Marker-based screening of maize inbred lines using support vector machine regression |
| |
| Authors: | Steven Maenhout Bernard De Baets Geert Haesaert Erik Van Bockstaele |
| |
| Institution: | (1) Department of Plant Production, University College Ghent, Voskenslaan 270, Ghent, 9000, Belgium;(2) Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, Ghent, 9000, Belgium;(3) Department of Plant Production, Ghent University, Coupure links 653, Ghent, 9000, Belgium;(4) ILVO, Institute for Agricultural and Fisheries Research, Van Gansberghelaan 96, Merelbeke, 9820, Belgium |
| |
| Abstract: | The phenomenon of heterosis is widely used in hybrid breeding programmes, despite the fact that no satisfactory molecular
explanation is available. Estimators of quantitative genetic components like GCA and SCA values are tools used by the plant
breeder to identify superior parental individuals and to search for high heterosis combinations. Obtaining these estimators
usually requires the creation of new parental combinations and testing their offspring in multi-environment field trials.
In this study we explore the use of ɛ-insensitive Support Vector Machine Regression (ɛ-SVR) for the prediction of GCA and
SCA values from the molecular marker scores of parental inbred lines as an alternative to these field trials. Prediction accuracies
are obtained by means of cross-validation on a grain maize data set from the private breeding company RAGT R2n. Results indicate
that the proposed method allows the routine screening of new inbred lines despite the fact that predicting the SCA value of
an untested hybrid remains problematic with the available molecular marker information and standard kernel functions. The
genotypical performance of a testcross hybrid, originating from a cross between an untested inbred line and a well-known complementary
tester, can be predicted with moderate to high accuracy while this cannot be said for a cross between two untested inbred
lines. |
| |
| Keywords: | BLUP Heterosis Maize Molecular markers Support Vector Machine Regression |
| 本文献已被 SpringerLink 等数据库收录！ |
|
