@article {584, title = {Megabase Length Hypermutation Accompanies Human Structural Variation at 17p11.2.}, journal = {Cell}, volume = {176}, year = {2019}, month = {2019 03 07}, pages = {1310-1324.e10}, abstract = {

DNA rearrangements resulting in human genome structural variants (SVs) are caused by diverse mutational mechanisms. We used long- and short-read sequencing technologies to investigate end products of de novo chromosome 17p11.2 rearrangements and query the molecular mechanisms underlying both recurrent and non-recurrent events. Evidence for an increased rate of clustered single-nucleotide variant (SNV) mutation in cis with non-recurrent rearrangements was found. Indel and SNV formation are associated with both copy-number gains and losses of 17p11.2, occur up to \~{}1 Mb away from the breakpoint junctions, and favor C > G transversion substitutions; results suggest that single-stranded DNA is formed during the genesis of the SV and provide compelling support for a microhomology-mediated break-induced replication (MMBIR) mechanism for SV formation. Our data show an additional mutational burden of MMBIR consisting of hypermutation confined to the locus and manifesting as SNVs and indels predominantly within genes.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2019.01.045}, author = {Beck, Christine R and Carvalho, Claudia M B and Akdemir, Zeynep C and Sedlazeck, Fritz J and Song, Xiaofei and Meng, Qingchang and Hu, Jianhong and Doddapaneni, Harsha and Chong, Zechen and Chen, Edward S and Thornton, Philip C and Liu, Pengfei and Yuan, Bo and Withers, Marjorie and Jhangiani, Shalini N and Kalra, Divya and Walker, Kimberly and English, Adam C and Han, Yi and Chen, Ken and Muzny, Donna M and Ira, Grzegorz and Shaw, Chad A and Gibbs, Richard A and Hastings, P J and Lupski, James R} } @article {9, title = {Identification of a RAI1-associated disease network through integration of exome sequencing, transcriptomics, and 3D genomics.}, journal = {Genome Med}, volume = {8}, year = {2016}, month = {2016 Nov 01}, pages = {105}, abstract = {

BACKGROUND: Smith-Magenis syndrome (SMS) is a developmental disability/multiple congenital anomaly disorder resulting from haploinsufficiency of RAI1. It is characterized by distinctive facial features, brachydactyly, sleep disturbances, and stereotypic behaviors.

METHODS: We investigated a cohort of 15 individuals with a clinical suspicion of SMS who showed neither deletion in the SMS critical region nor damaging variants in RAI1 using whole exome sequencing. A combination of network analysis (co-expression and biomedical text mining), transcriptomics, and circularized chromatin conformation capture (4C-seq) was applied to verify whether modified genes are part of the same disease network as known SMS-causing genes.

RESULTS: Potentially deleterious variants were identified in nine of these individuals using whole-exome sequencing. Eight of these changes affect KMT2D, ZEB2, MAP2K2, GLDC, CASK, MECP2, KDM5C, and POGZ, known to be associated with Kabuki syndrome 1, Mowat-Wilson syndrome, cardiofaciocutaneous syndrome, glycine encephalopathy, mental retardation and microcephaly with pontine and cerebellar hypoplasia, X-linked mental retardation 13, X-linked mental retardation Claes-Jensen type, and White-Sutton syndrome, respectively. The ninth individual carries a de novo variant in JAKMIP1, a regulator of neuronal translation that was recently found deleted in a patient with autism spectrum disorder. Analyses of co-expression and biomedical text mining suggest that these pathologies and SMS are part of the same disease network. Further support for this hypothesis was obtained from transcriptome profiling that showed that the expression levels of both Zeb2 and Map2k2 are perturbed in Rai1 mice. As an orthogonal approach to potentially contributory disease gene variants, we used chromatin conformation capture to reveal chromatin contacts between RAI1 and the loci flanking ZEB2 and GLDC, as well as between RAI1 and human orthologs of the genes that show perturbed expression in our Rai1 mouse model.

CONCLUSIONS: These holistic studies of RAI1 and its interactions allow insights into SMS and other disorders associated with intellectual disability and behavioral abnormalities. Our findings support a pan-genomic approach to the molecular diagnosis of a distinctive disorder.

}, keywords = {Animals, Embryo, Mammalian, Exome, Female, Gene Regulatory Networks, Genomics, Humans, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Mutation, Phenotype, Real-Time Polymerase Chain Reaction, Reverse Transcriptase Polymerase Chain Reaction, RNA, Messenger, Smith-Magenis Syndrome, Trans-Activators, Transcription Factors, Transcriptome}, issn = {1756-994X}, doi = {10.1186/s13073-016-0359-z}, author = {Loviglio, Maria Nicla and Beck, Christine R and White, Janson J and Leleu, Marion and Harel, Tamar and Guex, Nicolas and Niknejad, Anne and Bi, Weimin and Chen, Edward S and Crespo, Isaac and Yan, Jiong and Charng, Wu-Lin and Gu, Shen and Fang, Ping and Coban-Akdemir, Zeynep and Shaw, Chad A and Jhangiani, Shalini N and Muzny, Donna M and Gibbs, Richard A and Rougemont, Jacques and Xenarios, Ioannis and Lupski, James R and Reymond, Alexandre} } @article {88, title = {A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics.}, journal = {Genome Med}, volume = {8}, year = {2016}, month = {2016 Feb 02}, pages = {13}, abstract = {

BACKGROUND: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives.

METHODS: Our tool, "OMIM Explorer" ( http://www.omimexplorer.com ), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries--in essence, generating a computationally assisted differential diagnosis informed by the individual{\textquoteright}s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes.

RESULTS: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 \% of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants.

CONCLUSIONS: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.

}, keywords = {Computational Biology, Diagnosis, Differential, Disease, Genetic Variation, Genome-Wide Association Study, Humans, Phenotype, Pilot Projects, Retrospective Studies, Software}, issn = {1756-994X}, doi = {10.1186/s13073-016-0261-8}, author = {James, Regis A and Campbell, Ian M and Chen, Edward S and Boone, Philip M and Rao, Mitchell A and Bainbridge, Matthew N and Lupski, James R and Yang, Yaping and Eng, Christine M and Posey, Jennifer E and Shaw, Chad A} }