School of Medicine Publications and Presentations
Document Type
Conference Proceeding
Publication Date
10-18-2016
Abstract
The incorporation of longitudinal data into genetic epidemiological studies has the potential to provide valuable information regarding the effect of time on complex disease etiology. Yet, the majority of research focuses on variables collected from a single time point. This aim of this study was to test for main effects on a quantitative trait across time points using a constrained maximum-likelihood measured genotype approach. This method simultaneously accounts for all repeat measurements of a phenotype in families. We applied this method to systolic blood pressure (SBP) measurements from three time points using the Genetic Analysis Workshop 19 (GAW19) whole-genome sequence family simulated data set and 200 simulated replicates. Data consisted of 849 individuals from 20 extended Mexican American pedigrees. Comparisons were made among 3 statistical approaches: (a) constrained, where the effect of a variant or gene region on the mean trait value was constrained to be equal across all measurements; (b) unconstrained, where the variant or gene region effect was estimated separately for each time point; and (c) the average SBP measurement from three time points. These approaches were run for nine genetic variants with known effect sizes (>0.001) for SBP variability and a known gene-centric kernel (MAP4)-based test under the GAW19 simulation model across 200 replicates.
Recommended Citation
Melton, P.E., Peralta, J.M. & Almasy, L. Constrained multivariate association with longitudinal phenotypes. BMC Proc 10 (Suppl 7), 30 (2016). https://doi.org/10.1186/s12919-016-0051-8
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
BMC Proceedings
DOI
10.1186/s12919-016-0051-8
Academic Level
faculty
Mentor/PI Department
Office of Human Genetics
Comments
Copyright © 2016, The Author(s).