Predicting genetic merit for mastitis and fertility in dairy cattle using genome wide selection and high density SNP screens

Raadsma, H. W., Moser, G., Crump, R. E., Khatkar, M. S., Zenger, K. R., Cavanagh, J. A. L., Hawken, R. J., Hobbs, M., Barris, W., Solkner, J., Nicholas, F. W. and Tier, B. (2008). Predicting genetic merit for mastitis and fertility in dairy cattle using genome wide selection and high density SNP screens. In: Marie-Hélène Pinard, Animal genomics for animal health: 2007 proceedings of an international symposium organized by Agricultural research services (ARS). International Symposium on Animal Genomics for Animal Health, Paris France, (219-223). Oct 25-27, 2007.

Author Raadsma, H. W.
Moser, G.
Crump, R. E.
Khatkar, M. S.
Zenger, K. R.
Cavanagh, J. A. L.
Hawken, R. J.
Hobbs, M.
Barris, W.
Solkner, J.
Nicholas, F. W.
Tier, B.
Title of paper Predicting genetic merit for mastitis and fertility in dairy cattle using genome wide selection and high density SNP screens
Conference name International Symposium on Animal Genomics for Animal Health
Conference location Paris France
Conference dates Oct 25-27, 2007
Proceedings title Animal genomics for animal health: 2007 proceedings of an international symposium organized by Agricultural research services (ARS)   Check publisher's open access policy
Journal name Developments in Biologicals   Check publisher's open access policy
Place of Publication Basel, Switzerland
Publisher S. Karger
Publication Year 2008
Sub-type Fully published paper
ISBN 978-3-8055-8619-1
ISSN 1424-6074
1662-2960
Editor Marie-Hélène Pinard
Volume 132
Start page 219
End page 223
Total pages 5
Language eng
Abstract/Summary Two novel methods for genome wide selection (GWS) were examined for predicting the genetic merit of animals using SNP information alone. A panel of 1,546 dairy bulls with reliable EBVs was genotyped for 15,380 SNPs that spanned the whole bovine genome. Two complexity reduction methods were used, partial least squares (PLS) and regression using a genetic algorithm (GAR), to find optimal solutions of EBVs against SNP information. Extensive internal cross-validation was used to find the best predictive models followed by external validation (without direct use of the pedigree or SNP location). Both PLS and GAR provided both accurate fit to the training data set for somatic cell count (SCC) (max r = 0.83) and fertility (max r = 0.88) and showed an accuracy of prediction of r = 0.47 for SCC, and r = 0.72 for fertility. This is the first empirical demonstration that genome wide selection can account for a very high proportion of additive genetic variation in fitness traits whilst exploiting only a small percentage of available SNP information, without use of pedigree or QTL mapping. PLS was computationally more efficient than GAR.
Keyword Genome wide selection
SNP
Dairy cattle
Somatic cell count
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Collection: UQ Diamantina Institute Publications
 
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