DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants  

作　　者：Kelin Wang Muhammad Ali Abid Awais Rasheed Jose Crossa Sarah Hearne Huihui Li 

机构地区：[1]Institute of Crop Sciences,Chinese Academy of Agricultural Sciences(CAAS),CIMMYT-China Office,12 Zhongguancun South Street,Beijing 100081,China [2]Nanfan Research Institute,CAAS,Sanya,Hainan 572024,China [3]Department of Plant Sciences,Quaid-i-Azam University,Islamabad 45320,Pakistan [4]International Maize and Wheat Improvement Center(CIMMYT),Apdo.Postal 6-641,Texcoco,D.F.06600,Mexico

出　　处：《Molecular Plant》2023年第1期279-293,共15页分子植物（英文版）

基　　金：National Key R&D Program of China(2021YFD1201200);National Science Foundation of China(32022064);Project of Hainan Yazhou Bay Seed Lab(B21HJ0223);Innovation Program of the Chinese Academy of Agricultural Sciences.

摘　　要：Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants.Traditional methods typically use linear regression models with clear assumptions;such methods are unable to capture the complex relationships between genotypes and phenotypes.Non-linear models(e.g.,deep neural networks)have been proposed as a superior alternative to linear models because they can capture complex non-additive effects.Here we introduce a deep learning(DL)method,deep neural network genomic prediction(DNNGP),for integration of multi-omics data in plants.We trained DNNGP on four datasets and compared its performance with methods built with five classic models:genomic best linear unbiased prediction(GBLUP);two methods based on a machine learning(ML)framework,light gradient boosting machine(LightGBM)and support vector regression(SVR);and two methods based on a DL framework,deep learning genomic selection(DeepGS)and deep learning genome-wide association study(DLGWAS).DNNGP is novel in five ways.First,it can be applied to a variety of omics data to predict phenotypes.Second,the multilayered hierarchical structure of DNNGP dynamically learns features from raw data,avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation(rectified linear unit)functions.Third,when small datasets were used,DNNGP produced results that are competitive with results from the other five methods,showing greater prediction accuracy than the other methods when large-scale breeding data were used.Fourth,the computation time required by DNNGP was comparable with that of commonly used methods,up to 10 times faster than DeepGS.Fifth,hyperparameters can easily be batch tuned on a local machine.Compared with GBLUP,LightGBM,SVR,DeepGS and DLGWAS,DNNGP is superior to these existing widely used genomic selection(GS)methods.Moreover,DNNGP can generate robust assessments from diverse datasets,including omics data,and quickly incorporate complex and large datas

关 键 词：deep learning genomic selection multi-omics data prediction method 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]