354. A variational Bayes method for genomic prediction increases accuracy and computing speed-Reference-Cited by-同舟云学术

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354. A variational Bayes method for genomic prediction increases accuracy and computing speed

Published:2022-12-31 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)
language:
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Author:

Cheng J.¹,Cheng H.²,Fernando R.³,Maltecca C.¹,Ma L.⁴,Dekkers J.³,Jiang J.¹

Affiliation:

1. Department of Animal Science, North Carolina State University, Raleigh, NC 27695, USA.

2. Department of Animal Science, University of California, Davis, CA 95616, USA.

3. Department of Animal Science, Iowa State University, Ames, IA 50011, USA.

4. Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA.

Publisher

Wageningen Academic Publishers

Link

https://www.wageningenacademic.com/doi/pdf/10.3920/978-90-8686-940-4_354

Reference13 articles.

1. Beal M.J. (2003). Variational algorithms for approximate Bayesian inference. PhD thesis. University College London, London.

2. Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance

3. Genetic analysis of disease resilience in wean-to-finish pigs from a natural disease challenge model

4. Genome-wide association study of disease resilience traits from a natural polymicrobial disease challenge model in pigs identifies the importance of the major histocompatibility complex region

5. Jaakkola T.S., Qi Y. (2007). Advances in Neural Information Processing Systems, pp 1097-1104

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