Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach

Data that are collected for whole-genome prediction can also be used for genome-wide association studies (GWAS). This paper discusses how Bayesian multiple-regression methods that are used for whole-genome prediction can be adapted for GWAS. It is argued here that controlling the posterior type I error rate (PER) is more suitable than controlling the genomewise error rate (GER) for controlling false positives in GWAS. It is shown here that under ideal conditions, i.e., when the model is correctly specified, PER can be controlled by using Bayesian posterior probabilities that are easy to obtain. Computer simulation was used to examine the properties of this Bayesian approach when the ideal conditions were not met. Results indicate that even then useful inferences can be made.

• Publication year

  2014

• Venue

  Journal of Agricultural, Biological and Environmental Statistics

• Publication date

  2014-08-22

• Fields of study

  Biology, Computer Science

• Identifiers
  DOI 10.1007/S13253-017-0277-6
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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