Search

BackgroundThe contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined.MethodsWe developed a new computational method, EM-mosaic (Expectation-Maximization-based detection of mosaicism), to analyze mosaicism in exome sequences derived primarily from blood DNA of 2530 CHD proband-parent trios. To optimize this method, we measured mosaic detection power as a function of sequencing depth. In parallel, we analyzed our cohort using MosaicHunter, a Bayesian genotyping algorithm-based mosaic detection tool, and compared the two methods. The accuracy of these mosaic variant detection algorithms was assessed using an independent resequencing method. We then applied both methods to detect mosaicism in cardiac tissue-derived exome sequences of 66 participants for which matched blood and heart tissue was available.ResultsEM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The estimated true frequency of mosaic variants above 10% mosaicism was 0.14/person in blood and 0.21/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.ConclusionsWe estimate that ~ 1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants with relatively higher allele fraction. Although blood is a readily available DNA source, cardiac tissues analyzed contributed ~ 5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses.

BackgroundDe novo genic and copy number variants are enriched in patients with congenital heart disease, particularly those with extra-cardiac anomalies. The impact of de novo damaging variants on outcomes following cardiac repair is unknown.MethodsWe studied 2517 patients with congenital heart disease who had undergone whole-exome sequencing as part of the CHD GENES study (Congenital Heart Disease Genetic Network).ResultsTwo hundred ninety-four patients (11.7%) had clinically significant de novo variants. Patients with de novo damaging variants were 2.4 times more likely to have extra-cardiac anomalies (P=5.63×10-12). In 1268 patients (50.4%) who had surgical data available and underwent open-heart surgery exclusive of heart transplantation as their first operation, we analyzed transplant-free survival following the first operation. Median follow-up was 2.65 years. De novo variants were associated with worse transplant-free survival (hazard ratio, 3.51; P=5.33×10-04) and longer times to final extubation (hazard ratio, 0.74; P=0.005). As de novo variants had a significant interaction with extra-cardiac anomalies for transplant-free survival (P=0.003), de novo variants conveyed no additional risk for transplant-free survival for patients with these anomalies (adjusted hazard ratio, 1.96; P=0.06). By contrast, de novo variants in patients without extra-cardiac anomalies were associated with worse transplant-free survival during follow-up (hazard ratio, 11.21; P=1.61×10-05) than that of patients with no de novo variants. Using agnostic machine-learning algorithms, we identified de novo copy number variants at 15q25.2 and 15q11.2 as being associated with worse transplant-free survival and 15q25.2, 22q11.21, and 3p25.2 as being associated with prolonged time to final extubation.ConclusionsIn patients with congenital heart disease undergoing open-heart surgery, de novo variants were associated with worse transplant-free survival and longer times on the ventilator. De novo variants were most strongly associated with adverse outcomes among patients without extra-cardiac anomalies, suggesting a benefit for preoperative genetic testing even when genetic abnormalities are not suspected during routine clinical practice. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01196182.

A genetic etiology is identified for one-third of patients with congenital heart disease (CHD), with 8% of cases attributable to coding de novo variants (DNVs). To assess the contribution of noncoding DNVs to CHD, we compared genome sequences from 749 CHD probands and their parents with those from 1,611 unaffected trios. Neural network prediction of noncoding DNV transcriptional impact identified a burden of DNVs in individuals with CHD (n = 2,238 DNVs) compared to controls (n = 4,177; P = 8.7 × 10-4). Independent analyses of enhancers showed an excess of DNVs in associated genes (27 genes versus 3.7 expected, P = 1 × 10-5). We observed significant overlap between these transcription-based approaches (odds ratio (OR) = 2.5, 95% confidence interval (CI) 1.1-5.0, P = 5.4 × 10-3). CHD DNVs altered transcription levels in 5 of 31 enhancers assayed. Finally, we observed a DNV burden in RNA-binding-protein regulatory sites (OR = 1.13, 95% CI 1.1-1.2, P = 8.8 × 10-5). Our findings demonstrate an enrichment of potentially disruptive regulatory noncoding DNVs in a fraction of CHD at least as high as that observed for damaging coding DNVs.

Multiple tools have been developed to identify copy number variants (CNVs) from whole exome (WES) and whole genome sequencing (WGS) data. Current tools such as XHMM for WES and CNVnator for WGS identify CNVs based on changes in read depth. For WGS, other methods to identify CNVs include utilizing discordant read pairs and split reads and genome-wide local assembly with tools such as Lumpy and SvABA, respectively. Here, we introduce a new method to identify deletion CNVs from WES and WGS trio data based on the clustering of Mendelian errors (MEs). Using our Mendelian Error Method (MEM), we identified 127 deletions (inherited and de novo) in 2,601 WES trios from the Pediatric Cardiac Genomics Consortium, with a validation rate of 88% by digital droplet PCR. MEM identified additional de novo deletions compared with XHMM, and a significant enrichment of 15q11.2 deletions compared with controls. In addition, MEM identified eight cases of uniparental disomy, sample switches, and DNA contamination. We applied MEM to WGS data from the Genome In A Bottle Ashkenazi trio and identified deletions with 97% specificity. MEM provides a robust, computationally inexpensive method for identifying deletions, and an orthogonal approach for verifying deletions called by other tools.