Genetic diagnosis of motile ciliopathies is conducted by healthcare, commercial and private laboratories. 88 genes have been implicated in motile ciliopathies (PCD, male infertility and associated disorders). Gene-disease relationships are uncertain where evidence is limited, risking inaccurate reporting and diagnosis. The ClinGen Motile Ciliopathy Gene Curation Expert Panel (GCEP) was set up collaboratively with BEAT-PCD ERS CRC in 2021. The GCEP comprises geneticists, pulmonologists and biocurators (Canada, France, Germany, Norway, Poland, Spain, Tunisia, UK, USA) tasked with classifying clinical validity of gene-disease relationships in motile ciliopathies to aid interpretation of genetic results. As an early step, the GCEP drew up guidelines to capture the critical details of motile ciliopathy cases and to score genetic and experimental evidence conservatively and consistently. The GCEP meets monthly and so far has curated 33 gene-disease relationships (https://clinicalgenome.org/affiliation/40102/). 22 curations have reached a definitive classification as the role of the gene in disease has been repeatedly demonstrated and upheld over time, 4 were disputed. These efforts provide a basis for future classifications of gene-disease relationships. The goal of the GCEP is to leverage emerging research to enhance the reliability of genetic testing for improved clinical detection and diagnosis of motile ciliopathies.
<p>Supplementary Table S1: FH variants with population frequencies, interpretations of pathogenicity, catalytic efficiencies, and multimerization states</p>
Warts, Hypogammaglobulinemia, Infections, Myelokathexis (WHIM) syndrome is a rare, combined immunodeficiency disease predominantly caused by gain-of-function variants in the CXCR4 gene that typically results in truncation of the carboxyl terminus of C-X-C chemokine receptor type 4 (CXCR4) leading to impaired leukocyte egress from bone marrow to peripheral blood. Diagnosis of WHIM syndrome continues to be challenging and is often made through clinical observations and/or genetic testing. Detection of a pathogenic CXCR4 variant in an affected individual supports the diagnosis of WHIM syndrome but relies on an appropriate annotation of disease-causing variants. Understanding the genotypic-phenotypic associations in WHIM syndrome has the potential to improve time to diagnosis and guide appropriate clinical management, resulting in a true example of precision medicine. This article provides an overview of the spectrum of CXCR4 variants in WHIM syndrome and summarizes the various lines of clinical and functional evidence that can support interpretation of newly identified variants.
<p>Supplementary Table S1: FH variants with population frequencies, interpretations of pathogenicity, catalytic efficiencies, and multimerization states</p>
Correction to: Genetics in Medicine19:2017; https://doi.org/10.1038/gim.2017.37, published online 11 May 2017 There were errors in the author listing such that consortium group of authors was not named individually. The corrected author list is: Sienna Aguilar, MS; Swaroop Aradhya, PhD, FACMG; Daniel Beltran, PhD; Brandon Bunker, PhD; Amy Daly, MS; Anne Deucher, MD; Tali Ekstein, MS; Ali Entezam, PhD; Karl Erhard, PhD; Ed Esplin MD, PhD, FACMG, FACP; Jennifer Fulbright, MS; Amy Fuller, MS; Kristen McDonald Gibson, PhD, FACMG; Tina Hambuch, PhD, FACMG; Rachel Harte, PhD; Christy Hartshorne, MS; Eden Haverfield, PhD, FACMG; Nastaran Heidari, PhD; Michelle Hogue, MS; Daniela Iacoboni, MS; Britt Johnson, PhD, FACMG; Hio Chung Kang, PhD; Rachel Lewis, PhD; Shiloh Martin, PhD; Sarah McCalmon, PhD; Scott Michalski, MS; Cindy Morgan, MS; Laura Murillo, PhD; Piper Nicolosi, PhD; Karen Ouyang, PhD, FACMG; Carolina Pardo, PhD; Rita Quintana, PhD; Marina Rabideau, MS; Darlene Riethmaier, MS; Amanda Stafford, PhD; Jackie Tahiliani, MS; Chris Tan, MS; S. Paige Taylor, PhD; Shu-Huei Wang, PhD; Hannah White, MS; Ian Wilson, PhD, FACMG; Tom Winder, PhD, FACMG; and Michelle K. Zeman, PhD. The original article can be found online at https://doi.org/10.1038/gim.2017.37. Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteriaGenetics in MedicineVol. 19Issue 10PreviewThe 2015 American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG–AMP) guidelines were a major step toward establishing a common framework for variant classification. In practice, however, several aspects of the guidelines lack specificity, are subject to varied interpretations, or fail to capture relevant aspects of clinical molecular genetics. A simple implementation of the guidelines in their current form is insufficient for consistent and comprehensive variant classification. Full-Text PDF Open Access
Gene therapy products, initially developed to treat rare diseases, are now being studied to treat a wide range of conditions including inherited ophthalmologic diseases. Increasingly there is a need for the development of comprehensive, annotated databases of genetic variants for a particular disease. Commercial laboratories are in the unique position to partner with biopharmaceutical companies to accelerate access to the variant landscape in patients for gene(s) of interest through consented de-identified data sharing.
Abstract Background: As the number of laboratories offering genetic tests grows, the potential for inconsistent variant classifications increases. New resources can help address this: (a) ClinVar, a rapidly growing public database of clinical variants to which many (but not all) laboratories contribute; (b) the public release of thousands of BRCA1/2 reports from Myriad Genetics through the Sharing Clinical Reports Project (SCRP), the Free the Data (FTD) initiative, and recent publications; and (c) ExAC, a greatly improved database of population allele frequencies. These complement longstanding efforts, e.g. the ENIGMA consortium. In addition, the American College of Medical Genetics (ACMG) recently updated guidelines for the interpretation of sequence variants. Using these resources, we sought to investigate the consistency of variant classifications to help inform ongoing practice. Methods: Pathogenicity assessments for variants in hereditary cancer genes were collected from multiple sources. Among these were 15,364 BRCA1/2 submissions to ClinVar, including 5416 submissions from SCRP and 1062 from our prior work [1]. When 3 or more submissions for a variant were available, we determined a consensus interpretation requiring 2 of 3 submitters to agree (or 3 of 4, etc.) to identify outliers. For our own classifications, we established a point-based system based on the 2015 ACMG guidelines, and independently applied it to publicly available evidence of pathogenicity without regard to other labs' classifications. Results: Initially, discordance among ClinVar submissions appears high (20-30%). However, upon investigation much of this discordance is a result of (i) research submissions to ClinVar, (ii) differences in confidence (e.g. benign vs. likely benign), (iii) older data in ClinVar, (iv) single lab outliers, and (v) nuances in the detailed structure of the ClinVar database. A careful comparison using the consensus methodology of objectively filtered ClinVar data shows high concordance between our interpretations and consensus: 95% were identical and 99% were similar (e.g. benign and likely benign were considered similar). Where consensus was not achieved (8% of variants with 3 or more independent sources) or not possible (any variant with only 2 sources), pairwise comparisons showed that few of these remaining differences (5%) were clinically significant. Also, many variants with significant discordances appeared to be particularly rare in the human population, and thus would be present in few patients. The rate of discordances with SCRP/FTD data was similar with that of other ClinVar submitters. Conclusions: Evaluations of inter-laboratory concordance need to be done carefully to avoid over-counting differences. Laboratories generally agree on the clinical significance of the vast majority of variants. Furthermore, the inter-laboratory consensus classification is often reached using a careful implementation of the ACMG guidelines and publicly available data. Thoroughly understanding the remaining differences is challenging when the evidence used by any laboratory is not available for peer review. Detailed data and methods from this study are available for review and alternate analyses. [1] Lincoln et al. SABCS 2014; JMD 2015. Citation Format: Lincoln S, Nykamp K, Kobayashi Y, Yang S, Powers M, Anderson M, Monzon F, Topper S. Consistency of pathogenicity determinations for hereditary cancer gene mutations. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-09-11.
