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

Molecular Plant 2023年第1期16卷 279-293页

作者： Kelin Wang Muhammad Ali Abid Awais Rasheed Jose Crossa Sarah Hearne Huihui Li Institute of Crop Sciences Chinese Academy of Agricultural Sciences(CAAS)CIMMYT-China Office12 Zhongguancun South StreetBeijing 100081China Nanfan Research Institute CAASSanyaHainan 572024China Department of Plant Sciences Quaid-i-Azam UniversityIslamabad 45320Pakistan International Maize and Wheat Improvement Center(CIMMYT) Apdo.Postal 6-641TexcocoD.F.06600Mexico

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

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从GWAS到EigenGWAS和EnvGWAS

从GWAS到EigenGWAS和EnvGWAS

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2018中国作物学会学术年会

作者：李静 Denise Costich 王建康陈国波 Sarah Hearne 李慧慧中国农业科学院作物科学研究所国际玉米小麦改良中心杭州医学院

解析玉米的进化历程有助于基因挖掘和提高育种遗传进度,虽然目前已有大量的考古学和群体遗传学的研究,但是玉米进化过程仍存在很多谜团。由于玉米进化过程伴随着遗传漂变,分离玉米群体中的选择信号是很困难的。我们利用简化基因组测序(t... 详细信息

解析玉米的进化历程有助于基因挖掘和提高育种遗传进度,虽然目前已有大量的考古学和群体遗传学的研究,但是玉米进化过程仍存在很多谜团。由于玉米进化过程伴随着遗传漂变,分离玉米群体中的选择信号是很困难的。我们利用简化基因组测序(tGBS)技术对来自22个国家的1143个玉米种质进行了基因型鉴定,这些材料分布在热带低地,热带高原,亚热带或中纬度,温带地区,涵盖了玉米进化过程中的主要生态型。通过最新发展的EigenGWAS方法对获得的355,442个高质量的SNPs数据进行选择位点分析,在前10个特征向量上共检测到13个受选择基因组区域,其中10个区域首次被报道。在13个选择区域内共有146个富集于代谢途径的基因;同时利用温度,光照,土壤酸度,降雨量,气压,经纬度,适应性,玉米类型和籽粒类型相关的12个气象GIS数据进行了EnvGWAS分析,检测到10个与环境变化响应相关的区间,发现温度,地理位置,适应性和玉米类型为主要的环境响应因素。通过对遗传多样性较高的玉米大群体基因组和生态学数据分析,本研究揭示了玉米的多基因适应性模型,为帮助理解玉米进化和适应过程中分子机制和进化历程奠定了基础。

关键词：玉米选择位点/区域 EigenGWAS EnvGWAS

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Redesigning crop varieties to win the race between climate change and food security

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Molecular Plant 2023年第10期16卷 1590-1611页

作者： Kevin V.Pixley Jill E.Cairns Santiago Lopez-Ridaura Chris O.Ojiewo Maryam Abba Dawud Inoussa Drabo Taye Mindaye Baloua Nebie Godfrey Asea Biswanath Das Happy Daudi Haile Desmae Benoit Joseph Batieno Ousmane Boukar Clare T.M.Mukankusi Stanley T.Nkalubo sarah J.hearne Kanwarpal S.Dhugga Harish Gandhi Sieglinde Snapp Ernesto Adair Zepeda-Villarreal International Maize and Wheat Improvement Center(CIMMYT) TexcocoMexico International Maize and Wheat Improvement Center(CIMMYT) HarareZimbabwe International Maize and Wheat Improvement Center(CIMMYT) NairobiKenya Lake Chad Research Institute(LCRI) MaidiguriNigeria International Maize and Wheat Improvement Center(CIMMYT) DakarSenegal Ethiopian Institute of Agricultural Research(EIAR) Addis AbabaEthiopia National Agricultural Research Organization(NARO) KampalaUganda Tanzania Agricultural Research Institute(TARI) NaliendeleTanzania Institut de l'Environnement et de Recherches Agricoles(INERA) OuagadougouBurkina Faso International Institute of Tropicl Agriculture(IITA) KanoNigeria Alliance of Bioversity International and CIAT KampalaUganda

Climate change poses daunting challenges to agricultural production and food security.Rising temperatures,shifting weather patterns,and more frequent extreme events have already demonstrated their effects on local,regional,and global agricultural systems.Crop varieties that withstand climate-related stresses and are suitable for cultivation in innovative cropping systems will be crucial to maximize risk avoidance,productivity,and profitability under climate-changed environments.We surveyed 588 expert stakeholders to predict current and novel traits that may be essential for future pearl millet,sorghum,maize,groundnut,cowpea,and common bean varieties,particularly in sub-Saharan Africa.We then review the current progress and prospects for breeding three prioritized future-essential traits for each of these crops.Experts predict that most current breeding priorities will remain important,but that rates of genetic gain must increase to keep pace with climate challenges and consumer demands.Importantly,the predicted future-essential traits include innovative breeding targets that must also be prioritized;for example,(1)optimized rhizosphere microbiome,with benefits for P,N,and water use efficiency,(2)optimized performance across or in specific cropping systems,(3)lower nighttime respiration,(4)improved stover quality,and(5)increased early vigor.We further discuss cutting-edge tools and approaches to discover,validate,and incorporate novel genetic diversity from exotic germplasm into breeding populations with unprecedented precision,accuracy,and speed.We conclude that the greatest challenge to developing crop varieties to win the race between climate change and food security might be our innovativeness in defining and boldness to breed for the traits of tomorrow.

关键词： climate change food security crop breeding cropping systems expert survey sub-Saharan Africa

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