The genetic architecture of pediatric cardiomyopathy

To understand the genetic contribution to primary pediatric cardiomyopathy, we performed exome sequencing in a large cohort of 528 children with cardiomyopathy. Using clinical interpretation guidelines and targeting genes implicated in cardiomyopathy, we identified a genetic cause in 32% of affected individuals. Cardiomyopathy sub-phenotypes differed by ancestry, age at diagnosis, and family history. Infants < 1 year were less likely to have a molecular diagnosis (p < 0.001). Using a discovery set of 1,703 candidate genes and informatic tools, we identified rare and damaging variants in 56% of affected individuals. We see an excess burden of damaging variants in affected individuals as compared to two independent control sets, 1000 Genomes Project (p < 0.001) and SPARK parental controls (p < 1 × 10−16). Cardiomyopathy variant burden remained enriched when stratified by ancestry, variant type, and sub-phenotype, emphasizing the importance of understanding the contribution of these factors to genetic architecture. Enrichment in this discovery candidate gene set suggests multigenic mechanisms underlie sub-phenotype-specific causes and presentations of cardiomyopathy. These results identify important information about the genetic architecture of pediatric cardiomyopathy and support recommendations for clinical genetic testing in children while illustrating differences in genetic architecture by age, ancestry, and sub-phenotype and providing rationale for larger studies to investigate multigenic contributions. To understand the genetic contribution to primary pediatric cardiomyopathy, we performed exome sequencing in a large cohort of 528 children with cardiomyopathy. Using clinical interpretation guidelines and targeting genes implicated in cardiomyopathy, we identified a genetic cause in 32% of affected individuals. Cardiomyopathy sub-phenotypes differed by ancestry, age at diagnosis, and family history. Infants < 1 year were less likely to have a molecular diagnosis (p < 0.001). Using a discovery set of 1,703 candidate genes and informatic tools, we identified rare and damaging variants in 56% of affected individuals. We see an excess burden of damaging variants in affected individuals as compared to two independent control sets, 1000 Genomes Project (p < 0.001) and SPARK parental controls (p < 1 × 10−16). Cardiomyopathy variant burden remained enriched when stratified by ancestry, variant type, and sub-phenotype, emphasizing the importance of understanding the contribution of these factors to genetic architecture. Enrichment in this discovery candidate gene set suggests multigenic mechanisms underlie sub-phenotype-specific causes and presentations of cardiomyopathy. These results identify important information about the genetic architecture of pediatric cardiomyopathy and support recommendations for clinical genetic testing in children while illustrating differences in genetic architecture by age, ancestry, and sub-phenotype and providing rationale for larger studies to investigate multigenic contributions. IntroductionCardiomyopathy is a rare heart muscle disease that can lead to heart failure and mortality.1Lipshultz S.E. Orav E.J. Wilkinson J.D. Towbin J.A. Messere J.E. Lowe A.M. Sleeper L.A. Cox G.F. Hsu D.T. Canter C.E. et al.Risk stratification at diagnosis for children with hypertrophic cardiomyopathy: an analysis of data from the Pediatric Cardiomyopathy Registry.Lancet. 2013; 382: 1889-1897Abstract Full Text Full Text PDF PubMed Scopus (123) Google Scholar, 2Pahl E. Sleeper L.A. 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Med. 2015; 17: 880-888Abstract Full Text Full Text PDF PubMed Scopus (251) Google Scholar This is a problem because cardiomyopathy in children is more genetically heterogeneous and can encompass syndromic, metabolic, and neuromuscular causes in addition to primary cardiomyopathies.6Norrish G. Ding T. Field E. Ziólkowska L. Olivotto I. Limongelli G. Anastasakis A. Weintraub R. Biagini E. Ragni L. et al.Development of a Novel Risk Prediction Model for Sudden Cardiac Death in Childhood Hypertrophic Cardiomyopathy (HCM Risk-Kids).JAMA Cardiol. 2019; 4: 918-927Crossref PubMed Scopus (84) Google Scholar,12Hershberger R.E. Givertz M.M. Ho C.Y. Judge D.P. Kantor P.F. McBride K.L. Morales A. Taylor M.R.G. Vatta M. Ware S.M. Genetic Evaluation of Cardiomyopathy-A Heart Failure Society of America Practice Guideline.J. Card. Fail. 2018; 24: 281-302Abstract Full Text Full Text PDF PubMed Scopus (180) Google Scholar, 13Kindel S.J. Miller E.M. Gupta R. Cripe L.H. Hinton R.B. Spicer R.L. Towbin J.A. 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Res. 2017; 121: 855-873Crossref PubMed Scopus (125) Google Scholar While variants in sarcomeric genes are reported in children’s cardiomyopathy as well,18Morita H. Rehm H.L. Menesses A. McDonough B. Roberts A.E. Kucherlapati R. Towbin J.A. Seidman J.G. Seidman C.E. Shared genetic causes of cardiac hypertrophy in children and adults.N. Engl. J. Med. 2008; 358: 1899-1908Crossref PubMed Scopus (292) Google Scholar whether there are pediatric-specific genes is not clear. Indeed, a Finnish study of 66 children with cardiomyopathy referred for transplant evaluation over 20 years identified metabolic, sarcomeric, and syndromic causes in 39% of these sickest of children and identified at least one novel gene associated with disease.19Vasilescu C. Ojala T.H. Brilhante V. Ojanen S. Hinterding H.M. Palin E. Alastalo T.P. Koskenvuo J. Hiippala A. Jokinen E. et al.Genetic Basis of Severe Childhood-Onset Cardiomyopathies.J. Am. Coll. 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Heart Assoc. 2019; 8: e012531Crossref PubMed Scopus (15) Google Scholar, 26Ware S.M. Wilkinson J.D. Tariq M. Schubert J.A. Sridhar A. Colan S.D. Shi L. Canter C.E. Hsu D.T. Webber S.A. et al.Genetic Causes of Cardiomyopathy in Children: First Results From the Pediatric Cardiomyopathy Genes Study.J. Am. Heart Assoc. 2021; 10: e017731Crossref PubMed Scopus (10) Google Scholar despite guidelines that recommend genetic testing in children with cardiomyopathy.12Hershberger R.E. Givertz M.M. Ho C.Y. Judge D.P. Kantor P.F. McBride K.L. Morales A. Taylor M.R.G. Vatta M. Ware S.M. Genetic Evaluation of Cardiomyopathy-A Heart Failure Society of America Practice Guideline.J. Card. Fail. 2018; 24: 281-302Abstract Full Text Full Text PDF PubMed Scopus (180) Google ScholarThe expected genetic heterogeneity and the number of private (or infrequent) variants in pediatric cardiomyopathy present an additional challenge to defining genetic architecture. To be clinically actionable, variants must have multiple tiers of evidence including bioinformatic prediction, clinical phenotyping, familial segregation studies, and functional studies,27Richards S. Aziz N. Bale S. Bick D. Das S. Gastier-Foster J. Grody W.W. Hegde M. Lyon E. Spector E. et al.Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.Genet. Med. 2015; 17: 405-424Abstract Full Text Full Text PDF PubMed Scopus (13526) Google Scholar which makes confirming new disease-causing variants difficult. Furthermore, both nonsynonymous (missense) variants and loss-of-function (LoF) variants may cause the disease, depending on the specific gene, which complicates disease-specific bioinformatic predictions.8Mazzarotto F. Tayal U. Buchan R.J. Midwinter W. Wilk A. Whiffin N. Govind R. Mazaika E. de Marvao A. Dawes T.J.W. et al.Reevaluating the Genetic Contribution of Monogenic Dilated Cardiomyopathy.Circulation. 2020; 141: 387-398Crossref PubMed Scopus (90) Google Scholar,10Walsh R. Thomson K.L. Ware J.S. Funke B.H. Woodley J. McGuire K.J. Mazzarotto F. Blair E. Seller A. Taylor J.C. et al.Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples.Genet. Med. 2017; 19: 192-203Abstract Full Text Full Text PDF PubMed Scopus (394) Google Scholar,11Alfares A.A. Kelly M.A. McDermott G. Funke B.H. Lebo M.S. Baxter S.B. Shen J. McLaughlin H.M. Clark E.H. Babb L.J. et al.Results of clinical genetic testing of 2,912 probands with hypertrophic cardiomyopathy: expanded panels offer limited additional sensitivity.Genet. Med. 2015; 17: 880-888Abstract Full Text Full Text PDF PubMed Scopus (251) Google Scholar,28Nouhravesh N. Ahlberg G. Ghouse J. Andreasen C. Svendsen J.H. Haunsø S. Bundgaard H. Weeke P.E. Olesen M.S. Analyses of more than 60,000 exomes questions the role of numerous genes previously associated with dilated cardiomyopathy.Mol. Genet. Genomic Med. 2016; 4: 617-623Crossref PubMed Scopus (22) Google ScholarWhen multiple variants (oligogenic inheritance) rather than a single variant act to modify disease risk, these variants may not reach the threshold of clinical actionability due to low penetrance. Thus, to gain understanding of the genetic etiology of pediatric cardiomyopathy, consideration of a broader list of variants beyond those meeting clinical actionability criteria will be required as well as non-Mendelian inheritance models such as gene burden. As pediatric cases are rarer than adult cardiomyopathy cases (1 in 100,000 compared to 1 in 500), targeted discovery approaches will be essential as the number of pediatric cases will be orders of magnitude lower than in adult studies. Systems biologic approaches have been shown to effectively leverage current biological knowledge to inform which genes have the highest potential of contributing to cardiomyopathy and to reduce multiple testing burden.Given the relatively limited data on the genetics of cardiomyopathy in children, the purpose of this paper was to investigate the genetic architecture of pediatric-onset cardiomyopathy. To address this question, we analyzed a large cohort of children with cardiomyopathy in North America. We determine likely genetic causes, identifying the yield of testing by cardiomyopathy sub-phenotype, age of diagnosis, and ancestry. Second, we provide an exome-based assessment of the genetic architecture of pediatric cardiomyopathy and identify an over-representation of bioinformatically predicted damaging variant burden, some of which is ancestry dependent. These findings facilitate deeper insight into the genetic architecture of pediatric cardiomyopathy.Subjects and methodsCohort composition and exome sequencingParticipants with pediatric cardiomyopathy were recruited from 14 sites in the United States and Canada. The procedures followed were in accordance with the ethical standards and the responsible conduct on human and experimentation and was approved by the institutional review board (institutional and national). Proper informed consent was obtained. The research methods, including eligibility criteria, sample handling, and exome sequencing procedures, are described elsewhere.26Ware S.M. Wilkinson J.D. Tariq M. Schubert J.A. Sridhar A. Colan S.D. Shi L. Canter C.E. Hsu D.T. Webber S.A. et al.Genetic Causes of Cardiomyopathy in Children: First Results From the Pediatric Cardiomyopathy Genes Study.J. Am. Heart Assoc. 2021; 10: e017731Crossref PubMed Scopus (10) Google Scholar Briefly, individuals with familial or idiopathic hypertrophic cardiomyopathy (HCM [MIM: 192600]), dilated cardiomyopathy (DCM [MIM: 115200]), restrictive cardiomyopathy (RCM [MIM: 115210]), or left ventricular noncompaction (LVNC [MIM: 604169]) were eligible if the diagnosis was made before age 18. Individuals with LVNC sub-phenotype (n = 17) or LVNC with HCM, DCM, and/or RCM in combination were given the designation of “LVNC/mixed.” Individuals with more than one sub-phenotype without LVNC in combination were given the designation of “non-LVNC mixed” sub-phenotype. Exome sequencing was performed at Cincinnati Children’s Hospital Medical Center with Nimblegen sequence capture (SeqCap EZ Human Exome 2.0) and an Illumina HiSeq2500. The mean sequence coverage over all samples was 79× (range: 31 to 155). Alignment was performed as described previously.26Ware S.M. Wilkinson J.D. Tariq M. Schubert J.A. Sridhar A. Colan S.D. Shi L. Canter C.E. Hsu D.T. Webber S.A. et al.Genetic Causes of Cardiomyopathy in Children: First Results From the Pediatric Cardiomyopathy Genes Study.J. Am. Heart Assoc. 2021; 10: e017731Crossref PubMed Scopus (10) Google ScholarAncestry estimationTo estimate ancestry, variants with a minor allele frequency (MAF) greater than 10% were identified in the dataset. These variants were linkage disequilibrium (LD) pairwise pruned with the PLINK procedure (window size, 50; step, 5; r2 threshold, 0.5). From the LD-pruned variants, 5,000 variants were randomly selected. Because principal-component analysis (PCA) can be performed only on complete data, SNPs not called in all pediatric cardiomyopathy (PCM) cohort samples were excluded (the final sample was 3,027 variants). We performed PCA on these SNPs in the 1000 Genomes dataset to establish super population clusters. The distance from each population centroid with three principal components was calculated. Ancestry for PCM participants was based on being localized within 3 standard deviations of the population centroid.Clinical variant interpretation of curated genesAt study initiation and participant enrollment from 2013–2016, 37 genes were curated from the literature and from available clinical genetic testing panels as genes in which pathogenic variation is potentially causative in infants and children with idiopathic or familial cardiomyopathy (Table S1). Two independent bioinformatic groups (CCHMC and CUMC) identified rare variants (MAF < 0.005) for further classification with the PCM exome files. Variants (N = 549) in this 37 gene curated gene list were interpreted as per American College of Medical Genetics and Genomics (ACMG) clinical-variant interpretation guidelines (Table S1).27Richards S. Aziz N. Bale S. Bick D. Das S. Gastier-Foster J. Grody W.W. Hegde M. Lyon E. Spector E. et al.Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.Genet. Med. 2015; 17: 405-424Abstract Full Text Full Text PDF PubMed Scopus (13526) Google Scholar Given the large number of variants in TTN (MIM: 188840) and the strong evidence for truncating variants causing DCM, variant interpretation was limited to nonsense and frameshift variants within the A-band region of the protein.29Herman D.S. Lam L. Taylor M.R. Wang L. Teekakirikul P. Christodoulou D. Conner L. DePalma S.R. McDonough B. Sparks E. et al.Truncations of titin causing dilated cardiomyopathy.N. Engl. J. Med. 2012; 366: 619-628Crossref PubMed Scopus (861) Google Scholar,30Golbus J.R. Puckelwartz M.J. Fahrenbach J.P. Dellefave-Castillo L.M. Wolfgeher D. McNally E.M. Population-based variation in cardiomyopathy genes.Circ Cardiovasc Genet. 2012; 5: 391-399Crossref PubMed Scopus (108) Google Scholar Variant interpretations by the two bioinformatic groups were 98% concordant with adjudication between the two groups performed for the remaining seven variants to arrive at consensus interpretation. The curated gene set and variant interpretations were frozen January 2019 and used for subsequent analyses. The variant results and interpretation criteria are provided in Table S1. Variant reinterpretation was performed October 2021 for likely pathogenic (LP) and pathogenic (P) variants as noted in Table S1.Compiling cardiac discovery gene list and sub-listsCurated geneThe curated gene set included the following 37 genes: ABCC9 (MIM: 601439), ACTC1 (MIM: 102540), ACTN2 (MIM: 102573), ANKRD1 (MIM: 609599), BAG3 (MIM: 603883), CAV3 (MIM: 601253), CRYAB (MIM: 123590), CSRP3 (MIM: 600824), DES (MIM: 125660), EMD (MIM: 300384), LAMP2 (MIM 309060), LDB3 (MIM: 605906), LMNA (MIM: 150330), MYBPC3 (MIM: 600958), MYH6 (MIM: 160710), MYH7 (MIM: 160760), MYL2 (MIM: 160781), MYL3 (MIM: 160790), MYPN (MIM: 608517), NEBL (MIM: 605491), NEXN (MIM: 613121), PLN (MIM: 172405), PRKAG2 (MIM: 602743), RBM20 (MIM: 613171), SCN5A (MIM: 600163), SCO2 (MIM: 604272), SGCD (MIM: 601411), SURF1 (MIM: 185620), TAZ (MIM: 300394), TCAP (MIM: 604488), TNNC1 (MIM: 191040), TNNI3 (MIM: 191044), TNNT2 (MIM: 191045), TPM1 (MIM: 191010), TTR (MIM: 176300), VCL (MIM: 193065), TTN (MIM: 188840). We next compiled multiple gene lists of cardiac discovery genes by using multiple primary sources, including the Online Mendelian Inheritance in Man (OMIM) compendium, ClinVar data, the Gene Ontology (GO) initiative, UniProt data, functional domains, and phenotype associations through the ToppGene Suite (Figure S1).31Chen J. Bardes E.E. Aronow B.J. Jegga A.G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization.Nucleic Acids Res. 2009; 37: W305-11Crossref PubMed Scopus (1705) Google Scholar,32Chen J. Xu H. Aronow B.J. Jegga A.G. Improved human disease candidate gene prioritization using mouse phenotype.BMC Bioinformatics. 2007; 8: 392Crossref PubMed Scopus (179) Google Scholar We focused on finding and aggregating additional genes either known or potentially associated with cardiomyopathy, heart development, and cardiac muscle structure by using available human phenotype, mouse phenotype, and co-expression data. The provenance of the gene lists is detailed in Table S2. This search identified our broadest list of 1,703 potential cardiac discovery genes. We also compiled several smaller lists within the cardiac discovery gene set. Table S3 provides each gene list.ClinVar gene setThe ClinVar gene list consisted of 70 genes associated with cardiomyopathy having a P or LP variant (ClinVar Version August 2020): ABCC9, ACTC1, ACTN2, ALPK3 (MIM: 617608), BAG3, BRAF (MIM: 164757), CRYAB, CSRP3, DES, DMD (MIM: 300377), DPM3 (MIM: 605951), DSG2 (MIM: 125671), DSP (MIM: 125647), DTNA (MIM: 601239), EYA4 (MIM: 603550), FKTN (MIM: 607440), FLNC (MIM: 102565), GATAD1 (MIM: 614518), GLA (MIM: 300644), HAND2 (MIM: 602407), JPH2 (MIM: 605267), LAMA4 (MIM: 600133), LAMP2, LDB3, LIMS2 (MIM: 607908), LMNA, MIPEP (MIM: 602241), MYBPC3, MYH6, MYH7, MYL2, MYL3, MYLK2 (MIM: 606566), MYO6 (MIM: 600970), MYOZ2 (MIM: 605602), MYPN, MYZAP (MIM: 614071), NCF1 (MIM: 608512), NDUFB11 (MIM: 300403), NEXN, NKX2-5 (MIM: 600584), PKP2 (MIM: 602861), PLN, PMPCA (MIM: 613036), PPC3 (MIM: 609853), PRDM16 (MIM: 605557), PRKAG2, PSEN1 (MIM: 104311), PTPN11 (MIM: 176876), RAF1 (MIM: 164760), RBM20, RYR2 (MIM: 180902), SCN1B (MIM: 600235), SCN5A, SCO2, SDHA (MIM: 600857), SDHD (MIM: 602690), SGCD, TAZ, TCAP, TMEM43 (MIM: 612048), TNNC1, TNNI3, TNNI3K (MIM: 613932), TNNT2, TPM1, TSFM (MIM: 604723), TTN, TTR, VCL.LoF-intolerant gene setThe LoF-intolerant gene set is the genes within the cardiac discovery gene set that are highly intolerant to a LoF variant. The Exome Aggregation Consortium (ExAC) uses the observed and expected variant counts to determine the probability that a given gene is highly intolerant to haploinsufficiency.33Lek M. Karczewski K.J. Minikel E.V. Samocha K.E. Banks E. Fennell T. O’Donnell-Luria A.H. Ware J.S. Hill A.J. Cummings B.B. et al.Analysis of protein-coding genetic variation in 60,706 humans.Nature. 2016; 536: 285-291Crossref PubMed Scopus (6396) Google Scholar,34Karczewski K.J. Weisburd B. Thomas B. Solomonson M. Ruderfer D.M. Kavanagh D. Hamamsy T. Lek M. Samocha K.E. Cummings B.B. et al.The ExAC browser: displaying reference data information from over 60 000 exomes.Nucleic Acids Res. 2017; 45: D840-D845Crossref PubMed Scopus (365) Google Scholar The probability of LoF intolerance (pLI) ranges from 0 to 1, where 1 indicates complete intolerance. To identify genes that are highly intolerant to LoF variants, we used a pLI [ExAC] score of 0.9 or greater. There were 457 genes found in cardiac discovery with a pLi > 0.9, of which 18 genes were found to have a damaging LoF variant per CADD35Rentzsch P. Witten D. Cooper G.M. Shendure J. Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome.Nucleic Acids Res. 2019; 47: D886-D894Crossref PubMed Scopus (1274) Google Scholar > 20.Missense-intolerant gene setThe missense-intolerant genes are the genes within the cardiac discovery gene set that are highly intolerant to a damaging missense variant. Given the high frequency of missense variants, we developed a damaging missense ratio (see methods below) to identify genes specifically intolerant to damaging missense variants; unlike MisZ [ExAC], which can be used to identify genes intolerant to any missense variant, we used the top 20% of the ranked genes in the cardiac discovery gene set to create our missense-intolerant gene list. There were 337 genes found in cardiac discovery with a missense-intolerant score in the top 20 percentile, of which 89 genes were found to have a damaging missense variant per Meta-SVM.Damaging missense ratioTo evaluate the tolerance of pathogenic-like variants within a gene, we compiled the number of synonymous variants and the number of damaging missense variants (as per Meta-SVM) seen in our 1,703 genes of interest across the participants in the 1000 Genomes data. We ranked the genes by taking the ratio of non-synonymous damaging variation to synonymous variation and selected 20% of the genes as those most intolerant to damaging non-synonymous variation. The combined analysis includes all damaging variants found across LoF-intolerant, missense-intolerant, curated, and ClinVar gene lists (Table S3, Figure S1).Control cohorts1000 Genomes1000 Genomes Phase 3 individuals (n = 2,504) were used as control individuals.36Auton A. Brooks L.D. Durbin R.M. Garrison E.P. Kang H.M. Korbel J.O. Marchini J.L. McCarthy S. McVean G.A. Abecasis G.R. 1000 Genomes Project ConsortiumA global reference for human genetic variation.Nature. 2015; 526: 68-74Crossref PubMed Scopus (7998) Google Scholar We also analyzed the cohort by super population ancestry per 1000 Genomes: 503 European (EUR), 347 admixed American (AMR), and 661 African (AFR) individuals. In PCA (Figure S2), we observed that individuals in the PCM cohort overlap only a portion of the African population in 1000 Genomes. Therefore, we limited our analysis to the African ancestry of Southwest USA (ASW; n = 61) within the 661 African (AFR) population in 1000 Genomes to better match the genetic background of our African American participants.Random genes in the Pediatric Cardiomyopathy cohortAs an additional control, we examined the damaging variant burden of 1,703 cardiac discovery genes compared to the average damaging variant burden of 1,703 random genes over 1,000 iterations. We performed the same analysis for all gene lists. We also compared burden distributions of damaging variants in individuals for selected and random genes for all gene lists.Simons Foundation Powering Autism Research for Knowledge (SPARK)Control individuals (n = 14,478) from unrelated parents in SPARK were used. The case and control samples were called with GATK37McKenna A. Hanna M. Banks E. Sivachenko A. Cibulskis K. Kernytsky A. Garimella K. Altshuler D. Gabriel S. Daly M. DePristo M.A. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.Genome Res. 2010; 20: 1297-1303Crossref PubMed Scopus (14169) Google Scholar and jointly with GLnexus. We used the same principal components calculated from the 1000 Genomes dataset to estimate the ancestry for the SPARK control individuals. Control individuals were matched to affected individuals with the smal