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Post-zygotic Point Mutations Are an Underrecognized Source of De Novo Genomic Variation.

ABSTRACT: De novo mutations are recognized both as an important source of genetic variation and as a prominent cause of sporadic disease in humans. Mutations identified as de novo are generally assumed to have occurred during gametogenesis and, consequently, to be present as germline events in an individual. Because Sanger sequencing does not provide the sensitivity to reliably distinguish somatic from germline mutations, the proportion of de novo mutations that occur somatically rather than in the germline remains largely unknown. To determine the contribution of post-zygotic events to de novo mutations, we analyzed a set of 107 de novo mutations in 50 parent-offspring trios. Using four different sequencing techniques, we found that 7 (6.5%) of these presumed germline de novo mutations were in fact present as mosaic mutations in the blood of the offspring and were therefore likely to have occurred post-zygotically. Furthermore, genome-wide analysis of "de novo" variants in the proband led to the identification of 4/4,081 variants that were also detectable in the blood of one of the parents, implying parental mosaicism as the origin of these variants. Thus, our results show that an important fraction of de novo mutations presumed to be germline in fact occurred either post-zygotically in the offspring or were inherited as a consequence of low-level mosaicism in one of the parents.

SUBMITTER: Acuna-Hidalgo R

PROVIDER: S-EPMC4571017 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Post-zygotic Point Mutations Are an Underrecognized Source of De Novo Genomic Variation.

Acuna-Hidalgo Rocio R Bo Tan T Kwint Michael P MP van de Vorst Maartje M Pinelli Michele M Veltman Joris A JA Hoischen Alexander A Vissers Lisenka E L M LE Gilissen Christian C

American journal of human genetics 20150606 1

De novo mutations are recognized both as an important source of genetic variation and as a prominent cause of sporadic disease in humans. Mutations identified as de novo are generally assumed to have occurred during gametogenesis and, consequently, to be present as germline events in an individual. Because Sanger sequencing does not provide the sensitivity to reliably distinguish somatic from germline mutations, the proportion of de novo mutations that occur somatically rather than in the germli ...[more]

PMID: 26054435

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Project description:BackgroundThe multiple de novo copy number variant (MdnCNV) phenotype is described by having four or more constitutional de novo CNVs (dnCNVs) arising independently throughout the human genome within one generation. It is a rare peri-zygotic mutational event, previously reported to be seen once in every 12,000 individuals referred for genome-wide chromosomal microarray analysis due to congenital abnormalities. These rare families provide a unique opportunity to understand the genetic factors of peri-zygotic genome instability and the impact of dnCNV on human diseases.MethodsChromosomal microarray analysis (CMA), array-based comparative genomic hybridization, short- and long-read genome sequencing (GS) were performed on the newly identified MdnCNV family to identify de novo mutations including dnCNVs, de novo single-nucleotide variants (dnSNVs), and indels. Short-read GS was performed on four previously published MdnCNV families for dnSNV analysis. Trio-based rare variant analysis was performed on the newly identified individual and four previously published MdnCNV families to identify potential genetic etiologies contributing to the peri-zygotic genomic instability. Lin semantic similarity scores informed quantitative human phenotype ontology analysis on three MdnCNV families to identify gene(s) driving or contributing to the clinical phenotype.ResultsIn the newly identified MdnCNV case, we revealed eight de novo tandem duplications, each ~ 1 Mb, with microhomology at 6/8 breakpoint junctions. Enrichment of de novo single-nucleotide variants (SNV; 6/79) and de novo indels (1/12) was found within 4 Mb of the dnCNV genomic regions. An elevated post-zygotic SNV mutation rate was observed in MdnCNV families. Maternal rare variant analyses identified three genes in distinct families that may contribute to the MdnCNV phenomenon. Phenotype analysis suggests that gene(s) within dnCNV regions contribute to the observed proband phenotype in 3/3 cases. CNVs in two cases, a contiguous gene duplication encompassing PMP22 and RAI1 and another duplication affecting NSD1 and SMARCC2, contribute to the clinically observed phenotypic manifestations.ConclusionsCharacteristic features of dnCNVs reported here are consistent with a microhomology-mediated break-induced replication (MMBIR)-driven mechanism during the peri-zygotic period. Maternal genetic variants in DNA repair genes potentially contribute to peri-zygotic genomic instability. Variable phenotypic features were observed across a cohort of three MdnCNV probands, and computational quantitative phenotyping revealed that two out of three had evidence for the contribution of more than one genetic locus to the proband's phenotype supporting the hypothesis of de novo multilocus pathogenic variation (MPV) in those families.

Project description:BackgroundThe advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence, there is no definitive treatment for most cancer types. This reveals the importance of developing new pipelines to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types.ResultsIn this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in many samples to identify cancer subtypes. We apply our pipeline to 12,270 samples collected from the international cancer genome consortium, covering 19 cancer types. As a result, we identify 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways.ConclusionsThis new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. Additionally, we analyze the mutational signatures for samples in each subtype, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on "gene-motif" suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer .

Dataset Information

Post-zygotic Point Mutations Are an Underrecognized Source of De Novo Genomic Variation.

Publications

Post-zygotic Point Mutations Are an Underrecognized Source of De Novo Genomic Variation.

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