DePalma, S. | bioRxiv Science

bioRxiv2020

Whole Genome De Novo Variant Identification with FreeBayes and Neural Network Approaches

MotivationDe novo variant (DNV) calling typically relies on heuristic filters intrinsic to specific platforms and variant calling algorithms. FreeBayes and neural network approaches have overcome this limitation for variant calling, and we implemented a similar approach for DNV identification. ResultsWe developed a DNV calling framework that uses Genome Analysis Toolkit (GATK), FreeBayes and a neural network trained on Integrative Genomics Viewer pile-up plots (IGV-bot). We identified DNVs in 2,390 WGS trios and benchmarked results against heuristics based on GATK parameters. Results were validated in silico and with Sanger sequencing, with the latter showing true positive rates of 98.4% and 97.3% for SNVs and indels, respectively. Taken together we describe a scalable framework for DNV identification based on both FreeBayes and neural network methods. AvailabilitySource code and documentation are available at https://github.com/ShenLab/igv-classifier and https://github.com/frichter/dnv_pipeline under the MIT license. Contactys2411@cumc.columbia.edu

genomics↗

bioRxiv2019

Early post-zygotic mutations contribute to congenital heart disease

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.\n\nResultsWe 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.\n\nConclusionsWe 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.

bioinformatics↗