Performance of the No-U-Turn sampler in multi-trait variance component estimation using genomic data.

ABSTRACT:

Background

Multi-trait genetic parameter estimation is an important topic for target traits with few records and with a low heritability and when the genetic correlation between target and secondary traits is strong. However, estimating correlations between multiple traits is difficult for both Bayesian and non-Bayesian inferences. We extended a Hamiltonian Monte Carlo approach using the No-U-Turn Sampler (NUTS) to a multi-trait animal model and investigated the performance of estimating (co)variance components and breeding values, compared to those for restricted maximum likelihood and Gibbs sampling with a population size of 2314 and 578 in a simulated and real pig dataset, respectively. For real data, we used publicly available data for three traits from the Pig Improvement Comp

SUBMITTER: Nishio M 

PROVIDER: S-EPMC9275044 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature