Settling the score: variant prioritization and Mendelian disease

By Karen Eilbeck, Aaron R. Quinlan, Mark Yandell. 2017

217 citations recorded by the source corpus.

When investigating Mendelian disease using exome or genome sequencing, distinguishing disease-causing genetic variants from the multitude of candidate variants is a complex, multidimensional task. Many prioritization tools and online interpretation resources exist, and professional organizations have offered clinical guidelines for review and return of prioritization results. In this Review, we describe the strengths and weaknesses of widely used computational approaches, explain their roles in the diagnostic and discovery process and discuss how they can inform (and misinform) expert reviewers. We place variant prioritization in the wider context of gene prioritization, burden testing and genotype–phenotype association, and we discuss opportunities and challenges introduced by whole-genome sequencing.

Subfields: Genetics

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Citations in the corpus

ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants. (2018, background): “37,38 Furthermore, progress in whole-genome sequencing data will bring the need to accurately predict the effect of non-coding variants.”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, background): “However, disease gene prioritization and disease-causing variation discovery are still difficult [19, 20].”
The DisGeNET knowledge platform for disease genomics: 2019 update (2019, background): “Nevertheless, the interpretation of the re-sults of genomic analysis and the identification of variant of clinical relevance remain a significant challenge (1).”
The DisGeNET cytoscape app: Exploring and visualizing disease genomics data (2021, background): “Sequencing of an individual’s genome can produce several millions of genomic variants; determining which of these variants lead to a disease phenotype remains a signiﬁcant challenge [3].”