Integrated Framework for Selection of Additive and Nonadditive Genetic Markers for Genomic Selection

Journal of computational biology

article

Sayanti Guha Majumdar¹  Anil Rai¹  Dwijesh C. Mishra¹ 

[1] Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute

关键词: additive and nonadditive models;    genetic markers;    GEBV;    genomic selection;    prediction accuracy;    redundancy rate.;   

DOI  :  10.1089/cmb.2019.0223

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Genomic selection is a modified form of marker-assisted selection in which the markers from the whole genome are used to estimate the genomic-estimated breeding value (GEBV). Several estimators are available to estimate GEBV. These estimators are able to capture either additive genetic effects or nonadditive genetic effects. However, there is hardly any procedure available that could capture both the effects simultaneously. Therefore, this study has been conducted to develop an integrated framework that is able to capture both additive and nonadditive effects efficiently. This integrated framework has been developed after evaluating existing additive and nonadditive models for marker selection. Furthermore, two efficient additive and nonadditive methods, that is, sparse additive models (SpAM) and Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC LASSO), have been combined to select both additive and nonadditive genetic markers for estimation of GEBV. The performance of the proposed framework has been evaluated on the basis of prediction accuracy, fraction of correctly selected features, and redundancy rate, along with standard error of mean for estimation of GEBV, compared with the individual performances of SpAM and HSIC LASSO separately. The newly developed framework is found to be satisfactory in terms of its performance and found to be robust for estimation of GEBV.

【 授权许可】

Unknown