Longitudinal Data Analysis for Genetic Studies in the Whole-Genome Sequencing Era
Wu, Z and Hu, Y and Melton, PE, Longitudinal Data Analysis for Genetic Studies in the Whole-Genome Sequencing Era, Genetic Epidemiology, 38, (Suppl 1) pp. S74-80. ISSN 0741-0395 (2014) [Refereed Article]
The analysis of whole-genome sequence (WGS) data using longitudinal phenotypes offers a potentially rich
resource for the examination of the genetic variants and their covariates that affect complex phenotypes over time. We
summarize eight contributions to the Genetic Analysis Workshop 18, which applied a diverse array of statistical genetic
methods to analyze WGS data in combination with data from genome-wide association studies (GWAS) from up to four
different time points on blood pressure phenotypes. The common goal of these analyses was to develop and apply appropriate
methods that utilize longitudinal repeated measures to potentially increase the analytic efficiency of WGS and GWAS data.
These diverse methods can be grouped into two categories, based on the way they model dependence structures: (1) linear
mixed-effects (LME) models, where the random effect terms in the linear models are used to capture the dependence structures;
and (2) variance-components models, where the dependence structures are constructed directly based on multiple components
of variance-covariance matrices for the multivariate Gaussian responses. Despite the heterogeneous nature of these analytical
methods, the group came to the following conclusions: (1) the use of repeat measurements can gain power to identify variants
associated with the phenotype; (2) the inclusion of family data may correct genotyping errors and allow for more accurate
detection of rare variants than using unrelated individuals only; and (3) fitting mixed-effects and variance-components models
for longitudinal data presents computational challenges. The challenges and computational burden demanded by WGS data
were addressed in the eight contributions.
family studies; longitudinal data; rare variants; repeat measurements; whole-genome sequencing.