Longitudinal analytical approaches to genetic data
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Chiu, Yen Feng, Anne E Justice, and Phillip E Melton. Longitudinal Analytical Approaches to Genetic Data. BioMed Central, 2016. https://doi.org/10.17615/59jc-yq22APA
Chiu, Y., Justice, A., & Melton, P. (2016). Longitudinal analytical approaches to genetic data. BioMed Central. https://doi.org/10.17615/59jc-yq22Chicago
Chiu, Yen Feng, Anne E Justice, and Phillip E Melton. 2016. Longitudinal Analytical Approaches to Genetic Data. BioMed Central. https://doi.org/10.17615/59jc-yq22- Creator
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Chiu, Yen-Feng
- Other Affiliation: Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan, ROC
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Justice, Anne E
- Affiliation: Gillings School of Global Public Health, Department of Epidemiology
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Melton, Phillip E
- Other Affiliation: Centre for Genetic Origins of Health and Disease, University of Western Australia, Perth, WA, Australia
- 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. 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.
- Date of publication
- February 3, 2016
- DOI
- Identifier
- Resource type
- Article
- Rights statement
- In Copyright
- Rights holder
- Chiu et al.
- Journal title
- BMC Genomic Data
- Journal volume
- 17
- Journal issue
- Suppl 2
- Language
- English
- Bibliographic citation
- BMC Genetics. 2016 Feb 03;17(Suppl 2):4
- Publisher
- BioMed Central
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