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

• Published in: Journal of agricultural, biological, and environmental statistics Vol. 22; no. 2; pp. 172 - 193
• Main Authors: Fernando, Rohan, Toosi, Ali, Wolc, Anna, Garrick, Dorian, Dekkers, Jack
• Format: Journal Article
• Language: English
• Published: New York Springer 01.06.2017; Springer US; Springer Nature B.V
• Subjects: Agriculture; Bayesian analysis; Bayesian theory; Biostatistics; Computer simulation; Data processing; Environmental statistics; Genetic research; Genome-wide association studies; genome-wide association study; Genomes; Genomics; Health Sciences; Mathematics and Statistics; Medicine; Methods; Monitoring/Environmental Analysis; prediction; Prediction models; regression analysis; Statistics; Statistics for Life Sciences; Genome-wide association studies; Whole-genome prediction; Posterior type I error rate; Genomewise error rate; Bayesian multiple regression
• ISSN: 1085-7117; 1537-2693
• DOI: 10.1007/s13253-017-0277-6

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.