Bayesian analysis of longitudinal traits in the Korea Association Resource (KARE) cohortopen access
- Authors
- Chung, W.; Hwang, H.; Park, T.
- Issue Date
- Jun-2022
- Publisher
- Korea Genome Organization
- Keywords
- Bayesian mixed model; KARE data; longitudinal data; obesity-related traits
- Citation
- Genomics and Informatics, v.20, no.2
- Journal Title
- Genomics and Informatics
- Volume
- 20
- Number
- 2
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/42418
- DOI
- 10.5808/gi.22022
- ISSN
- 1598-866X
- Abstract
- Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions. © 2022 Korea Genome Organization.
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