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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Copyright Owner
The Authors
Copyright Date
2017
Language
en
File Format
application/pdf
Recommended Citation
Fernando, Rohan; Toosi, Ali; Wolc, Anna; Garrick, Dorian; and Dekkers, Jack, "Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach" (2017). Animal Science Publications. 632.
https://lib.dr.iastate.edu/ans_pubs/632
Included in
Agriculture Commons, Animal Sciences Commons, Computational Biology Commons, Statistical Models Commons
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.