Prediction of maize phenotype based on whole-genome single nucleotide polymorphisms using deep belief networks
Prediction of maize phenotype based on whole-genome single nucleotide polymorphisms using deep belief networks
复制标题
使用深度信念网络基于全基因组单核苷酸多态性预测玉米表型
DOI：
10.1088/1742-6596/835/1/012003
复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Lailan Sahrina Hasibuan
中科院分区：
文献类型：
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作者：
H. Rachmatia;W. Kusuma;Lailan Sahrina Hasibuan
植物育种中的选择如果以基因组数据为基础，可能会更有效、更高效。基因组选择（Genomic selection， GS）是一种利用基因组数据进行植物育种选择的新方法，其机制被称为基因组预测（Genomic prediction， GP）。大多数GP模型采用线性方法，忽略了基因间相互作用的影响和高阶非线性的影响。深度信念网络（Deep belief network， DBN）是深度学习方法的体系结构之一，它能够对涉及数据非线性效应的数据进行高度抽象的建模。本研究利用全基因组单核苷酸多态性（snp）作为训练和测试数据，利用DBN开发GP模型。案例研究是玉米的一组性状。玉米数据集来自CIMMYT（国际玉米和小麦改良中心）的全球玉米计划。基于Pearson相关性，DBN优于其他方法，核希尔伯特空间（RKHS）回归，贝叶斯LASSO (BL)，最佳线性无偏预测器（BLUP），以防止所谓的非加性特征。DBN在-1到1的范围内实现了0.579的相关性。
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.