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Crop Breeding Chips and Genotyping Platforms： Progress, Challenges, and Perspectives

Molecular Plant 2017年第8期10卷 1047-1064页

作者： awais rasheed Yuanfeng Hao Xianchun Xia awais Khan Yunbi Xu Rajeev K. Varshney Zh-onghu He Institute of Crop Sciences National Wheat Improvement Center Chinese Academy of Agricultural Sciences (CAAS) Beijing 100081 China International Maize and Wheat Improvement Center (CIMMY-F) c/o CAAS Beijing 100081 China Department of Plant Pathology and Plant-Microbe Biology Cornell University Geneva NY USA International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) Patancheru 502324 India

There is a rapidly rising trend in the development and application of molecular marker assays for gene map- ping and discovery in field crops and trees. Thus far, more than 50 SNP arrays and 15 different types of genotyping-by-sequencing （GBS） platforms have been developed in over 25 crop species and perennial trees. However, much less effort has been made on developing ultra-high-throughput and cost-effective genotyping platforms for applied breeding programs. In this review, we discuss the scientific bottlenecks in existing SNP arrays and GBS technologies and the strategies to develop targeted platforms for crop mo- lecular breeding. We propose that future practical breeding platforms should adopt automated genotyping technologies, either array or sequencing based, target functional polymorphisms underpinning economic traits, and provide desirable prediction accuracy for quantitative traits, with universal applications under wide genetic backgrounds in crops. The development of such platforms faces serious challenges at both the technological level due to cost ineffectiveness, and the knowledge level due to large genotype- phenotype gaps in crop plants. It is expected that such genotyping platforms will be achieved in the next ten years in major crops in consideration of （a） rapid development in gene discovery of important traits, （b） deepened understanding of quantitative traits through new analytical models and population designs, （c） integration of multi-layer -omics data leading to identification of genes and pathways responsible for important breeding traits, and （d） improvement in cost effectiveness of large-scale genotyping. Crop breeding chips and genotyping platforms will provide unprecedented opportunities to accelerate the development of cultivars with desired yield potential, quality, and enhanced adaptation to mitigate the effects of climate change.

关键词： Single nucleotide polymorphisms (SNPs) Genotyping-by-sequencing (GBS) SNP arrays Crop breeding Genotyping platforms

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

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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 *** methods typically use linear regression models with clear assumptions;such methods are unable to capture the complex relationships between genotypes and ***-linear models(e.g.,deep neural networks)have been proposed as a superior alternative to linear models because they can capture complex non-additive *** we introduce a deep learning(DL)method,deep neural network genomic prediction(DNNGP),for integration of multi-omics data in *** 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 ***,it can be applied to a variety of omics data to predict ***,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)***,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 ***,the computation time required by DNNGP was comparable with that of commonly used methods,up to 10 times faster than ***,hyperparameters can easily be batch tuned on a local *** with GBLUP,LightGBM,SVR,DeepGS and DLGWAS,DNNGP is superior to these existing widely used genomic selection(GS)***,DNNGP can generate robust assessments from diverse datasets,including omics data

关键词： deep learning genomic selection multi-omics data prediction method

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Orchestrating seed storage protein and starch accumulation toward overcoming yield–quality trade-off in cereal crops

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Journal of Integrative Plant Biology 2024年第3期66卷 468-483页

作者： Shuanghe Cao Bingyan Liu Daowen Wang awais rasheed Lina Xie Xianchun Xia Zhonghu He State Key Laboratory of Crop Gene Resources and Breeding/National Wheat Improvement Center Institute of Crop SciencesBeijing 100081China College of Agronomy Henan Agricultural UniversityZhengzhou 450002China International Maize and Wheat Improvement Center(CIMMYT)China Office Chinese Academy of Agricultural SciencesBeijing 100081China

Achieving high yield and good quality in crops is essential for human food security and ***,there is usually disharmony between yield and *** storage protein(SSP)and starch,the predominant components in cereal grains,determine yield and quality,and their coupled synthesis causes a yield–quality ***,dissection of the underlying regulatory mechanism facilitates simultaneous improvement of yield and ***,we summarize current findings about the synergistic molecular machinery underpinning SSP and starch synthesis in the leading staple cereal crops,including maize,rice and *** further evaluate the functional conservation and differentiation of key regulators and specify feasible research approaches to identify additional regulators and expand *** also present major strategies to leverage resultant information for simultaneous improvement of yield and quality by molecular ***,future perspectives on major challenges are proposed.

关键词： cereal crops grain yield and quality seed storage protein starch synergistic regulation

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Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies

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The Crop Journal 2022年第5期10卷 1303-1311页

作者： Lei Li Muhammad Adeel Hassan Shurong Yang Furong Jing Mengjiao Yang awais rasheed Jiankang Wang Xianchun Xia Zhonghu He Yonggui Xiao Institute of Crop Sciences National Wheat Improvement CentreChinese Academy of Agricultural Sciences(CAAS)Beijing 100081China Electronic Information School Foshan PolytechnicFoshan 528137GuangdongChina International Maize and Wheat Improvement Centre(CIMMYT)China Office c/o CAASBeijing 100081China Department of Plant Sciences Quaid-i-Azam UniversityIslamabad 44000Pakistan

Spike number(SN) per unit area is one of the major determinants of grain yield in wheat. Development of high-throughput techniques to count SN from large populations enables rapid and cost-effective selection and facilitates genetic studies. In the present study, we used a deep-learning algorithm, i.e., Faster Region-based Convolutional Neural Networks(Faster R-CNN) on Red-Green-Blue(RGB) images to explore the possibility of image-based detection of SN and its application to identify the loci underlying SN. A doubled haploid population of 101 lines derived from the Yangmai 16/Zhongmai 895 cross was grown at two sites for SN phenotyping and genotyped using the high-density wheat 660 K SNP *** of manual spike number(MSN) in the field, image-based spike number(ISN), and verification of spike number(VSN) by Faster R-CNN revealed significant variation(P < 0.001) among genotypes, with high heritability ranged from 0.71 to 0.96. The coefficients of determination(R^(2)) between ISN and VSN was 0.83, which was higher than that between ISN and MSN(R^(2)= 0.51), and between VSN and MSN(R^(2)= 0.50). Results showed that VSN data can effectively predict wheat spikes with an average accuracy of 86.7% when validated using MSN data. Three QTL ***-4 DS, ***-7 DS, and ***-7 DL were identified based on MSN, ISN and VSN data, while ***-7 DS was detected in all the three data sets. These results indicate that using Faster R-CNN model for image-based identification of SN per unit area is a precise and rapid phenotyping method, which can be used for genetic studies of SN in wheat.

关键词： Deeping learning High-throughput phenotyping QTL mapping RGB imaging

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Phosphorylation of KAT-2B by WKS1/Yr36 redirects the lipid flux to jasmonates to enhance resistance against wheat stripe rust

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Journal of Genetics and Genomics 2023年第11期50卷 872-882页

作者： Yan Yan Xiao-Ming Li Yun Chen Tian-Tian Wu Ci-Hang Ding Mei-Qi Zhang Yue-Ting Guo Chu-Yang Wang Junli Zhang Xuebin Zhang awais rasheed Shengchun Xu Meng-Lu Wang Zhongfu Ni Qixin Sun 缑金营 Frontiers Science Center for Molecular Design Breeding(MOE) China Agricultural UniversityBeijing 100193China Xianghu Laboratory HangzhouZhejiang 311231China School of Life Sciences Fudan UniversityShanghai 200438China Institute of Metabolism and Integrative Biology Fudan UniversityShanghai 200438China State Key Laboratory of Crop Stress Adaptation and Improvement Henan Joint International Laboratory for Crop Multi-Omics ResearchSchool of Life SciencesHenan UniversityKaifengHenan 475004China International Maize and Wheat Improvement Center(CIMMYT) Chinese Academy of Agricultural SciencesBeijing 100081China

Wheat(Triticum aestivum)is one of the most essential human energy and protein ***,wheat production is threatened by devastating fungal diseases such as stripe rust,caused by Puccinia striiformis ***(Pst).Here,we reveal that the alternations in chloroplast lipid profiles and the accumulation of jasmonate(JA)in the necrosis region activate JA signaling and trigger the host *** collapse of chloroplasts in the necrosis region results in accumulations of polyunsaturated membrane lipids and the lipid-derived phytohormone JA in transgenic lines of Yr36 that encodes Wheat Kinase START 1(WKS1),a high-temperature-dependent adult plant resistance ***1.1,a protein encoded by a full-length splicing variant of WKS1,phosphorylates and enhances the activity of keto-acyl thiolase(KAT-2B),a critical enzyme catalyzing theβ-oxidation reaction in JA *** premature stop mutant,kat-2b,accumulates less JA and shows defects in the host defense against ***,overexpression of KAT-2B results in a higher level of JA and limits the growth of ***,JA inhibits the growth and reduces pustule densities of *** study illustrates the WKS1.1-KAT-2B-JA pathway for enhancing wheat defense against fungal pathogens to attenuate yield loss.

关键词： Wheat Stripe rust Cell death Jasmonate Hostdefense

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China-CIMMYT collaboration enhances wheat improvement in China

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Frontiers of Agricultural Science and Engineering 2019年第3期6卷 233-239页

作者： Zhonghu HE Xianchun XIA Yong ZHANG Yan ZHANG Yonggui XIAO Xinmin CHEN Simin LI Yuanfeng HAO awais rasheed Zhiyong XIN Qiaosheng ZHUANG Ennian YANG Zheru FAN Jun YAN Ravi SINGH Hans-Joachim BRAUN Institute of Crop Sciences Chinese Academy of Agricultural SciencesBeijing100081China CIMMYT China Office Beijing100081China Crop Research Institute Sichuan Academy of Agricultural SciencesChengdu610066China Research Institute of Nuclear and Biotechnology Xinjiang Academy of Agricultural SciencesUrumqi830091China Cotton Research Institute Chinese Academy of Agricultural Sciences(CAAS)Anyang455000China International Maize and Wheat Improvement Center(CIMMYT) Apdo.Postal6-64106600MexicoD.F.Mexico

China and CIMMYT have collaborated on wheat improvement for over 40 years and significant progress has been achieved in five aspects in China.A standardized protocol for testing Chinese noodle quality has been established with three selection criteria, i.e.,gluten quality, starch viscosity and flour color are identified as being responsible for noodle *** approaches have been used to develop and validate genespecific markers, leading to the establishment of a KASP platform, and seven cultivars have been released through application of molecular marker *** for breeding adult-plant resistance to yellow rust, leaf rust and powdery mildew, based on the pleiotropic effect of minor genes has been established, resulting in release of six *** than 330 cultivars derived from CIMMYT germplasm have been released and are now grown over 9% of the Chinese wheat production ***, physiological approaches have been used to characterize yield potential and develop high-efficiency phenotyping *** has also provided valuable training for Chinese *** of climate-resilient cultivars with application of new technology will be the priority for future collaboration.

关键词： adult-plant resistance bread wheat breeding gene-specific marker germplasm exchange processing quality

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利用GWAS和EigenGWAS挖掘合成小麦中优异遗传变异

利用GWAS和EigenGWAS挖掘合成小麦中优异遗传变异

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第十九届中国作物学会学术年会

作者：单丹婷 Mohsin Ali 王建康 awais rasheed 李慧慧中国农业科学院作物科学研究所 Department of Plant Sciences Quaid-i-Azam University

随着全球人口增长、气候变化、耕地面积减少以及人们生活水平日益提高,作物产量和营养价值的需求也在不断增加,作物对环境的适应性要求也逐渐增加。小麦(Triticum aestivum L.)作为世界三大作物之一,在保障粮食安全中发挥着重要的作用... 详细信息

随着全球人口增长、气候变化、耕地面积减少以及人们生活水平日益提高,作物产量和营养价值的需求也在不断增加,作物对环境的适应性要求也逐渐增加。小麦(Triticum aestivum L.)作为世界三大作物之一,在保障粮食安全中发挥着重要的作用。由于基因组大数据的爆发式增长,全基因组关联分析(GWAS, Genome-wide association studies)成为数量性状基因定位的重要手段。特征值全基因组关联分析(EigenGWAS)是一种高效的选择位点定位方法,用来挖掘研究群体中受选择的遗传变异。本研究利用495份小麦品种,其中包括194份合成小麦以及合成小麦衍生的93份普通小麦。通过简化基因组测序(GBS, genotyping by sequencing)技术,共获得47,121个分子标记,经过杂合率≤0.2和最小等位基因频率≥0.05的数据过滤后,得到33,788个标记用于后续分析。通过表型鉴定,共获得4大类15种农艺性状(适应性性状、产量相关性状、叶片相关性状、穗部相关性状)的表型。本研究通过GWAS分析,共定位到43个控制小麦株高、穗长、开花期等的QTL,其中包括23个一因多效QTL;共注释到96个基因,其中包括生长期、开花期、盐性等相关基因;12个是本研究首次发现的QTL。EigenGWAS结果一共定位到100个受选择基因组区域,共注释到75个基因,其中与千粒重和生物量相关的3个QTL同时也受到选择,表明小麦千粒重和生物量与选择适应性密切相关,该研究发掘出的新位点为揭示合成小麦基因组渗入在小麦育种中的重要性、为小麦全基因组辅助育种提供了重要信息。

关键词：合成小麦全基因组关联分析(GWAS) 特征值全基因组关联分析(EigenGWAS) 数量性状基因座(QTL) 受选择位点

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分子标记在品质和抗病性育种中的应用

分子标记在品质和抗病性育种中的应用

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2018全国植物生物学大会

作者：何中虎夏先春陈新民张勇闫俊 awais rasheed 杨恩年中国农业科学院作物科学研究所 CIMMYT中国办事处四川省农业科学院作物研究所中国农业科学院棉花研究所

针对白粉病、条锈病和叶锈病三大病害,对兼抗型成株抗性的鉴定方法、QTL定位、育种方法建立与材料创制进行了系统研究,建了立兼抗三种病害的成株抗性育种新方法。揭示了平原50等持久抗性品种的遗传机制,聚合3-5个效应较大的QTL即可在甘... 详细信息

针对白粉病、条锈病和叶锈病三大病害,对兼抗型成株抗性的鉴定方法、QTL定位、育种方法建立与材料创制进行了系统研究,建了立兼抗三种病害的成株抗性育种新方法。揭示了平原50等持久抗性品种的遗传机制,聚合3-5个效应较大的QTL即可在甘肃和四川等锈病重发区实现持久抗性。在QTL定位的基础上,在北京用鲁麦21和百农64杂交,在四川用引进的含成株抗性基因的CIMMYT种质与四川品种进行杂交并回交,结合分子标记检测,育成农艺性状优良的兼抗型成株抗性育种新材料100多份。四川农科院育成的兼抗型品种川麦82于2017年通过四川省品种审定,我们育成的兼抗型品种中麦255和中麦578,表现高产优质兼抗,将于2018年底通过河南省品种审定。

关键词：普通小麦成株抗性基因定位育种

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小麦NAS基因家族分析及锌铁相关基因鉴定

小麦NAS基因家族分析及锌铁相关基因鉴定

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第二十届中国作物学会学术年会

作者：孙梦静刘洋洋郝晨阳仝靖洋卞英杰张业伦郑建敏蒲宗君 awais rasheed 夏先春郭自峰张学勇何中虎郝元峰中国农业科学院作物科学研究所/国家小麦改良中心/国家农作物基因资源与基因改良重大科学工程中国科学院植物研究所/植物分子生理重点实验室河北省农林科学院粮油作物研究所/河北省作物遗传育种实验室四川省农业科学院作物研究所/农业农村部西南地区小麦生物学与遗传育种重点实验室

【研究背景】尼克胺（NA）是一种非蛋白质氨基酸,为麦根酸类铁载体（MAs）的生物合成前体,对Zn、Fe和Fe等金属阳离子的长距离和短距离运输均起着重要作用。尼克胺合酶（NAS）基因是编码催化S-腺苷甲硫氨酸（SAM）合成NA的关键酶,其对籽... 详细信息

【研究背景】尼克胺（NA）是一种非蛋白质氨基酸,为麦根酸类铁载体（MAs）的生物合成前体,对Zn、Fe和Fe等金属阳离子的长距离和短距离运输均起着重要作用。尼克胺合酶（NAS）基因是编码催化S-腺苷甲硫氨酸（SAM）合成NA的关键酶,其对籽粒锌铁含量的影响在水稻和玉米上有较多报道。本研究对小麦NAS基因家族进行基因组水平分析,利用候选基因关联分析鉴定与籽粒锌铁含量相关的基因,进而探究NAS基因在调控小麦锌铁稳态中的作用。【材料与方法】利用中国春参考基因组（RefSeq v1.1）,在全基因组水平上检索NAS基因家族成员,并对其染色体定位、基因结构、进化关系及表达模式进行分析。根据145份重测序小麦品种的NAS基因SNP变异及5个环境的籽粒锌铁表型数据,采用一般线性模型进行关联分析,显著性阈值–log10（P）设为3.0。【结果与分析】鉴定到35个NAS基因,其中12,10和10个基因分别分布在A、B和D亚基因组,另外3个位于Un染色体;所有NAS基因均只有一个外显子,根据系统发育树可分为A和B两个亚家族。转录组数据显示,NAS基因主要在根系和叶片中表达,其中A亚家族的基因普遍在根系高表达,在其他组织不表达或表达量很低,B亚家族的基因相反,在茎秆和叶片中的表达量较高,在根系等其他组织的表达量很低,说明A亚家族基因可能主要负责MAs生物合成和金属离子的短距离运输,而B亚家族基因主要参与维管组织金属离子的装载。对35个NAS基因在145份小麦重测序品种的自然变异进行分析,发现24个NAS基因存在自然变异,去除缺失率大于20%和最小等位基因小于5%的SNP,最终筛选到307个有效SNP用于锌铁候选基因关联分析;发现3个基因在两个及以上环境与籽粒锌含量相关,7个基因在三个及以上环境与籽粒铁含量相关,其中TaNAS4-5A.1（TraesCS5A02G552000）在所有五个环境同时与籽粒锌和铁含量相关;该基因全长1002bp,编码333个氨基酸,属于A亚家族,有两种单倍型。进一步利用Ta NAS4-5A.1编码区第467位G到A的SNP（Gly156Asp）开发出高通量的KASP标记,并在343份小麦品种或品系的自然群体中验证了其遗传效应。【结论】在小麦中检测出35个NAS基因,通过关联分析发现Ta NAS4-5A.1为重要的锌铁候选基因,开发的分子标记可用于生物强化育种。

关键词：小麦 NAS 锌铁候选基因关联分析

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Mobilizing Crop Biodiversity

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Molecular Plant 2020年第10期13卷 1341-1344页

作者： Susan McCouch Zahra Katy Navabi Michael Abberton Noelle L.Anglin Rosa Lia Barbieri Michael Baum Kirstin Bett Helen Booker Gerald L.Brown Glenn J.Bryan Luigi Cattivelli David Charest Kellye Eversole Marcelo Freitas Kioumars Ghamkhar Dario Grattipaglia Robert Henry Maria Cleria Valadares Inglis Tofazzal Islam Zakaria Kehel Paul J.Kersey Graham J.King Stephen Kresovich Emily Marden Sean Mayes Marie Noelle Ndjiondjiop Henry T.Nguyen Samuel Rezende Paiva Roberto Papa Peter W.B.Phillips awais rasheed Christopher Richards Mathieu Flouard Maria Jose Amstalden Sampaio Uwe Scholz Paul D.Shaw Brad Sherman S.Evan Staton Nils Stein Jan Svensson Mark Tester Jose Francisco Montenegro Valls Rajeev Varshney Stephen Visscher Eric von Wettberg Robbie Waugh Peter Wenzl Loren H.Rieseberg Plant Breeding and Genetics School of Integrated Plant SciencesCornell UniversityIthacaNY14853USA DivSeek Global Institute for Food Security110 Gymnasium PlaceUniversity of SaskatchewanSaskatoonSKS7N 0W9Canada lnternational Institute of Tropical Agriculture(IITA) PMB 5320Oyo RdIbadanNigeria International Potato Center(CIP)1895 Avenida La Molina Lima Peru 12Lima 15023Peru Embrapa Genetic Resources and Biotechnology Parque Estacjao BiologicaFinal Av W5 NorteCaixa Postal 0237270770-917-Brasilia DFBrazil International Center for Agricultural Research in the Dry Areas(I CAR DA) Station Exp.INRA-Quich.Rue Hafiane Cherkaoui.Agdal.Rabat-Instituts10111RabatMorocco Department of Plant Sciences University of Saskatchewan51 Campus Dr.SaskatoonSK S7N 5A8Canada Department of Plant Agriculture University of GuelphRm 316Crop Science Bldg50 Stone Rd EGuelphON N1G 2W1Canada Genome Prairie 111 Research DriveSuite 101SaskatoonSKS7N 3R2Canada The James Hutt on Institute Errol RoadInvergowrieDundeeDD25DAUK CREA Research Centre for Genomics and Bioinformaticsvia San Protaso 302Fiorenzuola d'Arda29017Italy Genome British Columbia 400-575 West 8th AvenueVancouverBCV5Z 0C4Canada International Wheat Genome Sequencing Consortium 2841 NE Marywood CtLee's SummitMO.64086USA Forage Science Grasslands Research CentreAgResearchPalmerston North4410New Zealand Queensla nd Alliance for Agriculture and Food Inn ovation University of QueenslandBrisbaneQLD 4072Australia Institute of Biotechnology and Genetic Engineering(IBGE) Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur 1706Bangladesh Southern Cross University PO Box 157LismoreNSW 2480Australia Royal Botanic Gardens KewRichmondSurreyTW93AEUK Feed the Future Innovation Lab for Crop Improvement 431 Weill HallCornell UniversityIthacaNY14853USA Department of Botany and Biodiversity Research Centre University of British ColumbiaVancouverBC V6R 2A5Canada Crops For the Future(UK)CIC 76-80 Baddow Road ChelmsfordEs

Over the past 70 years,the world has witnessed extraordinary growth in crop productivity,enabled by a suite of technological advances,including higher yielding crop varieties,improved farm management,synthetic agrochemicals,and agricultural *** this"Green Revolution"intensified crop production,and is credited with reducing famine and malnutrition,its benefits were accompanied by several undesirable collateral effects(Pingali,2012).These include a narrowing of agricultural biodiversity,stemming from increased monoculture and greater reliance on a smaller number of crops and crop varieties for the majority of our *** reduction in diversity has created vulnerabilities to pest and disease epidemics,climate variation,and ultimately to human health(Harlan,1972).

关键词： intensified crops agricultural

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