genomic.prediction {GSelection}

Genomic Prediction

Description

Prediction of phenotypic values based on selected markers with integrated model framework using both additive (Sparse Additive Models) and non-additive (HSIC LASSO) statistical models.

Usage

genomic.prediction(x,spam_error_var,hsic_error_var,
spam_selected_feature_index,hsic_selected_feature_index,
coefficient.spam,coefficient.hsic)

Arguments

x

a matrix of markers or explanatory variables for which phenotype will be predicted. Each column contains one marker and each row represents an individual.

spam_error_var

estimated error variance of genomic prediction by Sparse Additive Model.

hsic_error_var

estimated error variance of genomic prediction by HSIC LASSO.

spam_selected_feature_index

index of selected markers from x using Sparse Additive Model

hsic_selected_feature_index

index of selected markers from x using HSIC LASSO.

coefficient.spam

coefficient values of selected markers using Sparse Additive Model.

coefficient.hsic

coefficient values of selected markers using HSIC LASSO.

Details

Phenotypic values will be predicted for given genotype of markers by using previously fitted model object. Integrated model framework is used for this purpose which is developed by combining selected features from SpAm and HSIC LASSO.

Value

Integrated_y

returns predicted phenotype

Author(s)

Sayanti Guha Majumdar <[e-mail]>, Anil Rai, Dwijesh Chandra Mishra

References

Guha Majumdar, S., Rai, A. and Mishra, D. C. (2019). Integrated framework for selection of additive and non-additive genetic markers for genomic selection. Journal of Computational Biology. doi:10.1089/cmb.2019.0223
Ravikumar, P., Lafferty, J., Liu, H. and Wasserman, L. (2009). Sparse additive models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(5), 1009-1030. doi:10.1111/j.1467-9868.2009.00718.x
Yamada, M., Jitkrittum, W., Sigal, L., Xing, E. P. and Sugiyama, M. (2014). High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso. Neural Computation, 26(1):185-207. doi:10.1162/NECO_a_00537

Examples

library(GSelection)
data(GS)
x_trn <- GS[1:40,1:110]
y_trn <- GS[1:40,111]
x_tst <- GS[41:60,1:110]
y_tst <- GS[41:60,111]

## estimate spam_var from function spam.var.ensemble or spam.var.rcv
spam_var <- 2.681972
## estimate hsic_var from function hsic.var.ensemble or hsic.var.rcv
hsic_var <- 10.36974

fit <- feature.selection(x_trn,y_trn,d=10)
pred_y <- genomic.prediction(x_tst,spam_var,hsic_var,
fit$spam_selected_feature_index,fit$hsic_selected_feature_index,
fit$coefficient.spam,fit$coefficient.hsic)