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Whole-genome sequencing analysis of clozapine-induced myocarditis


One of the concerns limiting the use of clozapine in schizophrenia treatment is the risk of rare but potentially fatal myocarditis. Our previous genome-wide association study and human leucocyte antigen analyses identified putative loci associated with clozapine-induced myocarditis. However, the contribution of DNA variation in cytochrome P450 genes, copy number variants and rare deleterious variants have not been investigated. We explored these unexplored classes of DNA variation using whole-genome sequencing data from 25 cases with clozapine-induced myocarditis and 25 demographically-matched clozapine-tolerant control subjects. We identified 15 genes based on rare variant gene-burden analysis (MLLT6, CADPS, TACC2, L3MBTL4, NPY, SLC25A21, PARVB, GPR179, ACAD9, NOL8, C5orf33, FAM127A, AFDN, SLC6A11, PXDN) nominally associated (p < 0.05) with clozapine-induced myocarditis. Of these genes, 13 were expressed in human myocardial tissue. Although independent replication of these findings is required, our study provides preliminary insights into the potential role of rare genetic variants in susceptibility to clozapine-induced myocarditis.


Clozapine initiation for patients suffering from treatment-resistant schizophrenia is recommended at the earliest possible opportunity by clinical guidelines [1, 2], and there is evidence for its superior efficacy in this difficult-to-treat population compared with other pharmacological treatments [3]. Regulatory requirements around serious adverse event monitoring have largely addressed the risk of undiagnosed agranulocytosis. However, there has been ongoing concern about a serious idiosyncratic adverse drug reaction (ADR) involving inflammation of the myocardium.

In 1999, Kilian and colleagues [4] reported the rate of myocarditis (both fatal and non-fatal) as 1.9 per 1000 patients starting clozapine. However, more recent projections suggest rates as high as 3% in the context of systematic monitoring, with fatality rates ranging from 7% to 75% [5]. Despite these alarming rates, our current ability to identify those at greatest risk for clozapine-induced myocarditis is inadequate. This uncertainty has led to reduced clozapine prescription and arguably poorer patient outcomes. Although research to uncover risk factors has identified a few clinical markers [6, 7], these factors do not account for all the risk. As such, identification of additional markers is needed to reduce or prevent fatalities and morbidity resulting from clozapine-induced myocarditis, potentially facilitating expanded and safer use of this highly effective medication.

Genomic analysis is a promising and largely unexplored approach for identifying those at risk of idiosyncratic ADRs. In fact, our recent genome-wide association study (GWAS) and human leucocyte antigen (HLA) analyses of clozapine-induced myocarditis identified four independent loci (SIX3, EFHC1, ADAM7, and GNA15) suggestive of increased myocarditis risk (P < 1 × 10−6), with odds ratios ranging from 5.5 to 13.7. We also found that the HLA-C*07:01 allele was associated with 2.89-fold (95% CI: 1.11–7.53) greater odds of clozapine-induced myocarditis [8]. To build on these promising findings, we undertook the first whole-genome sequencing study to examine different classes of DNA variation not measured in our previous genetic study, including rare deleterious coding variants, copy number variants, and candidate variation in pharmacogenes associated with clozapine metabolism. Herein, we report our results among a cohort of individuals with and without a history of clozapine-induced myocarditis, in collaboration with members of the Pharmacogenomics of Clozapine-Induced Myocarditis (PROCLAIM) Consortium [9].


Study participants

Participants with (n = 25) and without (n = 25) a history of clozapine-induced myocarditis were recruited by investigators affiliated with the PROCLAIM Consortium at the University of Calgary, University of Melbourne, and Monash University. For this study, we included 34 participants (19 cases and 19 controls) included in our previous GWAS study [8]. For each recruited case of clozapine-induced myocarditis, an age (±5 years), sex, and ancestry-matched clozapine-tolerant control was recruited from the same site. Genetic ancestry of controls and cases was further assessed by principal component analysis using the smartpca module of the EIGENSOFT package (Supplementary Fig. S1). Individuals were eligible for inclusion into the study if they: (a) were aged 18 to 65 years, (b) had a clinical diagnosis of a schizophrenia-spectrum disorder, (c) had accessible medical records, and (d) had a history of clozapine therapy. For cases, an additional inclusion criterion was a history of myocarditis evidenced by abnormal troponin I/T levels (>2 times upper limit of normal) or signs of left ventricular dysfunction with or without elevated C-reactive protein (CRP) levels (>100 mg/L) during the first 30 days of clozapine therapy, which aligns with current monitoring guidelines [10]. Importantly, cases with an infection at the onset of clozapine-induced myocarditis were excluded as part of the differential diagnosis of myocarditis. An additional inclusion criterion for clozapine-tolerant controls was evidence of clozapine therapy for a minimum of 45 days with no documented history or clinical suspicion of myocarditis. The present study was approved by the Melbourne Health Human Research Ethics Committee and University of Calgary Conjoint Health and Research Ethics Board. The study also complied with the Declaration of Helsinki and its subsequent revisions. All participants or next of kin provided written informed consent prior to participation.

Whole genome sequencing

Genomic DNA (750–1000 ng, A260/A280 ratio >1.8) was isolated from whole blood samples using standard procedures and shipped to the Centre for Applied Genomics at the Hospital for Sick Children (Toronto, Canada) for library preparation and sequencing. Libraries were prepared for all samples using the Illumina TruSeq PCR-free DNA library preparation kit. Each library was subjected to 150 base, paired end sequencing on three lanes of the Illumina HiSeqX.

The Illumina DRAGEN (version 07.021.382.3.4.9, Dynamic Read Analysis for GENomics) Bio-IT platform was used for aligning reads to the human genome (reference build hg19). After alignment, sorting and duplicate marking was performed to generate aligned BAM files. Next, sample-wise gVCF files were generated using DRAGEN’s haplotype calling algorithm and then these files were run through the joint calling algorithm to generate a joint VCF file. dbSNP (v150) was used to annotate variants. The jointly called VCF file was further recalibrated using the Variant Quality Score Recalibration (VQSR) algorithm to produce a high-quality variant dataset. The VQSR model was trained for single nucleotide polymorphisms (SNPs) and insertion/deletions (indels) using high quality variant data from different resources (see Supplementary Methods for more details). For SNPs, the VQSR model was trained using data from HapMap3.3, 1000 Genomes Project (Phase 1 SNP sites and Omni 2.5 M SNP array) and dbSNP (v150), with a sensitivity threshold of 99.5%. For indels, the VQSR was trained using data in the 1000 Genomes Project (gold standard sites) and Illumina Platinum sites, with a sensitivity threshold of 99.0%. Only “PASS” calls were annotated using ANNOVAR after VQSR implementation.

Pharmacogenetic variant analysis

The Stargazer v1.08 [11] tool was used to call diplotypes, calculate activity scores, and assign genotype-inferred metabolizer phenotypes for seven genes (CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP2E1) relevant to clozapine metabolism [12]. Stargazer uses whole genome sequencing data to find phased haplotypes comprised of SNPs, small indels, and large structural variants, and then maps these haplotypes to star alleles based on translation tables maintained by the Pharmcogene Variation Consortium [13]. Notably, previous work has shown Stargazer recalled 100% of star alleles present in the Genetic Testing Reference Material Coordination Program’s consensus genotypes [11]. Furthermore, a comparison of Stargazer-predicted diplotypes for the seven target pharmcogenes with the diplotypes generated by the PharmacoScan Solution (Thermofisher, Carlsbad, USA) array in a subset of our sample (n = 22) tested on both platforms found high diplotype (94.8%) and phenotype (98.1%) concordance (Supplementary Table S1).

Prior to analysis, activity scores for the seven target pharmacogenes were corrected for concomitant inhibitors or inducers (Supplementary Table S2) using a method previously described [14]. In brief, activity scores for one or more of the seven genes were multiplied by zero if a strong inhibitor of the corresponding enzyme was present. For moderate inhibitors the activity score was multiplied by 0.5 and for inducers the activity score was multiplied by 1.5. In cases where both an inhibitor and inducer of the same enzyme were present, the uncorrected activity score for the corresponding gene was retained, as consensus on how to correct activity scores in this situation has not been established. The corrected activity scores were then used to assign a corrected metabolizer phenotype for each of the seven target genes. To determine differences in activity scores and metabolizer phenotypes between the cases and controls, we used the Mann-Whitney U and chi-square tests, respectively. P values were corrected for multiple testing using the Benjamini–Hochberg (B–H) step-up procedure [15].

Copy number variant (CNV) analysis

CNVs (variants with length ≥1 kb) were called using the DRAGEN algorithm and self-normalization method. A merged CNV file was created by compiling copy number state and segmentation mean values for all samples. Segmentation mean values were log transformed to construct copy number variable regions (CNVRs). Ten regions where segmentation mean and log transformed segmentation values had conflicting CNV status were excluded (Supplementary Table S3). We used two different tools - CNVRuler [16] and CoNVaQ [17] to construct CNVRs and perform CNVR association analyses. Using CNVRuler, logistic regression was performed using the first two principal components as covariates, and a minor allele frequency threshold of greater than 0.1. CNVRuler performs multiple testing using the B–H step-up procedure [15]. CNVR association analysis was also performed using CoNVaQ with permutation (n = 4000) testing by swapping phenotype labels. CNVR encompassing genes were retrieved from the UCSC Genome Browser.

Gene burden analysis and functional annotation of candidate genes

We performed burden analysis using rare and deleterious variants, defined based on the minor allele frequency from global databases and combined annotation dependent depletion (CADD) annotation score using ANNOVAR software [18]. A variant was considered rare if the maximum allele frequency was less than 0.05 in the 1000 Genomes Project [19], ESP6500 [20], ExAC [21], CG46 [22] or gnomAD databases [23]. A deleterious variant was defined by a CADD Phred score greater than 20. We used CADD scores to include functionally important variants from protein-coding and non-coding regions. Variants that met both criteria were extracted using BCFtools (v1.9) [24] and burden analysis was performed on these selected variants. Two different methods were used––Combined and Multivariate Collapsing (CMC) and the sequencing kernel association test (SKAT-O), both implemented in RVTESTS [25]. Permutation analysis (10,000 iterations) was carried out in R by permuting phenotype labels (case and control) of observations (i.e. total allele counts) to derive empirical p values. We also used TRAPD (Testing Rare vAriants using Public Data) [26] software to compute the frequency of rare and deleterious alleles of prioritised genes using genome data from 15,708 individuals in the gnomAD v2 database (build hg19) [23] and then compared these frequencies with those observed in our cases and controls using a two proportion Z-test. P values were corrected using the Bonferroni method.

To evaluate the tolerance of genes for functional genetic variation, Residual Variation Intolerance Score (RVIS) scores were fetched using the web-based tool Gene Intolerance [27]. RVIS scores are used to rank genes in terms of their functional tolerance [28]. Genes with negative scores represent less tolerance to functional variations. Gene enrichment analysis was performed using ToppFun [29]. Finally, we assessed the mRNA expression of candidate genes in left ventricular-septal tissue from 21 healthy adult donors using published RNASeq data [30]. Raw RNASeq data was retrieved from the Zenodo Data repository [30]. Raw read counts were normalized with DESeq2, using the median of ratios method [31]. We also downloaded RNA expression data from the Human Protein Atlas [32] to analyze the expression of candidate genes in 62 other tissues.


A summary of the study design and key results are shown in Fig. 1.

Fig. 1: PROCLAIM study design, analysis and key results.
figure 1

Age, sex and ethnicity matched treatment resistant schizophrenia patients who did (cases) or did not (controls) develop myocarditis following clozapine exposure were recruited. Using whole genome sequencing data, candidate pharmacogenetic variants, copy number variable regions and rare variants were examined for their association with clozapine-induced myocarditis.

Study participants and sequencing

Participant characteristics and relevant clinical information for the two patient groups are shown in Table 1. Cases and controls did not differ significantly in their demographic or clinical characteristics, with the exception of titration slope. On average, cases received a faster titration than controls (p = 0.006). However, post hoc comparisons of case and control titration slopes with those recommended in titration protocols used in Canada [33], Australia [34], and United Kingdom [35] showed that neither case nor control slopes differed from these national recommendations (Fig. 2). In addition, post hoc examination showed no difference in titration slopes by ancestry (Supplementary Fig. S2).

Table 1 Demographic and clinical characteristics of participants with (N = 25) and without (N = 25) a history of clozapine-induced myocarditis.
Fig. 2: Distribution of titration slopes by history of clozapine-induced myocarditis.
figure 2

Cases differed significantly from controls (p = 0.006). Dashed lines represent titration slopes recommended by clozapine dosing protocols in Canada, United Kingdom, and Australia. Case titration slopes did not differ from slopes recommended by protocols used in Canada (p = 0.544, United Kingdom (p = 0.795), or Australia (p = 0.259). Likewise, control titration slopes did not differ from slopes recommended by protocols used in Canada (p = 0.187, United Kingdom (p = 0.248), or Australia (p = 0.869).

One of the control samples failed initial quality assessment post-sequencing, resulting in 25 cases and 24 controls available for further analysis with an average depth of reads and average read mapping across the samples of 33.65x and 92.9%, respectively (Supplementary Fig. S3). Ti/Tv (transition/transversion) ratio for both raw (2.00) and filtered (2.02) calls were in the acceptable whole genome range. On average, there were 3,833,700 SNPs, 953,459 indels and 619 CNVs per individual (Supplementary Table S4). The final jointly called dataset contained 16,014,407 variants (both SNPs and indels).

Pharmacogenetic variants

Observed diplotype frequencies for the seven targeted pharmacogenes, along with the corresponding uncorrected activity scores and genotype-inferred metabolizer phenotypes among cases and controls, are shown in Supplementary Table S5. No significant differences in uncorrected or corrected activity scores were found between cases and controls (Supplementary Table S6) nor were differences detected in the distribution of genotype-inferred metabolizer phenotypes (Supplementary Table S7). Furthermore, titration slopes were not correlated with activity scores for the seven targeted pharmacogenes in cases or controls (Supplementary Table S8). Importantly, post hoc gene burden analysis did not reveal any differences in enrichment of rare and deleterious variants in the seven genes (CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP2E1) known to be involved in clozapine metabolism.

Copy number variants

We identified 30,340 CNVs across the 49 samples. Comparing the average number of CNVs between cases and controls revealed no statistically significant differences in losses (cases: mean = 498, ±standard deviation = 28; controls: 503 ± 19, p = 0.42) or gains (cases: 102 ± 10 vs. controls: 117 ± 8, p = 0.26) (Supplementary Table S9). We also did not detect significant differences with respect to the average size of the losses (cases: 10.54 ± 61.6 kb vs. controls: 10.57 ± 59.5 kb, p = 0.97) or gains (cases: mean 141.37 kb, sd = 721.9 kb; controls: mean = 134.01 kb, sd = 533.5 kb, p = 0.66) (Supplementary Table S9). The CNVRuler constructed 3,178 copy number variable regions (CNVRs) among which five were found to be significant (p < 0.01, Supplementary Table S10), while the CoNVaQ tool resulted in ten significant CNVRs (p < 0.01, Supplementary Table S11). However, no CNVR survived permutation analysis or correction for multiple testing.

Rare and deleterious single nucleotide variants

A total of 22,703 rare and deleterious variants from 8,442 genes (out of 22,480 genic regions scanned) met criteria for gene burden analysis. On average, cases and controls had 739 ± 51, and 735 ± 52 rare and deleterious variants per sample, respectively (p = 0.779). We identified 114 and 44 genes (p < 0.05) using CMC and SKAT-O, respectively and selected 42 common genes for prioritization analysis (Supplementary Fig. S4). Out of 42 genes, 15 genes had a rare and deleterious variant in our cohort (Table 2). All genes survive permutation analysis with MLLT6 as the top candidate (p = 4.2 × 10−3) but no gene survived multiple testing correction. However, compared to the frequencies of rare and deleterious variants in the gnomAD cohort, cases had significantly greater frequencies for seven (MLLT6, CADPS, TACC2, L3MBTL4, NOL8, FAM172A, AFDN) of the 15 candidate genes, after Bonferroni correction (Table 2). Frequencies in controls did not differ from those observed in the gnomAD cohort for any of the 15 genes. Average depth of rare and deleterious variants in the candidate genes was 36.59x. Sample-wise allelic depth of variants is also provided in Supplementary Table 12. A sensitivity analysis confined to individuals of European ancestry (17 cases, 18 controls) identified seven genes (MLLT6, TACC2, FAM172A, NPY, NOL8, SLC6A11, MLLT4), all of which were included in the 15 genes identified in the full cohort analysis (Supplementary Table 13).

Table 2 Top hits from gene burden analysis.

Gene intolerance analysis showed CADPS, AFDN, and MLLT6 were the most intolerant to rare and deleterious variation, respectively ranking amongst the 2nd, 5th, and 6th percentile of the most intolerant genes. Gene enrichment analysis of the 15 nominally significant genes revealed no significant functional category or pathway. Likewise, gene enrichment analysis including of the 15 nominally significant genes and the four candidate genes from our previous GWAS (i.e, SIX3, EFHC1, ADAM7, GNA15) revealed no significant functional category or pathway. However, all 15 genes, with the exception of GPR179 and SLC6A11, were expressed in left ventricular septal tissue from healthy human adult donors with TACC2 and MLLT6 having the highest expression (Fig. 3). Among the GWAS candidates, only SIX3, EFHC1, and GNA15 were expressed in left ventricular septal tissue (Supplementary Fig. S5). Expression of the 15 candidate genes in 62 other tissues included in the Human Protein Atlas are provided in Supplementary Table 14.

Fig. 3: Normalised RNASeq read counts of the 15 nominally associated loci identified in gene burden analysis in left ventricular septal tissue of healthy adult donors (n = 21).
figure 3

MLLT6 PHD Finger Containing, CADPS Calcium Dependent Secretion Activator, TACC2 Transforming Acidic Coiled-Coil Containing Protein 2, L3MBTL4 L3MBTL Histone Methyl-Lysine Binding Protein 4, NPY Neuropeptide Y, SLC25A21 Solute Carrier Family 25 Member 21, PARVB Parvin Beta, GPR179 G Protein-Coupled Receptor 179, ACAD9 Acyl-CoA Dehydrogenase Family Member 9, NOL8 Nucleolar Protein 8, NADK2 NAD Kinase 2, Mitochondrial, FAM172A Family With Sequence Similarity 172 Member A, AFDN Afadin, Adherens Junction Formation Factor, SLC6A11 Solute Carrier Family 6 Member 11, PXDN Peroxidasin.


Using a whole-genome sequencing approach, our study sought to identify associations between three different and unexplored classes of DNA variation (pharmacogenetic, CNVs, rare variants) and clozapine-induced myocarditis [8]. Although we did not identify a single robust genetic marker for differentiating cases from controls, we identified 15 novel genes nominally associated with this important and sometimes fatal ADR - which continues to limit the use of clozapine, a highly effective medication for schizophrenia. Among the novel candidate genes identified, 13 were found to be expressed in human myocardium, specifically the ventricular septum, providing biological plausibility of a link between these genes and clozapine-induced myocarditis. Although the mechanisms by which these genes may increase risk for clozapine-induced myocarditis remain unclear and require further study, including replication in a separate patient cohort, the genes identified now represent novel targets to inform future functional and clinical studies. Such studies may subsequently improve our understanding of the role played by genetic risk of this serious ADR.

Our findings add to the list of candidate genes identified in our previous GWAS (GNA15, ADAM7, SIX3, EFHC1) and HLA (HLA-C*07:01) studies of clozapine-induced myocarditis [8] and suggests that the genetic architecture of this serious ADR is complex and likely polygenic. In fact, each of the candidate genes identified to date have low sensitivity and positive predictive value, limiting their clinical utility. This notion aligns with genomic studies of clozapine-induced agranulocytosis, another rare clozapine ADR, that has been associated with variants from several genes with modest sensitivity (range: 11–36%) [36]. This current state of the evidence coupled with low incidence of clozapine-induced myocarditis highlights the need for international collaboration to facilitate the rapid ascertainment of cases that will ultimately provide the statistical power required to unravel its complex genetic etiology. Recognizing this need, we formed the PROCLAIM Consortium in 2017 that currently comprises 15 recruitment sites across four countries (Canada, USA, Australia, Turkey) [9] and are actively looking for new sites to join the consortium.

Our gene enrichment analysis was inconclusive and post hoc exploration of the 15 genes using the published literature showed limited to no associations with phenotypes related to myocarditis, inflammation, or adverse drug reactions with few exceptions (see Supplementary Discussion). However, this is not unsurprising given the lack of previously published genetic studies examining this ADR. One notable exception was neuropeptide Y (NPY), which has a well-established role in modulating cardiac contractibility, protein degradation, and proliferation of cardiomyocytes and has been implicated in the pathogenesis of various cardiovascular morbidities (e.g., cardiac hypertrophy, arrhythmias, heart failure) [37]. Furthermore, clinical associations between NPY genetic variation and antipsychotic-induced weight gain have been reported [38]. Weight gain and related increases in body mass index (BMI) related to antipsychotic treatment could be clinically relevant given the number of antipsychotic trials patients undergo prior to commencing clozapine and previous work showing high BMI increases clozapine-induced myocarditis fatal outcomes [6, 39]. Notably, BMI did not statistically differ between our cases and controls, although there was a trend (p = 0.107, Cohen’s d = 0.42) toward higher BMIs in cases. Given the paucity of genetic markers for clozapine-induced myocarditis, future investigation of rare genetic variation in NPY and the other 14 putative candidate genes should be considered.

Our findings should be interpreted in the context of notable caveats. Since clozapine-induced myocarditis is a relatively rare event––even despite our international consortium - our patient sample size is small and had suboptimal statistical power to detect small-moderate effect sizes. This was partially mitigated by our demographically-matched case-control design and the deployment of an analysis plan appropriate for small sample sizes. Nevertheless, our results are preliminary. We did not validate the candidate rare variants using an alternative method (e.g., Sanger sequencing) and as such independent replication is required before firm conclusions can be drawn. Likewise, our negative pharmacogenetic results should be interpreted cautiously. Our cohort had few non-normal metabolizers, which inhibits us from ruling out a potential association between altered clozapine metabolism and clozapine-induced myocarditis. Furthermore, we examined each CYP450 activity score separately rather than in combination with each other. Although a combinatorial approach could be insightful, it was not applied because the in vivo data required to weight each CYP450 activity score by its relative contribution to clozapine metabolism is not currently available. Finally, functional characterization of the identified genes was limited to existing gene expression data derived from healthy adult human left ventricular septal tissue due to the limited biological material and clinical data available from our study participants. Notably, the PROCLAIM Consortium is currently developing a novel in vitro model using patient-derived induced pluripotent stem cells differentiated into beating cardiomyocytes to understand how clozapine induces myocardial inflammation and to investigate the impact common and rare genetic variants and/or epigenetic modifications have on cardiomyocyte structure and function when exposed to clozapine.

In summary, our whole-genome sequencing analysis identified 15 genes nominally associated with clozapine-induced myocarditis that provide novel candidates for future studies. With the possible exception of NPY, the mechanism by which these genes increase risk for myocarditis is unclear. Although these candidate genes will require independent validation and their roles in the development of clozapine-induced myocarditis remain to be tested, they provide potential insights into the genomic underpinnings and mechanisms by which clozapine induces myocardial inflammation and damage, that may help improve clinical management in the future.


  1. McGorry P, Killackey E, Lambert T, Lambert M, Jackson H, Codyre D, et al. Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for the treatment of schizophrenia and related disorders. Aust NZ J psychiatry. 2005;39:1–30.

    Article  Google Scholar 

  2. NICE (2014). Psychosis and schizophrenia in adults: treatment and management.

  3. Siskind D, McCartney L, Goldschlager R, Kisely S. Clozapine v. first- and second-generation antipsychotics in treatment-refractory schizophrenia: systematic review and meta-analysis. Br J Psychiatry. 2016;209:385–92.

    Article  Google Scholar 

  4. Kilian JG, Kerr K, Lawrence C, Celermajer DS. Myocarditis and cardiomyopathy associated with clozapine. Lancet. 1999;354:1841–5.

    CAS  Article  Google Scholar 

  5. Ronaldson KJ, Fitzgerald PB, McNeil JJ. Clozapine-induced myocarditis, a widely overlooked adverse reaction. Acta Psychiatr Scandinavica. 2015;132:231–40.

    CAS  Article  Google Scholar 

  6. Ronaldson KJ, Fitzgerald PB, Taylor AJ, Topliss DJ, Wolfe R, McNeil JJ. Rapid clozapine dose titration and concomitant sodium valproate increase the risk of myocarditis with clozapine: A case-control study. Schizophrenia Res. 2012;141:173–8.

    Article  Google Scholar 

  7. Youssef DL, Narayanan P, Gill N. Incidence and risk factors for clozapine-induced myocarditis and cardiomyopathy at a regional mental health service in Australia. Australas Psychiatry. 2016;24:176–80.

    Article  Google Scholar 

  8. Lacaze P, Ronaldson KJ, Zhang EJ, Alfirevic A, Shah H, Newman L, et al. Genetic associations with clozapine-induced myocarditis in patients with schizophrenia. Transl Psychiatry. 2020;10:37.

    CAS  Article  Google Scholar 

  9. Bousman CA, Greenway SC, Tarailo-Graovac M, Long Q, Sellmer R, Crockford D, et al. The pharmacogenomics of clozapine-induced myocarditis (PROCLAIM) consortium. Neuropsychopharmacology. 2018;43:S97–8.

    Google Scholar 

  10. Ronaldson KJ, Fitzgerald PB, Taylor AJ, Topliss DJ, McNeil JJ. A new monitoring protocol for clozapine-induced myocarditis based on an analysis of 75 cases and 94 controls. Aust Nz J Psychiat. 2011;45:458–65.

    Article  Google Scholar 

  11. Lee SB, Wheeler MM, Thummel KE, Nickerson DA. Calling star alleles with stargazer in 28 pharmacogenes with whole genome sequences. Clin Pharm Ther. 2019;106:1328–37.

    CAS  Article  Google Scholar 

  12. Thorn CF, Müller DJ, Altman RB, Klein TE. PharmGKB summary: clozapine pathway, pharmacokinetics. Pharmacogenet Genomics. 2018;28:214–22.

    CAS  Article  Google Scholar 

  13. Gaedigk A, Ingelman-Sundberg M, Miller NA, Leeder JS, Whirl-Carrillo M, Klein TE, et al. The Pharmacogene Variation (PharmVar) Consortium: Incorporation of the Human Cytochrome P450 (CYP) Allele Nomenclature Database. Clin Pharm Ther. 2018;103:399–401.

    CAS  Article  Google Scholar 

  14. Lesche D, Mostafa S, Everall I, Pantelis C, Bousman CA. Impact of CYP1A2, CYP2C19, and CYP2D6 genotype- and phenoconversion-predicted enzyme activity on clozapine exposure and symptom severity. Pharmacogenomics J. 2020;20:192–201.

    CAS  Article  Google Scholar 

  15. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol). 1995;57:289–300.

    Google Scholar 

  16. Kim JH, Hu HJ, Yim SH, Bae JS, Kim SY, Chung YJ. CNVRuler: a copy number variation-based case-control association analysis tool. Bioinformatics. 2012;28:1790–2.

    CAS  Article  Google Scholar 

  17. Larsen SJ, do Canto LM, Rogatto SR, Baumbach J. CoNVaQ: a web tool for copy number variation-based association studies. BMC Genomics. 2018;19:369.

    Article  Google Scholar 

  18. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.

    Article  Google Scholar 

  19. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.

    Article  Google Scholar 

  20. Fu W, O’Connor TD, Jun G, Kang HM, Abecasis G, Leal SM, et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature. 2013;493:216–20.

    CAS  Article  Google Scholar 

  21. Karczewski KJ, Weisburd B, Thomas B, Solomonson M, Ruderfer DM, Kavanagh D, et al. The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res. 2017;45(D1):D840–5.

    CAS  Article  Google Scholar 

  22. Drmanac R, Sparks AB, Callow MJ, Halpern AL, Burns NL, Kermani BG, et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science. 2010;327:78–81.

    CAS  Article  Google Scholar 

  23. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–43.

    CAS  Article  Google Scholar 

  24. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27:2987–93.

    CAS  Article  Google Scholar 

  25. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ. RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data. Bioinformatics. 2016;32:1423–6.

    CAS  Article  Google Scholar 

  26. Guo MH, Plummer L, Chan YM, Hirschhorn JN, Lippincott MF. Burden Testing of Rare Variants Identified through Exome Sequencing via Publicly Available Control Data. Am J Hum Genet. 2018;103:522–34.

    CAS  Article  Google Scholar 

  27. Petrovski S, Wang Q, Heinzen EL, Allen AS, Goldstein DB. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 2013;9:e1003709.

    CAS  Article  Google Scholar 

  28. Petrovski S, Gussow AB, Wang Q, Halvorsen M, Han Y, Weir WH, et al. The intolerance of regulatory sequence to genetic variation predicts gene dosage sensitivity. PLoS Genet. 2015;11:e1005492.

    Article  Google Scholar 

  29. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37:W305–11.

    CAS  Article  Google Scholar 

  30. Hahn VS, Knutsdottir H, Luo X, Bedi K, Margulies KB, Haldar SM, et al. Myocardial gene expression signatures in human heart failure with preserved ejection fraction. Circulation. 2021;143:120–34.

    CAS  Article  Google Scholar 

  31. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  Google Scholar 

  32. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map Hum proteome Sci. 2015;347:1260419.

    Google Scholar 

  33. Health Canada. Clozapine Product Monograph. Health Canada: Ottawa. 2020.

  34. ACSQHC. National Adult Clozapine Titration Chart User Guide. Australian Commission on Safety and Quality in Health Care Sydney. 2012.

  35. NHS (2019). CLOZAPINE TREATMENT GUIDELINES. National Health Service Foundation Trust: United Kingdom.

  36. Legge SE, Walters JT. Genetics of clozapine-associated neutropenia: recent advances, challenges and future perspective. Pharmacogenomics. 2019;20:279–90.

    CAS  Article  Google Scholar 

  37. Tan CMJ, Green P, Tapoulal N, Lewandowski AJ, Leeson P, Herring N. The role of neuropeptide Y in cardiovascular health and disease. Front Physiol. 2018;9:1281.

    Article  Google Scholar 

  38. Shams TA, Müller DJ. Antipsychotic induced weight gain: genetics, epigenetics, and biomarkers reviewed. Curr Psychiatry Rep. 2014;16:473.

    Article  Google Scholar 

  39. Ronaldson KJ, Fitzgerald PB, Taylor AJ, Topliss DJ, McNeil JJ. Clinical course and analysis of ten fatal cases of clozapine-induced myocarditis and comparison with 66 surviving cases. Schizophrenia Res. 2011;128:161–5.

    Article  Google Scholar 

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The work was supported in part by the University of Calgary Cumming School of Medicine, Alberta Children’s Hospital Research Institute, and University of Melbourne Establishment Grant. P.L is supported by a National Heart Foundation Future Leader Fellowship (102604). JMCN is supported by an NHMRC Leadership award (IG1173690). CP was supported by a National Health and Medical Research Council (NHMRC) L3 Investigator Grant (1196508) and NHMRC Program Grant (ID: 566529). Access to high performance computing was provided by The Centre for Health Genomics and Informatics (CHGI) at University of Calgary.

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CB and CP conceptualized the study. AN and CB conducted the analyses and wrote the first draft of the manuscript. PL, KR, JM, MJ, NT, RS, DC, RS, SG, and CP contributed to subsequent drafts of the manuscript. All authors approved the final manuscript.

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Correspondence to Chad A. Bousman.

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CAB is the founder and a shareholder of Sequence2Script Inc and a member of the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Pharmacogene Variation Consortium (PharmVar). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Narang, A., Lacaze, P., Ronaldson, K.J. et al. Whole-genome sequencing analysis of clozapine-induced myocarditis. Pharmacogenomics J 22, 173–179 (2022).

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