Abstract
AbstractMotivationBayes factor has advantages over p-value as test statistics for association, particularly when comparing multiple alternative models. A software package to compute Bayes factor for linear mixed model is lacking.ResultsWe transformed the standard linear mixed model as Bayesian linear regression, substituting the random effect by fixed effects with eigenvectors as covariates whose prior effect sizes are proportional to their corresponding eigenvalues. Using conjugate normal inverse gamma priors on regression parameters, Bayes factors can be computed in a closed form. We then showed that the transformed Bayesian linear regression produced identical estimates to those of the best linear unbiased prediction (BLUP), providing a new derivation to a known connection between BLUP and Bayesian estimates.Availability and implementationMethods described in this note are implemented in the software IDUL as two new functionalities: computing Bayes factors and residuals for the linear mixed model. IDUL and its source code are freely available athttps://github.com/haplotype/idul.
Publisher
Cold Spring Harbor Laboratory
Reference14 articles.
1. Carlin, B. P. and T. A. Louis (2002). Empirical bayes: Past, present, and future. In A. E. Raftery , M. A. Tanner , and M. T. Wells (Eds.), Statistics in the 21st Century, pp. 312–318. Chapman & Hall.
2. Bayesian Methods in Animal Breeding Theory
3. Guan, Y. and D. Levy (2024a). Asymptotically exact fit for linear mixed model. bioRxiv.
4. Estimation of inbreeding and kinship coefficients via latent identity-by-descent states;Bioinformatics,2024
5. Bayesian variable selection regression for genome-wide association studies and other large-scale problems;The Annals of Applied Statistics,2011
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