A novel method for analyzing genetic association with longitudinal phenotypes

Douglas Londono, Kuo Mei Chen, Anthony Musolf, Ruixue Wang, Tong Shen, January Brandon, John A. Herring, Carol A. Wise, Hong Zou, Meilei Jin, Lei Yu, Stephen J. Finch, Tara C. Matise, Derek Gordon

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Knowledge of genes influencing longitudinal patterns may offer information about predicting disease progression. We developed a systematic procedure for testing association between SNP genotypes and longitudinal phenotypes. We evaluated false positive rates and statistical power to localize genes for disease progression. We used genome-wide SNP data from the Framingham Heart Study. With longitudinal data from two real studies unrelated to Framingham, we estimated three trajectory curves from each study. We performed simulations by randomly selecting 500 individuals. In each simulation replicate, we assigned each individual to one of the three trajectory groups based on the underlying hypothesis (null or alternative), and generated corresponding longitudinal data. Individual Bayesian posterior probabilities (BPPs) for belonging to a specific trajectory curve were estimated. These BPPs were treated as a quantitative trait and tested (using the Wald test) for genome-wide association. Empirical false positive rates and power were calculated. Our method maintained the expected false positive rate for all simulation models. Also, our method achieved high empirical power for most simulations. Our work presents a method for disease progression gene mapping. This method is potentially clinically significant as it may allow doctors to predict disease progression based on genotype and determine treatment accordingly.

Original languageEnglish (US)
Pages (from-to)241-261
Number of pages21
JournalStatistical Applications in Genetics and Molecular Biology
Issue number2
StatePublished - May 2013


  • Disease course
  • Methodology
  • Mixture model
  • Mixtures
  • Mplus

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics


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