title: Mapping genes with longitudinal phenotypes via Bayesian posterior probabilities.
abstract: 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 for most specified significance levels (α). We believe that this method can be useful to aid in the discovery of genes that affect severity or progression of disease.
authors.affiliation: Department of Genetics, Rutgers University, 145 Bevier Road, Piscataway, NJ 08854, USA.
authors.forename: Anthony
authors.initials: A
authors.lastname: Musolf
authors.affiliation: Department of Genetics, Rutgers University, 145 Bevier Road, Piscataway, NJ 08854, USA.
authors.forename: Alejandro Q
authors.initials: AQ
authors.lastname: Nato
authors.affiliation: Department of Genetics, Rutgers University, 145 Bevier Road, Piscataway, NJ 08854, USA.
authors.forename: Douglas
authors.initials: D
authors.lastname: Londono
authors.affiliation: Department of Genetics, Rutgers University, 145 Bevier Road, Piscataway, NJ 08854, USA.
authors.forename: Lisheng
authors.initials: L
authors.lastname: Zhou
authors.affiliation: Department of Genetics, Rutgers University, 145 Bevier Road, Piscataway, NJ 08854, USA.
authors.forename: Tara C
authors.initials: TC
authors.lastname: Matise
authors.affiliation: Department of Genetics, Rutgers University, 145 Bevier Road, Piscataway, NJ 08854, USA.
authors.forename: Derek
authors.initials: D
authors.lastname: Gordon