LAUSR

Introduction: The genetic basis of congenital heart disease (CHD) is incompletely understood. Approximately 10-20% of infants with CHD have abnormal chromosome microarray (CMA) results, however, the spectrum of genomic and phenotypic abnormalities remains incompletely characterized. Methods: All study patients had abnormal CMA on clinical testing and abnormal cardiac findings on echocardiogram, as identified at 9 pediatric cardiac centers. Clinical genetic testing results, diagnoses, and echocardiogram reports for detailed cardiac phenotyping were collected. CMA results were denoted as abnormal for any reported findings that included copy number variants (CNVs) and/or regions of homozygosity. Cardiac classification was performed with minor modification to National Birth Defect Prevention Study methods. Recurrent CNVs were identified algorithmically. Functional enrichment analysis of genes predicted to be intolerant of loss-of-function (LOF) in CNVs and genes located in monogenic CNVs was performed using ToppGene. Results: The study included 1369 patients. Genetic syndromes highly associated with CHD such as Trisomy 21, Turner syndrome, and 22q11.2 deletion syndrome among others were identified in 401 patients and elucidated less recognized cardiac phenotypes in these groups. Analyzing genes known to be causative of CHD in the remaining patients (N=968) showed enrichment in CNVs < 5 megabases (N=51; 7%). Common recurrent loci included duplications in 15q13.2-13.3 (N=16 patients) and 16p11.13 (N=12; 8 with left-sided cardiac lesion), and deletions in 15q11.2 (N=14; 6 with total anomalous pulmonary venous return) and 2q13 (N=10; 8 with conotruncal defect). Genes predicted to be intolerant of LOF (495 genes) or located in monogenic CNVs (137 genes) were enriched for neuronal processes. Conclusions: The consortium provides the largest data set of abnormal clinical CMA results in patients with detailed CHD phenotyping to our knowledge. The findings provide novel understanding of CHD phenotypes, a roadmap of recurrent loci, and pathways and candidate genes in CNV-associated CHD. The consortium’s data will lead to improved genetic understanding and clinical care of patients with CHD.