title: Longitudinal analytical approaches to genetic data.
abstract: BACKGROUND
  Longitudinal phenotypic data provides a rich potential resource for genetic studies which may allow for greater understanding of variants and their covariates over time. Herein, we review 3 longitudinal analytical approaches from the Genetic Analysis Workshop 19 (GAW19). These contributions investigated both genome-wide association (GWA) and whole genome sequence (WGS) data from odd numbered chromosomes on up to 4 time points for blood pressure-related phenotypes. The statistical models used included generalized estimating equations (GEEs), latent class growth modeling (LCGM), linear mixed-effect (LME), and variance components (VC). The goal of these analyses was to test statistical approaches that use repeat measurements to increase genetic signal for variant identification.
  
  
  RESULTS
  Two analytical methods were applied to the GAW19: GWA using real phenotypic data, and one approach to WGS using 200 simulated replicates. The first GWA approach applied a GEE-based model to identify gene-based associations with 4 derived hypertension phenotypes. This GEE model identified 1 significant locus, GRM7, which passed multiple test corrections for 2 hypertension-derived traits. The second GWA approach employed the LME to estimate genetic associations with systolic blood pressure (SBP) change trajectories identified using LCGM. This LCGM method identified 5 SBP trajectories and association analyses identified a genome-wide significant locus, near ATOX1 (p = 1.0E(-8)). Finally, a third VC-based model using WGS and simulated SBP phenotypes that constrained the β coefficient for a genetic variant across each time point was calculated and compared to an unconstrained approach. This constrained VC approach demonstrated increased power for WGS variants of moderate effect, but when larger genetic effects were present, averaging across time points was as effective.
  
  
  CONCLUSIONS
  In this paper, we summarize 3 GAW19 contributions applying novel statistical methods and testing previously proposed techniques under alternative conditions for longitudinal genetic association. We conclude that these approaches when appropriately applied have the potential to: (a) increase statistical power; (b) decrease trait heterogeneity and standard error;
authors: Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan, ROC. yfchiu@nhri.org.tw.
authors: Yen-Feng
authors: YF
authors: Chiu
authors: Department of Epidemiology, University of North Carolina, Chapel Hill, NC, 27514, USA. anne.justice@unc.edu.
authors: Anne E
authors: AE
authors: Justice
authors: Centre for Genetic Origins of Health and Disease, University of Western Australia, Perth, WA, Australia. phillip.melton@uwa.edu.au.
authors: Phillip E
authors: PE
authors: Melton
mesh_terms: D001794:Blood Pressure / Q000235:genetics*; D002877:Chromosomes, Human; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D006973:Hypertension / Q000235:genetics*; D008137:Longitudinal Studies; D008957:Models, Genetic; D015233:Models, Statistical; D017422:Sequence Analysis, DNA