The challenge of comprehensive and consistent sequence variant interpretation between clinical laboratories

Abstract

Purpose: Genetic testing has shifted from academic laboratories with expertise in specific genes to commercial laboratories that offer tests of a diverse array of genes. The purpose of this comparative study was to determine whether one academic laboratory's model of variant interpretation is similar to that of several commercial laboratories. Methods:The Collagen Diagnostic Laboratory (CDL) received, over a 14-month period, 38 requests to interpret variants originally identified by an outside laboratory (OL). The interpretations by the OL and CDL were compared and discrepancies were assessed. Results:Interpretations from the OL and CDL were concordant in 11 inquiries (29%); discrepancies were moderate in 11 instances (29%) and significant in 16 (42%). Factors that caused discrepancies included the following: (i) private data were not shared in a public database (n = 9); (ii) publicly available allele frequency data were not referenced and used as evidence (n = 5); and (iii) important aspects of protein structure and function were not taken into account (n = 13). Conclusion:Comprehensive interpretation of sequence variants depends on good functional tests and well-curated variant databases. Provision of clinical information to the clinical laboratory, mandatory submission of identified variants with phenotype data to common resources, and collaboration between clinical laboratories and recognized experts is likely to improve consistency in variant interpretation among clinical laboratories.

The current state of clinical interpretation of sequence variants

“…Detection of a pathogenic or “actionable” variant may influence a patient's diagnosis and/or prognosis, family planning, and lifelong health management. But variant classification has historically been inconsistent between clinical molecular genetic laboratories [1•,2] and potentially even inaccurate [3]. These issues arose in part due to a lack of appreciation of the diversity and complexity of the human genome [4,5] and the differing classification methodologies used by individual clinical laboratories [2,6,7].…”

“…It is now known that this variant is present in 2.6% (76/2968) of tested African chromosomes (data from the Exome Aggregation Consortium (ExAC) browser [11•]), a frequency higher than the prevalence of hypertrophic cardiomyopathy (~1:500) [19]. Furthermore, classification discrepancies often exist between clinical laboratories [1•,2]. Such discrepancies may arise from methodological differences, in particular differing weights assigned to certain data or reliance on unpublished in-house data [2,6].…”

“…Furthermore, classification discrepancies often exist between clinical laboratories [1•,2]. Such discrepancies may arise from methodological differences, in particular differing weights assigned to certain data or reliance on unpublished in-house data [2,6]. In response to these discrepancies, the American College of Medical Genetics and Genomics (ACMG), joined by the Association of Molecular Pathology (AMP) and College of American Pathologists (CAP), updated the standards and guidelines for variant interpretation for Mendelian disorders to include recommendations for weighing particular evidence [20••].…”

Defining the Clinical Value of a Genomic Diagnosis in the Era of Next-Generation Sequencing

“…A second layer of complexity and subjectivity comes with interpretation at the variant level, which can differ between accredited laboratories for the same variant (90). The recently updated ACMG guidelines for assessing variant pathogenicity are more structured and systematic than previous guidelines, which may lead to greater consistency in the use of different types of evidence (101).…”

ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants

“…Although ACMG/AMP guidelines provide a general framework for Mendelian sequence variant interpretation, it was always recognized that this framework would evolve and be customized to meet requirements for specific genes and disease conditions [2, 16–19]. To address these anticipated needs, the Calculator is designed to support guideline evolution and customization without programming.…”