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

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

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)2512-2520
Number of pages9
JournalGenetics in Medicine
Volume21
Issue number11
DOIs
StatePublished - Nov 1 2019

Keywords

  • disease prevalence
  • limb-girdle muscular dystrophy
  • rare disease

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