Skip to content

Advertisement

  • Research
  • Open Access

Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6

  • †1, 2,
  • †3,
  • 4,
  • 5,
  • 6, 7,
  • 8,
  • 9,
  • 10,
  • 11,
  • 6, 12,
  • 13,
  • 13,
  • 13,
  • 13,
  • 10,
  • 14,
  • 15, 16,
  • 17,
  • 18, 19,
  • 20,
  • 21,
  • 22,
  • 23, 24,
  • 25,
  • 26,
  • 27, 28, 29,
  • 30, 31,
  • 32,
  • 33, 34,
  • 6, 7,
  • 11,
  • 13,
  • 35,
  • 36, 37,
  • 34, 38, 39,
  • 6, 7,
  • 40, 41,
  • 42,
  • 5, 43,
  • 44,
  • 38,
  • 45,
  • 46,
  • 47,
  • 48, 49,
  • 41, 50,
  • 51,
  • 25,
  • 52,
  • 47,
  • 53, 54, 55,
  • 56, 57,
  • 58,
  • 12, 31,
  • 51,
  • 59,
  • 60, 61, 62,
  • 15, 16,
  • 10,
  • 63,
  • 29, 64, 65,
  • 66,
  • 67, 68,
  • 69, 70,
  • 71,
  • 72,
  • 28, 29,
  • 73,
  • 74,
  • 27, 75,
  • 5, 76,
  • 6, 7,
  • 53, 77,
  • 78,
  • 44,
  • 38,
  • 40, 41,
  • 41, 79,
  • 29, 64, 80,
  • 81,
  • 82,
  • 35, 83, 84, 85,
  • 41, 50,
  • 19,
  • 86, 87,
  • 5, 88,
  • 36, 37,
  • 10,
  • 28, 29,
  • 89,
  • 14,
  • 23, 24,
  • 48, 90,
  • 49,
  • 20, 91,
  • 92,
  • 34, 93,
  • 33, 34,
  • 94,
  • 15, 16,
  • 17,
  • 95,
  • 5,
  • 96,
  • 11,
  • 97,
  • 3,
  • 38, 83, 98,
  • 5, 76,
  • 6, 7,
  • 99,
  • 13 and
  • 1, 100Email authorView ORCID ID profile
†Contributed equally
Genome Biology201819:87

https://doi.org/10.1186/s13059-018-1457-6

©  The Author(s). 2018

  • Received: 2 March 2018
  • Accepted: 23 May 2018
  • Published:

Abstract

Background

Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear.

Results

Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction.

Conclusions

Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes.

Keywords

  • Exome chip
  • Conduction
  • ADAMTS6
  • Meta-analysis

Background

In the heart, the ventricular conduction system propagates the electrical impulses that coordinate ventricular chamber contraction. The QRS interval on an electrocardiogram (ECG) is used clinically to quantify duration of ventricular depolarization in the heart. Prolonged QRS duration is an independent predictor of mortality in both the general population [1–4] and in patients with cardiac disease [5–10].

QRS interval duration is a quantitative trait influenced by multiple genetic and environmental factors and is known to be influenced by both age and gender [11, 12]. The heritability of QRS duration is estimated to be 35–55% from twin and family studies [13–16].

We previously performed a genome-wide association meta-analysis in 40,407 individuals and identified 22 genetic loci associated with QRS duration [17]. The QRS-associated loci highlighted novel biological processes such as kinase inhibitors, but also pointed to genes with established roles in ventricular conduction such as sodium channels, transcription factors, and calcium-handling proteins. However, the common risk variants identified in genome-wide association studies (GWAS) reside overwhelmingly in regulatory regions, making inference of the underlying causative genes difficult. Furthermore, as with most complex traits, the variants discovered to date explain only a small proportion of the total heritability (the “missing heritability” paradigm), suggesting additional variants are yet to be identified. In fact, the role of rare and low frequency variants, which cannot currently be detected using standard genome-wide single nucleotide polymorphism (SNP) chip arrays, have not been fully investigated. Here we used the Illumina HumanExome BeadChip to focus on rare (MAF < 1%), low frequency (MAF = 1–5%), and common (MAF ≥ 5%) putative functional coding variation associated with changes in ventricular depolarization.

Results and Discussion

We combined genotype data from 77,898 participants of European ancestry and 7695 of African descent participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Exome-Chip EKG consortium (Additional file 1: Table S1). A total of 228,164 polymorphic markers on the exome-chip array passed quality control and were used as a basis for our analyses. Through single variant analysis in the combined European and African datasets, we identified 34 variants across 28 loci associated with QRS duration that passed the exome-chip-wide significance threshold (P < 6.17 × 10−8 for single variants [Table 1, Additional file 2: Figure S1]). Eight of the identified loci were novel and five of these were driven by low frequency (MAF < 5%) and common (MAF ≥ 5%) non-synonymous coding variation. We confirmed 20 of the 29 previously identified QRS duration loci [14, 17–19], the remaining loci were not covered by the Exome-Chip and/or did not pass quality control (QC) (Additional file 1: Table S2). As might be anticipated when combining two ancestries in association analyses, we detected heterogeneity of effects for one variant (Cochran’s heterogeneity P < 1.47 × 10−3, a Bonferroni corrected P value of α=0.05/34 variants), Additional file 1: Table S2). We did not observe evidence for inflation of test statistics for any of the analyses (λGC = 1.049, European and African ancestries, combined, Additional file 2: Figure S2, individual ancestry results, Additional file 2: Figures S3–S6). We next sought to replicate the 34 lead variants of our 28 loci in a replication meta-analysis of 111,874 individuals from the UK Biobank [20] and deCODE genetics [21] cohorts. In the replication meta-analysis, 30 lead variants for 25 loci replicated (P ≤ 1.47 × 10−3 = 0.05/34 variants), seven of which were novel, ten of which are known (Additional file 1: Table S2). The remaining four variants that did not replicate in UK Biobank encompass two previously established loci (one in locus SCN5A/SCN10A for which the other five variants replicated) and two novel loci (SENP2, IGF1R). This is likely due to differences in phenotype acquisition methods (UK Biobank having exercise ECGs measured), though effect size directions between discovery and replication remained consistent and P values of non-replicating variants were all below nominal significance (P < 0.05).
Table 1

Lead SNPs for 28 loci identified for QRS duration in a combined European and African American ancestry meta-analysis

Locus

Band

dbSNPID

A1/A2

cMAF

beta(se)

P

n

Nearest gene

Annotation

Novel loci

1

2q31.2

rs17362588

A/G

0.081

0.52 (0.08)

4.20 × 10−11

85,593

CCDC141

Non-synonymous

2

3p22.2

rs116202356

A/G

0.015

− 1.63 (0.17)

1.23 × 10−20

85,593

DLEC1

Non-synonymous

3

3q27.2

rs6762208

A/C

0.357

− 0.31 (0.05)

3.45 × 10−12

85,593

SENP2

Non-synonymous

4

6q22.32

rs4549631

C/T

0.481

0.28 (0.04)

5.56 × 10−11

85,593

PRELID1P1

Intergenic

5

8q24.13

rs16898691

G/C

0.040

− 0.92 (0.11)

5.71 × 10−16

79,976

KLHL38

Non-synonymous

6

12q13.3

rs2926743

A/G

0.257

− 0.32 (0.05)

9.40 × 10−11

85,593

NACA

Non-synonymous

7

15q26.3

rs4966020

G/A

0.387

− 0.27 (0.04)

2.99 × 10−9

85,593

IGF1R

Intronic

8

20p12.3

rs961253

A/C

0.357

0.30 (0.04)

1.20 × 10−11

85,593

CASC20

Intergenic

Previously identified loci

9

1p32.3

rs11588271

A/G

0.333

− 0.34 (0.05)

7.59 × 10−14

85,593

CDKN2C

Intergenic

10

1p13.1

rs4074536

C/T

0.305

− 0.29 (0.05)

8.27 × 10−10

85,593

CASQ2

Non-synonymous

11

2p22.2

rs7562790

G/T

0.424

0.37 (0.04)

4.34 × 10−17

85,593

CRIM1

Intronic

12

2p22.2

rs17020136

C/T

0.185

0.38 (0.07)

1.02 × 10−8

59,876

HEATR5B

Intronic

13

3p22.2

rs6795970

A/G

0.371

0.80 (0.05)

9.19 × 10−70

85,593

SCN10A

Non-synonymous

14

3p21.1

rs4687718

A/G

0.164

− 0.36 (0.06)

1.19 × 10−8

83,134

TKT

Intronic

15

5q33.2

rs13165478

A/G

0.377

− 0.68 (0.04)

6.74 × 10−52

85,593

HAND1

Intergenic

16

6p21.2

rs9470361

A/G

0.249

0.84 (0.05)

1.21 × 10−63

85,593

CDKN1A

Intergenic

17

6q22.31

rs11153730

C/T

0.475

0.56 (0.04)

1.99 × 10−38

85,593

SLC35F1

Intergenic

18

7p14.2

rs1362212

A/G

0.144

0.55 (0.06)

1.22 × 10−18

85,593

TBX20

Intergenic

19

7p12.3

rs7784776

G/A

0.397

0.27 (0.04)

1.18 × 10−9

85,593

IGFBP3

Intergenic

20

7q31.2

rs3807989

A/G

0.427

0.40 (0.04)

2.14 × 10−19

85,593

CAV1

Intronic

21

12q24.21

rs3825214

G/A

0.200

0.46 (0.05)

1.10 × 10−17

85,593

TBX5

Intronic

22

12q24.21

rs7966651

T/C

0.270

− 0.38 (0.05)

6.74 × 10−15

85,593

TBX3

Intergenic

23

13q22.1

rs1886512

A/T

0.380

− 0.36 (0.05)

3.17 × 10−13

70,887

KLF12

Intronic

24

14q24.2

rs11848785

G/A

0.237

− 0.44 (0.05)

5.59 × 10−18

85,593

SIPA1L1

Intronic

25

17q21.32

rs17608766

C/T

0.127

0.70 (0.07)

9.81 × 10−27

85,593

GOSR2

UTR3

26

17q24.2

rs9912468

G/C

0.416

0.43 (0.05)

2.34 × 10−21

79,976

PRKCA

Intronic

27

18q12.3

rs663651

G/A

0.446

− 0.44 (0.05)

6.59 × 10−18

61,604

SETBP1

Non-synonymous

28

20q11.22

rs3746435

C/G

0.190

− 0.36 (0.06)

2.67 × 10−10

79,976

MYH7B

Non-synonymous

Top panel: novel loci; bottom panel: previously identified loci

Locus index number for each independent locus, Band cytogenetic band in which the lead SNP for the locus resides, dbSNPID dbSNP rs-number of the lead SNP of the locus, A1/A2 coded/non-coded alleles, cMAF cumulative minor allele frequency, beta(se) effect size (standard error) in ms, P P value, n total number of individuals analyzed for this variant, Nearest gene (nearest) gene, Annotation variant function (protein coding)

Sex-specific associations with QRS duration

Sex differences in QRS duration are well established (men have significantly longer QRS durations than women [22, 23]), and might be attributable to differential effects of genetic variation in men and women. Therefore, we performed sex-stratified association analyses (Additional file 1: Table S3, Additional file 2: Figures S7 and S8). We included only those studies that had both male and female participants to mitigate potential bias due to contributions from single-sex cohorts. In total, up to 31,702 men and 39,907 women were included from both European and African ancestry studies. We found suggestive evidence for a sex-specific locus that was not identified in the combined analysis. The non-synonymous variant rs17265513 (p.Asn310Ser) in ZHX3 (zinc fingers and homeoboxes 3) showed a significant association only in men (Pmale = 4.89 × 10−8, β(SE) = − 0.52(0.09)), whereas no effect was observed for women (Pfemale = 0.86, β(SE) = − 0.01(0.08)); however, there was no significant difference consistent with an interaction with sex (P = 2.3 × 10−5). Additionally, no further evidence was observed in the replication analyses alone (Pmale = 7.95 × 10−4, β(SE) = − 0.30(0.09), Nmales = 50,457), (Pfemale = 3.55 × 10−2, β(SE) = − 0.17(0.08), Nfemales = 61,417).

Association of coding and non-coding variants with QRS duration

Among the eight newly identified loci in the sex-combined analysis, five had lead variants that were non-synonymous: CCDC141 (Coiled-Coil Domain Containing 141); KLHL38 (Kelch Like Family Member 38); DLEC1 (Deleted in Lung and Esophageal Cancer 1); NACA (Nascent Polypeptide-Associated Complex Alpha subunit); and SENP2 (SUMO1/Sentrin/SMT3 Specific Protease 2). Suggestive evidence for association of the same non-synonymous variant in CCDC141 (rs17362588; P = 4.75 × 10−7) and an intronic variant in KLHL38 (rs11991744; P = 1.25 × 10−7) with QRS duration was shown in two earlier GWAS [24, 25]. DLEC1 has recently been suggested to have a possible role as a tumor suppressor [26], and while specific roles for KLHL38 and CCDC141 (a centrosome associated protein) have not yet been elucidated, they show the highest expression in skeletal and/or cardiac tissue, respectively, among the tissues examined in the Genotype-Tissue Expression (GTEx) Portal database (http://www.gtexportal.org) [27]. Two of the novel loci, NACA and SENP2, have established roles in cardiac development and dysfunction. NACA produces the isoform skNAC (skeletal NACA) and acts as a skeletal muscle- and heart-specific transcription factor and is critical for ventricular cardiomyocyte expansion [28]. Cardiac-specific knockdown of skNAC in a Drosophila Hand4.2-Gal4 driver cell-line results in severe cardiac defects [19]. Cardiac-specific overexpression of SENP2, a SUMO-specific protease, leads to congenital heart defects and cardiac dysfunction [29].

In the sex-stratified analysis, the association with ZHX3 (Zinc Fingers and Homeoboxes 3) was also driven by an amino acid changing variant. ZHX3 encodes a transcriptional repressor whose functions are largely unknown. However, the sex-specific association might be explained by hormonal changes that have previously been hypothesized to explain a variety of sex-specific differences observed in ECG measures and conduction disorders [30, 31]. A sex-specific association of ZHX3 has also been previously shown for total cholesterol levels (the effect is only significant in men) [32].

We further identified an intronic variant in the IGF1R (Insulin Like Growth Factor 1 Receptor) locus and two intergenic variants: rs4549631 at locus 6q22.32 and rs961253 at locus 20p12.3. Interestingly, when queried against results from the GTEx project portal [27] for blood and eight tissues (including adipose [subcutaneous], artery [aorta, coronary, tibial], heart [atrium, appendage, left ventricle], lung, muscle [skeletal], nerve [tibial], skin [sun exposed], and thyroid), the lead intronic variant in IGF1R (rs4966020; MAF EA/AA 0.36/0.63) is a left ventricle tissue-specific cis-eQTL (P = 2.4 × 10−7). The variant is also in strong linkage disequilibrium with the strongest cis-eQTL for this tissue (rs4966021, P = 5 × 10−8). IGF1R promotes physiological hypertrophy but protects against cardiac fibrosis [33]; the signaling pathways induced by its binding partner, IGF1, regulate contractility, metabolism, hypertrophy, autophagy, senescence, and apoptosis in the heart [34]. The nearest genes for the two intergenic variants are PRELID1P1 (PRELI Domain Containing 1 Pseudogene 1 [locus 6q22.32]) and CASC20 (Cancer Susceptibility Candidate 20 [non-protein-coding]; locus 20p12.3)—the former a pseudogene and the latter a non-protein-coding gene, both with currently uncharacterized function.

Rare ADAMTS6 variants are associated with QRS duration

By collapsing rare variants in genes as functional units and jointly testing these for association, substantial statistical power-gains can be achieved [35]. We, therefore, performed gene-based analyses using both the Sequence Kernel Association Test (SKAT) (Additional file 1: Table S4) and burden test (T1) (Additional file 1: Table S5), because these tests have optimal power under different scenarios. Analyses were restricted to variants with MAF < 1% in a total of 16,085 genes. One gene-based significant association (P < 5.18 × 10−7) was identified in ADAMTS6 (A Disintegrin-Like And Metalloproteinase with Thrombospondin Type 1 Motif 6; PSKAT = 8.18 × 10−8, Table 2), when including only variants classified as damaging (see “Methods”). Four additional genes showed suggestive evidence of association (P < 1 × 10−4) (Table 2).
Table 2

Gene-based test association results (for genes with variants classified as damaging)

Gene

NSNPs

cMAF

beta(se)T1-Burden

PT1-Burden

PSKAT

Protein function

Cardiac-specific involvement

ADAMTS6

12

0.0097

− 0.72 (0.23)

1.48 × 10−3

8.18 × 10−8

Zinc-dependent protease

–

CSRP3

3

0.0048

1.38 (0.31)

9.65 × 10−6

9.10 × 10−6

Regulator of myogenesis

Myocyte cytoarchitecture maintenance

FHOD3

17

0.0171

0.00 (0.17)

9.86 × 10−1

1.82 × 10−5

Actin filament assembly

Myofibril development and repair

ISM1

5

0.0037

1.47 (0.36)

5.05 × 10−5

5.88 × 10−5

Angiogenesis inhibitor

–

TBX5

8

0.0171

− 0.32 (0.17)

5.21 × 10−2

7.80 × 10−5

T-box transcription factor

Cardiac development and cell cycle control

Displayed are the top five genes that have the lowest P values in the SKAT test (for genes with damaging variants)

Gene gene in which variants were collapsed, NSNPs number of variants used in the collapsed variant test, cMAF cumulative minor allele frequency of variants in the test, beta(se)T1burden effect size (standard error) in ms, PT1-Burden P value of T1-burden test, PSKAT P value of SKAT test, Protein function function of the protein encoded by respective gene, Cardiac-specific involvement, literature support for physiological involvement of the protein in the heart

The ADAMTS6 gene-based signal is driven by two rare non-synonymous variants: rs61736454 (p.Ser90Leu) and rs114007286 (p.Arg603Trp), which have allele frequencies of 0.0018 and 0.0021, respectively (Additional file 1: Table S6). Notably, a look-up in the independent deCODE QRS duration analysis showed that rs61736454 was highly significant, however not exome-wide ([P = 2.65 × 10−7, β(SE) = 3.01(0.58)], MAF = 0.002, N = 59,903), and was extremely well imputed (info score = 0.995). Importantly, after meta-analysis with discovery exome summary statistics, the signal reached exome-wide significance ([P = 8.96 × 10−13, β(SE) = 2.75(0.38)], N = 145,496), underscoring the robustness of our initial discovery signal driver. Data for rs114007286 were not available. ADAMTS6 is a highly constrained gene, with a probability of loss of function intolerance score of 1.0 (pLI = 1.0) (Exome Aggregation Consortium [ExAC], Cambridge, MA, USA; http://exac.broadinstitute.org/). The p.Ser90Leu variant lies within the ADAMTS6 propeptide, which is predicted to be important for initiation of folding, because the homologous ADAMTS9 propeptide is an intramolecular chaperone essential for its secretion [36]. The second variant, p.Arg603Trp, is located in the N-terminal-most TSR domain (TSR1) of ADAMTS6. This domain is the target of protein-O-fucosylation, which is a QC signal that prevents secretion of ADAMTS proteins that are improperly folded [37].

ADAMTS6 is necessary for cardiac development and expression of gap junction protein Cx43

ADAMTS6 belongs to a family of metalloproteases that mediates extracellular proteolytic processing of extracellular matrix (ECM) components and other secreted molecules. ADAMTS6 is closely related to ADAMTS10, which interacts with and accelerates assembly of fibrillin-1, mutations in which cause Marfan syndrome [38]. This suggests that ADAMTS6 could regulate cardiac ECM. While no specific ADAMTS6 substrates have been unequivocally identified, it was reported to regulate focal adhesions, epithelial cell–cell interactions, and microfibril assembly in cultured cells [39]. We show by RNA in situ hybridization that Adamts6 is expressed in the atrioventricular and septal cushions and myocardium of the embryonic heart, with expression persisting into adult ventricular, trabecular, and septal myocardium (Fig. 1a–d).
Fig. 1
Fig. 1

Adamts6 cardiac expression, sequence conservation, and cardiac anomalies in Adamts6-deficient mice. a–d Adamts6 (red punctate signal) is expressed in the outflow tract (a, blue arrowhead), heart valves (a, yellow arrowhead), atria (a, green arrowhead), and ventricular myocardium (a, orange arrowhead, b-d). e, f Diagram of the two Adamts6 mutant alleles recovered: Met1Ile and Ser149Arg. The sequence alignment shows conservation of the Ser149 residue in ADAMTS6 across species. g–l Congenital heart defects observed in Adamts6 Ser149Arg (Adamts6m/m) mutant embryos. A WT mouse heart with normal atrial, ventricular, and outflow tract anatomy (g), an intact atrioventricular septum (d), and normal ventricular myocardium (i). Homozygous Adamts6 Ser149Arg mutants (Adamts6m/m) exhibit a spectrum of congenital heart defects, such as a double outlet right ventricle (j, in which the aorta and pulmonary artery both arise from the right ventricle; see Additional file 3: Video S1) or an atrioventricular septal defect (AVSD) (k, in which the atrial and ventricular septa fail to form). Thickening of the ventricular wall is commonly observed, indicating ventricular hypertrophy (l). These mutant hearts (j–l) are shown at embryonic day (E)16.5 but their development is delayed, giving an appearance similar to WT hearts at E14.5 (as shown in (g–i)). Ao aorta, AVSD atrioventricular septal defect, LA left atrium, LV left ventricle, Pa pulmonary artery, RA right atrium, RV right ventricle. Scale bar: (a) 500 μm; (b–d) 50 μm; (g–l) 1 mm

Mice with recessive Adamts6 mutations were recovered in a forward genetic screen [40] (Fig. 1e and f). One mutation (p.Met1Ile) affects the start codon and is predicted null. The second mutation (p.Ser149Arg) lies in the propeptide. Both mutations cause prenatal/neonatal lethality with identical congenital heart defect phenotypes (Additional file 1: Table S7), comprising double outlet right ventricle (Fig. 1j, Additional file 3: Video S1), atrioventricular septal defect (Fig. 1k), and ventricular hypertrophy (Fig. 1j and l).

Ventricular conduction relies on cardiomyocyte coupling through gap junctions, with connexin 43 (Cx43) being the predominant myocardial gap junction protein in the human and mouse myocardium. Gja1 (encoding Cx43) knockout mice exhibit slow conduction, QRS prolongation, and increased susceptibility to ventricular arrhythmias [41–43], consistent with its role in mediating electrical coupling required for efficient propagation of ventricular depolarization. While Adamts6 heterozygous (Adamts6m/+) adult mice are viable and without structural heart defects (Additional file 2: Figure S9), their ventricular myocardium shows reduced Cx43 staining (Fig. 2a and b). Western blot shows reduction of Cx43 protein in the adult Adamts6m/+ myocardium (Fig. 2c and d). Interestingly, parallel quantitative real-time polymerase chain reaction (qRT-PCR) shows unchanged Gja1 messenger RNA (mRNA) expression (Fig. 2e), suggesting post-transcriptional regulation. Analysis of embryonic day 14.5 homozygote Adamts6m/m mutants shows Cx43 is completely absent in the ventricular myocardium (Fig. 2a and b). Thus, whereas Adamts6m/m mice have severe structural heart defects and Cx43 deficiency, Adamts6m/+ hemizygosity leads to reduction in Cx43 expression in the ventricles without defects in cardiac morphogenesis. Together these findings suggest the QRS prolongation in individuals with rare pathogenic ADAMTS6 variants could arise from impaired myocardial connectivity due to Cx43 reduction.
Fig. 2
Fig. 2

Reduction of Cx43 intercalated disk gap junction staining in Adamts6-deficient mice. a, b Cx43 staining (green) (a) is reduced throughout ventricular myocardium in embryonic day (E) 14.5 Adamts6m/m embryos and 6-week and 12-month Adamts6m/+ mice and quantified in (b). DAPI (blue) was used to visualize cell nuclei. c, d Representative western blot (c) and quantification (d) shows reduced Cx43 in three pairs of 6-week Adamts6m/+ and WT myocardium controls. Gapdh was used as a loading control. e No change in Gja1 RNA level in 6-week and 12-month Adamts6m/+ myocardium as compared to control. Scale bar: 50 μm. *P ≤ 0.01. E embryonic, W weeks, M months

Rare ADAMTS6 coding variants lead to impaired ADAMTS6 secretion

To determine the functional consequences of the two predicted pathogenic human ADAMTS6 coding variants from the exome-chip analysis (p.Ser90Leu and p.Arg603Trp), myc-tagged ADAMTS6 constructs with the variants introduced by site-directed mutagenesis were expressed in HEK293F cells. Western blotting was used to compare the levels of mutant and wild type (WT) myc-tagged ADAMTS6 in the transfected cell lysates and medium. As positive and negative controls, respectively, we transfected the known pathogenic murine variant (p.Ser149Arg) and two rare non-synonymous human ADAMTS6 variants predicted to be benign (p.Ser210Leu and p.Met752Val). Western blotting confirmed that the Adamts6 p.Ser149Arg variant was not secreted (Fig. 3a). The predicted human pathogenic variants show much reduced secretion compared to the WT and benign variants (Fig. 3b–d). Significantly, the molecular masses of the secreted p.Ser90Leu and p.Arg603Trp variants observed in cell lysate are comparable to that of the WT protein, indicating normal glycosylation and propeptide excision, which are essential for ADAMTS zymogen conversion to their mature forms [44]. These results suggest that heterozygous individuals have a reduction of secreted ADAMTS6 to 50% of normal, implying reduced proteolytic activity. The resulting disruption of proteolytic remodeling could potentially affect cell–cell and cell–matrix interactions essential for efficient Cx43 gap junction assembly. However, the rs61736454 (p.Ser90Leu) and rs114007286 (p.Arg603Trp) variants were associated with longer and shorter QRS duration, respectively. The reduced secretion observed was more profound for the rs61736454 variant compared to rs114007286, and the assay does not predict what impact a small amount of secreted protein may have, nor how it interacts in the presence of other modifier genes/variants carried by the same individual. Additionally, the two variants might affect overall protein function and interaction with binding partners in different ways.
Fig. 3
Fig. 3

A mouse Adamts6 ENU mutant and predicted damaging ADAMTS6 variants have impaired secretion. a, b Representative western blots using anti-Myc antibody show a major molecular species of 150 kDa in HEK293F cell lysates, corresponding to the ADAMTS6 zymogen (Z). In contrast, the culture medium of cells transfected with WT ADAMTS6 shows a 130 kDa species, corresponding to mature (M, i.e. furin-processed) ADAMTS6. a The p.Ser149Arg murine variant is not secreted into the culture medium. b The predicted damaging human variants, p.Ser90Leu and p.Arg603Trp, have reduced secretion, whereas the predicted benign variants, p.Ser210Leu and p.Met752Val, are secreted normally. Lysate and medium of HEK293F cells transfected with an empty vector (EV) lack immunoreactivity. The membrane was subsequently re-blotted using an anti-GAPDH monoclonal antibody to demonstrate comparable sample loading. c, d Densitometry of ADAMTS6 signal in lysates (c) and medium (d) shows reduced secretion of p.Ser90Leu and p.Arg603Trp variants and normal secretion of p.Ser210Leu and p.Met752Val into the medium, relative to the WT control (*P ≤ 0.01 for n = 3 transfections of each vector)

Conclusions

In a meta-analysis of data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Exome-Chip ECG consortium, we identified 28 loci associated with QRS duration. With the addition of 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts, all 34 variants across the 28 loci passed the exome-chip-wide significance threshold, indicating our results are robust. Furthermore, effect size directions between discovery and replication remained consistent and P values of non-replicating variants in the replication analysis alone were all below nominal significance (P < 0.05). Novel loci include genes involved in cardiac development and dysfunction, some of which are highly expressed in skeletal and/or cardiac tissue. To establish further evidence for these novel loci and mechanisms underlying each association, future functional experiments are essential.

The present study also highlights the efficacy of large-scale population-based exome-chip analysis for discovery of non-synonymous coding variants with significant functional effects. In gene-based tests, we identified an association between ventricular depolarization and rare non-synonymous variants in ADAMTS6, a gene not previously implicated in cardiac conduction. We chose to focus on this novel locus and seek functional validation as the association was driven by multiple rare coding variants that were predicted to be damaging by in silico tools. The coding variants driving the association in the population study and the mutations identified in the mouse forward genetic screen all impair ADAMTS6 secretion, indicating reduction/loss of function. Significantly, although heterozygosity of the variants in mice is not associated with structural heart defects, we detected reduction of Cx43 gap junctions in the ventricular myocardium. Homozygous Adamts6 mutants show complete loss of Cx43 gap junctions as well as structural heart defects, implying a dosage effect. Together, these findings indicate that ADAMTS6 has a novel role in regulating gap junction-mediated ventricular depolarization, with quantitative reduction in ADAMTS6 causing cardiac conduction perturbation. While our study focuses on cardiac conduction, the findings support the potential broad utility of large-scale exome-chip analysis for interrogating coding variants associated with other physiological or clinical parameters.

Methods

Discovery association analyses

Study cohorts

All participating studies formed the CHARGE EKG exome-chip consortium, including those belonging to the CHARGE consortium and external studies to investigate the role of functional variation in electrocardiographic traits. Twenty-two cohorts participated in the QRS duration analysis effort representing a maximum total sample size of 85,593 samples, consisting of 77,898 participants of European ancestry (91%) and 7695 of African descent. Individual study details and characteristics are summarized in Additional file 1: Table S1.

Phenotype measurements

We analyzed QRS duration measured in milliseconds. In each study, individuals were excluded from the analyses if these had a QRS duration of > 120 ms, atrial fibrillation (AF) on baseline electrocardiogram, a history of myocardial infarction or heart failure, had Wolff–Parkinson–White syndrome (WPW), a pacemaker, or used Class I and class III blocking medications (those medications with prefix C01B* according to the Anatomical Therapeutic Chemical (ATC) Classification System, http://www.whocc.no/atcddd/) [45]. For cohorts that were disease case-control studies, we included only the control subjects in our analyses irrespective of the nature of the case disease.

Genotyping and quality control

Each participating study performed genotyping using the Illumina HumanExome BeadChip / HumanCoreExome platforms. Owing to the difficulty of accurately detecting and assign genotype calls for rare variants (MAF < 1%), an initial core set of CHARGE cohorts, comprising approximately 62,000 samples, assembled intensity data into a single project for a joint improved calling. The quality of the joint calling was assessed through investigating the concordance of genotypes in samples having both exome-chip and exome-sequence data, described extensively elsewhere [46, 47]. Using the curated clustering files from the CHARGE central calling effort, several cohorts within our study re-called their genotypes. The remainder of participating studies used either Gencall [48] or zCall [49], or a combination of both. Full details concerning the genotyping and quality control for each cohort are summarized in Additional file 1: Table S1. Individual studies performed sample-level genotype QC filtering for call rate, removing autosomal heterozygosity outliers, gender mismatches, duplicates as established by identity by descent (IBD) analysis, and removed ethnic outliers as determined by multidimensional scaling. Poorly called variants were typically removed by filtering for Hardy-Weinberg equilibrium test P value (pHWE), call rate, and filtering removing poorly clustering variants. Each study aligned their data reference strand to the Illumina forward strand using a central SNP allele reference and annotation file (SNP info file) [46] for the Illumina Exome Chip. Variants were all mapped to GRCh37/hg19. Only variants present within the SNP info file were initially considered for analyses, 247,871 in total. Next, we filtered out 9252 variants that failed QC in the joint calling effort, as well as 6591 variants with inconsistent reference alleles across studies (a total of 11,392 unique SNPs), and considered furthermore only autosomal and chromosome X variants, and only those that were polymorphic in our study, leaving an initial set of 228,164 variants for analysis. For our single variant analyses, we only included variants with MAF > 0.012% (equal to a minor allele count [MAC] of 10), 162,199 in total.

Statistical methods

All association analyses were carried out using the R-package seqMeta [50]. Each study ran the “prepScores” function and adjusted their analyses for age, gender, body mass index (BMI), height, principal components, and study-specific covariates when appropriate (details in Additional file 1: Table S1). The output of this function is an R “list” object (“a prepScores object”), stored in an .RData file, where each element corresponds to a gene, and contains the scores and MAFs for variants, as well as a matrix of the covariance between the scores at all pairs of SNPs within a gene. All studies performed both gender combined and separated analyses, in addition to separation by ancestry. Using the prepScores objects from each study, we performed meta-analyses using the “singlesnpMeta()” for single variant meta-analyses, and the “burdenMeta” and “skatMeta()” functions of SeqMeta. Coefficients and standard errors from seqMeta can be interpreted as a “one-step” approximation to the maximum likelihood estimates. Ancestry groups were analyzed both separate and combined at the meta-analysis level.

For single variant meta-analyses, we included all variants with a MAC ≥ 10 in order to have well-calibrated type I error rates [51]. Statistical significance was defined using Bonferroni corrections. For single variants, maximally 162,199 variants were included in five separate analyses after filtering for MAC: European and African ancestry separated and combined (n = 3); and sex-stratified analyses (n = 2), resulting in a Bonferroni corrected P value of α=0.05 / 162,199 variants / 5 analyses = 6.17 × 10−8.

Suggestive sexually dimorphic associations were identified by performing sex-stratified meta-analyses, totaling 39,907 women and 31,702 men, including only from cohorts that had both male and female samples. Variants were deemed to be suggestive sex-specific when reaching below a P value threshold of exome-wide significance (P < 6.17 × 10−8) in one sex and above nominal significance in the other (P > 0.05).

For gene-based tests, also performed using seqMeta using the “prepScores” objects from individual cohorts, we assigned variants to genes by annotating all variants on the Exome Chip using ANNOVAR [52] following RefSeq [53] gene definitions mapped to human genome build 37 (hg19). In the collapsed variant tests, we included only variants with MAF < 1% and included only genes for which two or more variants were present (n = 16,085). We performed both SKAT [54] and T1 burden [55] tests, for three different functional sets of variants limited to the following: (I) all variants; (II) missense, nonsense, splice, and indel variants; (III) “damaging”: the same variants as in group II, except for missense only including those that are predicted to be damaging by at least two out of four functional prediction algorithms (Polyphen2 [56], SIFT [57], Mutation Taster [58], and LRT [59]). For the gene-based tests, we used a Bonferroni corrected P value significance threshold of α=0.05 / 16,085 genes / 2 different tests / 3 functional variant classes = 5.18 × 10−7.

We define a physically independent locus as the genomic region that contains variants within 250 kb on either side of LD-independent lead SNPs (exome-wide significant variants with r2 < 0.1), where LD calculations were based on European ancestry. Following this definition, in certain cases LD-independent lead variants are present in overlapping regions, complicating the definition and reporting of associated genetic loci and harbored genes. Therefore, we annealed loci if LD-independent exome-wide significant variants were < 250 kb from each other. Where lead SNPs from previous analyses were not contained in these regions, we considered these as novel. LD calculations were performed on the Illumina Exome Chip genotype data from the TwinsUK cohort [60] (n = 1194), using PLINK 1.9 [61].

Replication association analyses

Study cohort: UK biobank (UKB)

UK Biobank (www.ukbiobank.ac.uk) is a prospective study of 500,000 volunteers, comprising relatively even numbers of men and women aged 40–69 years old at recruitment, with extensive baseline, and follow-up clinical, biochemical, genetic, and outcome measures. Approximately 95,000 individuals were recruited for a Cardio test using a stationary bicycle in conjunction with a four-lead electrocardiograph device at the initial assessment (2006–2008) and ~ 20,000 individuals performed the test again (the first repeat assessment: 2011–2013). The Cardio test, thereafter known as the exercise test, started with 15 s of rest (pre-test), followed by 6 min of exercise (cycling) with an increasing workload, and a 1-min recovery period without exercise. To improve accuracy, we calculated an average QRS waveform by aligning all QRS complexes present in a window of 15 s from the resting stage. Ectopic beats and artifacts were removed. Then, we calculated the correlation between each individual QRS complex and the average QRS waveform and removed those with a correlation coefficient < 0.8. Finally, we repeated the calculation of the average QRS waveform by only considering those highly correlated individual QRS complexes. The QRS width was measured from the average QRS waveform as the interval between the onset of the Q wave and the end of the S wave. Genotyping was performed by UKB using the Applied Biosystems UK BiLEVE Axiom Array or the UKB AxiomTM Array. Single Nucleotide Variants (SNVs) were imputed centrally by UKB using a merged UK10K sequencing + 1000 Genomes imputation reference panel (https://www.biorxiv.org/content/early/2017/07/20/166298). Following phenotype and genotype QC, a total of 51,971 unrelated individuals of European ancestry remained for analysis. Thirty-four QRS discovery lead variants selected for replication were extracted from UKB imputed files, all being of high quality (Hardy-Weinberg P > 1 × 10−4 and an info score > 0.5) using QCTOOL v2 and the association analysis was performed using SNPTEST v2.5.4 assuming an additive genetic model.

Study cohort: deCODE

ECGs obtained in Landspitali—The National University Hospital of Iceland, Reykjavik, the largest and only tertiary care hospital in Iceland—have been digitally stored since 1998. For this analysis, we used information on mean QRS duration in milliseconds from 151,667 sinus rhythm ECGs from 59,903 individuals. Individuals with permanent pacemakers or history of myocardial infarction, heart failure, atrial fibrillation, or WPW were excluded, as well as ECGs with QRS duration > 120 ms. ECG measurements were adjusted for sex, year of birth, and age at measurement. Due to limited availability of information, height, BMI, or drugs were not accounted for in the analysis. The genotypes in the deCODE study were derived from whole-genome sequencing of 28,075 Icelanders using Illumina standard TruSeq methodology to a mean depth of 35X (SD 8X) with subsequent imputation into 160,000 chip-typed individuals and their close relatives [21]. Selected replication variants from the meta-analysis for association with QRS duration were tested in accounting for relatedness using a mixed effects model as implemented by BOLT-LMM [62] followed by LD score regression [63].

Statistical analysis

We first performed a fixed-effects inverse variance weighted meta-analysis combining the summary statistics data from the UKB and deCODE analyses, followed by a combined analysis of the discovery and replication summary statistics using GWAMA v2.2.2 [64].

Mouse and cell models

Western blot analysis

A plasmid vector for expression of the full-length Adamts6 open reading frame was generated via PCR using Phusion High-Fidelity DNA Polymerase (catalog no. M0530 L; New England Biolabs) and embryonic mouse heart complementary DNA (cDNA) as the template and inserted into PSecTag2B (V900–20; Life Technologies). ADAMTS6 variants p.Ser90Leu and p.Arg603Trp were created in the Adamts6 cDNA using Q5 Site-Directed Mutagenesis Kit (catalog no. E0554S; New England BioLabs). Primer sequences used for cloning and mutagenesis are available upon request. Each plasmid insert was verified by sequencing. Human embryonic kidney (HEK293) cells obtained from ATCC were maintained in medium supplemented with 10% fetal bovine serum and 100 U/mL penicillin and 100 μg/mL streptomycin. The constructs were transfected with Lipofectamine 3000 Transfection Kit (catalog no. L3000; Invitrogen) following manufacturer’s instructions. After 72 h in serum-free medium, cell lysates were collected in lysis buffer (0.1% NP-40, 0.01% sodium dodecyl sulfate, and 0.05% sodium deoxycholate in phosphate buffered saline [PBS], pH 7.4). Extracts were electrophoresed by reducing SDS-PAGE on 10% Tris-Glycine gels. Proteins were electroblotted to Immobilon-FL membranes (catalog no. IPFL00010, EMD Millipore), incubated with primary antibody anti-myc (Hybridoma core facility; 1:1000; Cleveland Clinic), anti-GAPDH (catalog no. MAB374; 1:5000; EMD Millipore), and anti-Cx43 (catalog no. C6219; 1:2000; Sigma-Aldrich), overnight at 4 °C, followed by IRDye secondary antibodies goat anti-mouse or anti-rabbit (926–68,170, 827–08365; 1:10000; LI-COR) for 1 h at room temperature and visualized by Odyssey CLx (LI-COR). Band intensity was measured using ImageJ (NIH, Bethesda, MD, USA).

Statistics

All values are expressed as mean ± SEM. A paired two-tailed Student’s t-test was used to assess statistical significance.

Recovery and phenotyping of Adamts6 mutant mice

Adamts6 mutant mice were recovered from a recessive ethynitrosourea (ENU) mouse mutagenesis screen conducted using non-invasive in utero fetal echocardiography [40]. Mutants detected with congenital heart defects by ultrasound imaging were recovered either as fetuses or at term and further analyzed by necropsy, followed by histopathology for detailed analysis of intracardiac anatomy with three-dimensional reconstructions using episcopic confocal microscopy. From the screen, ten independent Adamts6 mutant lines were recovered, all exhibiting the identical phenotype. Mouse histology, immunostaining and RT-PCR experiments were approved by the Cleveland Clinic Institutional Animal Care and Use Committee (protocol # 2015–1458, IACUC number: 18052990).

Mouse mutation recovery

Mutation recovery was conducted by whole-exome capture using SureSelect Mouse All Exon kit V1, with sequencing carried out using Illumina HiSeq 2000 with minimum 50X average coverage (BGI Americas). Sequence reads were aligned to the C57BL/6 J mouse reference genome (mm9) and analyzed using CLCBio Genomic Workbench and GATK software. All homozygous mutations were genotyped across all mutants recovered in the mutant line and only the Adamts6 mutation was consistently homozygous across all mutants recovered in the line, the pathogenic identifying it as mutation. Of the ten mutant lines, nine were identified to have the same missense mutation (c.C447G: p.S149R), while one mutant line exhibited loss of the start codon (c.G3A: p.M1I) and was confirmed to be null with no Adamts6 transcripts detected with transcript analysis. The Adamts6 missense mutation was subsequently identified as a spontaneous mutation in the C57BL/6 J production colony at the Jackson Laboratory.

Histology and immunofluorescence staining and RNA in situ hybridization

Tissues were fixed in 4% paraformaldehyde in PBS at 4 °C overnight followed by paraffin embedding. Sections of 7 μm were used for hematoxylin and eosin staining, picrosirius red staining, and immunofluorescence for Cx43 (catalog no. C6219; 1:800; Sigma-Aldrich) followed by secondary goat anti-rabbit antibody (catalog no. 111–035-144; 1:2000; Jackson Immunoresearch Laboratories Inc.). Antigen retrieval, i.e. immersion of slides in citrate-EDTA buffer (10 mM/L citric acid, 2 mM/L EDTA, 0.05% v/v Tween-20, pH 6.2) and microwaving for 1.5 min at 50% power four times in a microwave oven with 30-s intervals intervening was used before immunofluorescence. Immunofluorescence was quantified by the ratio of Cx43 signal to DAPI-positive cell nuclei integrated density (ImageJ; National Institutes of Health, n = 3, with three samples of each myocardium). Adamts6 RNA in situ hybridization was performed using RNAScope (Advanced Cell Diagnostics) following the manufacturer’s protocol. Briefly, 7-μm sections were deparaffinized and hybridized to a mouse Adamts6 probe set (catalog no. 428301; Advanced Cell Diagnostics) using a HybEZ™ oven (Advanced Cell Diagnostics) and the RNAScope 2.5 HD Detection Reagent Kit (catalog no. 322360; Advanced Cell Diagnostics).

Quantitative real-time PCR

Total RNA was isolated using TRIzol (catalog no. 15596018, Invitrogen) and 1 μg of RNA was reverse-transcribed into cDNA with SuperScript III Cells Direct cDNA synthesis system (catalog no. 46–6321, Invitrogen). qPCR was performed with Bullseye EvaGreen qPCR MasterMix (catalog no. BEQPCR-S; MIDSCI) using an Applied Biosystems 7500 instrument. The experiments were performed with three independent samples and confirmed reproducibility. Gapdh was used as a control for mRNA quantity. The ∆∆Ct method was used to calculate relative mRNA expression levels of target genes. Primer sequences are as follows: Gapdh: 5’ TGGAGAAACCTGCCAAGTATGA 3′ and 5’ CTGTTGAAGTCGCAGGAGACA 3′; Gja1: 5’ CCTGCTGAGAACCTACATCATC 3′ and 5’CGCCCTTGAAGAAGACATAGAA 3′.

Web resources

Databases

Genotype-Tissue Expression (GTEx) Portal database: http://www.gtexportal.org

Software

seqMeta: http://cran.r-project.org/web/packages/seqMeta/

EasyStrata: https://cran.r-project.org/web/packages/EasyStrata/

PLINK 1.9: https://www.cog-genomics.org/plink

SNPTEST v2.5.4: https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html

GWAMA v.2.2.2: https://www.geenivaramu.ee/en/tools/gwama

Notes

Declarations

Funding

This work was funded by a grant to YJ from the British Heart Foundation (PG/12/38/29615).

AGES: This study has been funded by NIH contracts N01-AG-1-2100 and 271201200022C, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee, VSN: 00–063. The researchers are indebted to the participants for their willingness to participate in the study.

ARIC: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. Funding support for “Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium” was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419).

BRIGHT: The Exome Chip genotyping was funded by Wellcome Trust Strategic Awards (083948 and 085475). This work was also supported by the Medical Research Council of Great Britain (Grant no. G9521010D); and by the British Heart Foundation (Grant no. PG/02/128). AFD was supported by the British Heart Foundation (Grant nos. RG/07/005/23633 and SP/08/005/25115); and by the European Union Ingenious HyperCare Consortium: Integrated Genomics, Clinical Research, and Care in Hypertension (grant no. LSHM-C7–2006-037093). The BRIGHT study is extremely grateful to all the patients who participated in the study and the BRIGHT nursing team. We would also like to thank the Barts Genome Centre staff for their assistance with this project.

CHS: This Cardiovascular Health Study (CHS) research was supported by NHLBI contracts HHSN268201800001C, HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants R01HL068986, U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

ERF: The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947) and also received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4–2007-201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (no. QLG2-CT-2002-01254). The ERF study was further supported by ENGAGE consortium and CMSB. High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). We are grateful to all study participants and their relatives, general practitioners, and neurologists for their contributions to the ERF study and to P Veraart for her help in genealogy, J Vergeer for the supervision of the laboratory work, and P Snijders for his help in data collection.

FHS: The Framingham Heart Study (FHS) research reported in this article was supported by a grant from the National Heart, Lung, and Blood Institute (NHLBI), HL120393.

Generation Scotland: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping of the Generation Scotland and Scottish Family Health Study samples was carried out by the Genetics Core Laboratory at the Clinical Research Facility, Edinburgh, Scotland and was funded by the UK’s Medical Research Council.

GOCHA: The Genetics of Cerebral Hemorrhage with Anticoagulation was carried out as a collaborative study supported by grants R01NS073344, R01NS059727, and 5K23NS059774 from the NIH–National Institute of Neurological Disorders and Stroke (NIH-NINDS).

GRAPHIC: The GRAPHIC Study was funded by the British Heart Foundation (BHF/RG/2000004). NJS and CPN are supported by the British Heart Foundation and is a NIHR Senior Investigator. This work falls under the portfolio of research supported by the NIHR Leicester Cardiovascular Biomedical Research.

INGI-FVG: This study has been funded by Regione FVG (L.26.2008).

INTER99: The Inter99 was initiated by Torben Jørgensen (PI), Knut Borch-Johnsen (co-PI), Hans Ibsen and Troels F. Thomsen. The steering committee comprises the former two and Charlotta Pisinger. The study was financially supported by research grants from the Danish Research Council, the Danish Centre for Health Technology Assessment, Novo Nordisk Inc., Research Foundation of Copenhagen County, Ministry of Internal Affairs and Health, the Danish Heart Foundation, the Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, the Becket Foundation, and the Danish Diabetes Association. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk).

JHS: We thank the Jackson Heart Study (JHS) participants and staff for their contributions to this work. The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. Dr. Wilson is supported by U54GM115428 from the National Institute of General Medical Sciences.

KORA: The KORA study was initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ.

Korcula: This work was funded by the Medical Research Council UK, The Croatian Ministry of Science, Education and Sports (grant 216–1080315-0302), the Croatian Science Foundation (grant 8875), the Centre of Excellence in Personalized health care, and the Centre of Competencies for Integrative Treatment, Prevention and Rehabilitation using TMS.

LifeLines: The LifeLines Cohort Study and generation and management of GWAS genotype data for the LifeLines Cohort Study are supported by The Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation, and Dutch Diabetes Research Foundation. Niek Verweij is supported by NWO-VENI (016.186.125) and Marie Sklodowska-Curie GF (call: H2020-MSCA-IF-2014, Project ID: 661395).

UHP: Folkert W. Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre. Ilonca Vaartjes is supported by a Dutch Heart Foundation grant DHF project “Facts and Figures.”

MGH-CAMP: Dr. Patrick Ellinor is funded by NIH grants (2R01HL092577, 1R01HL128914, R01HL104156, and K24HL105780) and American Heart Association Established Investigator Award 13EIA14220013 (Ellinor). Dr. Steve Lubitz is funded by NIH grants K23HL114724 and a Doris Duke Charitable Foundation Clinical Scientist Development Award 2014105.

NEO: The authors of the NEO study thank all individuals who participated in the Netherlands Epidemiology in Obesity study, all participating general practitioners for inviting eligible participants, and all research nurses for collection of the data. We thank the NEO study group, Pat van Beelen, Petra Noordijk, and Ingeborg de Jonge for the coordination, lab, and data management of the NEO study. We also thank Arie Maan for the analyses of the electrocardiograms. The genotyping in the NEO study was supported by the Centre National de Génotypage (Paris, France), headed by Jean-Francois Deleuze. The NEO study is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. Dennis Mook-Kanamori is supported by Dutch Science Organization (ZonMW-VENI Grant 916.14.023).

RS-I: The generation and management of the Illumina Exome Chip v1.0 array data for the Rotterdam Study (RS-I) was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The Exome chip array dataset was funded by the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, from the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO)-sponsored Netherlands Consortium for Healthy Aging (NCHA; project nr. 050–060-810); the Netherlands Organization for Scientific Research (NWO; project number 184021007); and by the Rainbow Project (RP10; Netherlands Exome Chip Project) of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL; www.bbmri.nl). We thank Ms. Mila Jhamai, Ms. Sarah Higgins, and Mr. Marijn Verkerk for their help in creating the exome chip database, and Carolina Medina-Gomez, MSc, Lennard Karsten, MSc, and Linda Broer PhD for QC and variant calling. Variants were called using the best practice protocol developed by Grove et al. as part of the CHARGE consortium exome chip central calling effort. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study, and the participating general practitioners and pharmacists. The work of Bruno H. Stricker is supported by grants from the Netherlands Organization for Health Research and Development (ZonMw) (Priority Medicines Elderly 113102005 to ME and DoelmatigheidsOnderzoek 80–82500–98-10208 to BHS). The work of Mark Eijgelsheim is supported by grants from the Netherlands Organization for Health Research and Development (ZonMw) (Priority Medicines Elderly 113102005 to ME and DoelmatigheidsOnderzoek 80–82500–98-10208 to BHS).

SHIP: SHIP is supported by the BMBF (grants 01ZZ9603, 01ZZ0103, and 01ZZ0403) and the German Research Foundation (Deutsche Forschungsgemeinschaft [DFG]; grant GR 1912/5–1). SHIP and SHIP-TREND are part of the Community Medicine Research net (CMR) of the Ernst-Moritz-Arndt University Greifswald (EMAU) which is funded by the BMBF as well as the Ministry for Education, Science and Culture and the Ministry of Labor, Equal Opportunities, and Social Affairs of the Federal State of Mecklenburg-West Pomerania. The CMR encompasses several research projects that share data from SHIP. The EMAU is a member of the Center of Knowledge Interchange (CKI) program of the Siemens AG. SNP typing of SHIP and SHIP-TREND using the Illumina Infinium HumanExome BeadChip (version v1.0) was supported by the BMBF (grant 03Z1CN22). We thank all SHIP and SHIP-TREND participants and staff members as well as the genotyping staff involved in the generation of the SNP data.

TWINSUK: TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.

UKBB: This research has been conducted using the UK Biobank Resource (application 8256 - Understanding genetic influences in the response of the cardiac electrical system to exercise) and is supported by Medical Research Council grant MR/N025083/1. We also wish to acknowledge the support of the NIHR Cardiovascular Biomedical Research Unit at Barts and Queen Mary University of London, UK. PD Lambiase acknowledges support from the UCLH Biomedicine NIHR. MO is supported by an IEF 2013 Marie Curie fellowship. JR acknowledges support from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007–2013) under REA grant agreement no. 608765.

YFS: The Young Finns Study has been financially supported by the Academy of Finland: grants 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; and Diabetes Research Foundation of Finnish Diabetes Association. The expert technical assistance in the statistical analyses by Irina Lisinen is gratefully acknowledged.

Cell culture and biochemistry: Funding was provided by the National Institutes of Health (Program of Excellence in Glycoscience award HL107147 to SSA and F32AR063548 to TJM) and the David and Lindsay Morgenthaler Postdoctoral Fellowship (to TJM) and by the Allen Distinguished Investigator Program, through support made by The Paul G. Allen Frontiers Group and the American Heart Association (to SSA).

Mutant mouse model: Adamts6 mutant mice were generated and further propagated and analyzed by funding provided by NIH grants HL098180 and HL132024 (to CWL) and by the Allen Distinguished Investigator Program, through support made by The Paul G. Allen Frontiers Group and the American Heart Association (to SSA).

Availability of data and materials

Summary statistics: The discovery summary statistics for both European and African-American ancestry meta-analyses are available at https://doi.org/10.17632/7jgbckpdr4.1 (DOI:https://doi.org/10.17632/7jgbckpdr4.1) and PhenoScanner [65] http://www.phenoscanner.medschl.cam.ac.uk/phenoscanner.

Individual cohort data:

Cardiovascular Health Study (CHS) Cohort: an NHLBI-funded observational study of risk factors for cardiovascular disease in adults aged 65 years or older. dbGaP. https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000287.v6.p1 [66].

Authors’ contributions

Supervision and management of the project: YJ. Study design: BPP, TJM, SSA, CWL, DEA, YJ. The manuscript was critically revised in detail by members of the writing team before circulation to all co-authors. Manuscript writing group: BPP, TJM, SSA, FJC, DEA, PvdH, CWL, YJ. All co-authors revised and approved the manuscript. Exome chip data analysis: BPP, JAB, NAB, MvdB, JB-J, SC, NG, JH, LMH, AI, AI, RL-G, HL, C-TL, L-PL, JM, HM, MM-N, SP, FR, AR, MS, JvS, AVS, NV, HRW, SW, CPN. In vitro/in vivo data acquisition and analysis: TJM, NTK, GCG, XL, CG. Genetic data acquisition: ARIC: AA, ML, EZS, LRGP: MLB, Lifelines: RAdB, PvdM, Bright: AFD, RS-I: ME, MGH-CAMP: PE, PLH, ZX, MESA: XG, SHIP: SBF, UV, HV, AGES: TBH, LJL, Generation Scotland: CH, CHS: SRH, BMP, KMR, JIR, NEO: JWJ, ST, YFS: MK, OTR, ERF: JAK, INTER99: AL, OP, WHI: MP, KORA: AP, MFS, KS, MW, Korcula: OP, RS-I: AU, LRGP: IV, JHS: JGW. Replication study: UKBB: IN, SV-D, MO, JR, PDL, AT, PBM; deCODE: GS, DOA, UT, DFG, KS, HH. Data interpretation and cohort oversight: LRGP: FWA, SHIP: MD, ERF: CMvD, INGI-CARL: PG AGES: VG, INTER99: TH, JKK, KORA: SK, WHI: CK, YFS: TL, MESA: HJL, MGH-CAMP: SAL, NEO: DOM-K, FHS: CHN-C, GOCHA: JR, Korcula: IR, GRAPHIC: NJS, INGI-CARL: GS, Generation Scotland: BHS, RS-I: BHS, INGI-FVG: SU, UHP: FWA, BRIGHT: PBM, CHS: NS, ARIC: DEA, TwinsUK: TDS, YJ, In vitro studies: TJM, SSA, In vivo studies: TJM, SSA, CWL.

Ethics approval and consent to participate

All participating studies received approval by their respective local institutional review boards and ensured that written informed consent was obtained from all study participants, following the recommendations of the Declaration of Helsinki.

Exome discovery and replication analyses

AGES: The study is approved by the Icelandic National Bioethics Committee, (VSN: 00–063) and the Data Protection Authority.

ARIC: Institutional Review Board approvals were obtained by each participating ARIC study center (the Universities of NC, MS, MN, and John Hopkins University) and the coordinating center (University of NC); the research was conducted in accordance with the principles described in the Helsinki Declaration. All participants in the ARIC study gave informed consent. For more information see dbGaP Study Accession: phs000280.v2.p1. JHSPH IRB number H.34.99.07.02.A1. Manuscript proposal number MS2572.

BRIGHT: All individuals in the BRIGHT study participated as volunteers and were recruited via hypertension registers from the MRC General Practice Framework in the UK. Ethics Committee approval was obtained from the multi-and local research committees of the partner institutes, and all participants gave written informed consent.

CHS: CHS was approved by institutional review committees at each site, the participants gave informed consent, and those included in the present analysis consented to the use of their genetic information for the study of cardiovascular disease. It is the position of the UW IRB that these studies of de-identified data, with no patient contact, do not constitute human subjects research. Therefore, we have neither an approval number, nor an exemption.

deCODE: The deCODE Electrocardiogram (ECG) study was approved by the Data Protection Commission of Iceland and the National Bioethics Committee of Iceland (VSNb2015030024/03.01). Written informed consent was obtained from individuals donating samples. Personal identifiers associated with medical information and samples were encrypted with a third-party encryption system as provided by the Data Protection Commission of Iceland.

ERF: The Medical Ethics Committee of the Erasmus University Medical Center approved the ERF study protocol and all participants, or their legal representatives, provided written informed consent.

FHS: The Boston University Medical Campus Institutional Review Board approved the FHS genome-wide genotyping (protocol number H-226671).

Generation Scotland: Data were collected for GS:SFHS during 2006–2011 with ethical approval from the NHS Tayside Committee on Medical Research Ethics A (ref 05/S1401/89). All participants gave written informed consent. GS:SFHS is now a Research Tissue Bank approved by the East of Scotland Research Ethics Service (ref 15/ES/0040).

GOCHA: The Institutional Review Board at MGH reviewed and approved the study. Participants or their next of kin provided informed consent at the time of enrolment.

GRAPHIC: GRAPHIC was approved by the Leicestershire Research Ethics Committee (LREC Ref no. 6463).

Inter99: Written informed consent was obtained from all participants and the study was approved by the Scientific Ethics Committee of the Capital Region of Denmark (KA98155, H-3-2012-155) and was in accordance with the principles of the Declaration of Helsinki II.

KORA: Written informed consent was obtained from all participants and the study was approved by the local ethics committee (Bayerische Landesärztekammer).

KORCULA: Ethical approval was given for recruitment of all Korcula study participants by ethics committees in both Scotland and Croatia. All volunteers gave informed consent before participation.

Lifelines: The Lifelines study followed the recommendations of the Declaration of Helsinki and was in accordance with research code of the University Medical Center Groningen (UMCG). The LifeLines study is approved by the medical ethical committee of the UMCG, the Netherlands. All participants signed an informed consent form before they received an invitation for the physical examination. For a comprehensive overview of the data collection, please visit the LifeLines catalog at https://catalogue.lifelines.nl/menu/main/protocolviewer.

MGH CAMP: The Institutional Review Board at MGH reviews the study protocol annually. Each participant provided written, informed consent before enrolment.

NEO: The Netherlands Epidemiology of obesity (NEO) study is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. All participants gave written informed consent and the Medical Ethical Committee of the Leiden University Medical Center (LUMC) approved the study design.

RS: The Rotterdam Study has been approved by the medical ethics committee according to the Population Study Act Rotterdam Study, executed by the Ministry of Health, Welfare and Sports of the Netherlands. Written informed consent was obtained from all participants.

SHIP: The SHIP study followed the recommendations of the Declaration of Helsinki. The study protocol of SHIP was approved by the medical ethics committee of the University of Greifswald. Written informed consent was obtained from each of the study participants. The SHIP study is described in PMID: 20167617.

TwinsUK: The study has ethical approval from the NRES Committee London–Westminster, London, UK (EC04/015). Written consent was obtained from all participants. Research was carried out in accordance with the Helsinki declaration.

UKBB: The UKB study has approval from the North West Multi-Centre Research Ethics Committee and all participants provided informed consent.

UHP: The Utrecht Health Project has been approved by the Medical Ethics Committee of the University Medical Centre Utrecht. All participants give written informed consent. The masking of all personal data for researchers and for other possible users of UHP has been regulated in a legal document.

WHI: All WHI participants provided written and informed consent. All study sites received approval to conduct this research from local Institutional Review Boards at the Fred Hutchinson Cancer research Center.

YFS: The Young Finns Study was approved by the local ethics committees (University Hospitals of Helsinki, Turku, Tampere, Kuopio, and Oulu) and was conducted following the guidelines of the Declaration of Helsinki. All participants gave their written informed consent.

In vivo mouse work

Cleveland Clinic Lerner Research Institute: All mouse experiments were approved by the Cleveland Clinic Institutional Animal Care and Use Committee (protocol no. 2015–1458, IACUC number: 18052990), and by the University of Pittsburgh Institutional Animal Care and Use Committee.

Competing interests

MGH-CAMP: Dr. Ellinor is the PI on a grant from Bayer HealthCare to the Broad Institute focused on the genetics and therapeutics of atrial fibrillation.

CHS: Dr. Bruce Psaty serves on the DSMB of a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson.

deCODE: G. Sveinbjornsson, D.O. Arnar, U. Thorsteinsdottir, D.F.Gudbjartsson, H. Holm, K. Stefansson are employed by deCODE genetics/Amgen, Inc.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Genetics Research Centre, Molecular and Clinical Sciences Institute, St George’s University of London, London, SW17 0RE, UK
(2)
Department of Public Health and Primary Care, MRC/BHF Cardiovascular Epidemiology Unit, University of Cambridge, Strangeways Research Laboratory, Worts’ Causeway, Cambridge, CB1 8RN, UK
(3)
Department of Biomedical Engineering, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA
(4)
Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98101, USA
(5)
deCODE genetics/Amgen, Inc., 101 Reykjavik, Iceland
(6)
Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
(7)
NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
(8)
Predoctoral Training Program in Human Genetics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
(9)
Department of Medical Informatics Erasmus MC - University Medical Center, P.O. Box 2040, Rotterdam, 3000, CA, The Netherlands
(10)
The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
(11)
Institute for Maternal and Child Health - IRCCS “Burlo Garofolo”, 34137 Trieste, Italy
(12)
Institute of Cardiovascular Science, University College London, London, WC1E 6BT, UK
(13)
Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15201, USA
(14)
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
(15)
Department of Cardiovascular Sciences, University of Leicester, Cardiovascular Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
(16)
Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK
(17)
Cardiovascular Department, Ospedali Riuniti and University of Trieste, 34100 Trieste, Italy
(18)
CARIM School for Cardiovascular Diseases, Maastricht Center for Systems Biology (MaCSBio), and Department of Biochemistry, Maastricht University, Universiteitssingel 60, Maastricht, 6229 ER, The Netherlands
(19)
Department of Epidemiology, Genetic Epidemiology Unit, Erasmus University Medical Center, Rotterdam, The Netherlands
(20)
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
(21)
Department of Medicine, Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA
(22)
Biostatistics Department, Boston University School of Public Health, Boston, MA 02118, USA
(23)
Department of Clinical Chemistry, Fimlab Laboratories, 33520 Tampere, Finland
(24)
Department of Clinical Chemistry, Faculty of Medicine and Life Sciences, University of Tampere, 33014 Tampere, Finland
(25)
Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
(26)
Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS 39216, USA
(27)
Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
(28)
Department of Medicine I, University Hospital Munich, Ludwig-Maximilians University, Munich, Germany
(29)
German Centre for Cardiovascular Research (DZHK); partner site: Munich Heart Alliance, Munich, Germany
(30)
Mechanical Engineering Department, University College London, London, WC1E 6BT, UK
(31)
Barts Heart Centre, St Bartholomews Hospital, London, EC1A 7BE, UK
(32)
Institute of Cardiovascular and Medical Sciences, University of Glasgow, BHF GCRC, Glasgow, G12 8TA, UK
(33)
Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA
(34)
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
(35)
Division Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Utrecht, the Netherlands
(36)
Icelandic Heart Association, 201 Kopavogur, Iceland
(37)
Department of Cardiology, Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
(38)
University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
(39)
Cardiovascular Research Center and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 2114.0, USA
(40)
Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, 17475 Greifswald, Germany
(41)
DZHK (German Centre for Cardiovascular Research); Partner site Greifswald, 17475 Greifswald, Germany
(42)
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
(43)
Department of Medicine, Landspitali University Hospital, 101 Reykjavik, Iceland
(44)
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
(45)
Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
(46)
Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
(47)
Cardiovascular Research Center, Massachusetts General Hospital, Charlestown, MA 02114, USA
(48)
The Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
(49)
Division of Genomic Outcomes, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
(50)
Department of Internal Medicine B - Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine, University Medicine Greifswald, 17475 Greifswald, Germany
(51)
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD 20892, USA
(52)
Cardiovascular Health Research Unit and the Department of Epidemiology, University of Washington, Seattle, WA 98101, USA
(53)
Department of Cardiology, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
(54)
Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands
(55)
Interuniversity Cardiology Institute of The Netherlands, Utrecht, The Netherlands
(56)
Department of Clinical Physiology, Tampere University Hospital, 33521 Tampere, Finland
(57)
Department of Clinical Physiology, Faculty of Medicine and Life Sciences, University of Tampere, 33014 Tampere, Finland
(58)
Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
(59)
Division of Nephrology & Hypertension, Internal Medicine, School of Medicine, University of Utah, Salt Lake City, UT 84109, USA
(60)
Research Centre for Prevention and Health, Capital Region of Denmark, 2600 Glostrup, Denmark
(61)
Department of Clinical Experimental Research, Rigshospitalet, 2600 Glostrup, Denmark
(62)
Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200.0 Copenhagen, Denmark
(63)
Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA
(64)
Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
(65)
German Center for Diabetes Research, Neuherberg, Germany
(66)
Faculty of Medicine, University of Split, Split, Croatia
(67)
Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA 98101, USA
(68)
Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA
(69)
Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, 20521 Turku, Finland
(70)
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20014 Turku, Finland
(71)
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
(72)
The Institute for Translational Genomics and Population Sciences and Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
(73)
Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
(74)
Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
(75)
Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
(76)
Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
(77)
Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
(78)
Human Genotyping Facility Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
(79)
Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany
(80)
Research unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
(81)
Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA
(82)
TCM Clinical Basis Institute, Zhejiang Chinese Medical University, Hangzhou, 310000, Zhejiang, China
(83)
Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, the Netherlands
(84)
Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
(85)
Farr Institute of Health Informatics Research and Institute of Health Informatics, University College London, London, UK
(86)
Department of Medical, Surgical and Health Sciences, University of Trieste, 34100 Trieste, Italy
(87)
Division of Experimental Genetics, Sidra Medical and Research Center, Doha, Qatar
(88)
School of Engineering and Natural Sciences, University of Iceland, 101 Reykjavik, Iceland
(89)
Laboratory of Experimental Cardiology, University of Copenhagen, 2200 Copenhagen, Denmark
(90)
Division of Medical Genetics, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, 90502, USA
(91)
Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, 2300RC, The Netherlands
(92)
Dubowitz Neuromuscular Centre, Institute of Child Health, University College London, 30 Guilford Street, London, WC1N 1EH, UK
(93)
Center for Human Genetic Research and Cardiovascular Research Center, Harvard Medical School and Massachusetts General Hospital, Boston, MA 02114, USA
(94)
Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH8 9AG, UK
(95)
Division of Population Health Sciences, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK
(96)
Department of Epidemiology Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
(97)
Division of Cardiology, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA 98101, USA
(98)
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
(99)
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
(100)
Genetics Research Centre, Molecular and Clinical Sciences Institute, St George’s University of London, London, UK

References

  1. Mentz RJ, Greiner MA, DeVore AD, Dunlay SM, Choudhary G, Ahmad T, et al. Ventricular conduction and long-term heart failure outcomes and mortality in African Americans: insights from the Jackson heart study. Circ Heart Fail. 2015;8:243–51.View ArticlePubMedGoogle Scholar
  2. Dhingra R, Pencina MJ, Wang TJ, Nam B-H, Benjamin EJ, Levy D, et al. Electrocardiographic QRS duration and the risk of congestive heart failure: the Framingham heart study. Hypertension. 2006;47:861–7.View ArticlePubMedGoogle Scholar
  3. Aro AL, Anttonen O, Tikkanen JT, Junttila MJ, Kerola T, Rissanen HA, et al. Intraventricular conduction delay in a standard 12-lead electrocardiogram as a predictor of mortality in the general population. Circ Arrhythm Electrophysiol. 2011;4:704–10.View ArticlePubMedGoogle Scholar
  4. Badheka AO, Singh V, Patel NJ, Deshmukh A, Shah N, Chothani A, et al. QRS duration on electrocardiography and cardiovascular mortality (from the National Health and nutrition examination survey-III). Am J Cardiol. 2013;112:671–7.View ArticlePubMedGoogle Scholar
  5. Kashani A, Barold SS. Significance of QRS complex duration in patients with heart failure. J Am Coll Cardiol. 2005;46:2183–92.View ArticlePubMedGoogle Scholar
  6. Konstam MA, Gheorghiade M, Burnett JC, Grinfeld L, Maggioni AP, Swedberg K, et al. Effects of oral tolvaptan in patients hospitalized for worsening heart failure: the EVEREST outcome trial. JAMA. 2007;297:1319–31.View ArticlePubMedGoogle Scholar
  7. Wang NC, Maggioni AP, Konstam MA, Zannad F, Krasa HB, Burnett JC, et al. Clinical implications of QRS duration in patients hospitalized with worsening heart failure and reduced left ventricular ejection fraction. JAMA. 2008;299:2656–66.View ArticlePubMedGoogle Scholar
  8. Zimetbaum PJ, Buxton AE, Batsford W, Fisher JD, Hafley GE, Lee KL, et al. Electrocardiographic predictors of arrhythmic death and total mortality in the multicenter unsustained tachycardia trial. Circulation. 2004;110:766–9.View ArticlePubMedGoogle Scholar
  9. Bongioanni S, Bianchi F, Migliardi A, Gnavi R, Pron PG, Casetta M, et al. Relation of QRS duration to mortality in a community-based cohort with hypertrophic cardiomyopathy. Am J Cardiol. 2007;100:503–6.View ArticlePubMedGoogle Scholar
  10. Morin DP, Oikarinen L, Viitasalo M, Toivonen L, Nieminen MS, Kjeldsen SE, et al. QRS duration predicts sudden cardiac death in hypertensive patients undergoing intensive medical therapy: the LIFE study. Eur Heart J. 2009;30:2908–14.View ArticlePubMedGoogle Scholar
  11. Vicente J, Johannesen L, Galeotti L, Strauss DG. Mechanisms of sex and age differences in ventricular repolarization in humans. Am Heart J. 2014;168:749–56.View ArticlePubMedGoogle Scholar
  12. Mieszczanska H, Pietrasik G, Piotrowicz K, McNitt S, Moss AJ, Zareba W. Gender-related differences in electrocardiographic parameters and their association with cardiac events in patients after myocardial infarction. Am J Cardiol. 2008;101:20–4.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Nolte IM, Jansweijer JA, Riese H, Asselbergs FW, van der Harst P, Spector TD, et al. A comparison of heritability estimates by classical twin modeling and based on genome-wide genetic relatedness for cardiac conduction traits. Twin Res Hum Genet. 2017;20:489–98.View ArticlePubMedGoogle Scholar
  14. Holm H, Gudbjartsson DF, Arnar DO, Thorleifsson G, Thorgeirsson G, Stefansdottir H, et al. Several common variants modulate heart rate, PR interval and QRS duration. Nat Genet. 2010;42:117–22.View ArticlePubMedGoogle Scholar
  15. Li J, Huo Y, Zhang Y, Fang Z, Yang J, Zang T, et al. Familial aggregation and heritability of electrocardiographic intervals and heart rate in a rural Chinese population. Ann Noninvasive Electrocardiol. 2009;14:147–52.View ArticlePubMedGoogle Scholar
  16. Mutikainen S, Ortega-Alonso A, Alén M, Kaprio J, Karjalainen J, Rantanen T, et al. Genetic influences on resting electrocardiographic variables in older women: a twin study. Ann Noninvasive Electrocardiol. 2009;14:57–64.View ArticlePubMedGoogle Scholar
  17. Sotoodehnia N, Isaacs A, de Bakker PIW, Dörr M, Newton-Cheh C, Nolte IM, et al. Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction. Nat Genet. 2010;42:1068–76.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Ritchie MD, Denny JC, Zuvich RL, Crawford DC, Schildcrout JS, Bastarache L, et al. Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk. Circulation. 2013;127:1377–85.View ArticlePubMedPubMed CentralGoogle Scholar
  19. van der Harst P, van Setten J, Verweij N, Vogler G, Franke L, Maurano MT, et al. 52 genetic loci influencing myocardial mass. J Am Coll Cardiol. 2016;68:1435–48.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Gudbjartsson DF, Helgason H, Gudjonsson SA, Zink F, Oddson A, Gylfason A, et al. Large-scale whole-genome sequencing of the Icelandic population. Nat Genet. 2015;47:435–44.View ArticlePubMedGoogle Scholar
  22. Macfarlane PW, McLaughlin SC, Devine B, Yang TF. Effects of age, sex, and race on ECG interval measurements. J Electrocardiol. 1994;27(Suppl):14–9.View ArticlePubMedGoogle Scholar
  23. Okin PM, Roman MJ, Devereux RB, Kligfield P. Gender differences and the electrocardiogram in left ventricular hypertrophy. Hypertension. 1995;25:242–9.View ArticlePubMedGoogle Scholar
  24. den Hoed M, Eijgelsheim M, Esko T, Brundel BJJM, Peal DS, Evans DM, et al. Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders. Nat Genet. 2013;45:621–31.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Sano M, Kamitsuji S, Kamatani N, Hong K-W, Han B-G, Kim Y, et al. Genome-wide association study of electrocardiographic parameters identifies a new association for PR interval and confirms previously reported associations. Hum Mol Genet. 2014;23:6668–76.View ArticlePubMedGoogle Scholar
  26. Wang Z, Li L, Su X, Gao Z, Srivastava G, Murray PG, et al. Epigenetic silencing of the 3p22 tumor suppressor DLEC1 by promoter CpG methylation in non-Hodgkin and Hodgkin lymphomas. J Transl Med. 2012;10:209.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Consortium GTE. Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.View ArticleGoogle Scholar
  28. Park CY, Pierce SA, von Drehle M, Ivey KN, Morgan JA, Blau HM, et al. skNAC, a Smyd1-interacting transcription factor, is involved in cardiac development and skeletal muscle growth and regeneration. Proc Natl Acad Sci U S A. 2010;107:20750–5.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Kim EY, Chen L, Ma Y, Yu W, Chang J, Moskowitz IP, et al. Enhanced desumoylation in murine hearts by overexpressed SENP2 leads to congenital heart defects and cardiac dysfunction. J Mol Cell Cardiol. 2012;52:638–49.View ArticlePubMedGoogle Scholar
  30. James AF, Choisy SCM, Hancox JC. Recent advances in understanding sex differences in cardiac repolarization. Prog Biophys Mol Biol. 2007;94:265–319.View ArticlePubMedGoogle Scholar
  31. Yang P-C, Clancy CE. Gender-based differences in cardiac diseases. J Biomed Res. 2011;25:81–9.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466:707–13.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Huynh K, McMullen JR, Julius TL, Tan JW, Love JE, Cemerlang N, et al. Cardiac-specific IGF-1 receptor transgenic expression protects against cardiac fibrosis and diastolic dysfunction in a mouse model of diabetic cardiomyopathy. Diabetes. 2010;59:1512–20.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Troncoso R, Ibarra C, Vicencio JM, Jaimovich E, Lavandero S. New insights into IGF-1 signaling in the heart. Trends Endocrinol Metab. 2014;25:128–37.View ArticlePubMedGoogle Scholar
  35. Lee S, Abecasis GR, Boehnke M, Lin X. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet. 2014;95:5–23.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Koo B-H, Longpré J-M, Somerville RPT, Alexander JP, Leduc R, Apte SS. Regulation of ADAMTS9 secretion and enzymatic activity by its propeptide. J Biol Chem. 2007;282:16146–54.View ArticlePubMedGoogle Scholar
  37. Wang LW, Dlugosz M, Somerville RPT, Raed M, Haltiwanger RS, Apte SS. O-fucosylation of thrombospondin type 1 repeats in ADAMTS-like-1/punctin-1 regulates secretion: implications for the ADAMTS superfamily. J Biol Chem. 2007;282:17024–31.View ArticlePubMedGoogle Scholar
  38. Kutz WE, Wang LW, Bader HL, Majors AK, Iwata K, Traboulsi EI, et al. ADAMTS10 protein interacts with fibrillin-1 and promotes its deposition in extracellular matrix of cultured fibroblasts. J Biol Chem. 2011;286:17156–67.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Cain SA, Mularczyk EJ, Singh M, Massam-Wu T, Kielty CM. ADAMTS-10 and -6 differentially regulate cell-cell junctions and focal adhesions. Sci Rep. 2016;6:35956.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Li Y, Klena NT, Gabriel GC, Liu X, Kim AJ, Lemke K, et al. Global genetic analysis in mice unveils central role for cilia in congenital heart disease. Nature. 2015;521:520–4.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Thomas SA, Schuessler RB, Berul CI, Beardslee MA, Beyer EC, Mendelsohn ME, et al. Disparate effects of deficient expression of connexin43 on atrial and ventricular conduction: evidence for chamber-specific molecular determinants of conduction. Circulation. 1998;97:686–91.View ArticlePubMedGoogle Scholar
  42. Gutstein DE, Morley GE, Tamaddon H, Vaidya D, Schneider MD, Chen J, et al. Conduction slowing and sudden arrhythmic death in mice with cardiac-restricted inactivation of connexin43. Circ Res. 2001;88:333–9.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Danik SB, Liu F, Zhang J, Suk HJ, Morley GE, Fishman GI, et al. Modulation of cardiac gap junction expression and arrhythmic susceptibility. Circ Res. 2004;95:1035–41.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Longpré J-M, McCulloch DR, Koo B-H, Alexander JP, Apte SS, Leduc R. Characterization of proADAMTS5 processing by proprotein convertases. Int J Biochem Cell Biol. 2009;41:1116–26.View ArticlePubMedGoogle Scholar
  45. World Health Organization. WHO | The Anatomical Therapeutic Chemical Classification System with Defined Daily Doses (ATC/DDD). http://www.who.int/classifications/atcddd/en/. Accessed 12 Dec 2017.
  46. Grove ML, Yu B, Cochran BJ, Haritunians T, Bis JC, Taylor KD, et al. Best practices and joint calling of the HumanExome BeadChip: the CHARGE consortium. PLoS One. 2013;8:e68095.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Wessel J, Chu AY, Willems SM, Wang S, Yaghootkar H, Brody JA, et al. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun. 2015;6:5897.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Illumina Inc. Illumina GenCall Data Analysis Software. GenCall software algorithms for clustering, calling, and scoring genotypes. San Diego: Technology Spotlight. 2005. http://www.illumina.com/Documents/products/technotes/technote_gencall_data_analysis_software.pdf.
  49. Goldstein JI, Crenshaw A, Carey J, Grant GB, Maguire J, Fromer M, et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics. 2012;28:2543–5.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Voorman A, Brody J, Chen H, Lumley T, Davis B. seqMeta: Meta-Analysis of Region-Based Tests of Rare DNA Variants. 2017. https://cran.r-project.org/web/packages/seqMeta/index.html Google Scholar
  51. Ma C. Statistical Methods for Low-frequency and Rare Genetic Variants. 2014. https://deepblue.lib.umich.edu/handle/2027.42/110435. Accessed 12 Dec 2017.Google Scholar
  52. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Pruitt KD, Tatusova T, Maglott DR. NCBI Reference sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005;33:D501–4.View ArticlePubMedGoogle Scholar
  54. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89:82–93.View ArticlePubMedPubMed CentralGoogle Scholar
  55. Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008;83:311–21.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–9.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31:3812–4.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. 2014;11:361–2.View ArticlePubMedGoogle Scholar
  59. Chun S, Fay JC. Identification of deleterious mutations within three human genomes. Genome Res. 2009;19:1553–61.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Spector TD, Williams FMK. The UK adult twin registry (TwinsUK). Twin Res Hum Genet. 2006;9:899–906.View ArticlePubMedGoogle Scholar
  61. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Loh P-R, Tucker G, Bulik-Sullivan BK, Vilhjálmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet. 2015;47:284–90.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.View ArticlePubMedPubMed CentralGoogle Scholar
  64. Mägi R, Morris AP. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics. 2010;11:288.View ArticlePubMedPubMed CentralGoogle Scholar
  65. Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics. 2016;32:3207–9.View ArticlePubMedPubMed CentralGoogle Scholar
  66. Prins BP, Mead TJ, Brody JA, Sveinbjornsson G, Ntalla I, Bihlmeyer NA, et al. Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6, Data sets. dbGAP. https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000287.v6.p1

Copyright

© The Author(s). 2018

Advertisement

Genome Biology

ISSN: 1474-760X

Contact us