Affiliation:
1. Division of Genomics & Translational Biomedicine, College of Health & Life Sciences, Hamad Bin Khalifa University, B-147, Penrose House, PO Box 34110, Education City, Doha, Qatar
2. Quantitative Genomics Laboratories (qGenomics), Barcelona, Catalonia, Spain
Abstract
Abstract
Rare diseases occur in a smaller proportion of the general population, which is variedly defined as less than 200 000 individuals (US) or in less than 1 in 2000 individuals (Europe). Although rare, they collectively make up to approximately 7000 different disorders, with majority having a genetic origin, and affect roughly 300 million people globally. Most of the patients and their families undergo a long and frustrating diagnostic odyssey. However, advances in the field of genomics have started to facilitate the process of diagnosis, though it is hindered by the difficulty in genome data analysis and interpretation. A major impediment in diagnosis is in the understanding of the diverse approaches, tools and datasets available for variant prioritization, the most important step in the analysis of millions of variants to select a few potential variants. Here we present a review of the latest methodological developments and spectrum of tools available for rare disease genetic variant discovery and recommend appropriate data interpretation methods for variant prioritization. We have categorized the resources based on various steps of the variant interpretation workflow, starting from data processing, variant calling, annotation, filtration and finally prioritization, with a special emphasis on the last two steps. The methods discussed here pertain to elucidating the genetic basis of disease in individual patient cases via trio- or family-based analysis of the genome data. We advocate the use of a combination of tools and datasets and to follow multiple iterative approaches to elucidate the potential causative variant.
Funder
College of Health & Life Sciences, Hamad Bin Khalifa University
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
6 articles.
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