4.0 Article

Utilizing Twins Concordance Rates to Infer the Predisposition to Myasthenia Gravis

Journal

TWIN RESEARCH AND HUMAN GENETICS
Volume 14, Issue 2, Pages 129-136

Publisher

CAMBRIDGE UNIV PRESS
DOI: 10.1375/twin.14.2.129

Keywords

autoimmunity; genetics; mathematical modeling; complex disease

Funding

  1. Swedish Research Council
  2. Palle Ferb Foundation
  3. US National Institute of Allergy and Infectious Diseases [U19Al067152]

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Myasthenia gravis (MG) is an autoimmune disorder in which patients experience muscular fatigability due to the presence of anti-acetylcholine receptor (AChR) antibodies which inhibit signal transduction across the neuromuscular junction. Like all complex disorders, disease is caused by an interaction between genetic and environmental factors. Although several genes have been identified which appear to be associated with MG, both classic twin studies and current multi-gene models are insufficient to explain either disease pathogenesis or inheritance. We examined the literature on MG to determine both mono- and dizygotic twin concordance rates, and used this data to (1) estimate the proportion of the population with underlying genetic predisposition to MG and the frequency of the environmental component and (2) derive the number of inherited genetic regions that are required to confer predisposition to MG. Using a MZ twin concordance rate of 35.5%, and a dizygotic rate of approximately 4-5% (based on family data), the probability of encountering environmental components necessary to develop MG in an individual with genetic predisposition is approximately 52.4%, making the frequency of predisposition (1:5240) roughly twice the rate of incidence. Furthermore, the number of genetic regions co-inherited between affected individuals is between two and four, which may be large haplotypes with interacting activity. Determining these haplotypes, by fully sequencing associated regions in cases and controls to identify mutations present, may therefore be a practically step toward the understanding of complex disease.

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