Ryan R. Brinkman

A new model for prediction of the age of onset and penetrance for Huntington's disease based on CAG length.

Huntingtons disease (HD) is a neurodegenerative disorder caused by an unstable CAG repeat. For patients at risk, participating in predictive testing and learning of having CAG expansion, a major unanswered question shifts from “Will I get HD?” to “When will it manifest?” Using the largest cohort of HD patients analyzed to date (2913 individuals from 40 centers worldwide), we developed a parametric survival model based on CAG repeat length to predict the probability of neurological disease onset (based on motor neurological symptoms rather than psychiatric onset) at different ages for individual patients. We provide estimated probabilities of onset associated with CAG repeats between 36 and 56 for individuals of any age with narrow confidence intervals. For example, our model predicts a 91% chance that a 40‐year‐old individual with 42 repeats will have onset by the age of 65, with a 95% confidence interval from 90 to 93%. This model also defines the variability in HD onset that is not attributable to CAG length and provides information concerning CAG‐related penetrance rates.

Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project

The Minimum Information for Biological and Biomedical Investigations (MIBBI) project aims to foster the coordinated development of minimum-information checklists and provide a resource for those exploring the range of extant checklists.

Long-Term Propagation of Distinct Hematopoietic Differentiation Programs In Vivo

Heterogeneity in the differentiation behavior of hematopoietic stem cells is well documented but poorly understood. To investigate this question at a clonal level, we isolated a subpopulation of adult mouse bone marrow that is highly enriched for multilineage in vivo repopulating cells and transplanted these as single cells, or their short-term clonal progeny generated in vitro, into 352 recipients. Of the mice, 93 showed a donor-derived contribution to the circulating white blood cells for at least 4 months in one of four distinct patterns. Serial transplantation experiments indicated that two of the patterns were associated with extensive self-renewal of the original cell transplanted. However, within 4 days in vitro, the repopulation patterns subsequently obtained in vivo shifted in a clone-specific fashion to those with less myeloid contribution. Thus, primitive hematopoietic cells can maintain distinct repopulation properties upon serial transplantation in vivo, although these properties can also alter rapidly in vitro.

Critical assessment of automated flow cytometry data analysis techniques

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

Modeling biomedical experimental processes with OBI

BackgroundExperimental descriptions are typically stored as free text without using standardized terminology, creating challenges in comparison, reproduction and analysis. These difficulties impose limitations on data exchange and information retrieval.ResultsThe Ontology for Biomedical Investigations (OBI), developed as a global, cross-community effort, provides a resource that represents biomedical investigations in an explicit and integrative framework. Here we detail three real-world applications of OBI, provide detailed modeling information and explain how to use OBI.ConclusionWe demonstrate how OBI can be applied to different biomedical investigations to both facilitate interpretation of the experimental process and increase the computational processing and integration within the Semantic Web. The logical definitions of the entities involved allow computers to unambiguously understand and integrate different biological experimental processes and their relevant components.AvailabilityOBI is available at http://purl.obolibrary.org/obo/obi/2009-11-02/obi.owl

A Worldwide Assessment of the Frequency of Suicide, Suicide Attempts, or Psychiatric Hospitalization after Predictive Testing for Huntington Disease

Prior to the implementation of predictive-testing programs for Huntington disease (HD), significant concern was raised concerning the likelihood of catastrophic events (CEs), particularly in those persons receiving an increased-risk result. We have investigated the frequency of CEs-that is, suicide, suicide attempt, and psychiatric hospitalization-after an HD predictive-testing result, through questionnaires sent to predictive-testing centers worldwide. A total of 44 persons (0.97%) in a cohort of 4,527 test participants had a CE: 5 successful suicides, 21 suicide attempts, and 18 hospitalizations for psychiatric reasons. All persons committing suicide had signs of HD, whereas 11 (52.4%) of 21 persons attempting suicide and 8 (44.4%) of 18 who had a psychiatric hospitalization were symptomatic. A total of 11 (84.6%) of 13 asymptomatic persons who experienced a CE during the first year after HD predictive testing received an increased-risk result. Factors associated with an increased risk of a CE included (a) a psychiatric history </=5 years prior to testing and (b) unemployed status. The frequency of CEs did not differ between those persons receiving results of predictive testing through linkage analysis in whom there was only changes in direction of risk and those persons receiving definitive results after analysis for the mutation underlying HD. These findings provide insights into the frequency, associated factors, and timing of CEs in a worldwide cohort of persons receiving predictive-testing results and, as such, highlight persons for whom ongoing support may be beneficial.

MIFlowCyt: The Minimum Information About a Flow Cytometry Experiment

A fundamental tenet of scientific research is that published results are open to independent validation and refutation. Minimum data standards aid data providers, users, and publishers by providing a specification of what is required to unambiguously interpret experimental findings. Here, we present the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, stating the minimum information required to report flow cytometry (FCM) experiments. We brought together a cross‐disciplinary international collaborative group of bioinformaticians, computational statisticians, software developers, instrument manufacturers, and clinical and basic research scientists to develop the standard. The standard was subsequently vetted by the International Society for Advancement of Cytometry (ISAC) Data Standards Task Force, Standards Committee, membership, and Council. The MIFlowCyt standard includes recommendations about descriptions of the specimens and reagents included in the FCM experiment, the configuration of the instrument used to perform the assays, and the data processing approaches used to interpret the primary output data. MIFlowCyt has been adopted as a standard by ISAC, representing the FCM scientific community including scientists as well as software and hardware manufacturers. Adoptionof MIFlowCyt by the scientific and publishing communities will facilitate third‐party understanding and reuse of FCM data.

Clinical markers of early disease in persons near onset of Huntington’s disease

Objective: There is increasing evidence that neuron loss precedes the phenotypic expression of Huntington’s disease (HD). As genes for late-onset neurodegenerative diseases are identified, the need for accurate assessment of phenoconversion (i.e., the transition from health to the disease phenotype) will be important. Methods: Prospective longitudinal evaluation using the Unified Huntington’s Disease Rating Scale (UHDRS) was conducted by Huntington Study Group members from 36 sites. There were 260 persons considered “at risk” for HD who initially did not have manifest disease and had at least one subsequent evaluation. Repeat UHDRS data, obtained an average of 2 years later, showed that 70 persons were given a diagnosis of definite HD based on the quantified neurologic examination. Results: Baseline cognitive performances were consistently worse for the at-risk group who demonstrated conversion to a definitive diagnosis compared with those who did not. Longitudinal change scores showed that the at-risk group who did not demonstrate manifest disease during the follow-up study period demonstrated improvements in all cognitive tests, whereas performances in the at-risk group demonstrating conversion to disease during the study declined across cognitive domains. Conclusions: Neuropsychological measures show impairment 2 years before the development of more manifest motor disease. Findings suggest that these brief cognitive measures administered over time may capture early striatal neural loss in HD.

Automated gating of flow cytometry data via robust model‐based clustering

The capability of flow cytometry to offer rapid quantification of multidimensional characteristics for millions of cells has made this technology indispensable for health research, medical diagnosis, and treatment. However, the lack of statistical and bioinformatics tools to parallel recent high‐throughput technological advancements has hindered this technology from reaching its full potential. We propose a flexible statistical model‐based clustering approach for identifying cell populations in flow cytometry data based on t‐mixture models with a Box–Cox transformation. This approach generalizes the popular Gaussian mixture models to account for outliers and allow for nonelliptical clusters. We describe an Expectation‐Maximization (EM) algorithm to simultaneously handle parameter estimation and transformation selection. Using two publicly available datasets, we demonstrate that our proposed methodology provides enough flexibility and robustness to mimic manual gating results performed by an expert researcher. In addition, we present results from a simulation study, which show that this new clustering framework gives better results in terms of robustness to model misspecification and estimation of the number of clusters, compared to the popular mixture models. The proposed clustering methodology is well adapted to automated analysis of flow cytometry data. It tends to give more reproducible results, and helps reduce the significant subjectivity and human time cost encountered in manual gating analysis.

flowCore: a Bioconductor package for high throughput flow cytometry

BackgroundRecent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates.ResultsWe developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry.ConclusionThe software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.