Systematic analysis of copy number variation associated with congenital diaphragmatic hernia.

Congenital diaphragmatic hernia (CDH), characterized by malformation of the diaphragm and hypoplasia of the lungs, is one of the most common and severe birth defects, and is associated with high morbidity and mortality rates. There is growing evidence demonstrating that genetic factors contribute to CDH, although the pathogenesis remains largely elusive. Single-nucleotide polymorphisms have been studied in recent whole-exome sequencing efforts, but larger copy number variants (CNVs) have not yet been studied on a large scale in a case control study. To capture CNVs within CDH candidate regions, we developed and tested a targeted array comparative genomic hybridization platform to identify CNVs within 140 regions in 196 patients and 987 healthy controls, and identified six significant CNVs that were either unique to patients or enriched in patients compared with controls. These CDH-associated CNVs reveal high-priority candidate genes including HLX, LHX1, and HNF1B We also discuss CNVs that are present in only one patient in the cohort but have additional evidence of pathogenicity, including extremely rare large and/or de novo CNVs. The candidate genes within these predicted disease-causing CNVs form functional networks with other known CDH genes and play putative roles in DNA binding/transcription regulation and embryonic development. These data substantiate the importance of CNVs in the etiology of CDH, identify CDH candidate genes and pathways, and highlight the importance of ongoing analysis of CNVs in the study of CDH and other structural birth defects.

Biological explanations for discordant noninvasive prenatal test results: Preliminary data and lessons learned.

OBJECTIVE: Maternal plasma cell-free DNA (cfDNA) analysis is a powerful screening tool for Down syndrome. In a pilot series, we examined biologic causes of discordance between the cfDNA test results and the fetal karyotype. We also explored the feasibility of obtaining trio biospecimens by using parental engagement. METHODS: A convenience sample of women with discordant cfDNA results were recruited by their care providers. We provided shipping materials and instructions for biospecimen collection. Maternal, newborn, and placental samples were examined with droplet digital PCR. RESULTS: Thirteen of 15 women successfully had biospecimens obtained remotely. High-quality DNA was extracted in 12 of 13 women. Presumed biologic etiologies for discordance were identified in 7 of 12 women: 3 cases from additional clinical review (male renal transplant, vanishing twin, and colon cancer) and 4 cases from additional laboratory investigation using droplet digital PCR (3 with confined placental mosaicism and 1 with true fetal mosaicism). CONCLUSIONS: Understanding the biology behind cfDNA-fetal karyotype discordancy is useful for follow-up clinical care. Our study suggests that most cases could be resolved by using a trio biospecimen protocol and parental involvement. To improve accuracy, additional sequencing of biospecimens will be required.

JAX-CNV: A Whole-genome Sequencing-based Algorithm for Copy Number Detection at Clinical Grade Level.

We aimed to develop a whole-genome sequencing (WGS)-based copy number variant (CNV) calling algorithm with the potential of replacing chromosomal microarray assay (CMA) for clinical diagnosis. JAX-CNV is thus developed for CNV detection from WGS data. The performance of this CNV calling algorithm was evaluated in a blinded manner on 31 samples and compared to the 112 CNVs reported by clinically validated CMAs for these 31 samples. The result showed that JAX-CNV recalled 100% of these CNVs. Besides, JAX-CNV identified an average of 30 CNVs per individual, respresenting an approximately seven-fold increase compared to calls of clinically validated CMAs. Experimental validation of 24 randomly selected CNVs showed one false positive, i.e., a false discovery rate (FDR) of 4.17%. A robustness test on lower-coverage data revealed a 100% sensitivity for CNVs larger than 300 kb (the current threshold for College of American Pathologists) down to 10× coverage. For CNVs larger than 50 kb, sensitivities were 100% for coverages deeper than 20×, 97% for 15×, and 95% for 10×. We developed a WGS-based CNV pipeline, including this newly developed CNV caller JAX-CNV, and found it capable of detecting CMA-reported CNVs at a sensitivity of 100% with about a FDR of 4%. We propose that JAX-CNV could be further examined in a multi-institutional study to justify the transition of first-tier genetic testing from CMAs to WGS. JAX-CNV is available at https://github.com/TheJacksonLaboratory/JAX-CNV.

Oncogenic extrachromosomal DNA functions as mobile enhancers to globally amplify chromosomal transcription.

Extrachromosomal, circular DNA (ecDNA) is emerging as a prevalent yet less characterized oncogenic alteration in cancer genomes. We leverage ChIA-PET and ChIA-Drop chromatin interaction assays to characterize genome-wide ecDNA-mediated chromatin contacts that impact transcriptional programs in cancers. ecDNAs in glioblastoma patient-derived neurosphere and prostate cancer cell cultures are marked by widespread intra-ecDNA and genome-wide chromosomal interactions. ecDNA-chromatin contact foci are characterized by broad and high-level H3K27ac signals converging predominantly on chromosomal genes of increased expression levels. Prostate cancer cells harboring synthetic ecDNA circles composed of characterized enhancers result in the genome-wide activation of chromosomal gene transcription. Deciphering the chromosomal targets of ecDNAs at single-molecule resolution reveals an association with actively expressed oncogenes spatially clustered within ecDNA-directed interaction networks. Our results suggest that ecDNA can function as mobile transcriptional enhancers to promote tumor progression and manifest a potential synthetic aneuploidy mechanism of transcription control in cancer.

FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods.

Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE .