Genomic Selection (GS) is the most prevalent method in
today’s scenario to access the genetic merit of individual
under study. It selects the candidates for next breeding
cycle on the basis of its genetic merit. GS has successfully
been used in various plant and animal studies in last decade.
Several parametric statistical models have been proposed
and being used successfully in various GS studies.
However, performance of parametric methods becomes very
poor when we have non additive kind of genetic architecture.
In such cases, generally performance of non-parametric
methods are quite satisfactory as these methods do not
require strict statistical assumptions. This article presents
comparative performance of few most commonly used nonparametric methods for complex genetic architecture i.e.
non-additive, using simulated dataset generated at different
level of heritability and varying combination of population
size. Among several non-parametric methods, SVM
outperformed across a range of genetic architecture.
Keywords: Genomic selection, epistasis, nonparametric, SVM and ANN
Article DOI: 10.31742/IJGPB.80.4.4
Print ISSN: 0019-5200
Online ISSN: 0975-6906
Neeraj Budhlakoti, Anil Rai, D. C. Mishra, Seema Jaggi, Mukesh Kumar and A. R. Rao info_circle