The Gene Curation Coalition: A global effort to harmonize gene-disease evidence resources.

PURPOSE: Several groups and resources provide information that pertains to the validity of gene-disease relationships used in genomic medicine and research; however, universal standards and terminologies to define the evidence base for the role of a gene in disease and a single harmonized resource were lacking. To tackle this issue, the Gene Curation Coalition (GenCC) was formed. METHODS: The GenCC drafted harmonized definitions for differing levels of gene-disease validity on the basis of existing resources, and performed a modified Delphi survey with 3 rounds to narrow the list of terms. The GenCC also developed a unified database to display curated gene-disease validity assertions from its members. RESULTS: On the basis of 241 survey responses from the genetics community, a consensus term set was chosen for grading gene-disease validity and database submissions. As of December 2021, the database contained 15,241 gene-disease assertions on 4569 unique genes from 12 submitters. When comparing submissions to the database from distinct sources, conflicts in assertions of gene-disease validity ranged from 5.3% to 13.4%. CONCLUSION: Terminology standardization, sharing of gene-disease validity classifications, and resolution of curation conflicts will facilitate collaborations across international curation efforts and in turn, improve consistency in genetic testing and variant interpretation.

The Clinical Genome Resource (ClinGen) Familial Hypercholesterolemia Variant Curation Expert Panel consensus guidelines for LDLR variant classification.

PURPOSE: 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.

ClinVar database of global familial hypercholesterolemia-associated DNA variants.

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; 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.

Clinical Genetic Testing for Familial Hypercholesterolemia: JACC Scientific Expert Panel.

Although awareness of familial hypercholesterolemia (FH) is increasing, this common, potentially fatal, treatable condition remains underdiagnosed. Despite FH being a genetic disorder, genetic testing is rarely used. The Familial Hypercholesterolemia Foundation convened an international expert panel to assess the utility of FH genetic testing. The rationale includes the following: 1) facilitation of definitive diagnosis; 2) pathogenic variants indicate higher cardiovascular risk, which indicates the potential need for more aggressive lipid lowering; 3) increase in initiation of and adherence to therapy; and 4) cascade testing of at-risk relatives. The Expert Consensus Panel recommends that FH genetic testing become the standard of care for patients with definite or probable FH, as well as for their at-risk relatives. Testing should include the genes encoding the low-density lipoprotein receptor (LDLR), apolipoprotein B (APOB), and proprotein convertase subtilisin/kexin 9 (PCSK9); other genes may also need to be considered for analysis based on patient phenotype. Expected outcomes include greater diagnoses, more effective cascade testing, initiation of therapies at earlier ages, and more accurate risk stratification.

Analysis of the frequency and spectrum of mutations recognised to cause familial hypercholesterolaemia in routine clinical practice in a UK specialist hospital lipid clinic.

AIM: To determine the frequency and spectrum of mutations causing Familial Hypercholesterolaemia (FH) in patients attending a single UK specialist hospital lipid clinic in Oxford and to identify characteristics contributing to a high mutation detection rate. METHODS: 289 patients (272 probands) were screened sequentially over a 2-year period for mutations in LDLR, APOB and PCSK9 using standard molecular genetic techniques. The Simon Broome (SB) clinical diagnostic criteria were used to classify patients and a separate cohort of 409 FH patients was used for replication. RESULTS: An FH-causing mutation was found in 101 unrelated patients (LDLR = 54 different mutations, APOB p.(Arg3527Gln) = 10, PCSK9 p.(Asp374Tyr) = 0). In the 60 SB Definite FH patients the mutation detection rate was 73% while in the 142 with Possible FH the rate was significantly lower (27%, p < 0.0001), but similar (14%, p = 0.06) to the 70 in whom there was insufficient data to make a clinical diagnosis. The mutation detection rate varied significantly (p = 9.83 × 10(-5)) by untreated total cholesterol (TC) levels (25% in those <8.1 mmol/l and 74% in those >10.0 mmol/l), and by triglyceride levels (20% in those >2.16 mmol/l and 60% in those <1.0 mmol/l (p = 0.0005)), with both effects confirmed in the replication sample (p for trend = 0.0001 and p = 1.8 × 10(-6) respectively). There was no difference in the specificity or sensitivity of the SB criteria versus the Dutch Lipid Clinic Network score in identifying mutation carriers (AROC respectively 0.73 and 0.72, p = 0.68). CONCLUSIONS: In this genetically heterogeneous cohort of FH patients the mutation detection rate was significantly dependent on pre-treatment TC and triglyceride levels.

Mutation detection in Croatian patients with familial hypercholesterolemia.

Familial hypercholesterolemia (FH) is caused by mutations in the genes for LDLR, APOB or PCSK9, and identification of the causative mutation provides definitive diagnosis so that the patient can be treated, their relatives tested and, therefore, premature heart disease prevented. DNA of eight unrelated individuals with clinically diagnosed FH were analyzed using a High-Resolution Melting method (HRM) for the LDLR gene (coding region, promoter and intron/exon boundaries), the APOB gene (part exon 26) and the PCSK9 gene (exon7). Variations found were sequenced and the effect on function of confirmed variants examined using predictive algorithms. Gross deletions and insertions were analysed using MLPA. Three novel LDLR variants were found, p.(S470C), p.(C698R) and c.2312-2A>C. All were predicted to be pathogenic using predictive algorithms. Three previously reported disease-causing mutations were identified (p.(G20R), p.(N272T) and p.(S286R); the latter was also carried by a hypercholesterolaemic relative. One patient carried the pathogenic APOB variant p.(R3527Q). No large LDLR deletions nor insertions were found, neither were any PCSK9 variants identified. HRM is a sensitive method for screening for mutations. While the causative mutation has been identified in 88% of these clinically defined FH patients, there appears to be a high degree of allelic heterogeneity in Croatian patients.

Low-density lipoprotein receptor gene familial hypercholesterolemia variant database: update and pathological assessment.

Familial hypercholesterolemia (FH) is caused predominately by variants in the low-density lipoprotein receptor gene (LDLR). We report here an update of the UCL LDLR variant database to include variants reported in the literature and in-house between 2008 and 2010, transfer of the database to LOVDv.2.0 platform (https://grenada.lumc.nl/LOVD2/UCL-Heart/home.php?select_db=LDLR) and pathogenicity analysis. The database now contains over 1288 different variants reported in FH patients: 55% exonic substitutions, 22% exonic small rearrangements (<100 bp), 11% large rearrangements (>100 bp), 2% promoter variants, 10% intronic variants and 1 variant in the 3' untranslated sequence. The distribution and type of newly reported variants closely matches that of the 2008 database, and we have used these variants (n= 223) as a representative sample to assess the utility of standard open access software (PolyPhen, SIFT, refined SIFT, Neural Network Splice Site Prediction Tool, SplicePort and NetGene2) and additional analyses (Single Amino Acid Polymorphism database, analysis of conservation and structure and Mutation Taster) for pathogenicity prediction. In combination, these techniques have enabled us to assign with confidence pathogenic predictions to 8/8 in-frame small rearrangements and 8/9 missense substitutions with previously discordant results from PolyPhen and SIFT analysis. Overall, we conclude that 79% of the reported variants are likely to be disease causing.