Multivariate Analyses of Blood Pressure Related Phenotypes in a Longitudinal Framework: Insights From Genetic Analysis Workshop 18. Issue 1 (September 2014)

• Record Type: Journal Article
• Authors: Ghosh, Saurabh; Paterson, Andrew; Bickeböller, Heike; Almasy, Laura
• Is Part Of: Genetic epidemiology. Volume 38:Issue 1(2014)
• Page Start: S63
• Page End: S67
• Publication Date: 2014-09
• Subjects: Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21827
• Languages: English
• ISSNs: 0741-0395

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>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.</p> </abstract>