@article {515, title = {Clinical exome sequencing reveals locus heterogeneity and phenotypic variability of cohesinopathies.}, journal = {Genet Med}, volume = {21}, year = {2019}, month = {2019 03}, pages = {663-675}, abstract = {

PURPOSE: Defects in the cohesin pathway are associated with cohesinopathies, notably Cornelia de Lange syndrome (CdLS). We aimed to delineate pathogenic variants~in known and candidate cohesinopathy genes from a clinical exome perspective.

METHODS: We retrospectively studied patients referred for clinical exome sequencing (CES, N = 10,698). Patients with causative variants in novel or recently described cohesinopathy genes were enrolled for phenotypic characterization.

RESULTS: Pathogenic or likely pathogenic single-nucleotide and insertion/deletion variants (SNVs/indels) were identified in established disease genes including NIPBL (N = 5), SMC1A (N = 14), SMC3 (N = 4), RAD21 (N = 2), and HDAC8 (N = 8). The phenotypes in this genetically defined cohort skew towards the mild end of CdLS spectrum as compared with phenotype-driven cohorts. Candidate or recently reported cohesinopathy genes were supported by de novo SNVs/indels in STAG1 (N = 3), STAG2 (N = 5), PDS5A (N = 1), and WAPL (N = 1), and one inherited SNV in PDS5A. We also identified copy-number deletions affecting STAG1 (two de novo, one of unknown inheritance) and STAG2 (one of unknown inheritance). Patients with STAG1 and STAG2 variants presented with overlapping features yet without characteristic facial features of CdLS.

CONCLUSION: CES effectively identified disease-causing alleles at the mild end of the cohensinopathy spectrum and enabled characterization of candidate disease genes.

}, keywords = {Adolescent, Alleles, Antigens, Nuclear, Biological Variation, Population, Carrier Proteins, Cell Cycle Proteins, Child, Child, Preschool, Chromosomal Proteins, Non-Histone, Cohort Studies, De Lange Syndrome, Exome, Female, Gene Frequency, Genetic Heterogeneity, Humans, INDEL Mutation, Male, Mutation, Nuclear Proteins, Phenotype, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins, Retrospective Studies, Whole Exome Sequencing}, issn = {1530-0366}, doi = {10.1038/s41436-018-0085-6}, author = {Yuan, Bo and Neira, Juanita and Pehlivan, Davut and Santiago-Sim, Teresa and Song, Xiaofei and Rosenfeld, Jill and Posey, Jennifer E and Patel, Vipulkumar and Jin, Weihong and Adam, Margaret P and Baple, Emma L and Dean, John and Fong, Chin-To and Hickey, Scott E and Hudgins, Louanne and Leon, Eyby and Madan-Khetarpal, Suneeta and Rawlins, Lettie and Rustad, Cecilie F and Stray-Pedersen, Asbj{\o}rg and Tveten, Kristian and Wenger, Olivia and Diaz, Jullianne and Jenkins, Laura and Martin, Laura and McGuire, Marianne and Pietryga, Marguerite and Ramsdell, Linda and Slattery, Leah and Abid, Farida and Bertuch, Alison A and Grange, Dorothy and Immken, LaDonna and Schaaf, Christian P and Van Esch, Hilde and Bi, Weimin and Cheung, Sau Wai and Breman, Amy M and Smith, Janice L and Shaw, Chad and Crosby, Andrew H and Eng, Christine and Yang, Yaping and Lupski, James R and Xiao, Rui and Liu, Pengfei} } @article {339, title = {Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation.}, journal = {N Engl J Med}, volume = {376}, year = {2017}, month = {2017 01 05}, pages = {21-31}, abstract = {

BACKGROUND: Whole-exome sequencing can provide insight into the relationship between observed clinical phenotypes and underlying genotypes.

METHODS: We conducted a retrospective analysis of data from a series of 7374 consecutive unrelated patients who had been referred to a clinical diagnostic laboratory for whole-exome sequencing; our goal was to determine the frequency and clinical characteristics of patients for whom more than one molecular diagnosis was reported. The phenotypic similarity between molecularly diagnosed pairs of diseases was calculated with the use of terms from the Human Phenotype Ontology.

RESULTS: A molecular diagnosis was rendered for 2076 of 7374 patients (28.2\%); among these patients, 101 (4.9\%) had diagnoses that involved two or more disease loci. We also analyzed parental samples, when available, and found that de novo variants accounted for 67.8\% (61 of 90) of pathogenic variants in autosomal dominant disease genes and 51.7\% (15 of 29) of pathogenic variants in X-linked disease genes; both variants were de novo in 44.7\% (17 of 38) of patients with two monoallelic variants. Causal copy-number variants were found in 12 patients (11.9\%) with multiple diagnoses. Phenotypic similarity scores were significantly lower among patients in whom the phenotype resulted from two distinct mendelian disorders that affected different organ systems (50 patients) than among patients with disorders that had overlapping phenotypic features (30 patients) (median score, 0.21 vs. 0.36; P=1.77{\texttimes}10).

CONCLUSIONS: In our study, we found multiple molecular diagnoses in 4.9\% of cases in which whole-exome sequencing was informative. Our results show that structured clinical ontologies can be used to determine the degree of overlap between two mendelian diseases in the same patient; the diseases can be distinct or overlapping. Distinct disease phenotypes affect different organ systems, whereas overlapping disease phenotypes are more likely to be caused by two genes encoding proteins that interact within the same pathway. (Funded by the National Institutes of Health and the Ting Tsung and Wei Fong Chao Foundation.).

}, keywords = {Exome, Genetic Diseases, Inborn, Genetic Variation, Genotyping Techniques, High-Throughput Nucleotide Sequencing, Humans, Phenotype, Retrospective Studies, Sequence Analysis, DNA}, issn = {1533-4406}, doi = {10.1056/NEJMoa1516767}, author = {Posey, Jennifer E and Harel, Tamar and Liu, Pengfei and Rosenfeld, Jill A and James, Regis A and Coban Akdemir, Zeynep H and Walkiewicz, Magdalena and Bi, Weimin and Xiao, Rui and Ding, Yan and Xia, Fan and Beaudet, Arthur L and Muzny, Donna M and Gibbs, Richard A and Boerwinkle, Eric and Eng, Christine M and Sutton, V Reid and Shaw, Chad A and Plon, Sharon E and Yang, Yaping and Lupski, James R} } @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} }