Showing papers by "Steven E. Lipshultz published in 2022"

The genetic architecture of pediatric cardiomyopathy

Abstract: 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. Canter C.E. Hsu D.T. Lu M. Webber S.A. Colan S.D. Kantor P.F. Everitt M.D. Towbin J.A. et al.Incidence of and risk factors for sudden cardiac death in children with dilated cardiomyopathy: a report from the Pediatric Cardiomyopathy Registry.J. Am. Coll. Cardiol. 2012; 59: 607-615Crossref PubMed Scopus (122) Google Scholar, 3Webber S.A. Lipshultz S.E. Sleeper L.A. Lu M. Wilkinson J.D. Addonizio L.J. Canter C.E. Colan S.D. Everitt M.D. Jefferies J.L. et al.Outcomes of restrictive cardiomyopathy in childhood and the influence of phenotype: a report from the Pediatric Cardiomyopathy Registry.Circulation. 2012; 126: 1237-1244Crossref PubMed Scopus (125) Google Scholar, 4Wilkinson J.D. Landy D.C. Colan S.D. Towbin J.A. Sleeper L.A. Orav E.J. Cox G.F. Canter C.E. Hsu D.T. Webber S.A. Lipshultz S.E. The pediatric cardiomyopathy registry and heart failure: key results from the first 15 years.Heart Fail. Clin. 2010; 6: 401-413, viiAbstract Full Text Full Text PDF PubMed Scopus (148) Google Scholar, 5Daubeney P.E. Nugent A.W. Chondros P. Carlin J.B. Colan S.D. Cheung M. Davis A.M. Chow C.W. Weintraub R.G. National Australian Childhood Cardiomyopathy StudyClinical features and outcomes of childhood dilated cardiomyopathy: results from a national population-based study.Circulation. 2006; 114: 2671-2678Crossref PubMed Scopus (192) Google Scholar, 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, 7Bharucha T. Lee K.J. Daubeney P.E. Nugent A.W. Turner C. Sholler G.F. Robertson T. Justo R. Ramsay J. Carlin J.B. et al.Sudden death in childhood cardiomyopathy: results from a long-term national population-based study.J. Am. Coll. Cardiol. 2015; 65: 2302-2310Crossref PubMed Scopus (91) Google Scholar The age of onset of primary cardiomyopathy, defined as disease of the myocardium that does not affect other organs, is highly variable, ranging from infancy to adulthood. Autosomal dominant inheritance of cardiomyopathy in many families provides evidence of a strong genetic component with high penetrance and variable expressivity.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, 9Walsh R. Buchan R. Wilk A. John S. Felkin L.E. Thomson K.L. Chiaw T.H. Loong C.C.W. Pua C.J. Raphael C. et al.Defining the genetic architecture of hypertrophic cardiomyopathy: re-evaluating the role of non-sarcomeric genes.Eur. Heart J. 2017; 38: 3461-3468PubMed 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 Gene discovery efforts have implicated variation in sarcomeric genes as a cause of primary cardiomyopathy.Most gene discovery efforts have been limited to adults. In studies that include both children and adults, a full range of childhood ages is typically not well documented or represented.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 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. Ware S.M. Pediatric cardiomyopathy: importance of genetic and metabolic evaluation.J. Card. Fail. 2012; 18: 396-403Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar, 14Tariq M. Ware S.M. Importance of genetic evaluation and testing in pediatric cardiomyopathy.World J. Cardiol. 2014; 6: 1156-1165Crossref PubMed Google Scholar, 15Ware S.M. Evaluation of genetic causes of cardiomyopathy in childhood.Cardiol. Young. 2015; 25: 43-50Crossref PubMed Scopus (17) Google Scholar, 16Cox G.F. Sleeper L.A. Lowe A.M. Towbin J.A. Colan S.D. Orav E.J. Lurie P.R. Messere J.E. Wilkinson J.D. Lipshultz S.E. Factors associated with establishing a causal diagnosis for children with cardiomyopathy.Pediatrics. 2006; 118: 1519-1531Crossref PubMed Scopus (94) Google Scholar, 17Lee T.M. Hsu D.T. Kantor P. Towbin J.A. Ware S.M. Colan S.D. Chung W.K. Jefferies J.L. Rossano J.W. Castleberry C.D. et al.Pediatric Cardiomyopathies.Circ. 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. Cardiol. 2018; 72: 2324-2338Crossref PubMed Scopus (64) Google Scholar Thus, understanding of the genetic causes of primary and idiopathic cardiomyopathy presenting in childhood is still extremely limited and based on studies typically with less than 150 participants. The lack of larger pediatric studies may explain why there is marked practice variation20Ellepola C.D. Knight L.M. Fischbach P. Deshpande S.R. Genetic Testing in Pediatric Cardiomyopathy.Pediatr. Cardiol. 2018; 39: 491-500Crossref PubMed Scopus (13) Google Scholar, 21Ouellette A.C. Mathew J. Manickaraj A.K. Manase G. Zahavich L. Wilson J. George K. Benson L. Bowdin S. Mital S. Clinical genetic testing in pediatric cardiomyopathy: Is bigger better?.Clin. Genet. 2018; 93: 33-40Crossref PubMed Scopus (25) Google Scholar, 22Quiat D. Witkowski L. Zouk H. Daly K.P. Roberts A.E. Retrospective Analysis of Clinical Genetic Testing in Pediatric Primary Dilated Cardiomyopathy: Testing Outcomes and the Effects of Variant Reclassification.J. Am. Heart Assoc. 2020; 9: e016195Crossref PubMed Scopus (14) Google Scholar, 23Kaski J.P. Syrris P. Esteban M.T. Jenkins S. Pantazis A. Deanfield J.E. McKenna W.J. Elliott P.M. Prevalence of sarcomere protein gene mutations in preadolescent children with hypertrophic cardiomyopathy.Circ Cardiovasc Genet. 2009; 2: 436-441Crossref PubMed Scopus (134) Google Scholar, 24Miller E.M. Hinton R.B. Czosek R. Lorts A. Parrott A. Shikany A.R. Ittenbach R.F. Ware S.M. Genetic Testing in Pediatric Left Ventricular Noncompaction.Circ Cardiovasc Genet. 2017; 10: e001735Crossref PubMed Scopus (43) Google Scholar, 25Al-Wakeel-Marquard N. Degener F. Herbst C. Kühnisch J. Dartsch J. Schmitt B. Kuehne T. Messroghli D. Berger F. Klaassen S. RIKADA Study Reveals Risk Factors in Pediatric Primary Cardiomyopathy.J. Am. 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

Long-Term Outcomes after Adolescent Bariatric Surgery.

Abstract: BACKGROUND Metabolic and bariatric surgery (MBS) is a safe and effective treatment option for adolescents with severe obesity, but no long-term studies are available with >10 years of follow-up data to document sustained improved outcomes. METHODS A total of 96 patients who completed MBS at ≤21 years of age in a tertiary academic center 2002 to 2010 were contacted for a telehealth visit. Body weight, co-morbidity status, social/physical function status, and long-term complications were evaluated 10-to-18 years after surgery. RESULTS Mean participant (83% female, 75% Hispanic) age at MBS was 18.8 (±1.6) years (median age 19 years, range 15-21 years), and median pre-MBS body mass index was 44.7 kg/m2 (SD 6.5). At follow-up (mean 14.2 [±2.2] years) post-MBS (90.6% Roux-en-Y gastric bypass [RYGB] or 8.3% laparoscopic adjustable gastric banding [LAGB]) mean total body weight decreased by 31.3% (IQR 20.0%-38.9%); 32.0% (IQR, 21.3%-40.1%) among RYGB participants and 22.5% (IQR, 0.64%-28.3%) among LAGB participants. Patients with pre-MBS hyperlipidemia (14.6%), asthma (10.4%), and diabetes/hyperglycemia (5.2%) reported 100% remission at follow-up (p<0.05 for all). Pre-post decrease in hypertension (13.5% vs. 1%, p=0.001), sleep apnea (16.7% vs. 1.0%, p<0.001), gastroesophageal reflux disease (13.5% vs. 3.1%, p=0.016), anxiety (7.3% vs. 2.1%, p=0.169), and depression (27.1% vs. 4.2%, p<0.001) were also found. CONCLUSIONS Significant sustained reductions in weight and co-morbidities, and low rates of long-term complications, a decade or more after completing MBS as an adolescent were found. These findings have important implications for adolescents who may be considering MBS for weight reduction and overall health improvements that extend into adulthood.

Abstract 14006: Circulating microRNAs Predict Recovery in Pediatric Dilated Cardiomyopathy Patients

Abstract: Introduction: microRNAs (miRs) are small single stranded RNAs capable of targeting expression of several genes. Circulating miRs are stable and can be important biomarkers of disease progression and diagnosis. Hypothesis: We hypothesized circulating miRs could be a reliable prognostic biomarker of recovery for pediatric dilated cardiomyopathy (DCM). Methods: TaqMan Open Array miR panel (ThermoFisher Scientific) was used to investigate expression of 381 circulating miRs. Samples originated from the Pediatric Cardiomyopathy Registry (PCMR) and were collected within 3 months of a new diagnosis of DCM. Inclusion criteria included <18 years of age at diagnosis of idiopathic or familial DCM (EF<50% or left ventricular end-diastolic dimension [LVEDd] z-score ≥+2). Patients with DCM secondary to musculoskeletal diseases or anthracycline toxicity were excluded. Primary outcomes, occurring within 1 year of enrollment, were defined as: [1] Death, transplant (tx) listing or initiation of mechanical circulatory support; [2] Recovered- normalization of ventricular size and function (EF≥50% and LVEDd z-score <+2) or [3] Stable- persistent DCM with EF<50% or LVEDd z-score ≥+2. Statistical analysis included random forest, hierarchical clustering and area under the receiver operator curve (AUC). Results: N=10 subjects were included in Group 1, of which 70% were female, 6 were white, 2 Hispanic and 3 of other/unknown races, median age 9.5y. N=5 samples were included in Group 2, of which 60% were female, 3 were white, 1 black and 1 native American, median age 1.9y. N=20 samples were included in Group 3, of which 40% were female, 10 were white, 5 black, 1 Pacific islander, 2 Hispanic and 4 of other/unknown races, median age 2.1y. The top 3 miRs differentiating Group 2 (Recovered) from Group 1 (Death/Tx) (AUC of 0.92) were hsa-miR-655, -410 and -636. The top 3 miRs differentiating Group 2 (Recovered) from Group 3 (Stable) were hsa-miR-17, -410 and -345 (AUC of 0.76). Conclusions: These results indicate circulating miRs can predict recovery in the pediatric DCM population. A unique biomarker signature of miRs specific to patients who have the potential to recover would improve risk stratification of this population.

Relationship of NT-proBNP to All-Cause Death and Heart Transplant in Children with Ventricular Dysfunction and Compared to Adults with Heart Failure

Abstract: PurposePlasma NT-proBNP concentration is a widely used diagnostic biomarker for heart failure (HF) in adults and children. We sought to determine whether NT-proBNP concentrations predicted the risk of heart transplantation or death in pediatric HF patients.MethodsWe studied medical records from 109 children with HF in the IBM Watson Explorys database and from 150 children in the Cardiac Biomarkers in Pediatric Cardiomyopathy (PCMR) study. Nonlinear regression with an Emax function was used to model the relationship between NT-proBNP concentrations and the number of events in the two cohorts and with adult patients from Explorys.ResultsAll children in the PCMR cohort had dilated cardiomyopathy, whereas the Explorys cohort also included children with congenital heart diseases (Table). Mean age at diagnosis was 3 years in the Explorys cohort and 1.4 years in the PCMR cohort; median NT-proBNP concentrations were 1250 pg/mL and 183.5 pg/mL, respectively. The percentage of deaths and transplantations was higher in the Explorys cohort (7% and 22%, respectively, over 2 years) than in the PCMR cohort (3% and 16%, respectively, over 5 years). Mean estimates of NT-proBNP concentration indicative of half-maximum risk for events (EC50 values) at 2 and 5 years were 3730 pg/mL and 4199 pg/mL, values both close to the mean of 3880 pg/mL established for adults with HF (Figure).ConclusionNT-proBNP concentration is suitable for estimating the risk of mortality and morbidity in children with HF, independent of age, and is a candidate surrogate marker for clinical outcome both in adults and children with HF. Plasma NT-proBNP concentration is a widely used diagnostic biomarker for heart failure (HF) in adults and children. We sought to determine whether NT-proBNP concentrations predicted the risk of heart transplantation or death in pediatric HF patients. We studied medical records from 109 children with HF in the IBM Watson Explorys database and from 150 children in the Cardiac Biomarkers in Pediatric Cardiomyopathy (PCMR) study. Nonlinear regression with an Emax function was used to model the relationship between NT-proBNP concentrations and the number of events in the two cohorts and with adult patients from Explorys. All children in the PCMR cohort had dilated cardiomyopathy, whereas the Explorys cohort also included children with congenital heart diseases (Table). Mean age at diagnosis was 3 years in the Explorys cohort and 1.4 years in the PCMR cohort; median NT-proBNP concentrations were 1250 pg/mL and 183.5 pg/mL, respectively. The percentage of deaths and transplantations was higher in the Explorys cohort (7% and 22%, respectively, over 2 years) than in the PCMR cohort (3% and 16%, respectively, over 5 years). Mean estimates of NT-proBNP concentration indicative of half-maximum risk for events (EC50 values) at 2 and 5 years were 3730 pg/mL and 4199 pg/mL, values both close to the mean of 3880 pg/mL established for adults with HF (Figure). NT-proBNP concentration is suitable for estimating the risk of mortality and morbidity in children with HF, independent of age, and is a candidate surrogate marker for clinical outcome both in adults and children with HF.