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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.

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.ResultsWe developed a computational method, Expectation-Maximization-based detection of Mosaicism (EM-mosaic), to analyze mosaicism in exome sequences of 2530 CHD proband-parent trios. 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 frequency of mosaic variants above 10% mosaicism was 0.13/person in blood and 0.14/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 expressed at higher allele fraction compared to benign variants. 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.

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. In addition, we identified variants that are possibly associated with ASD in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.1 provides statistical support for 26 ASD risk genes. While most of these genes are already known ASD risk genes, BRSK2 has the strongest statistical support and reaches genome-wide significance as a risk gene for ASD (p-value = 2.3e−06). Future studies leveraging the thousands of individuals with ASD who have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate ASD research that incorporates genetic etiology.

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. Additionally, we identified variants that are possibly associated with autism in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.2 provides statistical support for 34 ASD risk genes with at least one damaging variant identified in SPARK. Nine of these genes (BRSK2, DPP6, EGR3, FEZF2, ITSN1, KDM1B, NR4A2, PAX5 and RALGAPB) are newly emerging genes in autism, of which BRSK2 has the strongest statistical support as a risk gene for autism (TADA q-value = 0.0015). Future studies leveraging the thousands of individuals with ASD that have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate autism research that incorporates genetic etiology.

The database of genotypes and phenotypes (dbGaP) developed by the National Center for Biotechnology Information (NCBI) is a resource that contains information on various genome-wide association studies (GWAS) and is currently available via NCBI's dbGaP Entrez interface. The database is an important resource, providing GWAS data that can be used for new exploratory research or cross-study validation by authorized users. However, finding studies relevant to a particular phenotype of interest is challenging, as phenotype information is presented in a non-standardized way. To address this issue, we developed PhenDisco (phenotype discoverer), a new information retrieval system for dbGaP. PhenDisco consists of two main components: (1) text processing tools that standardize phenotype variables and study metadata, and (2) information retrieval tools that support queries from users and return ranked results. In a preliminary comparison involving 18 search scenarios, PhenDisco showed promising performance for both unranked and ranked search comparisons with dbGaP's search engine Entrez. The system can be accessed at http://pfindr.net.