Data from electronic healthcare records expand our understanding of X-linked genetic diseases.

Disease specific cohort studies have reported details on X linked (XL) disorders affecting females. We investigated the spectrum and penetrance of XL disorders seen in electronic health records (EHR). We generated a cohort of individuals diagnosed with XL disorders at Vanderbilt University Medical Center over 20 years. Our cohort included 477 males and 203 females diagnosed with 108 different XL genetic disorders. We found large differences between the female/male (F/M) ratios for various XL disorders regardless of their OMIM annotated mode of inheritance. We identified four XL recessive disorders affecting women previously only described in men. Biomarkers for XL disease had unique gender-specific patterns differing between modes of inheritance. EHRs provide large cohorts of XL genetic disorders that give new insights compared to the literature. Differences in the F/M ratios and biomarkers of XL disorders observed likely result from disease specific and sex dependent penetrance. We conclude that observed gender ratios associated with specific XL disorders may be more useful than those predicted by Mendelian genetics provided by OMIM. Our findings of a gender specific penetrance and severity for XL disorders show unexpected differences from Mendelian predictions. Further work is required to validate our findings in larger combined EHR cohorts.

Probable digenic inheritance of Diamond-Blackfan anemia.

A 26-year-old female proband with a clinical diagnosis and consistent phenotype of Diamond-Blackfan anemia (DBA, OMIM 105650) without an identified genotype was referred to the Undiagnosed Diseases Network. DBA is classically associated with monoallelic variants that have an autosomal-dominant or -recessive mode of inheritance. Intriguingly, her case was solved by a detection of a digenic interaction between non-allelic RPS19 and RPL27 variants. This was confirmed with a machine learning structural model, co-segregation analysis, and RNA sequencing. This is the first report of DBA caused by a digenic effect of two non-allelic variants demonstrated by machine learning structural model. This case suggests that atypical phenotypic presentations of DBA may be caused by digenic inheritance in some individuals. We also conclude that a machine learning structural model can be useful in detecting digenic models of possible interactions between products encoded by alleles of different genes inherited from non-affected carrier parents that can result in DBA with an unrealized 25% recurrence risk.

A medical odyssey of a 72-year-old man with Charcot-Marie-Tooth disease type 2 newly diagnosed with biallelic variants in SORD gene causing sorbitol dehydrogenase deficiency.

A 72-year-old man was referred to the Undiagnosed Diseases Network (UDN) because of gradual progressive weakness in both lower extremities for the past 45 years. He was initially diagnosed as having Charcot-Marie-Tooth disease type 2 (CMT2) without a defined molecular genetic cause. Exome sequencing (ES) failed to detect deleterious neuromuscular variants. Very recently, biallelic variants in sorbitol dehydrogenase (SORD) were discovered to be a novel cause of inherited neuropathies including CMT2 or distal hereditary motor neuropathy (dHMN) referred to as Sorbitol Dehydrogenase Deficiency with Peripheral Neuropathy (SORDD, OMIM 618912). The most common variant identified was c.757delG; p.A253Qfs*27. Through the Vanderbilt UDN clinical site, this patient was formally diagnosed with SORDD after the identification of homozygosity for the above SORD frameshift through UDN Genome Sequencing (GS). His medical odyssey was solved by GS and detection of extremely high levels of sorbitol. The diagnosis provided him the opportunity to receive potential treatment with an investigational drug in a clinical trial for SORDD. We suggest that similar studies be considered in other individuals thought to possibly have CMT2 or dHMN.