Multiple osteochondromas (MO) is an autosomal dominant skeletal disease characterized by the formation of multiple cartilage‐capped bone tumors growing outward from the metaphyses of long tubular bones. MO is genetically heterogeneous, and is associated with mutations in Exostosin‐1 (EXT1) or Exostosin‐2 (EXT2), both tumor‐suppressor genes of the EXT gene family. All members of this multigene family encode glycosyltransferases involved in the adhesion and/or polymerization of heparin sulfate (HS) chains at HS proteoglycans (HSPGs). HSPGs have been shown to play a role in the diffusion of Ihh, thereby regulating chondrocyte proliferation and differentiation. EXT1 is located at 8q24.11–q24.13, and comprises 11 exons, whereas the 16 exon EXT2 is located at 11p12–p11. To date, an EXT1 or EXT2 mutation is detected in 70–95% of affected individuals. EXT1 mutations are detected in ±65% of cases, versus ±35% EXT2 mutations in MO patient cohorts. Inactivating mutations (nonsense, frame shift, and splice‐site mutations) represent the majority of MO causing mutations (75–80%). In this article, the clinical aspects and molecular genetics of EXT1 and EXT2 are reviewed together with 895 variants in MO patients. An overview of the reported variants is provided by the online Multiple Osteochondromas Mutation Database (http://medgen.ua.ac.be/LOVD). Hum Mutat 30:1–8, 2009.

Background: Multiple hereditary exostoses is an autosomal dominant skeletal disorder characterized by wide variation in clinical phenotype. The aim of this study was to evaluate whether the severity of the disease is linked with a specific genetic background. Methods: Five hundred and twenty-nine patients with multiple hereditary exostoses from two different European referral centers participated in the study. According to a new clinical classification based on the presence or absence of deformities and functional limitations, the phenotype of the patients was assessed as mild (the absence of both aspects), intermediate, or severe (the concurrent presence of both aspects). An identical molecular screening protocol with denaturing high-performance liquid chromatography and multiplex ligation-dependent probe amplification was performed in both institutions. Results: In our cohort of patients, variables such as female sex (odds ratio = 1.840; 95% confidence interval, 1.223 to 2.766), fewer than five skeletal sites with exostoses (odds ratio = 7.588; 95% confidence interval, 3.479 to 16.553), EXT2 mutations (odds ratio = 2.652; 95% confidence interval, 1.665 to 4.223), and absence of EXT1/2 mutations (odds ratio = 1.975; 95% confidence interval, 1.051 to 3.713) described patients with a mild phenotype; in contrast, a severe phenotype was associated with male sex (odds ratio = 2.431; 95% confidence interval, 1.544 to 3.826), EXT1 mutations (odds ratio = 6.817; 95% confidence interval, 1.003 to 46.348), and more than twenty affected skeletal sites (odds ratio = 2.413; 95% confidence interval, 1.144 to 5.091). Malignant transformation was observed in 5% of patients, and no evidence of association between chondrosarcoma onset and EXT mutation, sex, severity of disease, or number of lesions was detected. Conclusions: The identified “protective” and “risk” factors, as well as the proposed classification system, represent helpful tools for clinical management and follow-up of patients with multiple hereditary exostoses; moreover, homogeneous cohorts of patients, useful for studies on the pathogenesis of multiple hereditary exostoses, have been identified. Level of Evidence: Prognostic Level II. See Instructions to Authors for a complete description of levels of evidence.

Osteogenesis imperfecta (OI) is a rare genetic disorder of the connective tissue and 90% of cases are due to dominant mutations in COL1A1 and COL1A2 genes. To increase OI disease knowledge and contribute to patient follow-up management, a homogeneous Italian cohort of 364 subjects affected by OI types I–IV was evaluated. The study population was composed of 262 OI type I, 24 type II, 39 type III, and 39 type IV patients. Three hundred and nine subjects had a type I collagen affecting function mutations (230 in α1(I) and 79 in α2(I)); no disease-causing changes were noticed in 55 patients. Compared with previous genotype–phenotype OI correlation studies, additional observations arose: a new effect for α1- and α2-serine substitutions has been pointed out and heart defects, never considered before, resulted associated to quantitative mutations (P = 0.043). Moreover, some different findings emerged if compared with previous literature; especially, focusing the attention on the lethal form, no association with specific collagen regions was found and most of variants localized in the previously reported “lethal clusters” were causative of OI types I–IV. Some discrepancies have been highlighted also considering the “50–55 nucleotides rule,” as well as the relationship between specific collagen I mutated region and the presence of dentinogenesis imperfecta and/or blue sclera. Despite difficulties still present in defining clear rules to predict the clinical outcome in OI patients, this study provides new pieces for completing the puzzle, also thanks to the inclusion of clinical signs never considered before and to the large number of OI Italian patients.

Rare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. This situation calls for innovative solutions to support the decision processviaquantitative and automated tools. Machine learning brings to the stage a wealth of powerful inference methods; however, matching the health conditions with advanced statistical techniques raises methodological, technological, and even ethical issues. In this contribution, we critically point to the specificities of the dialog of rare diseases with machine learning techniques concentrating on the key steps and challenges that may hamper or create actionable knowledge and value for the patient together with some on-field methodological suggestions and considerations.