Neurofibromatosis type 1 (NF1), a common genetic disorder with a birth incidence of 1:2,000–3,000, is characterized by a highly variable clinical presentation. To date, only two clinically relevant intragenic genotype-phenotype correlations have been reported for NF1 missense mutations affecting p.Arg1809 and a single amino acid deletion p.Met922del. Both variants predispose to a distinct mild NF1 phenotype with neither externally visible cutaneous/plexiform neurofibromas nor other tumors. Here, we report 162 individuals (129 unrelated probands and 33 affected relatives) heterozygous for a constitutional missense mutation affecting one of five neighboring NF1 codons—Leu844, Cys845, Ala846, Leu847, and Gly848—located in the cysteine-serine-rich domain (CSRD). Collectively, these recurrent missense mutations affect ∼0.8% of unrelated NF1 mutation-positive probands in the University of Alabama at Birmingham (UAB) cohort. Major superficial plexiform neurofibromas and symptomatic spinal neurofibromas were more prevalent in these individuals compared with classic NF1-affected cohorts (both p < 0.0001). Nearly half of the individuals had symptomatic or asymptomatic optic pathway gliomas and/or skeletal abnormalities. Additionally, variants in this region seem to confer a high predisposition to develop malignancies compared with the general NF1-affected population (p = 0.0061). Our results demonstrate that these NF1 missense mutations, although located outside the GAP-related domain, may be an important risk factor for a severe presentation. A genotype-phenotype correlation at the NF1 region 844–848 exists and will be valuable in the management and genetic counseling of a significant number of individuals.

Clonal proliferation in myeloproliferative neoplasms (MPN) is driven by somatic mutations in JAK2, CALR or MPL, but the contribution of inherited factors is poorly characterized. Using a three-stage genome-wide association study of 3,437 MPN cases and 10,083 controls, we identify two SNPs with genome-wide significance in JAK2V617F-negative MPN: rs12339666 (JAK2; meta-analysis P=1.27 × 10−10) and rs2201862 (MECOM; meta-analysis P=1.96 × 10−9). Two additional SNPs, rs2736100 (TERT) and rs9376092 (HBS1L/MYB), achieve genome-wide significance when including JAK2V617F-positive cases. rs9376092 has a stronger effect in JAK2V617F-negative cases with CALR and/or MPL mutations (Breslow–Day P=4.5 × 10−7), whereas in JAK2V617F-positive cases rs9376092 associates with essential thrombocythemia (ET) rather than polycythemia vera (allelic χ2 P=7.3 × 10−7). Reduced MYB expression, previously linked to development of an ET-like disease in model systems, associates with rs9376092 in normal myeloid cells. These findings demonstrate that multiple germline variants predispose to MPN and link constitutional differences in MYB expression to disease phenotype.

Purpose Where multiple in silico tools are concordant, the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) framework affords supporting evidence toward pathogenicity or benignity, equivalent to a likelihood ratio of ~2. However, limited availability of “clinical truth sets” and prior use in tool training limits their utility for evaluation of tool performance. Methods We created a truth set of 9,436 missense variants classified as deleterious or tolerated in clinically validated high-throughput functional assays for BRCA1, BRCA2, MSH2, PTEN, and TP53 to evaluate predictive performance for 44 recommended/commonly used in silico tools. Results Over two-thirds of the tool–threshold combinations examined had specificity of <50%, thus substantially overcalling deleteriousness. REVEL scores of 0.8–1.0 had a Positive Likelihood Ratio (PLR) of 6.74 (5.24–8.82) compared to scores <0.7 and scores of 0–0.4 had a Negative Likelihood Ratio (NLR) of 34.3 (31.5–37.3) compared to scores of >0.7. For Meta-SNP, the equivalent PLR = 42.9 (14.4–406) and NLR = 19.4 (15.6–24.9). Conclusion Against these clinically validated “functional truth sets," there was wide variation in the predictive performance of commonly used in silico tools. Overall, REVEL and Meta-SNP had best balanced accuracy and might potentially be used at stronger evidence weighting than current ACMG/AMP prescription, in particular for predictions of benignity.

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Accurate classification of variants in cancer susceptibility genes (CSGs) is key for correct estimation of cancer risk and management of patients. Consistency in the weighting assigned to individual elements of evidence has been much improved by the American College of Medical Genetics (ACMG) 2015 framework for variant classification, UK Association for Clinical Genomic Science (UK-ACGS) Best Practice Guidelines and subsequent Cancer Variant Interpretation Group UK (CanVIG-UK) consensus specification for CSGs. However, considerable inconsistency persists regarding practice in the combination of evidence elements. CanVIG-UK is a national subspecialist multidisciplinary network for cancer susceptibility genomic variant interpretation, comprising clinical scientist and clinical geneticist representation from each of the 25 diagnostic laboratories/clinical genetic units across the UK and Republic of Ireland. Here, we summarise the aggregated evidence elements and combinations possible within different variant classification schemata currently employed for CSGs (ACMG, UK-ACGS, CanVIG-UK and ClinGen gene-specific guidance for PTEN, TP53 and CDH1). We present consensus recommendations from CanVIG-UK regarding (1) consistent scoring for combinations of evidence elements using a validated numerical ‘exponent score’ (2) new combinations of evidence elements constituting likely pathogenic’ and ‘pathogenic’ classification categories, (3) which evidence elements can and cannot be used in combination for specific variant types and (4) classification of variants for which there are evidence elements for both pathogenicity and benignity.

Advances in technology have led to a massive expansion in the capacity for genomic analysis, with a commensurate fall in costs. The clinical indications for genomic testing have evolved markedly; the volume of clinical sequencing has increased dramatically; and the range of clinical professionals involved in the process has broadened. There is general acceptance that our early dichotomous paradigms of variants being pathogenic–high risk and benign–no risk are overly simplistic. There is increasing recognition that the clinical interpretation of genomic data requires significant expertise in disease–gene-variant associations specific to each disease area. Inaccurate interpretation can lead to clinical mismanagement, inconsistent information within families and misdirection of resources. It is for this reason that ‘national subspecialist multidisciplinary meetings’ (MDMs) for genomic interpretation have been articulated as key for the new NHS Genomic Medicine Service, of which Cancer Variant Interpretation Group UK (CanVIG-UK) is an early exemplar. CanVIG-UK was established in 2017 and now has >100 UK members, including at least one clinical diagnostic scientist and one clinical cancer geneticist from each of the 25 regional molecular genetics laboratories of the UK and Ireland. Through CanVIG-UK, we have established national consensus around variant interpretation for cancer susceptibility genes via monthly national teleconferenced MDMs and collaborative data sharing using a secure online portal. We describe here the activities of CanVIG-UK, including exemplar outputs and feedback from the membership.

BackgroundNeurofibromatosis type 1 (NF1) predisposes to breast cancer (BC), but no genotype-phenotype correlations have been described.MethodsConstitutional NF1 mutations in 78 patients with NF1 with BC (NF1-BC) were compared with the NF1 Leiden Open Variation Database (n=3432).ResultsNo cases were observed with whole or partial gene deletions (HR 0.10; 95% CI 0.006 to 1.63; p=0.014, Fisher’s exact test). There were no gross relationships with mutation position. Forty-five (64.3%; HR 6.4–83) of the 70 different mutations were more frequent than expected (p<0.05), while 52 (74.3%; HR 5.3–83) were significant when adjusted for multiple comparisons (adjusted p≤0.125; Benjamini-Hochberg). Higher proportions of both nonsense and missense mutations were also observed (adjusted p=0.254; Benjamini-Hochberg). Ten of the 11 missense cases with known age of BC occurred at <50 years (p=0.041). Eighteen cases had BRCA1/2 testing, revealing one BRCA2 mutation.DiscussionThese data strongly support the hypothesis that certain constitutional mutation types, and indeed certain specific variants in NF1 confer different risks of BC. The lack of large deletions and excess of nonsenses and missenses is consistent with gain of function mutations conferring risk of BC, and also that neurofibromin may function as a dimer. The observation that somatic NF1 amplification can occur independently of ERBB2 amplification in sporadic BC supports this concept. A prospective clinical-molecular study of NF1-BC needs to be established to confirm and build on these findings, but regardless of NF1 mutation status patients with NF1-BC warrant testing of other BC-predisposing genes.

Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders.