Thuwal, Kingdom of Saudi Arabia – [April, 18, 2017]: Genetic diagnosis of disease and personalization of treatment have the potential to dramatically improve strategies for diagnosis and therapy. Around 80% or rare diseases are thought to have a genetic component, but currently many patients experience long delays in diagnosis or receive none at all. The challenge is to identify which of the hundreds of thousands of genetic differences between a patient and an unaffected individual might be responsible for their disease; a problem which has been described as "looking for needles in stacks of needles." What's new with this approach is the application of data from model experimental organisms.
How can we make use of the similarities between human and non-human disease models? Non-human model organisms offer significantly more insight because it is possible to induce variation into their genomes and observe the outcomes. This type of data has been collected for close to a century and has culminated into a "big-data" set of genotype-phenotype association data.
A framework called PhenomeNET finds disease genes in a patient-specific manner by matching a patient's phenotype (symptoms) to a large database of gene-to-phenotype associations, including those from non-human model organisms, such as mouse or zebrafish. By combining PhenomeNET with methods that find harmful variants in a genomics sequence, the team developed the PhenomeNET Variant Predictor (PVP) system, an algorithm that prioritizes these variants with their likelihood of involvement in human disease.
"PVP makes use of clinical and experimental data that have been collected for years and uses them to identify the genetic variants underlying the conditions of patients with genetic disorders," said Professor Hoehndorf.
Working with Dr. Nadia Schoenmakers at the Wellcome Trust-MRC Institute of Metabolic Science in Cambridge, the team were able to show that PVP can identify genetic changes in patients with congenital thyroid disease, and can reveal candidate genetic changes in "Mendelian" diseases where only a single gene is involved. Moving forward, the team will seek to determine whether a similar approach can be applied to complex diseases, such as diabetes, where multiple genes are involved.
The article detailing this work will be published in Issue xx of the PLOS journal PLOS Computational Biology (DOI:10.1371/journal.pcbi.1005500).
Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.
PROF. ROBERT HOEHNDORF'S BIO
Robert Hoehndorf is an Assistant Professor of Computer Science at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Before joining KAUST, Robert was a research fellow at Aberystwyth University, research associate at the University of Cambridge, and postdoctoral researcher at the European Bioinformatics Institute and the Max Planck Institute for Evolutionary Anthropology. His research is in developing methods for building and using ontologies for analysis of large and complex biological datasets, with primary applications in studying mechanisms underlying complex phenotypes and disease.
King Abdullah University of Science and Technology (KAUST)
KAUST advances science and technology through distinctive and collaborative research integrated with graduate education. Located on the Red Sea coast in Saudi Arabia, KAUST conducts curiosity-driven and goal-oriented research to address global challenges related to food, water, energy and the environment. Established in 2009, KAUST is a catalyst for innovation, economic development and social prosperity in Saudi Arabia and the world. The university currently educates and trains over 900 master's and doctoral students, supported by an academic community of 150 faculty members, 400 postdocs and 300 research scientists. With 100 nationalities working and living at KAUST, the university brings together people and ideas from all over the world. Visit kaust.edu.sa for more information.
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