title: Multivariate analyses of blood pressure related phenotypes in a longitudinal framework: insights from Genetic Analysis Workshop 18.
abstract: Our working group studied methods for joint analyses of multiple phenotypes using the data provided by Genetic Analysis Workshop 18. Two data sets were available: one containing genotypes obtained from a real human whole-genome sequencing study along with longitudinal measurements on systolic and diastolic blood pressure, age, sex, medication use, and tobacco smoking; and the other a simulated data set using the same set of genotypes and phenotype structure as the real data set. The nine sets of investigators in our working group focused on the statistical challenges posed by association analyses of multivariate phenotypes; they applied a wide spectrum of statistical methods, such as linear mixed models, copula models, and semiparametric regression models for simultaneous analyses of longitudinal data on the two blood pressure phenotypes at the genome-wide level. In this report, we discuss the various strategies explored by the different investigators whose common goal was improving the power to detect association with multivariate phenotypes.
authors.affiliation: Human Genetics Unit, Indian Statistical Institute, Kolkata, India.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Ghosh
mesh_terms: D000367:Age Factors; D001794:Blood Pressure / Q000235:genetics*; D005260:Female; D055106:Genome-Wide Association Study; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008957:Models, Genetic; D015999:Multivariate Analysis; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012737:Sex Factors; D012907:Smoking
keywords: correlated phenotypes; genetic association