Rare disorders have many faces: in silico characterization of rare disorder spectrum

Simona D Frederiksen,Vladimir Avramović,Laura Arbour,Maja Tarailo-Graovac,Tatiana Maroilley,Anna Lehman

doi:10.1186/s13023-022-02217-9

Simona D Frederiksen, Vladimir Avramović + Show 4 more

Open Access

https://doi.org/10.1186/s13023-022-02217-9

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Abstract

BackgroundThe diagnostic journey for many rare disease patients remains challenging despite use of latest genetic technological advancements. We hypothesize that some patients remain undiagnosed due to more complex diagnostic scenarios that are currently not considered in genome analysis pipelines. To better understand this, we characterized the rare disorder (RD) spectrum using various bioinformatics resources (e.g., Orphanet/Orphadata, Human Phenotype Ontology, Reactome pathways) combined with custom-made R scripts.ResultsOur in silico characterization led to identification of 145 borderline-common, 412 rare and 2967 ultra-rare disorders. Based on these findings and point prevalence, we would expect that approximately 6.53%, 0.34%, and 0.30% of individuals in a randomly selected population have a borderline-common, rare, and ultra-rare disorder, respectively (equaling to 1 RD patient in 14 people). Importantly, our analyses revealed that (1) a higher proportion of borderline-common disorders were caused by multiple gene defects and/or other factors compared with the rare and ultra-rare disorders, (2) the phenotypic expressivity was more variable for the borderline-common disorders than for the rarer disorders, and (3) unique clinical characteristics were observed across the disorder categories forming the spectrum.ConclusionsRecognizing that RD patients who remain unsolved even after genome sequencing might belong to the more common end of the RD spectrum support the usage of computational pipelines that account for more complex genetic and phenotypic scenarios.

Highlights

The diagnostic journey for many rare disease patients remains challenging despite use of latest genetic technological advancements
Borderline‐common disorders only comprise 4% of disorders in the spectrum yet represented more than 90% of patients in a fictive rare disorder cohort Worldwide and/or continent point prevalence were reported for 3,524 RDs in Orphadata (Additional File 1: Fig. S1)
Some clinical characteristics are more prevalent among the borderline‐common disorders than the rare and ultra‐rare disorders In our study, we found that only two Human Phenotype Ontology (HPO) terms in top 15 for the entire RD spectrum overlapped with those in top 15 for the borderline-common disorder category (Table 3)

Summary

Introduction

The diagnostic journey for many rare disease patients remains challenging despite use of latest genetic technological advancements. Other examples of factors that can result in diagnostic errors are low read depth in the genetic region of interest, relatively high allele frequencies in a reference population, existence of allelic imbalance/mosaicism, the causative variant being inherited from an unaffected parent, faulty pathogenicity predictions using computational tools, or the condition not being genetically inherited (e.g. arise from imprinting) [12] This is where the concept of missing heritability comes in—often used in the context of common disease. By considering more complex genetic scenarios in computational pipelines focused on rare disease diagnostics, we might be able to explain some of the missing heritability. Computational pipelines are not currently geared to address more complex genetic and phenotypic scenarios, and negate the whole palette of rare diseases (e.g., some rare diseases are more common than others) and the underlying genetic architecture might differ This consideration is based on our knowledge that common diseases are considered polygenic and multifactorial

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