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A Nested Mixture Model for Genomic Prediction Using Whole-Genome SNP Genotypes

Authors
  • Jian Zeng (Iowa State University)
  • Dorian J. Garrick (Iowa State University)
  • Jack C. Dekkers (Iowa State University)
  • Rohan L Fernando (Iowa State University)

Abstract

We propose a novel model (BayesN) for genomic prediction, where multiple markers in a small segment are simultaneously fitted to jointly capture the effect of major genes (QTL) in the segment. Compared with BayesB, in which the effects of neighboring markers are a prioriassumed to be independent, BayesN gave higher accuracies of prediction and required less computing effort. BayesN is an accurate and practical method for analyzing high-density markers, especially for traits influenced by rare QTL alleles

How to Cite:

Zeng, J., Garrick, D. J., Dekkers, J. C. & Fernando, R. L., (2016) “A Nested Mixture Model for Genomic Prediction Using Whole-Genome SNP Genotypes”, Iowa State University Animal Industry Report 13(1). doi: https://doi.org/10.31274/ans_air-180814-452

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Published on
01 Jan 2016
Peer Reviewed