@article {357, title = {Lessons learned from additional research analyses of unsolved clinical exome cases.}, journal = {Genome Med}, volume = {9}, year = {2017}, month = {2017 03 21}, pages = {26}, abstract = {

BACKGROUND: Given the rarity of most single-gene Mendelian disorders, concerted efforts of data exchange between clinical and scientific communities are critical to optimize molecular diagnosis and novel disease gene discovery.

METHODS: We designed and implemented protocols for the study of cases for which a plausible molecular diagnosis was not achieved in a clinical genomics diagnostic laboratory (i.e. unsolved clinical exomes). Such cases were recruited to a research laboratory for further analyses, in order to potentially: (1) accelerate novel disease gene discovery; (2) increase the molecular diagnostic yield of whole exome sequencing (WES); and (3) gain insight into the genetic mechanisms of disease. Pilot project data included 74 families, consisting mostly of parent-offspring trios. Analyses performed on a research basis employed both WES from additional family members and complementary bioinformatics approaches and protocols.

RESULTS: Analysis of all possible modes of Mendelian inheritance, focusing on both single nucleotide variants (SNV) and copy number variant (CNV) alleles, yielded a likely contributory variant in 36\% (27/74) of cases. If one includes candidate genes with variants identified within a single family, a potential contributory variant was identified in a total of ~51\% (38/74) of cases enrolled in this pilot study. The molecular diagnosis was achieved in 30/63 trios (47.6\%). Besides this, the analysis workflow yielded evidence for pathogenic variants in disease-associated genes in 4/6 singleton cases (66.6\%), 1/1 multiplex family involving three affected siblings, and 3/4 (75\%) quartet families. Both the analytical pipeline and the collaborative efforts between the diagnostic and research laboratories provided insights that allowed recent disease gene discoveries (PURA, TANGO2, EMC1, GNB5, ATAD3A, and MIPEP) and increased the number of novel genes, defined in this study as genes identified in more than one family (DHX30 and EBF3).

CONCLUSION: An efficient genomics pipeline in which clinical sequencing in a diagnostic laboratory is followed by the detailed reanalysis of unsolved cases in a research environment, supplemented with WES data from additional family members, and subject to adjuvant bioinformatics analyses including relaxed variant filtering parameters in informatics pipelines, can enhance the molecular diagnostic yield and provide mechanistic insights into Mendelian disorders. Implementing these approaches requires collaborative clinical molecular diagnostic and research efforts.

}, keywords = {Adenosine Triphosphatases, ATPases Associated with Diverse Cellular Activities, Computational Biology, DNA Copy Number Variations, DNA-Binding Proteins, Exome, Female, Genetic Diseases, Inborn, Genomics, GTP-Binding Protein beta Subunits, Humans, Male, Membrane Proteins, Metalloendopeptidases, Mitochondrial Proteins, Pilot Projects, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Transcription Factors}, issn = {1756-994X}, doi = {10.1186/s13073-017-0412-6}, author = {Eldomery, Mohammad K and Coban-Akdemir, Zeynep and Harel, Tamar and Rosenfeld, Jill A and Gambin, Tomasz and Stray-Pedersen, Asbj{\o}rg and K{\"u}ry, S{\'e}bastien and Mercier, Sandra and Lessel, Davor and Denecke, Jonas and Wiszniewski, Wojciech and Penney, Samantha and Liu, Pengfei and Bi, Weimin and Lalani, Seema R and Schaaf, Christian P and Wangler, Michael F and Bacino, Carlos A and Lewis, Richard Alan and Potocki, Lorraine and Graham, Brett H and Belmont, John W and Scaglia, Fernando and Orange, Jordan S and Jhangiani, Shalini N and Chiang, Theodore and Doddapaneni, Harsha and Hu, Jianhong and Muzny, Donna M and Xia, Fan and Beaudet, Arthur L and Boerwinkle, Eric and Eng, Christine M and Plon, Sharon E and Sutton, V Reid and Gibbs, Richard A and Posey, Jennifer 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} } @article {331, title = {Clan genomics and the complex architecture of human disease.}, journal = {Cell}, volume = {147}, year = {2011}, month = {2011 Sep 30}, pages = {32-43}, abstract = {

Human diseases are caused by alleles that encompass the full range of variant types, from single-nucleotide changes to copy-number variants, and these variations span a broad frequency spectrum, from the very rare to the common. The picture emerging from analysis of whole-genome sequences, the 1000 Genomes Project pilot studies, and targeted genomic sequencing derived from very large sample sizes reveals an abundance of rare and private variants. One implication of this realization is that recent mutation may have a greater influence on disease susceptibility or protection than is conferred by variations that arose in distant ancestors.

}, keywords = {Genetic Predisposition to Disease, Genetic Variation, Genetics, Population, Genome, Human, Genomics, Humans, Pedigree, Pilot Projects, Precision Medicine}, issn = {1097-4172}, doi = {10.1016/j.cell.2011.09.008}, author = {Lupski, James R and Belmont, John W and Boerwinkle, Eric and Gibbs, Richard A} }