Opportunities and challenges for the computational interpretation of rare variation in clinically important genes

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, however there is 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 consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with 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.

Figure 1

Diagram of current treatment workflow and proposed workflow that integrates genomics Simplified overview of the identification and treatment of patients with IEMs or PGx. We contrast the current approach with our proposed framework, which incorporates early sequencing and analysis of rare variants with machine learning and ethical considerations. (A) Current practice for IEMs and PGx begins with an observable phenotype. In IEMs, this may be an altered metabolite detected by newborn screening; in PGx, perhaps an adverse event. Phenotype can also include physical examination, medical history, family history, and relevant labs or studies. Genetic sequencing is then performed, which could include targeted single-gene sequencing with copy number variant detection, gene panel, whole-exome sequencing, or in some cases, family trio sequencing to assess phasing and identify de novo variants. If annotated pathogenic variants are identified in the target gene, a patient may be diagnosed with a disease and offered preventative services (as is the case with IEMs) or given a different drug or dose adjustment (as with PGx). Identification of VUSs may result in a diagnosis, depending on the other variants identified. In this simplified figure, VUS refers to a variant in the targeted gene of interest as opposed to an incidental finding not relevant to diagnosis. Patient diagnosis can occur without DNA sequencing, as is the case with some IEMs. (B) Hypothetical future approach to patient care in the fields of PGx and IEMs. All individuals undergo whole-genome sequencing at birth. Machine learning models use detected variants to predict phenotype (disease risk or differential drug response). Ethical considerations are addressed, and clinical action is taken accordingly.

Figure 2

ClinVar variants of uncertain significance in genes related to IEMs and PGx (A–D) The number of VUSs in ClinVar between 2015 and 2020 in IEM and PGx genes, respectively (A and B). All ClinVar variants in IEM and PGx genes that were reclassified between February 2018 and February 2019 (C and D). Height of bars is proportional to number of variants reclassified. A total of 293 variant reclassifications is shown in (C), and 434 variant reclassifications are shown in (D).

Figure 3

Proposed workflow for a genomic learning healthcare system Patients’ DNA samples are collected and sequenced with genomic data input to computational models. The model outputs a predicted phenotype for the patient; results are reviewed by clinicians and applied to the patient. Outcomes are evaluated and the model continues to learn from a feedback loop to improve outcomes for future patients. Icons are from The Noun Project., , ,