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A multilocus model of the genetic architecture of autoimmune thyroid disorder, with clinical implications |
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| Authors: | Vieland Veronica J Huang Yungui Bartlett Christopher Davies Terry F Tomer Yaron |
| Affiliation: | 1 Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, OH 43205, USA 2 Department of Pediatrics, Ohio State University, Columbus, OH 43205, USA 3 Department of Statistics, Ohio State University, Columbus, OH 43205, USA 4 Department of Medicine, Mount Sinai School of Medicine, and James J. Peters Veterans Affairs Medical Center, New York, NY 10029, USA 5 Department of Internal Medicine, Division of Endocrinology, Diabetes, and Metabolism, University of Cincinnati and Cincinnati Veterans Affairs Medical Center, Cincinnati, OH 45267, USA |
| Abstract: | We report here a preliminary model of the genetic architecture of Autoimmune Thyroid Disorder (AITD). Using a flexible class of mathematical modeling techniques, applied to an established set of data and supplemented with information both from candidate-gene and genome-wide-association studies and from basic bioinformatics, we find strong statistical support for a model in which AITD is the result of "hits" along three distinct genetic pathways: affected individuals have (1) a genetic susceptibility to clinical AITD, along with (2) a separate predisposition to develop the autoantibodies characteristic of AITD, and they also have (3) a predisposition to develop high levels of autoantibodies once they occur. Genes underlying each of these factors then appear to interact with one another to cause clinical AITD. We also find that a genetic variant in CTLA4 that increases risk for AITD in some people might actually protect against AITD in others, depending on which additional risk variants an individual carries. Our data show that the use of statistical methods for the incorporation of information from multiple sources, combined with careful modeling of distinct intermediate phenotypes, can provide insights into the genetic architecture of complex diseases. This model has several clinical implications, which we believe will prove relevant to other complex diseases as well. |
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