The influence of a first-order antedependence model and hyperparameters in BayesCπ for genomic prediction.

ABSTRACT:

Objective

The Bayesian first-order antedependence models, which specified single nucleotide polymorphisms (SNP) effects as being spatially correlated in the conventional BayesA/B, had more accurate genomic prediction than their corresponding classical counterparts. Given advantages of BayesCπ over BayesA/B, we have developed hyper-BayesCπ, ante-BayesCπ, and ante-hyper-BayesCπ to evaluate influences of the antedependence model and hyperparameters for vg and sg2 on BayesCπ.

Methods

Three public data (two simulated data and one mouse data) were used to validate our proposed methods. Genomic prediction performance of proposed methods was compared to traditional BayesCπ, ante-BayesA and ante-BayesB.

Results

Through both simulation and real data analyses, we found that hype

SUBMITTER: Li X 

PROVIDER: S-EPMC6212739 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature