Rapid Genome Sequencing Shows Diagnostic Utility In Infants With Congenital Heart Defects

Congenital heart disease (CHD) is the most common birth defect and a leading cause of infant mortality. CHD often has a genetic etiology and recent studies demonstrate utility in genetic testing. In clinical practice, decisions around genetic testing choices continue to evolve, and the incorporation of rapid genome sequencing (rGS) in CHD has not been well studied. Though smaller studies demonstrate the value of rGS, they also highlight the burden of results interpretation. We analyze genetic testing in CHD at two time-points, in 2018 and 2022-2023, across a change in clinical testing guidelines from chromosome microarray (CMA) to rGS. Analysis of 421 hospitalized infants with CHD demonstrated consistent genetic testing across time. Overall, after incorporation of rGS in 2022-2023, the diagnostic yield was 6.8% higher compared to 2018, and this pattern was consistent across all patient subtypes analyzed. In 2018, CMA was the most common test performed, with diagnostic results for CHD in 14.3%, while in 2022-2023, rGS was the most frequent test performed, with results diagnostic for CHD in 16.9%. Additionally, rGS identified 44% more unique genetic diagnoses than CMA. This is the largest study to highlight the value of rGS in CHD and has important implications for management.

Clinical exome sequencing efficacy and phenotypic expansions involving anomalous pulmonary venous return

Anomalous pulmonary venous return (APVR) frequently occurs with other congenital heart defects (CHDs) or extra-cardiac anomalies. While some genetic causes have been identified, the optimal approach to genetic testing in individuals with APVR remains uncertain, and the etiology of most cases of APVR is unclear. Here, we analyzed molecular data from 49 individuals to determine the diagnostic yield of clinical exome sequencing (ES) for non-isolated APVR. A definitive or probable diagnosis was made for 8 of those individuals yielding a diagnostic efficacy rate of 16.3%. We then analyzed molecular data from 62 individuals with APVR accrued from three databases to identify novel APVR genes. Based on data from this analysis, published case reports, mouse models, and/or similarity to known APVR genes as revealed by a machine learning algorithm, we identified 3 genes-EFTUD2, NAA15, and NKX2-1-for which there is sufficient evidence to support phenotypic expansion to include APVR. We also provide evidence that 3 recurrent copy number variants contribute to the development of APVR: proximal 1q21.1 microdeletions involving RBM8A and PDZK1, recurrent BP1-BP2 15q11.2 deletions, and central 22q11.2 deletions involving CRKL. Our results suggest that ES and chromosomal microarray analysis (or genome sequencing) should be considered for individuals with non-isolated APVR for whom a genetic etiology has not been identified, and that genetic testing to identify an independent genetic etiology of APVR is not warranted in individuals with EFTUD2-, NAA15-, and NKX2-1-related disorders.

A Multicenter Analysis of Abnormal Chromosomal Microarray Findings in Congenital Heart Disease

Background Chromosomal microarray analysis (CMA) provides an opportunity to understand genetic causes of congenital heart disease (CHD). The methods for describing cardiac phenotypes in patients with CMA abnormalities have been inconsistent, which may complicate clinical interpretation of abnormal testing results and hinder a more complete understanding of genotype-phenotype relationships. Methods and Results Patients with CHD and abnormal clinical CMA were accrued from 9 pediatric cardiac centers. Highly detailed cardiac phenotypes were systematically classified and analyzed for their association with CMA abnormality. Hierarchical classification of each patient into 1 CHD category facilitated broad analyses. Inclusive classification allowing multiple CHD types per patient provided sensitive descriptions. In 1363 registry patients, 28% had genomic disorders with well-recognized CHD association, 67% had clinically reported copy number variants (CNVs) with rare or no prior CHD association, and 5% had regions of homozygosity without CNV. Hierarchical classification identified expected CHD categories in genomic disorders, as well as uncharacteristic CHDs. Inclusive phenotyping provided sensitive descriptions of patients with multiple CHD types, which occurred commonly. Among CNVs with rare or no prior CHD association, submicroscopic CNVs were enriched for more complex types of CHD compared with large CNVs. The submicroscopic CNVs that contained a curated CHD gene were enriched for left ventricular obstruction or septal defects, whereas CNVs containing a single gene were enriched for conotruncal defects. Neuronal-related pathways were over-represented in single-gene CNVs, including top candidate causative genes NRXN3, ADCY2, and HCN1. Conclusions Intensive cardiac phenotyping in multisite registry data identifies genotype-phenotype associations in CHD patients with abnormal CMA.

Genetic Testing Guidelines Impact Care in Newborns with Congenital Heart Defects

OBJECTIVE

To evaluate genetic evaluation practices in newborns with the most common birth defect, congenital heart defects (CHD), we determined the prevalence and the yield of genetic evaluation across time and across patient subtypes, before and after implementation of institutional genetic testing guidelines.

STUDY DESIGN

This was a retrospective, cross-sectional study of 664 hospitalized newborns with CHD using multivariate analyses of genetic evaluation practices across time and patient subtypes.

RESULTS

Genetic testing guidelines for hospitalized newborns with CHD were implemented in 2014, and subsequently genetic testing increased (40% in 2013 and 75% in 2018, OR 5.02, 95% CI 2.84-8.88, P < .001) as did medical geneticists' involvement (24% in 2013 and 64% in 2018, P < .001). In 2018, there was an increased use of chromosomal microarray (P < .001), gene panels (P = .016), and exome sequencing (P = .001). The testing yield was high (42%) and consistent across years and patient subtypes analyzed. Increased testing prevalence (P < .001) concomitant with consistent testing yield (P = .139) added an estimated 10 additional genetic diagnoses per year, reflecting a 29% increase.

CONCLUSIONS

In patients with CHD, yield of genetic testing was high. After implementing guidelines, genetic testing increased significantly and shifted to newer sequence-based methods. Increased use of genetic testing identified more patients with clinically important results with potential to impact patient care.