Longitudinal analytical approaches to genetic data.
MetadataShow full item record
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. Conclusion: 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; (c) decrease computational burden in WGS; and (d) have the potential to identify genetic variants influencing subphenotypes important for understanding disease progression.
Showing items related by title, author, creator and subject.
Melton, Phillip; Peralta, J.; Almasy, L. (2016)© 2016 The Author(s). Background: 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. ...
Modelling the co-occurence of Streptococcus pneumoniae with other bacterial and viral pathogens in the upper respiratory tractJacoby, P.; Watson, K.; Bowman, J.; Taylor, A.; Riley, T.; Smith, D.; Lehmann, Deborah (2007)Go to ScienceDirect® Home Skip Main Navigation Links Brought to you by: The University of Western Australia Library Login: + Register Athens/Institution Login Not Registered? - User Name: Password: ...
Australian twin and molecular genetic study on attention deficit hyperactivity disorder and its co-morbidity with reading disabilitySheikhi, Abdullah (2008)Aim: This study aims to investigate the genetic components of Attention Deficit Hyperactivity Disorder (ADHD), Reading Disability (RD), and their comorbidity. Methods: Three approaches were applied to data from 2610 ...