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

Carla Chen, Kerrie Mengersen, Jonathan Keith, Nicholas Martin, Dale Nyholt

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

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
Original languageEnglish
Pages (from-to)591 - 604
Number of pages14
JournalHuman Genetics
Volume125
Issue number5-6
DOIs
Publication statusPublished - 2009
Externally publishedYes

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