中国科学院大学学报2024，Vol.41Issue(4)：468-476,9.DOI:10.7523/j.ucas.2023.046
基于卷积神经网络多尺度特征的大豆基因组表型预测
Multi-scale featured convolution neural network-based soybean phenotypic prediction
摘要
Abstract
In breeding,single nucleotide polymorphisms(SNPs)in the genome are often used to predict quantitative phenotypes to assist breeding,thereby improving breeding efficiency.The traditional statistical analysis method is limited by many factors including missing data,and its performance sometimes can not meet the requirements.In this paper,we proposed a multi-scale feature convolutional neural network model(MSF-CNN)to predict plant traits.The model extracted SNP features at three different scales through convolution and analyzed the significance of SNP sites through the weight of the SNPs input into the model.The test results showed that MSF-CNN model performed with higher accuracy than the known methods and other deep learning models in phenotype prediction on the datasets with missing genotypic data.This paper also studied the contribution of genotype to traits through saliency map,and discovered several significant SNP loci.These results showed that,compared with other known methods available at present,the deep learning model proposed in this paper can obtain more accurate prediction results of quantitative phenotypes,and can also effectively and efficiently identify SNPs associated with genome-wide association research.关键词
遗传筛选/深度学习/全基因组关联分析/大豆Key words
gene selection/deep learning/genome-wide association study/soybean分类
农业科技引用本文复制引用
林昱彤,王红,柴团耀..基于卷积神经网络多尺度特征的大豆基因组表型预测[J].中国科学院大学学报,2024,41(4):468-476,9.基金项目
国家重点研发计划(2019YFA0903901)、中国科学院战略性先导科技专项A类项目(XDA24010402)、国家自然科学基金(61972374)和中央高校基本科研业务费专项资助 （2019YFA0903901）
