Login / Signup

Fast parallelized sampling of Bayesian regression models for whole-genome prediction.

Tianjing ZhaoRohan FernandoDorian GarrickHao Cheng
Published in: Genetics, selection, evolution : GSE (2020)
We demonstrate the BayesXII algorithm using the prior for BayesC[Formula: see text], a Bayesian variable selection regression method, which is applied to simulated data with 50,000 individuals and a medium-density marker panel ([Formula: see text] 50,000 markers). To reach about the same accuracy as the conventional samplers for BayesC[Formula: see text] required less than 30 min using the BayesXII algorithm on 24 nodes (computer used as a server) with 24 cores on each node. In this case, the BayesXII algorithm required one tenth of the computation time of conventional samplers for BayesC[Formula: see text]. Addressing the heavy computational burden associated with Bayesian methods by parallel computing will lead to greater use of these methods.
Keyphrases
  • deep learning
  • smoking cessation
  • human milk
  • machine learning
  • big data
  • electronic health record
  • squamous cell carcinoma
  • risk factors
  • small molecule
  • early stage