Recommendations for clinical interpretation of variants found in non-coding regions of the genome

Abstract

Background The majority of clinical genetic testing focuses almost exclusively on regions of the genome that directly encode proteins. The important role of variants in non-coding regions in penetrant disease is, however, increasingly being demonstrated, and the use of whole genome sequencing in clinical diagnostic settings is rising across a large range of genetic disorders. Despite this, there is no existing guidance on how current guidelines designed primarily for variants in protein-coding regions should be adapted for variants identified in other genomic contexts. Methods We convened a panel of nine clinical and research scientists with wide-ranging expertise in clinical variant interpretation, with specific experience in variants within non-coding regions. This panel discussed and refined an initial draft of the guidelines which were then extensively tested and reviewed by external groups. Results We discuss considerations specifically for variants in non-coding regions of the genome. We outline how to define candidate regulatory elements, highlight examples of mechanisms through which non-coding region variants can lead to penetrant monogenic disease, and outline how existing guidelines can be adapted for the interpretation of these variants. Conclusions These recommendations aim to increase the number and range of non-coding region variants that can be clinically interpreted, which, together with a compatible phenotype, can lead to new diagnoses and catalyse the discovery of novel disease mechanisms.

Using CRISPR/Cas9 genome editing in human iPSCs for deciphering the pathogenicity of a novel CCM1 transcription start site deletion

“…According to the ACMG guidelines, well-established in vitro analyses can be strong evidence of a variant’s pathogenic or benign impact. Recently published recommendations aiming for a consistent clinical interpretation of non-coding variants have also highlighted the importance of functional evidence ( Ellingford et al, 2022 ). In this context, iPSC-based disease modeling, which has developed rapidly in recent years, can be very helpful.…”

“…As a result of clinical DNA sequencing, “variants of unknown clinical significance” (VUS) are detected in a significant proportion of patients ( Rehm et al, 2015 ). Remarkably, a classification as VUS is more likely for variants outside the coding region, with ∼40% of coding variants versus ∼60% of all UTR variants classified as VUS in ClinVar ( Ellingford et al, 2022 ).…”

Exploration of Tools for the Interpretation of Human Non-Coding Variants

“…However, the potential of WGS is not fully exploited because the current knowledge of non-coding regions is still lagging behind coding ones. Ellingford et al [ 7 ] report that 63.4% of untranslated region (UTR) variants in the ClinVar database are categorized as ‘variants of uncertain significance’ (VUS), thus demonstrating that non-coding regions are still widely under-ascertained in clinical databases, even when representing quite well-studied areas. In this context, novel algorithms are continuously being proposed to predict the pathogenicity of variants in such critical regions, and reviews are constantly published trying to shed light on the selection of best practices and methods to maximize successful results.…”

“…Although imperfect and in-progress, prediction tools currently represent the unique recognized way and necessary step for rapid and massive interpretation of ‘non-coding variants’. Nevertheless, they could hide pitfalls and issues and often require functional tests, such as RNA sequencing or targeted approaches, multiplexed assays of variant effects, chromatin interaction assays, and reporter gene assays to validate predictions [ 7 ]. Several other reviews have been published describing the limits and strengths of different tools [ 10 , 11 ], focusing on those currently available to visualize and functionally annotate WGS variants without performing comparative evaluation and benchmarking.…”

Intronic variants in inborn errors of metabolism: Beyond the exome

“…Many mechanisms by which non-coding variants can be responsible for disease have been established and comprehensively summarized by Ellingford and colleagues ( Ellingford et al, 2022 ). However, due to limited population-wide whole genome sequencing (WGS) data, there is a lack of understanding of normal human variation in these non-coding areas.…”