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Evaluation of random forest regression for prediction of breeding value from genomewide SNPs

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Title Evaluation of random forest regression for prediction of breeding value from genomewide SNPs
Not Available
 
Creator Rupam Kumar Sarkar
A R Rao
Prabina Kumar Meher
T Nepolean
T Mohapatra
 
Subject genomewide SNPs
penalized regression
prediction of breeding value
machine learning methods
 
Description Not Available
Genomic prediction is meant for estimating the breeding value using molecular marker data which has turned out to be a
powerful tool for efficient utilization of germplasm resources and rapid improvement of cultivars. Model-based techniques
have been widely used for prediction of breeding values of genotypes from genomewide association studies. However, application of the random forest (RF), a model-free ensemble learning method, is not widely used for prediction. In this study, the
optimum values of tuning parameters of RF have been identified and applied to predict the breeding value of genotypes based
on genomewide single-nucleotide polymorphisms (SNPs), where the number of SNPs (P variables) is much higher than the
number of genotypes (n observations) (P >> n). Further, a comparison was made with the model-based genomic prediction
methods, namely, least absolute shrinkage and selection operator (LASSO), ridge regression (RR) and elastic net (EN) under
P >> n. It was found that the correlations between the predicted and observed trait response were 0.591, 0.539, 0.431 and
0.587 for RF, LASSO, RR and EN, respectively, which implies superiority of the RF over the model-based techniques in
genomic prediction. Hence, we suggest that the RF methodology can be used
Not Available
 
Date 2022-08-11T07:04:24Z
2022-08-11T07:04:24Z
2015-06-02
 
Type Research Paper
 
Identifier 10.1007/s12041-015-0501-5
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/73757
 
Language English
 
Relation Not Available;
 
Publisher Not Available