CLCN7 and TCIRG1 mutations in a single family: Evidence for digenic inheritance of osteopetrosis

Clinical and genetic diagnosis of autosomal dominant osteopetrosis type II in a Chinese family: A case report

BACKGROUND

Osteopetrosis is a rare genetic disorder characterized by increased bone density due to defective bone resorption of osteoclasts. Approximately, 80% of autosomal dominant osteopetrosis type II (ADO-II) patients were usually affected by heterozygous dominant mutations in the chloride voltage-gated channel 7 (ClCN7) gene and present early-onset osteoarthritis or recurrent fractures. In this study, we report a case of persistent joint pain without bone injury or underlying history.

CASE SUMMARY

We report a 53-year-old female with joint pain who was accidentally diagnosed with ADO-II. The clinical diagnosis was based on increased bone density and typical radiographic features. Two heterozygous mutations in the ClCN7 and T-cell immune regulator 1 (TCIRG1) genes by whole exome sequencing were identified in the patient and her daughter. The missense mutation (c.857G>A) occurred in the CLCN7 gene p. R286Q, which is highly conserved across species. The TCIRG1 gene point mutation (c.714-20G>A) in intron 7 (near the splicing site of exon 7) had no effect on subsequent transcription.

CONCLUSION

This ADO-II case had a pathogenic CLCN7 mutation and late onset without the usual clinical symptoms. For the diagnosis and assessment of the prognosis for osteopetrosis, genetic analysis is advised.

Genetics and Epigenetics of Bone Remodeling and Metabolic Bone Diseases

Bone metabolism consists of a balance between bone formation and bone resorption, which is mediated by osteoblast and osteoclast activity, respectively. In order to ensure bone plasticity, the bone remodeling process needs to function properly. Mesenchymal stem cells differentiate into the osteoblast lineage by activating different signaling pathways, including transforming growth factor β (TGF-β)/bone morphogenic protein (BMP) and the Wingless/Int-1 (Wnt)/β-catenin pathways. Recent data indicate that bone remodeling processes are also epigenetically regulated by DNA methylation, histone post-translational modifications, and non-coding RNA expressions, such as micro-RNAs, long non-coding RNAs, and circular RNAs. Mutations and dysfunctions in pathways regulating the osteoblast differentiation might influence the bone remodeling process, ultimately leading to a large variety of metabolic bone diseases. In this review, we aim to summarize and describe the genetics and epigenetics of the bone remodeling process. Moreover, the current findings behind the genetics of metabolic bone diseases are also reported.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.