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| 1 | High-throughput Phenotyping and Genomic Selection:The Frontiers of Crop Breeding Converge显示文摘Genomic selection (GS) and high-throughput phenotyping have recently been captivating the interest of the crop breeding com-munity from both the public and private sectors world-wide.Both approaches promise to revolutionize the prediction of complex traits,including growth,yield and adaptation to stress.Whereas high-throughput phenotyping may help to improve understanding of crop physiology,most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and compa-rable to genomic selection.Despite the fact that the two method-ological approaches represent the extremes of what is understood as the breeding process (phenotype versus genome),they both consider the targeted traits (e.g.grain yield,growth,phenology,plant adaptation to stress) as a black box instead of dissecting them as a set of secondary traits (i.e.physiological) putatively related to the target trait.Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology.This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy (NIRS) and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield. | Lloren Cabrera-Bosquet Jos Crossa Jarislav von Zitzewitz María Dolors Serret Jos Luis Araus | 2012 | Journal of Integrative Plant Biology2012，54，5： | 11 |
| 2 | META-R:A software to analyze data from multi-environment plant breeding trials显示文摘META-R(multi-environment trial analysis in R)is a suite of R scripts linked by a graphical user interface(GUI)designed in Java language.The objective of META-R is to accurately analyze multi-environment plant breeding trials(METs)by fitting mixed and fixed linear models from experimental designs such as the randomized complete block design(RCBD)and the alpha-lattice/lattice designs.META-R simultaneously estimates the best linear and unbiased estimators(BLUEs)and the best linear and unbiased predictors(BLUPs).Additionally,it computes the variance-covariance parameters,as well as some statistical and genetic parameters such as the least significant difference(LSD)at 5%significance,the coefficient of variation in percentage(CV),the genetic variance,and the broad-sense heritability.These parameters are very important in the selection of top performing genotypes in plant breeding.META-R also computes the phenotypic and genetic correlations among environments and between traits,as well as their statistical significance.The genetic correlations between environments or traits can be visualized in a biplot graph or a tree diagram(dendrogram).Genetic correlations are very important for identifying environments with similar behavior or making indirect selection and identifying the most highly associated traits.META-R performs multi-environment analyses by using the residual maximum likelihood(REML)method;these analyses can be done by environment,across environments by grouping factors(stress conditions,nitrogen content,etc.)and across environments;the analyses across environments can be done with a pre-defined degree of heritability. | Gregorio Alvarado Francisco M.Rodríguez Angela Pacheco Juan Burgueño JoséCrossa Mateo Vargas Paulino Pérez-Rodríguez Marco A.Lopez-Cruz | 2020 | The Crop Journal2020，8，5： | 4 |
| 3 | Genome-wide association study and genomic prediction of Fusarium ear rot resistance in tropical maize germplasm显示文摘Fusarium ear rot(FER)is a destructive maize fungal disease worldwide.In this study,three tropical maize populations consisting of 874 inbred lines were used to perform genomewide association study(GWAS)and genomic prediction(GP)analyses of FER resistance.Broad phenotypic variation and high heritability for FER were observed,although it was highly influenced by large genotype-by-environment interactions.In the 874 inbred lines,GWAS with general linear model(GLM)identified 3034 single-nucleotide polymorphisms(SNPs)significantly associated with FER resistance at the P-value threshold of 1×10^(-5),the average phenotypic variation explained(PVE)by these associations was 3%with a range from 2.33%to 6.92%,and 49 of these associations had PVE values greater than 5%.The GWAS analysis with mixed linear model(MLM)identified 19 significantly associated SNPs at the P-value threshold of 1×10^(-4),the average PVE of these associations was 1.60%with a range from 1.39%to 2.04%.Within each of the three populations,the number of significantly associated SNPs identified by GLM and MLM ranged from 25 to 41,and from 5 to 22,respectively.Overlapping SNP associations across populations were rare.A few stable genomic regions conferring FER resistance were identified,which located in bins 3.04/05,7.02/04,9.00/01,9.04,9.06/07,and 10.03/04.The genomic regions in bins 9.00/01 and 9.04 are new.GP produced moderate accuracies with genome-wide markers,and relatively high accuracies with SNP associations detected from GWAS.Moderate prediction accuracies were observed when the training and validation sets were closely related.These results implied that FER resistance in maize is controlled by minor QTL with small effects,and highly influenced by the genetic background of the populations studied.Genomic selection(GS)by incorporating SNP associations detected from GWAS is a promising tool for improving FER resistance in maize. | Yubo Liu Guanghui Hu Ao Zhang Alexander Loladze Yingxiong Hu Hui Wang Jingtao Qu Xuecai Zhang Michael Olsen Felix San Vicente Jose Crossa Feng Lin Boddupalli M.Prasanna | 2021 | The Crop Journal2021，9，2： | 3 |
| 4 | DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants显示文摘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 datasets into usable models,making it a promising and practical approach for straightforward integration into existing GS platforms. | Kelin Wang Muhammad Ali Abid Awais Rasheed Jose Crossa Sarah Hearne Huihui Li | 2023 | Molecular Plant2023，16，1： | 3 |
| 5 | Genomic prediction of the performance of hybrids and the combining abilities for line by tester trials in maize显示文摘The two most important activities in maize breeding are the development of inbred lines with high values of general combining ability(GCA)and specific combining ability(SCA),and the identification of hybrids with high yield potentials.Genomic selection(GS)is a promising genomic tool to perform selection on the untested breeding material based on the genomic estimated breeding values estimated from the genomic prediction(GP).In this study,GP analyses were carried out to estimate the performance of hybrids,GCA,and SCA for grain yield(GY)in three maize line-by-tester trials,where all the material was phenotyped in 10 to 11 multiple-location trials and genotyped with a mid-density molecular marker platform.Results showed that the prediction abilities for the performance of hybrids ranged from 0.59 to0.81 across all trials in the model including the additive effect of lines and testers.In the model including both additive and non-additive effects,the prediction abilities for the performance of hybrids were improved and ranged from 0.64 to 0.86 across all trials.The prediction abilities of the GCA for GY were low,ranging between-0.14 and 0.13 across all trials in the model including only inbred lines;the prediction abilities of the GCA for GY were improved and ranged from 0.49 to 0.55 across all trials in the model including both inbred lines and testers,while the prediction abilities of the SCA for GY were negative across all trials.The prediction abilities for GY between testers varied from-0.66 to 0.82;the performance of hybrids between testers is difficult to predict.GS offers the opportunity to predict the performance of new hybrids and the GCA of new inbred lines based on the molecular marker information,the total breeding cost could be reduced dramatically by phenotyping fewer multiple-location trials. | Ao Zhang Paulino Pérez-Rodríguez Felix San Vicente Natalia Palacios-Rojas Thanda Dhliwayo Yubo Liu Zhenhai Cui Yuan Guan Hui Wang Hongjian Zheng Michael Olsen Boddupalli M.Prasanna Yanye Ruan Jose Crossa Xuecai Zhang | 2022 | The Crop Journal2022，10，1： | 3 |
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