Estimated prevalence of mucopolysaccharidoses from population-based exomes and genomes

Abstract

Background In this study, the prevalence of different types of mucopolysaccharidoses (MPS) was estimated based on data from the exome aggregation consortium (ExAC) and the genome aggregation database (gnomAD). The population-based allele frequencies were used to identify potential disease-causing variants on each gene related to MPS I to IX (except MPS II). Methods We evaluated the canonical transcripts and excluded homozygous, intronic, 3′, and 5′ UTR variants. Frameshift and in-frame insertions and deletions were evaluated using the SIFT Indel tool. Splice variants were evaluated using SpliceAI and Human Splice Finder 3.0 (HSF). Loss-of-function single nucleotide variants in coding regions were classified as potentially pathogenic, while synonymous variants outside the exon–intron boundaries were deemed non-pathogenic. Missense variants were evaluated by five in silico prediction tools, and only those predicted to be damaging by at least three different algorithms were considered disease-causing. Results The combined frequencies of selected variants (ranged from 127 in GNS to 259 in IDUA) were used to calculate prevalence based on Hardy–Weinberg's equilibrium. The maximum estimated prevalence ranged from 0.46 per 100,000 for MPSIIID to 7.1 per 100,000 for MPS I. Overall, the estimated prevalence of all types of MPS was higher than what has been published in the literature. This difference may be due to misdiagnoses and/or underdiagnoses, especially of the attenuated forms of MPS. However, overestimation of the number of disease-causing variants by in silico predictors cannot be ruled out. Even so, the disease prevalences are similar to those reported in diagnosis-based prevalence studies. Conclusion We report on an approach to estimate the prevalence of different types of MPS based on publicly available population-based genomic data, which may help health systems to be better prepared to deal with these conditions and provide support to initiatives on diagnosis and management of MPS.

Epidemiology of mucopolysaccharidoses (MPS) in United States: challenges and opportunities

“…Although a review of other countries also demonstrates heterogenous data, the smaller size of European countries and lack of state fragmentation makes it easier to estimate incidence and prevalence data for that geography. A recent report by Borges et al estimated the prevalence of MPS using publicly available population based genomic data [ 58 ]. This approach overestimated MPS prevalence due to limitations of (1) the database, (2) filtering steps and consensus scores used in the methodology, and (3) lack of estimation of false positive results, among others [ 58 ].…”

“…A recent report by Borges et al estimated the prevalence of MPS using publicly available population based genomic data [ 58 ]. This approach overestimated MPS prevalence due to limitations of (1) the database, (2) filtering steps and consensus scores used in the methodology, and (3) lack of estimation of false positive results, among others [ 58 ].…”

Type III mucopolysaccharidosis: literature review and clinical observation

“…Большинство случаев, не связанных с родственными браками, являются сложными гетерозиготами. Расчетная оценка заболеваемости, проведенная на основании анализа частоты носительства патогенных мутаций по данным популяционных геномных данных (Aggregation Consortium Exome и Genome Aggregation Database), определила показатели, превышаю щие данные региональных регистров пациентов [7]. В особенности это превышение касалось более редких подтипов (C, D), что может быть объяснено наличием стертых и поздних форм.…”

Gene therapies for mucopolysaccharidoses

“…Ten types of MPS have been recognized to date and their overall incidence might be as high as 0.5-7.1 in 100 000 live births. [1][2][3] In MPSs, accumulation of undegraded GAGs in lysosomes affects virtually every cell type resulting in multi-systemic diseases. Depending on the enzyme deficiency and the degree of residual activity, clinical features can vary but they are typically progressive and include coarse facial features, hepatosplenomegaly, cardiac valve disease, respiratory disease, and bone/joint abnormalities.…”