ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

BackgroundWhole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts.MethodsWe developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient's standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA).ResultsClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes.ConclusionsClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses

Review and evaluation of the methodological quality of the existing guidelines and recommendations for inherited neurometabolic disorders

BACKGROUND: Inherited neurometabolic disorders (iNMDs) represent a group of almost seven hundred rare diseases whose common manifestations are clinical neurologic or cognitive symptoms that can appear at any time, in the first months/years of age or even later in adulthood. Early diagnosis and timely treatments are often pivotal for the favorable course of the disease. Thus, the elaboration of new evidence-based recommendations for iNMD diagnosis and management is increasingly requested by health care professionals and patients, even though the methodological quality of existing guidelines is largely unclear. InNerMeD-I-Network is the first European network on iNMDs that was created with the aim of sharing and increasing validated information about diagnosis and management of neurometabolic disorders. One of the goals of the project was to determine the number and the methodological quality of existing guidelines and recommendations for iNMDs. ----- METHODS: We performed a systematic search on PubMed, the National Guideline Clearinghouse (NGC), the Guidelines International Network (G-I-N), the Scottish Intercollegiate Guideline Network (SIGN) and the National Institute for Health and Care Excellence (NICE) to identify all the published guidelines and recommendations for iNMDs from January 2000 to June 2015. The methodological quality of the selected documents was determined using the AGREE II instrument, an appraisal tool composed of 6 domains covering 23 key items. ----- RESULTS: A total of 55 records met the inclusion criteria, 11 % were about groups of disorders, whereas the majority encompassed only one disorder. Lysosomal disorders, and in particular Fabry, Gaucher disease and mucopolysaccharidoses where the most studied. The overall methodological quality of the recommendation was acceptable and increased over time, with 25 % of the identified guidelines strongly recommended by the appraisers, 64 % recommended, and 11 % not recommended. However, heterogeneity in the obtained scores for each domain was observed among documents covering different groups of disorders and some domains like 'stakeholder involvement' and 'applicability' were generally scarcely addressed. ----- CONCLUSIONS: Greater efforts should be devoted to improve the methodological quality of guidelines and recommendations for iNMDs and AGREE II instrument seems advisable for new guideline development. The elaboration of new guidelines encompassing still uncovered disorders is badly needed

Selective Elimination of Mitochondrial Mutations in the Germline by Genome Editing

SummaryMitochondrial diseases include a group of maternally inherited genetic disorders caused by mutations in mtDNA. In most of these patients, mutated mtDNA coexists with wild-type mtDNA, a situation known as mtDNA heteroplasmy. Here, we report on a strategy toward preventing germline transmission of mitochondrial diseases by inducing mtDNA heteroplasmy shift through the selective elimination of mutated mtDNA. As a proof of concept, we took advantage of NZB/BALB heteroplasmic mice, which contain two mtDNA haplotypes, BALB and NZB, and selectively prevented their germline transmission using either mitochondria-targeted restriction endonucleases or TALENs. In addition, we successfully reduced human mutated mtDNA levels responsible for Leber’s hereditary optic neuropathy (LHOND), and neurogenic muscle weakness, ataxia, and retinitis pigmentosa (NARP), in mammalian oocytes using mitochondria-targeted TALEN (mito-TALENs). Our approaches represent a potential therapeutic avenue for preventing the transgenerational transmission of human mitochondrial diseases caused by mutations in mtDNA.PaperCli

Personalized medicine in psychiatry: problems and promises

The central theme of personalized medicine is the premise that an individual’s unique physiologic characteristics play a significant role in both disease vulnerability and in response to specific therapies. The major goals of personalized medicine are therefore to predict an individual’s susceptibility to developing an illness, achieve accurate diagnosis, and optimize the most efficient and favorable response to treatment. The goal of achieving personalized medicine in psychiatry is a laudable one, because its attainment should be associated with a marked reduction in morbidity and mortality. In this review, we summarize an illustrative selection of studies that are laying the foundation towards personalizing medicine in major depressive disorder, bipolar disorder, and schizophrenia. In addition, we present emerging applications that are likely to advance personalized medicine in psychiatry, with an emphasis on novel biomarkers and neuroimaging