Variant Interpretation: Functional Assays to the Rescue

Abstract

Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of "lookup tables" of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.

Figure 1

Many Rare Missense Variants Have Been Discovered, and Most Are Presently Variants of Uncertain Significance (VUSs) (A) There are 4.6 million missense variants in the Genome Aggregation Database (gnomAD) (left). The vast majority of these variants are not in ClinVar and have no clinical interpretation. The plurality of variants in ClinVar are variants of uncertain significance (right). Variants with both likely benign and benign reports are categorized as likely benign in this plot. Variants with both likely pathogenic and pathogenic reports are categorized as likely pathogenic in this plot. The data in this plot were taken from the February 28, 2017, release of gnomAD and the April 5, 2017, release of ClinVar. (B) The number of registered tests correlates with the number of missense variants (Spearman’s ρ = 0.61), VUSs (bubble size), and conflicting significance reports (bubble color). The data in this plot were taken from the April 5, 2017, ClinVar variant summary and summary of conflicting interpretations.

Figure 2

Multiplex Assays of Variant Effect (MAVEs) Could Provide Functional Data for Most Variants in the Genome A set of MAVEs are shown for a hypothetical locus in the genome. In a MAVE, variants are synthesized, introduced into a model system, selected for a phenotype of interest, and sequenced for a readout of the effects of each variant in the assay. Variants in regulatory elements, such as enhancers, can be investigated by massively parallel reporter assays (MPRA) or self-transcribing active regulatory region sequencing (STARR-seq). Variants in coding regions can be investigated by deep mutational scanning (DMS) or splicing assays. The result of a MAVE is a sequence-function map describing the functional effects of every possible SNV in the functional element. Example sequence-function maps are shown with genome positions as columns and possible nucleotide substitutions as rows. Wild-type-like variants are shown in red, and loss-of-function variants are shown in blue; gray indicates missing data, and wild-type nucleotides are shown as gray dots.