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DOI 10.1038/s41436-019-0544-8

Медицинская библиотека · Genetics in medicine : official journal of the American College of Medical Genetics · 2019

Estimating prevalence for limb-girdle muscular dystrophy based on public sequencing databases.

Liu Wei, Pajusalu Sander, Lake Nicole J, Zhou Geyu, Ioannidis Nilah, Mittal Plavi, Johnson Nicholas E, Weihl Conrad C, Williams Bradley A, Albrecht Douglas E, Rufibach Laura E, Lek Monkol

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

PURPOSE: Limb-girdle muscular dystrophies (LGMD) are a genetically heterogeneous category of autosomal inherited muscle diseases. Many genes causing LGMD have been identified, and clinical trials are beginning for treatment of some genetic subtypes. However, even with the gene-level mechanisms known, it is still difficult to get a robust and generalizable prevalence estimation for each subtype due to the limited amount of epidemiology data and the low incidence of LGMDs.

METHODS

Taking advantage of recently published exome and genome sequencing data from the general population, we used a Bayesian method to develop a robust disease prevalence estimator.

RESULTS

This method was applied to nine recessive LGMD subtypes. The estimated disease prevalence calculated by this method was largely comparable with published estimates from epidemiological studies; however, it highlighted instances of possible underdiagnosis for LGMD2B and 2L.

CONCLUSION

The increasing size of aggregated population variant databases will allow for robust and reproducible prevalence estimates of recessive disease, which is critical for the strategic design and prioritization of clinical trials.

Для цитирования: Liu Wei, Pajusalu Sander, Lake Nicole J, Zhou Geyu, Ioannidis Nilah, Mittal Plavi, Johnson Nicholas E, Weihl Conrad C, Williams Bradley A, Albrecht Douglas E, Rufibach Laura E, Lek Monkol. Estimating prevalence for limb-girdle muscular dystrophy based on public sequencing databases.. Genetics in medicine : official journal of the American College of Medical Genetics. 2019. DOI: 10.1038/s41436-019-0544-8.

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