Prediction of maize phenotype based on whole-genome single nucleotide polymorphisms using deep belief networks

International Symposium on Bioinformatics, Chemometrics and Metabolomics

生物科学;化学

Rachmatia, H.^1 ; Kusuma, W.A.^1 ; Hasibuan, L.S.^1

Department of Computer Science, Bogor Agricultural University, Indonesia^1

关键词: Best linear unbiased predictor;    Deep belief network (DBN);    Deep belief networks;    High level of abstraction;    Higher order nonlinearity;    Kernel Hilbert space;    Single nucleotide polymorphisms;    Training and testing;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/835/1/012003/pdf
DOI  :  10.1088/1742-6596/835/1/012003

学科分类：生物科学（综合）

来源: IOP

【 摘 要 】

Selection in plant breeding could be more effective and more efficient if it is based on genomic data. Genomic selection (GS) is a new approach for plant-breeding selection that exploits genomic data through a mechanism called genomic prediction (GP). Most of GP models used linear methods that ignore effects of interaction among genes and effects of higher order nonlinearities. Deep belief network (DBN), one of the architectural in deep learning methods, is able to model data in high level of abstraction that involves nonlinearities effects of the data. This study implemented DBN for developing a GP model utilizing whole-genome Single Nucleotide Polymorphisms (SNPs) as data for training and testing. The case study was a set of traits in maize. The maize dataset was acquisitioned from CIMMYT's (International Maize and Wheat Improvement Center) Global Maize program. Based on Pearson correlation, DBN is outperformed than other methods, kernel Hilbert space (RKHS) regression, Bayesian LASSO (BL), best linear unbiased predictor (BLUP), in case allegedly non-additive traits. DBN achieves correlation of 0.579 within -1 to 1 range.