PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes

BMC Medical Genomics PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes Yannis J Trakadis 1 5 Caroline Buote 0 3 Jean-Franois Therriault 2 Pierre-tienne Jacques 2 6 Hugo Larochelle 2 Sbastien Lvesque 0 3 4 0 Department of Paediatrics, division of medical genetics, Faculty of Medicine and Health Sciences, Universite de Sherbrooke , Sherbrooke , Canada 1 Department of Medical Genetics, McGill University Health Centre , Montreal , Canada 2 Department of Computer Science, Faculte des Sciences, Universite de Sherbrooke , Sherbrooke , Canada 3 Department of Paediatrics, division of medical genetics, Faculty of Medicine and Health Sciences, Universite de Sherbrooke , Sherbrooke , Canada 4 Medical geneticist, Department of Paediatrics, Medical director molecular genetics laboratory, Centre Hospitalier Universitaire de Sherbrooke , Sherbrooke , Canada 5 Medical geneticist, Biochemical Genetics Fellow, McGill University Health Centre, The Montreal Children's Hospital , 2300 Tupper Street, Room A-604, Montreal H3H 1P3, Qc , Canada 6 Departement de Biologie, Faculte des Sciences, Universite de Sherbrooke , Sherbrooke , Canada Background: We propose a phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test. Twenty test-patients with varied syndromes were selected from the literature. For each patient, the mutation, phenotypic data, and genetic diagnosis were available. Next, control exome-files, each modified to include one of these twenty mutations, were assigned to the corresponding test-patients. These data were used by a geneticist blinded to the diagnoses to test the efficiency of our software, PhenoVar. The score assigned by PhenoVar to any genetic diagnosis listed in OMIM (Online Mendelian Inheritance in Man) took into consideration both the patient's phenotype and all variations present in the corresponding exome. The physician did not have access to the individual mutations. PhenoVar filtered the search using a cut-off phenotypic match threshold to prevent undesired discovery of incidental findings and ranked the OMIM entries according to diagnostic score. Results: When assigning the same weight to all variants in the exome, PhenoVar predicted the correct diagnosis in 10/20 patients, while in 15/20 the correct diagnosis was among the 4 highest ranked diagnoses. When assigning a higher weight to variants known, or bioinformatically predicted, to cause disease, PhenoVar's yield increased to 14/20 (18/20 in top 4). No incidental findings were identified using our cut-off phenotypic threshold. Conclusion: The phenotype-driven approach described could render widespread use of ES more practical, ethical and clinically useful. The implications about novel disease identification, advancement of complex diseases and personalized medicine are discussed. Genome; Exome; Encrypted; Sequencing; Clinic; PhenoVar; I-MPOS; I-MPOSE - Background Exome Sequencing (ES) allows simultaneous screening for variants in the coding portion of all genes present in a patients genome. Over the last few years, ES has aided in the elucidation of the genetic basis of multiple genetic syndromes (for a review of some examples see Ku et al. [1]). The relatively low cost of ES and its high diagnostic yield have stimulated discussion about its promising role in clinic [2-4]. However, despite the unprecedented success of ES as a research tool, its utilization as a genetic screening test in clinic remains largely prohibitive due to challenges associated with consent, incidental findings, and the management of the massive amounts of data generated (see Challenges of integrating ES in clinic subsection). Furthermore, in many families there is a single affected individual available, which adds further complexity to the analysis of the results [5], unless the genetic variant responsible for the disease is not present in the parents. clinical utilization of ES. We hereby present PhenoVar, a software consistent with this phenotype-driven approach, and provide preliminary evidence of its potential benefits. 1. Meaningful patient informed-consent may not be feasible Possibility of incidental findings, Multiple findings of uncertain clinical significance, Multiple issues to discuss leading to prohibitive requirements in time & resources 2. Potential emotional distress over disease risk even among healthy individuals 3. Genomic information is a powerful personal identifier Raising concerns about privacy, confidentiality, genetic discrimination 4. Very large amounts of genetic information generated Limited number of clinical geneticists for data interpretation and clinical care Substantial time and cost for data analysis and genetic counselling Dynamic/evolving nature of the interpretation as new knowledge is gained Duty to re-contact patients as knowledge changes over time To address these challenges, variant prioritization using bioinformatic tools (e.g. Berg et al. [7]; Berg et al. [8]) and practice guidelines/recommendations (e.g. Christenhusz et al. [9]; ACMG Policy statement on Genomic Sequencing, May 2012 [10,11]) have been suggested. These approaches, however, do not adequately address all the challenges summarized in the Challenges of integrating ES in clinic subsection (e.g. incidental findings, findings of uncertain clinical significance, risk for genetic discrimination, requirements in time & resources). Moreover, they are limited by the efficiency of the bioinformatic tools to accurately predict the clinical impact of different variants [12,13]. At present, different tools often lead to opposite predictions about the functional impact of the same variant [14]. Nonetheless, the ability of ES to facilitate diagnosis and inform therapy will likely lead to its premature introduction in clinic using an approach similar to the one followed for chromosomal microarray [15-20]. In the light of rapid developments in genomic technologies, medical genetics is shifting from the present phenotype-first medical model to a data-first model, which leads to multiple complexities. An alternative phenotype-driven approach was recently put forward [6]. This approach, namely Individualized Mutationweighed Phenotype On-line Search (I-MPOS), aims to address the above mentioned issues and facilitate widespread Implementation PhenoVar and phenotype-driven analysis of exome data Figure 1 summarizes the overall workflow of PhenoVar. In brief, PhenoVar automatically prioritizes diagnoses for validation based on both the phenotypic and genomic information of a proband. It calculates a patient-specific diagnostic score for each OMIM entry (Online Mendelian Inheritance in Man; http://www.ncbi.nlm.nih.gov/omim) with known molecular basis. The diagnostic score assigned to a given syndrome is the sum of its phenotypic and genotypic weight. Calculation of phenotypic weight For (...truncated)