Medical Genetics Commons^™

Mapping Genes With Longitudinal Phenotypes Via Bayesian Posterior Probabilities, Anthony Musolf, Alejandro Q. Nato Jr., Douglas Londono, Lisheng Zhou, Tara C. Matise, Derek Gordon

Biochemistry and Microbiology

Most association studies focus on disease risk, with less attention paid to disease progression or severity. These phenotypes require longitudinal data. This paper presents a new method for analyzing longitudinal data to map genes in both population-based and family-based studies. Using simulated systolic blood pressure measurements obtained from Genetic Analysis Workshop 18, we cluster the phenotype data into trajectory subgroups. We then use the Bayesian posterior probability of being in the high subgroup as a quantitative trait in an association analysis with genotype data. This method maintains high power (>80%) in locating genes known to affect the simulated phenotype …