Development of a Scalable Pharmacogenomic Clinical Decision Support Service

Advances in sequencing technology are making genomic data more accessible within the healthcare environment. Published pharmacogenetic guidelines attempt to provide a clinical context for specific genomic variants; however, the actual implementation to convert genomic data into a clinical report integrated within an electronic medical record system is a major challenge for any hospital. We created a two-part solution that integrates with the medical record system and converts genetic variant results into an interpreted clinical report based on published guidelines. We successfully developed a scalable infrastructure to support TPMT genetic testing and are currently testing approximately two individuals per week in our production version. We plan to release an online variant to clinical interpretation reporting system in order to facilitate translation of pharmacogenetic information into clinical practice

Clinical heterogeneity associated with KCNA1 mutations include cataplexy and nonataxic presentations

Mutations in the KCNA1 gene are known to cause episodic ataxia/myokymia syndrome type 1 (EA1). Here, we describe two families with unique presentations who were enrolled in an IRB-approved study, extensively phenotyped, and whole exome sequencing (WES) performed. Family 1 had a diagnosis of isolated cataplexy triggered by sudden physical exertion in multiple affected individuals with heterogeneous neurological findings. All enrolled affected members carried a KCNA1 c.941T>C (p.I314T) mutation. Family 2 had an 8-year-old patient with muscle spasms with rigidity for whom WES revealed a previously reported heterozygous missense mutation in KCNA1 c.677C>G (p.T226R), confirming the diagnosis of EA1 without ataxia. WES identified variants in KCNA1 that explain both phenotypes expanding the phenotypic spectrum of diseases associated with mutations of this gene. KCNA1 mutations should be considered in patients of all ages with episodic neurological phenotypes, even when ataxia is not present. This is an example of the power of genomic approaches to identify pathogenic mutations in unsuspected genes responsible for heterogeneous diseases

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

This is an Open Access article distributed under the terms of the Creative Commons Attribution License.-- et al.[Background]: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. [Results]: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. [Conclusions]: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.This work was supported by funds provided through the Gene Partnership and the Manton Center for Orphan Disease Research at Boston Children’s Hospital and the Center for Biomedical Informatics at Harvard Medical School and by generous donations in-kind of genomic sequencing services by Life Technologies (Carlsbad, CA, USA) and Complete Genomics (Mountain View, CA, USA).Peer Reviewe

Using network theory to identify the causes of disease outbreaks of unknown origin.

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known aetiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 80%), whereas others (e.g. Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic

In vivo characterization of the role of tissue-specific translation elongation factor 1A2 in protein synthesis reveals insights into muscle atrophy

Translation elongation factor 1A2 (eEF1A2), uniquely among translation factors, is expressed specifically in neurons and muscle. eEF1A2‐null mutant wasted mice develop an aggressive, early‐onset form of neurodegeneration, but it is unknown whether the wasting results from denervation of the muscles, or whether the mice have a primary myopathy resulting from loss of translation activity in muscle. We set out to establish the relative contributions of loss of eEF1A2 in the different tissues to this postnatal lethal phenotype. We used tissue‐specific transgenesis to show that correction of eEF1A2 levels in muscle fails to ameliorate the overt phenotypic abnormalities or time of death of wasted mice. Molecular markers of muscle atrophy such as Fbxo32 were dramatically upregulated at the RNA level in wasted mice, both in the presence and in the absence of muscle‐specific expression of eEF1A2, but the degree of upregulation at the protein level was significantly lower in those wasted mice without transgene‐derived expression of eEF1A2 in muscle. This provides the first in vivo confirmation that eEF1A2 plays an important role in translation. In spite of the inability of the nontransgenic wasted mice to upregulate key atrogenes at the protein level in response to denervation to the same degree as their transgenic counterparts, there were no measurable differences between transgenic and nontransgenic wasted mice in terms of weight loss, grip strength, or muscle pathology. This suggests that a compromised ability fully to execute the atrogene pathway in denervated muscle does not affect the process of muscle atrophy in the short term

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY challenge

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

AIFM1 mutation presenting with fatal encephalomyopathy and mitochondrial disease in an infant

Apoptosis-inducing factor mitochondrion-associated 1 (AIFM1), encoded by the gene AIFM1, has roles in electron transport, apoptosis, ferredoxin metabolism, reactive oxygen species generation, and immune system regulation. Here we describe a patient with a novel AIFM1 variant presenting unusually early in life with mitochondrial disease, rapid deterioration, and death. Autopsy, at the age of 4 mo, revealed features of mitochondrial encephalopathy, myopathy, and involvement of peripheral nerves with axonal degeneration. In addition, there was microvesicular steatosis in the liver, thymic noninvolution, follicular bronchiolitis, and pulmonary arterial medial hypertrophy. This report adds to the clinical and pathological spectrum of disease related to AIFM1 mutations and provides insights into the role of AIFM1 in cellular function

Returning Individual Research Results from Digital Phenotyping in Psychiatry

Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants’ locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant’s real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas