Genomic Prediction and QTL Mapping Using Bayesian Methods

  • Xiaochen Sun (Iowa State University)
  • David Habier (Pioneer Hi-Bred International)
  • Rohan L. Fernando (Iowa State University)
  • Dorian J. Garrick (Iowa State University)
  • Jack C.M. Dekkers (Iowa State University)


Several genomic selection methods were applied to a data set that was simulated for the 2010 QTLMAS workshop to predict the genomic breeding values (GEBV) of the offspring generation and to map the QTL. The GEBV had an accuracy of 0.894 with very small bias. QTL were detected based on the variance of 10 SNP windows. Using a threshold chosen for a 10% chromosome-wise type-I error rate, most of the large QTL were successfully detected with few false positives. Results for both prediction of breeding values and detection of QTL were among the best among all analyses of this data set by groups across the globe. Genomic selection method BayesCπ was identified to be appropriate for the 2010 QTLMAS dataset and also applicable to real cases with similar settings.

Keywords: ASL R2647

How to Cite:

Sun, X., Habier, D., Fernando, R. L., Garrick, D. J. & Dekkers, J. C., (2011) “Genomic Prediction and QTL Mapping Using Bayesian Methods”, Iowa State University Animal Industry Report 8(1). doi: https://doi.org/10.31274/ans_air-180814-959

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Published on
01 Jan 2011
Peer Reviewed