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 = 1.77×10−7). 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.)

PurposeWhole exome sequencing (WES) is increasingly used as a diagnostic tool in medicine, but prior reports focus on predominantly pediatric cohorts with neurologic or developmental disorders. We describe the diagnostic yield and characteristics of whole exome sequencing in adults.MethodsWe performed a retrospective analysis of consecutive WES reports for adults from a diagnostic laboratory. Phenotype composition was determined using Human Phenotype Ontology terms.ResultsMolecular diagnoses were reported for 17.5% (85/486) of adults, lower than a primarily pediatric population (25.2%; p=0.0003); the diagnostic rate was higher (23.9%) in those 18–30 years of age compared to patients over 30 years (10.4%; p=0.0001). Dual Mendelian diagnoses contributed to 7% of diagnoses, revealing blended phenotypes. Diagnoses were more frequent among individuals with abnormalities of the nervous system, skeletal system, head/neck, and growth. Diagnostic rate was independent of family history information, and de novo mutations contributed to 61.4% of autosomal dominant diagnoses.ConclusionEarly WES experience in adults demonstrates molecular diagnoses in a substantial proportion of patients, informing clinical management, recurrence risk and recommendations for relatives. A positive family history was not predictive, consistent with molecular diagnoses often revealed by de novo events, informing the Mendelian basis of genetic disease in adults.

Most new mutations are observed to arise in fathers, and increasing paternal age positively correlates with the risk of new variants. Interestingly, new mutations in X-linked recessive disease show elevated familial recurrence rates. In male offspring, these mutations must be inherited from mothers. We previously developed a simulation model to consider parental mosaicism as a source of transmitted mutations. In this paper, we extend and formalize the model to provide analytical results and flexible formulas. The results implicate parent of origin and parental mosaicism as central variables in recurrence risk. Consistent with empirical data, our model predicts that more transmitted mutations arise in fathers and that this tendency increases as fathers age. Notably, the lack of expansion later in the male germline determines relatively lower variance in the proportion of mutants, which decreases with paternal age. Subsequently, observation of a transmitted mutation has less impact on the expected risk for future offspring. Conversely, for the female germline, which arrests after clonal expansion in early development, variance in the mutant proportion is higher, and observation of a transmitted mutation dramatically increases the expected risk of recurrence in another pregnancy. Parental somatic mosaicism considerably elevates risk for both parents. These findings have important implications for genetic counseling and for understanding patterns of recurrence in transmission genetics. We provide a convenient online tool and source code implementing our analytical results. These tools permit varying the underlying parameters that influence recurrence risk and could be useful for analyzing risk in diverse family structures.

BackgroundGenome-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.MethodsOur 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’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.ResultsWe 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.ConclusionsOur integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0261-8) contains supplementary material, which is available to authorized users.

BackgroundGenomic disorders resulting from deletion or duplication of genomic segments are known to be an important cause of cardiovascular malformations (CVMs). In our previous study, we identified a unique individual with a de novo 17q25.3 deletion from a study of 714 individuals with CVM.MethodsTo understand the contribution of this locus to cardiac malformations, we reviewed the data on 60,000 samples submitted for array comparative genomic hybridization (CGH) studies to Medical Genetics Laboratories at Baylor College of Medicine, and ascertained seven individuals with segmental aneusomy of 17q25. We validated our findings by studying another individual with a de novo submicroscopic deletion of this region from Cytogenetics Laboratory at Cincinnati Children’s Hospital. Using bioinformatic analyses including protein-protein interaction network, human tissue expression patterns, haploinsufficiency scores, and other annotation systems, including a training set of 251 genes known to be linked to human cardiac disease, we constructed a pathogenicity score for cardiac phenotype for each of the 57 genes within the terminal 2.0 Mb of 17q25.3.ResultsWe found relatively high penetrance of cardiovascular defects (~60 %) with five deletions and three duplications, observed in eight unrelated individuals. Distinct cardiac phenotypes were present in four of these subjects with non-recurrent de novo deletions (range 0.08 Mb–1.4 Mb) in the subtelomeric region of 17q25.3. These included coarctation of the aorta (CoA), total anomalous pulmonary venous return (TAPVR), ventricular septal defect (VSD) and atrial septal defect (ASD). Amongst the three individuals with variable size duplications of this region, one had patent ductus arteriosus (PDA) at 8 months of age.ConclusionThe distinct cardiac lesions observed in the affected patients and the bioinformatics analyses suggest that multiple genes may be plausible drivers of the cardiac phenotype within this gene-rich critical interval of 17q25.3.Electronic supplementary materialThe online version of this article (doi:10.1186/s13023-015-0291-0) contains supplementary material, which is available to authorized users.

We provide our method as an R package for use in both human protein-protein interaction network analyses and analyses of arbitrary networks along with a tutorial at http://www.shawlab.org/NetComm/.

Summary: Many important data in current biological science comprise hundreds, thousands or more individual results. These massive data require computational tools to navigate results and effectively interact with the content. Mobile device apps are an increasingly important tool in the everyday lives of scientists and non-scientists alike. These software present individuals with compact and efficient tools to interact with complex data at meetings or other locations remote from their main computing environment. We believe that apps will be important tools for biologists, geneticists and physicians to review content while participating in biomedical research or practicing medicine. We have developed a prototype app for displaying gene expression data using the iOS platform. To present the software engineering requirements, we review the model-view-controller schema for Apple's iOS. We apply this schema to a simple app for querying locally developed microarray gene expression data. The challenge of this application is to balance between storing content locally within the app versus obtaining it dynamically via a network connection. Availability: The Hematopoietic Expression Viewer is available at http://www.shawlab.org/he_viewer. The source code for this project and any future information on how to obtain the app can be accessed at http://www.shawlab.org/he_viewer.

In single-particle electron microscopy, the electron dose is limited to avoid damaging the specimen. This results in images with low signal-to-noise ratios (SNR). Class averaging techniques are used to enhance the low-SNR electron micrograph images. A class is defined as a collection of projection images taken along nominally identical projection directions. The images in each class are aligned and averaged in order to cancel or reduce the background noise. The class-averaged images with high-SNR can be used for more accurate threedimensional reconstruction in single-particle electron microscopy. However, errors in the alignment process are inevitable due to noise in electron micrographs. This error results in blurry averaged images. Using the mean and variance of the background noise that is assumed to be Gaussian, we derive equations for the mean and variance of translational and rotational misalignments in the class averaging process. Furthermore, the blurring function representing the distribution of the misalignments is estimated using a Gaussian with the computed mean and variance of the misalignments. The blurring process in class averaging is formulated as convolution of an underlying clear image with the blurring function. We propose a deconvolution method to estimate the underlying image using the Fourier analysis in the appropriate domain. This deconvolution method is applied to artificial and experimental electron micrographs. The deburred class averages are assessed quantitatively and qualitatively.