Premium
Multivariate Analyses of Blood Pressure Related Phenotypes in a Longitudinal Framework: Insights From Genetic Analysis Workshop 18
Author(s) -
Ghosh Saurabh
Publication year - 2014
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21827
Subject(s) - multivariate statistics , multivariate analysis , phenotype , data set , genome wide association study , 1000 genomes project , computational biology , biology , longitudinal data , statistics , genotype , computer science , genetics , data mining , mathematics , single nucleotide polymorphism , gene
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.