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Longitudinal Data Analysis for Genetic Studies in the Whole‐Genome Sequencing Era
Author(s) -
Wu Zheyang,
Hu Yijuan,
Melton Phillip E.
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.21829
Subject(s) - genome wide association study , mixed model , multivariate statistics , variance (accounting) , computational biology , linear model , statistical power , generalized linear mixed model , covariance , whole genome sequencing , computer science , covariate , statistics , biology , genome , genetics , mathematics , genotype , single nucleotide polymorphism , accounting , gene , business
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.