Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions

Abstract

Background: As the availability of genomic testing grows, variant interpretation will increasingly be performed by genomic generalists, rather than domain-specific experts. Demand is rising for laboratories to accurately classify variants in inherited cardiac condition (ICC) genes, including secondary findings.

Methods: We analyse evidence for inheritance patterns, allelic requirement, disease mechanism and disease-relevant variant classes for 65 ClinGen-curated ICC gene-disease pairs. We present this information for the first time in a structured dataset, CardiacG2P, and assess application in genomic variant filtering.

Results: For 36/65 gene-disease pairs, loss of function is not an established disease mechanism, and protein truncating variants are not known to be pathogenic. Using the CardiacG2P dataset as an initial variant filter allows for efficient variant prioritisation whilst maintaining a high sensitivity for retaining pathogenic variants compared with two other variant filtering approaches.

Conclusions: Access to evidence-based structured data representing disease mechanism and allelic requirement aids variant filtering and analysis and is a pre-requisite for scalable genomic testing.

Keywords: Allelic requirement; Disease mechanism; Gene curation; Genomic variant filtering; Inheritance; Inherited cardiac conditions; Variant classification; Variant interpretation.

Fig. 1

Flow chart depicting the analysis of inheritance and disease mechanism in established inherited cardiac genes. A structured representation of the resulting data is available in the Additional files 2 and 3 and also through G2P (https://www.ebi.ac.uk/gene2phenotype/downloads), which is also searchable through the GenCC portal (https://thegencc.org/). ARVC, arrhythmogenic right ventricular cardiomyopathy; BrS, Brugada syndrome; CPVT, catecholaminergic polymorphic ventricular tachycardia; DCM, dilated cardiomyopathy; G2P, gene2phenotype; GenCC, Gene Curation Coalition; HCM, hypertrophic cardiomyopathy; LQTS, long QT syndrome; SQTS, short QT syndrome

Fig. 2

Validating CardiacG2P. Two generic variant prioritisation pipelines (pipelines 1 and 2) were compared to CardiacG2P (pipeline 3). All 3 pipelines interrogate the same gene list which includes 21 HCM and 12 DCM genes. Pipeline 1: filtered rare (gnomAD global allele frequency (AF) <0.0001) AND protein-altering variants. Pipeline 2: filtered rare (AF <0.0001) AND ((high impact variants (e.g. stop gained, frameshift) AND high confidence by LOFTEE (VEP plugin) LoF variants) OR ClinVar P/LP variants). CardiacG2P (pipeline 3): filtered rare variants (AF <0.0001) and incorporates allelic requirement, variant consequence, and gene-specific annotations of a restricted repertoire of pathogenic alleles appropriate for the disease under interrogation—e.g. restricted variant classes, specific variants, or restricted regions of the protein. Set 1: contains 285 unique variants identified and classified as P/LP for HCM or DCM by a specialist NHS cardiovascular genetics lab. A VCF file with these variants was created, annotated by VEP, and filtered according to the 3 pipelines. Sensitivity (number of P/LP variants retained) was assessed. Set 2a: is a merged VCF file with SNVs and indels from 200 patients with HCM or DCM. Set2b: is a merged VCF file with SNVs and indels from 200 healthy volunteers. Set2a and 2b were separately annotated by VEP and filtered according to the 3 pipelines. Positive rate (the number of variants retained for further analysis) was assessed. AF, allele frequency; DCM, dilated cardiomyopathy; HCM, hypertrophic cardiomyopathy; indels, insertion or deletion variants; LoF, loss of function; P/LP, pathogenic/likely pathogenic; SNVs, single nucleotide variants; VCF, variant call format; VEP, variant effect predictor

Fig. 3

A variant prioritisation approach that incorporates structured data representing disease mechanisms and allelic requirement for specific gene-disease pairs (CardiacG2P) outperforms other scalable variant-prioritisation approaches. A Comparison of the sensitivity of 3 variant filtering approaches to prioritise 285 variants classified as pathogenic/likely pathogenic (P/LP) for hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). Error bars = 95% confidence intervals (CI). Pipeline 1 (light blue) prioritises all rare protein-altering variants (PAV), sensitivity 0.95, 95% CI [0.92, 0.97]. Pipeline 2 (dark blue) prioritises all rare loss of function (LoF) variants, and those classified as P/LP by ClinVar, sensitivity 0.70, 95% CI [0.64, 0.75]. Pipeline 3 (orange) prioritises variant classes according to specific characteristics of each gene-disease pair (CardiacG2P), sensitivity 0.99, 95% CI [0.96, 1.0]. CardiacG2P has a higher sensitivity when compared to Pipeline 1, P_Fisher = 0.046 and Pipeline 2, P_Fisher ≤0.0001. B The positive rate (number of variants retained) by 3 variant-filtering approaches for cardiomyopathy cases (left panel), using a dataset of 5681 unique variants from 200 individuals with confirmed HCM/DCM, and healthy controls (right panel), using a dataset of 6060 unique variants from 200 healthy individuals. Pipeline 1 (light blue), filtering for rare PAV; Pipeline 2 (dark blue), filtering for rare LoF variants or those classified as P/LP by ClinVar. Pipeline 3 (orange), filtering using CardiacG2P. CardiacG2P demonstrated more efficient variant prioritisation compared to Pipeline 1 in both the disease cohort (P_Fisher = 0.001) and healthy controls (P_Fisher ≤0.001)