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

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

Posted 21 Dec 2018
bioRxiv DOI: 10.1101/502708 (published DOI: 10.1038/s41436-019-0544-8)

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 reliable 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 whole exome and genome sequencing data from the general population, we used a Bayesian method to develop a reliable disease prevalence estimator. Results: This method was applied to nine recessive LGMD subtypes. The estimated disease prevalence calculated by this method were largely comparable to published estimates from epidemiological studies, however highlighted instances of possible under-diagnosis 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.

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