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Background The 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. Methods We 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. Results EM-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. Conclusions We 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.

Mosaicism due to somatic mutations can cause multiple diseases including cancer, developmental and overgrowth syndromes, neurodevelopmental disorders, autoinflammatory diseases, and atrial fibrillation. With the increased use of next generation sequencing technology, multiple tools have been developed to identify low-frequency variants, specifically from matched tumor-normal tissues in cancer studies. To investigate whether mosaic variants are implicated in congenital heart disease (CHD), we developed a pipeline using the cancer somatic variant caller MuTect to identify mosaic variants in whole-exome sequencing (WES) data from a cohort of parent/affected child trios (n = 715) and a cohort of healthy individuals (n = 416). This is a novel application of the somatic variant caller designed for cancer to WES trio data. We identified two cases with mosaic KMT2D mutations that are likely pathogenic for CHD, but conclude that, overall, mosaicism detectable in peripheral blood or saliva does not account for a significant portion of CHD etiology.

Rationale: Congenital heart disease (CHD) occurs in about 1 in 100 live births, yet known genetic causes explain less than 20% of CHD cases. While variants that cause frameshift, nonsense, start/stop site gain or loss, and canonical splice site alterations are readily categorized as being pathogenic or loss-of-function (LOF), interpreting the clinical significance of variants without obvious functional consequences remains challenging. Here, we aim to improve classification of variants of unknown significance (VUS) in non-canonical RNA splice sites that may be pathogenic for CHD. Methods: We tested candidate LOF de novo (DNV) and rare (p < 2E-5) inherited variants from whole exome sequencing of 4474 CHD probands and their parents in the NHLBI Pediatric Cardiac Genetics Consortium. Briefly, variants underwent computational selection to prioritize VUS in splice regions that are likely to alter splicing (“high-likelihood VUS”). These variants then underwent in vitro analysis including Minigene construction, transfection, RNA isolation, and sequencing to confirm splicing outcomes. Results: Preliminary results limited to DNV variants showed that 163 of 2678 (6.08%) were high-likelihood VUS. Subsequent analysis in vitro assay of high-likelihood VUS yielded 53 as splice-altering (p < 0.05) and thus LOF. Combined with previously identified 366 DNV LOF variants, the addition of these splice-altering variants represents a 15.3% increase in total LOF DNV variant calls. This includes new pathogenic mutations in known CHD genes such as KMT2D . In one case, a CHD proband with features of Kabuki Syndrome without a definitive diagnosis was found to have a splice-altering variant in KMT2D . We have extended this assay to 34518 rare, inherited variants in the same cohort, of which 868 (2.54%) are in genes previously associated with CHD. Conclusion: Consideration of non-canonical RNA splice sites in this assay increased the yield of LOF mutations from traditional sequencing methods by 15.3% in the CHD cohort. Further analysis of splice-altering variants in both known and unknown pathogenic genes will improve diagnostic classification of VUS and gene-based diagnosis of CHD.

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 to XHMM, and also identified sample switches, DNA contamination, a significant enrichment of 15q11.2 deletions compared to controls and eight cases of uniparental disomy. 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.