Sander Pajusalu1,2,3, Nicole J Lake1,4, Wei Liu5, Geyu Zhou5, Nilah Ioannidis6,7, Plavi Mittal6,8, Nicholas E Johnson9, Conrad C Weihl10, Bradley A Williams6, Douglas E Albrecht6, Laura E Rufibach6, Monkol Lek11. 1. Department of Genetics, Yale School of Medicine, New Haven, CT, USA. 2. Department of Clinical Genetics, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia. 3. Department of Clinical Genetics, United Laboratories, Tartu University Hospital, Tartu, Estonia. 4. Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Australia. 5. Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. 6. Jain Foundation, Seattle, WA, USA. 7. Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA. 8. In-Depth Genomics, Bellevue, WA, USA. 9. Department of Neurology, Virginia Commonwealth University, Richmond, VA, USA. 10. Department of Neurology, Washington University School of Medicine, St. Louis,, MO, USA. 11. Department of Genetics, Yale School of Medicine, New Haven, CT, USA. monkol.lek@yale.edu.
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.
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.
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