Bayesian latent trait modeling of migraine symptom data |
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Authors: | Carla Chia Ming Chen Jonathan M Keith Dale R Nyholt Nicholas G Martin Kerrie L Mengersen |
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Institution: | (1) School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, 4001, Australia;(2) Neurogenetics Laboratory, Queensland Institute of Medical Research, PO Royal Brisbane Hospital, Brisbane, QLD, 4029, Australia;(3) Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, PO Royal Brisbane Hospital, Brisbane, QLD, 4029, Australia |
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Abstract: | Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical
diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously,
we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class
model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a
single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response
theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit.
We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis
using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both
models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar.
Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome
7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying
genes contributing to migraine susceptibility. |
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