Accurate and consistent variant classification is imperative for incorporation of rapidly developing sequencing technologies into genomic medicine for improved patient care. An essential requirement for achieving standardized and reliable variant interpretation is data sharing, facilitated by a centralized open-source database. Familial hypercholesterolemia (FH) is an exemplar of the utility of such a resource: it has a high incidence, a favorable prognosis with early intervention and treatment, and cascade screening can be offered to families if a causative variant is identified. ClinVar, an NCBI-funded resource, has become the primary repository for clinically relevant variants in Mendelian disease, including FH. Here, we present the concerted efforts made by the Clinical Genome Resource, through the FH Variant Curation Expert Panel and global FH community, to increase submission of FH-associated variants into ClinVar. Variant-level data was categorized by submitter, variant characteristics, classification method and available supporting data. To further reform interpretation of FH-associated variants, areas for improvement in variant submissions were identified and addressed; these include a need for more detailed submissions and submission of supporting variant-level data, both retrospectively and prospectively. Collaborating to provide thorough, reliable evidence-based variant interpretation will ultimately improve the care of FH patients.

PurposeFamilial hypercholesterolemia (FH) is an autosomal disorder of lipid metabolism presenting with increased cardiovascular risk. Although more than 1,700 variants have been associated with FH, the great majority have not been functionally proved to affect the low-density lipoprotein receptor cycle. We aimed to classify all described variants associated with FH and to establish the proportion of variants that lack evidence to support their pathogenicity.MethodsWe followed American College of Medical Genetics and Genomics (ACMG) guidelines for the classification, and collected information from a variety of databases and individual reports. A worldwide overview of publicly available FH variants was also performed.ResultsA total of 2,104 unique variants were identified as being associated with FH, but only 166 variants have been proven by complete in vitro functional studies to be causative of disease. Additionally, applying the ACMG guidelines, 1,097 variants were considered pathogenic or likely pathogenic. Only seven variants were found in all five continents.ConclusionThe lack of functional evidence for about 85% of all variants found in FH patients can compromise FH diagnosis and patient prognosis. ACMG classification improves variant interpretation, but functional studies are necessary to understand the effect of about 40% of all variants reported. Nevertheless, ACMG guidelines need to be adapted to FH for a better diagnosis.

In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published consensus standardized guidelines for sequence-level variant classification in Mendelian disorders. To increase accuracy and consistency, the Clinical Genome Resource Familial Hypercholesterolemia (FH) Variant Curation Expert Panel was tasked with optimizing the existing ACMG/AMP framework for disease-specific classification in FH. In this study, we provide consensus recommendations for the most common FH-associated gene, LDLR, where >2300 unique FH-associated variants have been identified. Methods: The multidisciplinary FH Variant Curation Expert Panel met in person and through frequent emails and conference calls to develop LDLR-specific modifications of ACMG/AMP guidelines. Through iteration, pilot testing, debate, and commentary, consensus among experts was reached. Results: The consensus LDLR variant modifications to existing ACMG/AMP guidelines include (1) alteration of population frequency thresholds, (2) delineation of loss-of-function variant types, (3) functional study criteria specifications, (4) cosegregation criteria specifications, and (5) specific use and thresholds for in silico prediction tools, among others. Conclusion: Establishment of these guidelines as the new standard in the clinical laboratory setting will result in a more evidence-based, harmonized method for LDLR variant classification worldwide, thereby improving the care of patients with FH.