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Use of gene expression data for predicting continuous phenotypes for animal production and breeding

Published online by Cambridge University Press:  01 October 2008

N. Robinson ,
M. Goddard  and
B. Hayes
N. Robinson*
Affiliation:
Nofima Akvaforsk-Fiskeriforskning AS, PO Box 5010, 1432 Ås, Norway
M. Goddard
Affiliation:
Primary Industries Research Victoria, 475 Mickleham Road Attwood, Victoria 3049, Australia Institute of Land and Food Resources, University of Melbourne, Parkville, Victoria 3052, Australia
B. Hayes
Affiliation:
Nofima Akvaforsk-Fiskeriforskning AS, PO Box 5010, 1432 Ås, Norway Primary Industries Research Victoria, 475 Mickleham Road Attwood, Victoria 3049, Australia
*
E-mail: nick.robinson@akvaforsk.no
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Abstract

Traits such as disease resistance are costly to evaluate and slow to improve using current methods. Analysis of gene expression profiles (e.g. DNA microarrays) has potential for predicting such phenotypes and has been used in an analogous way to classify cancer types in human patients. However, doubts have been raised regarding the use of classification methods with microarray data for this purpose. Here we propose a method using random regression with cross validation, which accounts for the distribution of variation in the trait and utilises different subsets of patients or animals to perform a complete validation of predictive ability. Published breast tumour data were used to test the method. Despite the small dataset (n < 100), the new approach resulted in a moderate but significant correlation between the predicted and actual phenotypes (0.32). Binary classification of the predicted phenotypes yielded similar classification error rates to those found by other authors (35%). Unlike other methods, the new method gave a quantitative estimate of phenotype that could be used to rank animals and select those with extreme phenotypic performance. Use of the method in an optimal way using larger sample sizes, and combining DNA microarrays and other testing platforms, is recommended.

Keywords

gene expressiondisease resistancerandom regressioncross validationselective breeding
Type
Full Paper
Information
animal , Volume 2 , Issue 10 , October 2008 , pp. 1413 - 1420
Copyright
Copyright © The Animal Consortium 2008

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