovascular Center, Osaka, Japan (K.S., W.S., T.A.). De- versity Medical Centre Utrecht, University of Utrecht, The Netherlands (J.P.v.T.). partment of Clinical Sciences, Unit of Paediatrics, Umeå University, Sweden (U.- AP-HP, Hôpital Bichat, Département de Cardiologie et Centre de Référence des B.D., A.R.). Molecular and Clinical Sciences Research Institute, St George’s Maladies Cardiaques Héréditaires, F-75018 Paris, France, Université de Paris IN- University of London and Cardiology Clinical Academic Group, St George’s Uni- SERM U1166, F-75013 France (I.D., A.L.). Department of Cardiovascular Medi- versity Hospitals NHS Foundation Trust, United Kingdom (Y.D.W., A.A., E.R.B.). cine, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan (W.S., National Hospital Organization Kagoshima Medical Center, Japan (Y.K., M.Y.). V.P.). Department of Cardiovascular Medicine, Kyoto University Graduate School Department of Cardiovascular Diseases, University Hospitals Leuven, Belgium of Medicine, Japan (T.M.). Center for Epidemiologic Research in Asia, Shiga Uni- (T.R.). Department of Cardiovascular Sciences, KU Leuven, Belgium (T.R.). Omics versity of Medical Science, Otsu, Japan (S.O., H.I., M.H.). Department of Biosci- Research Center, National Cerebral and Cardiovascular Center, Osaka, Japan (T.I.). ence and Genetics, National Cerebral and Cardiovascular Center, Suita, Japan Cardiovascular Institute, Hospital Clinic de Barcelona, Universitat de Barcelona, (S.O.). Lankenau Institute for Medical Research and Lankenau Heart Institute, Institut d’Investigació August Pi i Sunyer (IDIBAPS), and Centro de Investigacion Wynnewood, PA (C.A.). Sidney Kimmel Medical College, Thomas Jefferson Uni- Biomedica en Red de Enfermedades Cardiovasculares (CIBERCV), Spain (E.A.). versity, Philadelphia, PA (C.A.). Department of Cardiology, Antwerp University Department of Congenital Disorders, Statens Serum Institute, Copenhagen, Den- Hospital, Belgium (J.S.). Department of Forensic Medicine, Faculty of Medical Sci- mark (M.C.). The Lundbeck Foundation Initiative for Integrative Psychiatric Re- ences, University of Copenhagen, Denmark (J.T.-H.). Cardiac Inherited Disease search, iPSYCH, Copenhagen, Denmark (M.C.). Laboratory of Experimental Car- Group, Starship Children’s Hospital, Auckland, New Zealand (J.R.S.). Department diology, Department of Biomedical Sciences, University of Copenhagen, Denmark of Cardiology, University Medical Center Groningen, University of Groningen, The (M.C.). Department of Physiology, The University of Auckland, New Zealand Netherlands (M.P.v.d.B.). Laboratory for Molecular Cardiology, Department of (A.W.). The Department of Cardiology, The Heart Centre, Copenhagen University Cardiology, The Heart Centre, Rigshospitalet (Copenhagen University Hospital), Hospital, Rigshospitalet, Denmark (R.J., P.E.W., J.T.-H.). Cardiac Arrhythmia Ser- Denmark (M.S.O.). Department of Biomedical Sciences, University of Copenha- vice, Massachusetts General Hospital, Boston (S.A.L., P.T.E.). Cardiovascular Dis- gen, Denmark (M.S.O.). Arrhythmia Unit, Hospital Sant Joan de Déu, Institut ease Initiative and Program in Medical and Population Genetics, Broad Institute, d’Investigació August Pi i Sunyer (IDIBAPS), Cardiovascular Institute, and Hospital Cambridge, MA (S.A.L., P.T.E.). Department of Cardiology and Angiology I, Heart Clinic de Barcelona, Universitat de Barcelona, Spain (J.B.). Cardiovascular Genet- Center University of Freiburg, Medical Faculty, Germany (J.S., K.E.O.). Depart- ics Center, University of Girona-IDIBGI, and Medical Science Department, School ment of Medicine, University Medical Center Mannheim, and German Center for of Medicine, University of Girona, Spain (R.B.). Cardiology Service, Hospital Josep Cardiovascular Research (DZHK), Partner Site Heidelberg/Mannheim, Germany Trueta, Girona, Spain (R.B.). National Cerebral and Cardiovascular Center Re- (B.R., M.B.). Department of Clinical Genetics, Antwerp University Hospital, Bel- search Institute, Osaka, Japan (N.M.). Centre for Human Genetics, University Hos- gium (B.L.). Department of Biomedical Informatics (D.M.R.), Department of Med- pitals Leuven, Belgium (J.B.). INSERM, Sorbonne University, UMRS 1166, Institute icine (M.B.S., P.E.W., D.M.R.), and Department of Pharmacology (D.M.R.), of Cardiometabolism and Nutrition (ICAN), Paris, France (P.G.). Netherlands Heart Vanderbilt University Medical Center, Nashville, TN. Masonic Medical Research Institute, Utrecht (H.L.T.). Cardiovascular Research Center and Center for Genom- Institute, Utica, NY (R.P.). Heart Rhythm Services, Division of Cardiology, Depart- ic Medicine, Massachusetts General Hospital, Boston (C.N.-C.). Department of ment of Medicine, University of British Columbia, Vancouver, Canada (B.D., Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam UMC, Univer- A.D.K.). L’Institut du Thorax, CHU Nantes, Service de Cardiologie, France (A.A., sity of Amsterdam, The Netherlands (M.W.T.T.). J.-J.S., J.-B.G.). Arrhythmia, Inherited Heart Disease and Sudden Death Unit, Hos- pital Sant Joan de Déu, European Reference Center at the ERN GUARD-Heart Reference Network for Rare Cardiac Diseases, Barcelona, Spain (G.S.-B.). Medical Acknowledgments Science Department, School of Medicine, University of Girona, Spain (G.S.-B.). The authors thank Estelle Baron, Simon Lecointe, Annabelle Rajalu, Aurélie Thol- Cardiovascular Program, Research Institute of Sant Joan de Déu (IRSJD), Barcelo- let, and Florence Kyndt for their help in DNA sample preparation and regulatory na, Spain (G.S.-B., O.C.). Center for Biomedical Diagnosis, Hospital Clinic de Bar- processes at the Nantes center, the Nantes biological resource center for biobank- celona, Universitat de Barcelona; Institut d’Investigació August Pi i Sunyer ing (CHU Nantes, Hôtel Dieu, Center de Resources Biologiques, Nantes, F-44093, (IDIBAPS); Cardiovascular Genetics Center, University of Girona-IDIBGI; and Med- France [BRIF: BB-0033-00040]), and the National Referral Center for Inherited ical Science Department, School of Medicine, University of Girona, Spain (O.C., Cardiac Arrhythmias of Nantes and its associated competence centers. We thank R.B.). Centro de Investigación Biomédica en Red de Enfermedades Cardiovascu- Professor Pier D. Lambiase and Professor Patricia B. Munroe for sharing summary lares (CIBERCV), Madrid, Spain (O.C.). Center for Integrative Electrocardiology statistics. We thank Dr Nicky Whiffin for help with using CardioClassifier. The (CIEL), Department of Cardiology, Clinical Sciences, Lund University, Sweden KORA research platform (KORA, Cooperative Research in the Region of Augs- (P.G.P.). Department of Clinical Genetics, Amsterdam UMC, University of Amster- burg) was initiated and financed by the Helmholtz Zentrum München–German dam, The Netherlands (E.A.N., J.P.v.T.). Department of Neurology, UMC Utrecht Research Center for Environmental Health, which is funded by the German Fed- Brain Center, University Medical Center Utrecht, The Netherlands (J.H.V., eral Ministry of Education and Research and by the State of Bavaria. Furthermore, L.H.v.d.B.). King’s College Hospital, Bessemer Road, London, United Kingdom KORA research was supported within the Munich Center of Health Sciences (MC (A.A.-C.). Department of Basic and Clinical Neuroscience, King’s College London, Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. This study Maurice Wohl Clinical Neuroscience Institute, United Kingdom (A.A.-C., C.E.S.). makes use of data generated by the Wellcome Trust Case-Control Consortium. UK Dementia Research Institute, King’s College London, United Kingdom (C.E.S.). A full list of the investigators who contributed to the generation of the data are Sheffield Institute for Translational Neuroscience, University of Sheffield, United available from www.wtccc.org.uk. Funding for the project was provided by the Kingdom (P.J.S.). Faculty of Medicine, University of Southampton, University Wellcome Trust under award 076113, 085475, and 090355. Circulation. 2020;142:324–338. DOI: 10.1161/CIRCULATIONAHA.120.045956 July 28, 2020 335 Lahrouchi et al Genome-Wide Association Study in Long QT Syndrome Sources of Funding Supplemental Materials ORIGINAL RESEARCH Najim Lahrouchi was supported by the Kenneth M. Rosen Fellowship in Car- Methods diac Pacing and Electrophysiology Scholarship of the Heart Rhythm Society, Data Supplement Figures I–XII the CVON-PREDICT Young Talent Program of the Dutch Heart Foundation, Data Supplement Tables I–XIII ARTICLE and the SADS Foundation Courts K. Cleveland Jr. Young Investigator Award in References 45–57 Cardiac Channelopathy Research. Drs Bezzina, Lahrouchi, Wilde, van Tintelen, and Tan acknowledge the support from the Dutch Heart Foundation (CVON 2018-30 PREDICT2 project to Drs Bezzina, Tan, and Wilde) and the Netherlands REFERENCES Organization for Scientific Research (VICI fellowship, 016.150.610, to Dr Bez- zina). Dr Tan has received funding from the European Union’s Horizon 2020 1. Schwartz PJ, Stramba-Badiale M, Crotti L, Pedrazzini M, Besana A, Bosi G, research and innovation program under acronym ESCAPE-NET (The European Gabbarini F, Goulene K, Insolia R, Mannarino S, et al. 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The Cardiac Inherited Disease Reg- 10. Crotti L, Monti MC, Insolia R, Peljto A, Goosen A, Brink PA, istry has been supported by Cure Kids. AW is supported by the Hugh Green Greenberg DA, Schwartz PJ, George AL Jr. NOS1AP is a genetic modi- Foundation. Dr Ellinor is supported by the Fondation Leducq (14CVD01), by fier of the long-QT syndrome. Circulation. 2009;120:1657–1663. doi: 10.1161/CIRCULATIONAHA.109.879643 grants from the National Institutes of Health (1RO1HL092577, R01HL128914, 11. Tomás M, Napolitano C, De Giuli L, Bloise R, Subirana I, Malovini A, K24HL105780) and the American Heart Association (18SFRN34110082). SAL Bellazzi R, Arking DE, Marban E, Chakravarti A, et al. Polymorphisms in is supported by National Institutes for Health grant 1R01HL139731 and Ameri- the NOS1AP gene modulate QT interval duration and risk of arrhythmias can Heart Association 18SFRN34250007. Dr Loeys is supported by a European in the long QT syndrome. J Am Coll Cardiol. 2010;55:2745–2752. doi: Research Council consolidator grant. Dr Roden is supported by P50 GM115305 10.1016/j.jacc.2009.12.065 and MBS by K23 HL127704. Dr Antzelevitch is supported by grants from the 12. Duchatelet S, Crotti L, Peat RA, Denjoy I, Itoh H, Berthet M, Ohno S, National Institutes for Health, HL47678 and HL138103. Dr Postema is support- Fressart V, Monti MC, Crocamo C, et al. Identification of a KCNQ1 poly- ed by The Swedish Heart-Lung Foundation, grant #20180444. Drs Turkowski, morphism acting as a protective modifier against arrhythmic risk in Tester, Bos, and Ackerman were supported by the Mayo Clinic Windland Smith long-QT syndrome. Circ Cardiovasc Genet. 2013;6:354–361. doi: Rice Comprehensive Sudden Cardiac Death Program. 10.1161/CIRCGENETICS.113.000023 13. Kolder ICRM, Tanck MWT, Postema PG, Barc J, Sinner MF, Zumhagen Disclosures S, Husemann A, Stallmeyer B, Koopmann TT, Hofman N, et al. Analysis for genetic modifiers of disease severity in patients with long-QT syn- Dr Behr received previous research funds from Biotronik and consulting for drome type 2. Circ Cardiovasc Genet. 2015;8:447–456. doi: 10.1161/ Medtronic. Dr Arbelo received speaking fees from Biosense Webster. Dr Lub- CIRCGENETICS.114.000785 itz receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer 14. Schwartz PJ, Crotti L, George AL. Modifier genes for sudden cardiac AG, and Boehringer Ingelheim, and has consulted for Bristol Myers Squibb/Pfiz- death. Eur Heart J 2018;39:3925–3931. doi:10.1093/eurheartj/ehy502. er and Bayer AG. Dr Weeke is supported by a grant from Bayer AG to the Broad 15. Ackerman MJ, Priori SG, Willems S, Berul C, Brugada R, Calkins H, Institute focused on the genetics and therapeutics of cardiovascular diseases. Camm AJ, Ellinor PT, Gollob M, Hamilton R, et al; Heart Rhythm Soci- Dr Weeke has also served on advisory boards or consulted for Bayer AG, Quest ety (HRS); European Heart Rhythm Association (EHRA). HRS/EHRA ex- Diagnostics, and Novartis. Dr Cusi is a scientific consultant for Bio4Dreams. Dr pert consensus statement on the state of genetic testing for the chan- Ackerman is a consultant for Audentes Therapeutics, Boston Scientific, Gilead nelopathies and cardiomyopathies: this document was developed as a Sciences, Invitae, Medtronic, Myokardia, and St. Jude Medical. Dr Ackerman partnership between the Heart Rhythm Society (HRS) and the European and Mayo Clinic have an equity/royalty-based licensing agreement with Alive- Heart Rhythm Association (EHRA). Europace. 2011;13:1077–1109. doi: Cor. The other authors report no conflicts. 10.1093/europace/eur245 336 July 28, 2020 Circulation. 2020;142:324–338. 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