Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Opportunities and Challenges for Interpreting Rare Variation in Clinically Important Genes

Version 1 : Received: 17 November 2020 / Approved: 23 November 2020 / Online: 23 November 2020 (20:44:20 CET)

How to cite: McInnes, G.; Sharo, A.G.; Koleske, M.L.; Brown, J.E.H.; Norstad, M.; Adhikari, A.N.; Wang, S.; Brenner, S.E.; Halpern, J.; Koenig, B.A.; Magnus, D.C.; Gallagher, R.C.; Giacomini, K.M.; Altman, R.B. Opportunities and Challenges for Interpreting Rare Variation in Clinically Important Genes. Preprints 2020, 2020110599 (doi: 10.20944/preprints202011.0599.v1). McInnes, G.; Sharo, A.G.; Koleske, M.L.; Brown, J.E.H.; Norstad, M.; Adhikari, A.N.; Wang, S.; Brenner, S.E.; Halpern, J.; Koenig, B.A.; Magnus, D.C.; Gallagher, R.C.; Giacomini, K.M.; Altman, R.B. Opportunities and Challenges for Interpreting Rare Variation in Clinically Important Genes. Preprints 2020, 2020110599 (doi: 10.20944/preprints202011.0599.v1).

Abstract

Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, leaving a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequent frequent variants and associated mechanisms. Variants of unknown significance (VUS) in these genes are discovered at a rate that outpaces current ability to classify them using databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns, and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

Subject Areas

Precision medicine; Inborn errors of metabolism; Pharmacogenomics, Ethics; Genomics; Learning healthcare; Machine learning; Computational biology

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