Linkage and heritability analysis of migraine symptom groupings: A comparison of three different clustering methods on twin data

Chen, Carla C. M., Mengersen, Kerrie L., Keith, Jonathan M., Martin, Nicholas G. and Nyholt, Dale R. (2009) Linkage and heritability analysis of migraine symptom groupings: A comparison of three different clustering methods on twin data. Human Genetics, 125 5-6: 591-604. doi:10.1007/s00439-009-0652-7


Author Chen, Carla C. M.
Mengersen, Kerrie L.
Keith, Jonathan M.
Martin, Nicholas G.
Nyholt, Dale R.
Title Linkage and heritability analysis of migraine symptom groupings: A comparison of three different clustering methods on twin data
Journal name Human Genetics   Check publisher's open access policy
ISSN 0340-6717
Publication date 2009-03-20
Sub-type Article (original research)
DOI 10.1007/s00439-009-0652-7
Volume 125
Issue 5-6
Start page 591
End page 604
Total pages 14
Editor David N. Cooper
Thomas J. Hudson
Place of publication Germany
Publisher Springer
Collection year 2010
Language eng
Subject C1
Abstract Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods—latent class analysis, grade of membership and grade of membership “fuzzy” clustering (Fanny)—to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
School of Medicine Publications
 
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Created: Thu, 25 Mar 2010, 17:17:50 EST by Amanda Jones on behalf of Medicine - Royal Brisbane and Women's Hospital