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

Rohan Fernando (), Ali Toosi, Anna Wolc, Dorian Garrick and Jack Dekkers
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Rohan Fernando: Iowa State University
Ali Toosi: Iowa State University
Anna Wolc: Iowa State University
Dorian Garrick: Iowa State University
Jack Dekkers: Iowa State University

Journal of Agricultural, Biological and Environmental Statistics, 2017, vol. 22, issue 2, No 4, 172-193

Abstract: 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.

Keywords: Bayesian multiple regression; Genome-wide association studies; Genomewise error rate; Posterior type I error rate; Whole-genome prediction (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s13253-017-0277-6

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