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

Robinson, N., Goddard, M. and Hayes, B. (2008) Use of gene expression data for predicting continuous phenotypes for animal production and breeding. Animal, 2 10: 1413-1420. doi:10.1017/S1751731108002632


Author Robinson, N.
Goddard, M.
Hayes, B.
Title Use of gene expression data for predicting continuous phenotypes for animal production and breeding
Journal name Animal   Check publisher's open access policy
ISSN 1751-7311
1751-732X
Publication date 2008-10
Sub-type Article (original research)
DOI 10.1017/S1751731108002632
Open Access Status Not yet assessed
Volume 2
Issue 10
Start page 1413
End page 1420
Total pages 8
Place of publication Cambridge, United Kingdom
Publisher Cambridge University Press
Language eng
Formatted 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.
Keyword Cross validation
Disease resistance
Gene expression
Random regression
Selective breeding
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
 
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