Campus Units

Animal Science

Document Type

Article

Publication Version

Published Version

Publication Date

4-11-2017

Journal or Book Title

Journal of Agricultural, Biological and Environmental Statistics

Volume

22

First Page

172

Last Page

193

DOI

10.1007/s13253-017-0277-6

Abstract

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.

Comments

This article is published as Fernando, R., Toosi, A., Wolc, A. et al. Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach. JABES 22, 172–193 (2017). doi: 10.1007/s13253-017-0277-6.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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