Smart breeding approaches in post-genomics era for developing climate-resilient food crops

Improving the crop traits is highly required for the development of superior crop varieties to deal with climate change and the associated abiotic and biotic stress challenges. Climate change-driven global warming can trigger higher insect pest pressures and plant diseases thus affecting crop production sternly. The traits controlling genes for stress or disease tolerance are economically imperative in crop plants. In this scenario, the extensive exploration of available wild, resistant or susceptible germplasms and unraveling the genetic diversity remains vital for breeding programs. The dawn of next-generation sequencing technologies and omics approaches has accelerated plant breeding by providing the genome sequences and transcriptomes of several plants. The availability of decoded plant genomes offers an opportunity at a glance to identify candidate genes, quantitative trait loci (QTLs), molecular markers, and genome-wide association studies that can potentially aid in high throughput marker-assisted breeding. In recent years genomics is coupled with marker-assisted breeding to unravel the mechanisms to harness better better crop yield and quality. In this review, we discuss the aspects of marker-assisted breeding and recent perspectives of breeding approaches in the era of genomics, bioinformatics, high-tech phonemics, genome editing, and new plant breeding technologies for crop improvement. In nutshell, the smart breeding toolkit in the post-genomics era can steadily help in developing climate-smart future food crops.

[1]  N. Tuteja,et al.  Genome editing (CRISPR-Cas)-mediated virus resistance in potato (Solanum tuberosum L.) , 2022, Molecular Biology Reports.

[2]  Shizhong Xu,et al.  Graph pangenome captures missing heritability and empowers tomato breeding , 2022, Nature.

[3]  K. Nazari,et al.  QTL Mapping of Adult Plant Resistance to Stripe Rust in a Doubled Haploid Wheat Population , 2022, Frontiers in Genetics.

[4]  M. Kumar,et al.  Germplasm, Breeding, and Genomics in Potato Improvement of Biotic and Abiotic Stresses Tolerance , 2022, Frontiers in Plant Science.

[5]  M. Kumar,et al.  CRISPR/Cas Genome Editing in Potato: Current Status and Future Perspectives , 2022, Frontiers in Genetics.

[6]  S. Khan,et al.  Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed , 2021, Biomolecules.

[7]  M. Cáccamo,et al.  Reap the crop wild relatives for breeding future crops. , 2021, Trends in biotechnology.

[8]  R. Varshney,et al.  Designing Future Crops: Genomics-Assisted Breeding Comes of Age. , 2021, Trends in plant science.

[9]  R. Varshney,et al.  Can omics deliver temperature resilient ready-to-grow crops? , 2021, Critical reviews in biotechnology.

[10]  Gurdeep Singh Malhi,et al.  Impact of Climate Change on Agriculture and Its Mitigation Strategies: A Review , 2021, Sustainability.

[11]  M. Bhatta,et al.  Need for speed: manipulating plant growth to accelerate breeding cycles. , 2021, Current opinion in plant biology.

[12]  Z. Nikoloski,et al.  Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. , 2020, Journal of plant physiology.

[13]  R. Singh,et al.  Prospects of Improving Nitrogen Use Efficiency in Potato: Lessons From Transgenics to Genome Editing Strategies in Plants , 2020, Frontiers in Plant Science.

[14]  Atul K. Jain,et al.  Recent global decline of CO2 fertilization effects on vegetation photosynthesis , 2020, Science.

[15]  G. Kootstra,et al.  Machine learning in plant science and plant breeding , 2020, iScience.

[16]  Jianbing Yan,et al.  Application of deep learning in genomics , 2020, Science China Life Sciences.

[17]  H. Dempewolf,et al.  The Potential of Payment for Ecosystem Services for Crop Wild Relative Conservation , 2020, Plants.

[18]  M. Gumma,et al.  Harnessing wild relatives of pearl millet for germplasm enhancement: Challenges and opportunities , 2020, Crop Science.

[19]  Raja Purushothaman,et al.  Deep learning based assessment of disease severity for early blight in tomato crop , 2020, Multimedia Tools and Applications.

[20]  G. Moghe,et al.  Machine learning: A powerful tool for gene function prediction in plants , 2020, Applications in plant sciences.

[21]  K. Glenn,et al.  The role of conventional plant breeding in ensuring safe levels of naturally occurring toxins in food crops , 2020 .

[22]  T. Widiez,et al.  Puzzling out plant reproduction by haploid induction for innovations in plant breeding , 2020, Nature Plants.

[23]  R. Singh,et al.  Genome-wide identification and characterization of microRNAs by small RNA sequencing for low nitrogen stress in potato , 2020, PloS one.

[24]  I. Żur,et al.  Candidate Genes for Freezing and Drought Tolerance Selected on the Basis of Proteome Analysis in Doubled Haploid Lines of Barley , 2020, International journal of molecular sciences.

[25]  R. Singh,et al.  Transcriptome analysis of potato shoots, roots and stolons under nitrogen stress , 2020, Scientific Reports.

[26]  E. Buckler,et al.  Deep learning for plant genomics and crop improvement. , 2020, Current opinion in plant biology.

[27]  P. Bayer,et al.  Plant pangenomics: approaches, applications and advancements. , 2020, Current opinion in plant biology.

[28]  D. Carputo,et al.  Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding , 2019, Plants.

[29]  F. Saleem,et al.  Modern Trends in Plant Genome Editing: An Inclusive Review of the CRISPR/Cas9 Toolbox , 2019, International journal of molecular sciences.

[30]  M. Rafii,et al.  Drought Resistance in Rice from Conventional to Molecular Breeding: A Review , 2019, International journal of molecular sciences.

[31]  Xin Zhang,et al.  A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images , 2019, Remote. Sens..

[32]  M. Tester,et al.  Breeding crops to feed 10 billion , 2019, Nature Biotechnology.

[33]  C. Gersbach,et al.  The next generation of CRISPR–Cas technologies and applications , 2019, Nature Reviews Molecular Cell Biology.

[34]  R. H. Mumm,et al.  African Orphan Crops Consortium (AOCC): status of developing genomic resources for African orphan crops , 2019, Planta.

[35]  D. Hunter,et al.  The potential of neglected and underutilized species for improving diets and nutrition , 2019, Planta.

[36]  Jong-Wook Kim,et al.  A Comparative Study of Deep CNN in Forecasting and Classifying the Macronutrient Deficiencies on Development of Tomato Plant , 2019, Applied Sciences.

[37]  S. Jackson,et al.  Machine learning and complex biological data , 2019, Genome Biology.

[38]  G. Fitzgerald,et al.  Grain mineral quality of dryland legumes as affected by elevated CO2 and drought: a FACE study on lentil (Lens culinaris) and faba bean (Vicia faba) , 2019, Crop and Pasture Science.

[39]  P. Kersey,et al.  Plant genome sequences: past, present, future. , 2019, Current opinion in plant biology.

[40]  Peter McCloskey,et al.  A Mobile-Based Deep Learning Model for Cassava Disease Diagnosis , 2019, Front. Plant Sci..

[41]  M. K. Mejía-Guerra,et al.  A k-mer grammar analysis to uncover maize regulatory architecture , 2019, BMC Plant Biology.

[42]  Gerrit Polder,et al.  Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images , 2019, Front. Plant Sci..

[43]  R. Jun,et al.  Development and Application of CRISPR/Cas System in Rice , 2019, Rice Science.

[44]  N. N. Das Relevance of Poly-Omics in System Biology Studies of Industrial Crops , 2019, OMICS-Based Approaches in Plant Biotechnology.

[45]  L. Hickey,et al.  Q&A: modern crop breeding for future food security , 2019, BMC Biology.

[46]  R. Henry,et al.  Exploring and Exploiting Pan-genomics for Crop Improvement. , 2019, Molecular plant.

[47]  N. Provart,et al.  An updated gene atlas for maize reveals organ‐specific and stress‐induced genes , 2019, The Plant journal : for cell and molecular biology.

[48]  Jacob D. Washburn,et al.  Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence , 2019, Proceedings of the National Academy of Sciences.

[49]  M. Gawłowska,et al.  Production of wheat-doubled haploids resistant to eyespot supported by marker-assisted selection , 2019, Electronic Journal of Biotechnology.

[50]  S. Rhee,et al.  QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice , 2018, G3: Genes, Genomes, Genetics.

[51]  R. Snowdon,et al.  Connecting genome structural variation with complex traits in crop plants , 2018, Theoretical and Applied Genetics.

[52]  A. Crane-Droesch Machine learning methods for crop yield prediction and climate change impact assessment in agriculture , 2018, Environmental Research Letters.

[53]  D. Edwards,et al.  Bottlenecks for genome-edited crops on the road from lab to farm , 2018, Genome Biology.

[54]  T. Iizumi,et al.  Crop production losses associated with anthropogenic climate change for 1981–2010 compared with preindustrial levels , 2018, International Journal of Climatology.

[55]  S. Lam,et al.  Effects of Elevated CO2 on Nutritional Quality of Vegetables: A Review , 2018, Front. Plant Sci..

[56]  K. Dassanayake,et al.  Interaction of Elevated Carbon Dioxide and Temperature on Strawberry (Fragaria × ananassa) Growth and Fruit Yield , 2018 .

[57]  Patrizia Busato,et al.  Machine Learning in Agriculture: A Review , 2018, Sensors.

[58]  Huwaida S. Rabie,et al.  Mapping of novel salt tolerance QTL in an Excalibur × Kukri doubled haploid wheat population , 2018, Theoretical and Applied Genetics.

[59]  Marcela A. Mendoza-Suárez,et al.  Speed breeding in growth chambers and glasshouses for crop breeding and model plant research , 2018, bioRxiv.

[60]  M. Otim,et al.  Grain-yield stability among tropical maize hybrids derived from doubled-haploid inbred lines under random drought stress and optimum moisture conditions , 2018, Crop and Pasture Science.

[61]  P. Larmande,et al.  Evaluating Named-Entity Recognition approaches in plant molecular biology , 2018, bioRxiv.

[62]  C. K. Chan,et al.  Variation in abundance of predicted resistance genes in the Brassica oleracea pangenome , 2018, Plant biotechnology journal.

[63]  M. Zaman-Allah,et al.  Translating High-Throughput Phenotyping into Genetic Gain , 2018, Trends in plant science.

[64]  Kenneth L. McNally,et al.  Genomic variation in 3,010 diverse accessions of Asian cultivated rice , 2018, Nature.

[65]  Baskar Ganapathysubramanian,et al.  An explainable deep machine vision framework for plant stress phenotyping , 2018, Proceedings of the National Academy of Sciences.

[66]  Zhi Wei,et al.  DeepPolyA: A Convolutional Neural Network Approach for Polyadenylation Site Prediction , 2018, IEEE Access.

[67]  W. Heyer,et al.  Homologous recombination and the repair of DNA double-strand breaks , 2018, The Journal of Biological Chemistry.

[68]  A. Rasheed,et al.  Fast-Forwarding Genetic Gain. , 2018, Trends in plant science.

[69]  Qun Xu,et al.  Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice , 2018, Nature Genetics.

[70]  C. K. Chan,et al.  Homoeologous exchange is a major cause of gene presence/absence variation in the amphidiploid Brassica napus , 2018, Plant biotechnology journal.

[71]  E. Waltz With a free pass, CRISPR-edited plants reach market in record time , 2018, Nature Biotechnology.

[72]  Wendy S. Schackwitz,et al.  Extensive gene content variation in the Brachypodium distachyon pan-genome correlates with population structure , 2017, Nature Communications.

[73]  Yang Lu,et al.  Identification of rice diseases using deep convolutional neural networks , 2017, Neurocomputing.

[74]  Armin Scheben,et al.  Towards CRISPR/Cas crops - bringing together genomics and genome editing. , 2017, The New phytologist.

[75]  G. de los Campos,et al.  Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. , 2017, Trends in plant science.

[76]  J. Yosinski,et al.  Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning. , 2017, Phytopathology.

[77]  G. S. Miglani Genome editing in crop improvement: Present scenario and future prospects , 2017 .

[78]  C. Gardner,et al.  Emerging Avenues for Utilization of Exotic Germplasm. , 2017, Trends in plant science.

[79]  O. Mitrofanova,et al.  New genetic resources in wheat breeding for increased grain protein content , 2017, Russian Journal of Genetics: Applied Research.

[80]  C. K. Chan,et al.  The pangenome of hexaploid bread wheat , 2017, The Plant journal : for cell and molecular biology.

[81]  Bing Yang,et al.  New variants of CRISPR RNA‐guided genome editing enzymes , 2017, Plant biotechnology journal.

[82]  Justin E. Anderson,et al.  Past and Future Use of Wild Relatives in Crop Breeding , 2017 .

[83]  Xiao Zhang,et al.  Genome-wide comparative analysis of NBS-encoding genes in four Gossypium species , 2017, BMC Genomics.

[84]  Jiawei Zhao,et al.  A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa) , 2017, The Plant journal : for cell and molecular biology.

[85]  Armin Scheben,et al.  Genome editors take on crops , 2017, Science.

[86]  C. K. Chan,et al.  The pangenome of an agronomically important crop plant Brassica oleracea , 2016, Nature Communications.

[87]  S. Brady,et al.  Plant developmental responses to climate change. , 2016, Developmental biology.

[88]  V. Ravindra babu,et al.  Marker assisted introgression of blast (Pi-2 and Pi-54) genes in to the genetic background of elite, bacterial blight resistant indica rice variety, Improved Samba Mahsuri , 2016, Euphytica.

[89]  D. Edwards,et al.  Advances in genomics for adapting crops to climate change , 2016 .

[90]  R. Qin,et al.  Rapid improvement of grain weight via highly efficient CRISPR/Cas9-mediated multiplex genome editing in rice. , 2016, Journal of genetics and genomics = Yi chuan xue bao.

[91]  Hongyu Wang,et al.  ARGOS8 variants generated by CRISPR‐Cas9 improve maize grain yield under field drought stress conditions , 2016, Plant biotechnology journal.

[92]  K. Schmid,et al.  Crossing Methods and Cultivation Conditions for Rapid Production of Segregating Populations in Three Grain Amaranth Species , 2016, Front. Plant Sci..

[93]  Emily Waltz,et al.  CRISPR-edited crops free to enter market, skip regulation , 2016, Nature Biotechnology.

[94]  Emily Waltz,et al.  Gene-edited CRISPR mushroom escapes US regulation , 2016, Nature.

[95]  J. Batley,et al.  Towards plant pangenomics. , 2016, Plant biotechnology journal.

[96]  Ashutosh Kumar Singh,et al.  Machine Learning for High-Throughput Stress Phenotyping in Plants. , 2016, Trends in plant science.

[97]  Jiming Jiang,et al.  Genome Reduction Uncovers a Large Dispensable Genome and Adaptive Role for Copy Number Variation in Asexually Propagated Solanum tuberosum[OPEN] , 2016, Plant Cell.

[98]  A. Paterson,et al.  Global agricultural intensification during climate change: a role for genomics , 2015, Plant biotechnology journal.

[99]  N. Riaz,et al.  Identification of stripe rust resistant genes in resistant synthetic hexaploid wheat accessions using linked markers , 2015, Plant Genetic Resources.

[100]  N. Kandemir,et al.  Apomixis: new horizons in plant breeding , 2015 .

[101]  Rajeev K. Varshney,et al.  Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects , 2015, Front. Plant Sci..

[102]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[103]  Yong Liang,et al.  Genome-wide analysis of the gene families of resistance gene analogues in cotton and their response to Verticillium wilt , 2015, BMC Plant Biology.

[104]  William Stafford Noble,et al.  Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.

[105]  Peter J. Bradbury,et al.  High-resolution genetic mapping of maize pan-genome sequence anchors , 2015, Nature Communications.

[106]  U. D. Singh,et al.  Development and evaluation of near-isogenic lines for major blast resistance gene(s) in Basmati rice , 2015, Theoretical and Applied Genetics.

[107]  G. Moore Strategic pre-breeding for wheat improvement , 2015, Nature Plants.

[108]  Jing Zhao,et al.  A maize wall-associated kinase confers quantitative resistance to head smut , 2014, Nature Genetics.

[109]  Jan-Peter Nap,et al.  Prioritization of candidate genes in QTL regions based on associations between traits and biological processes , 2014, BMC Plant Biology.

[110]  Doreen Ware,et al.  Whole genome de novo assemblies of three divergent strains of rice, Oryza sativa, document novel gene space of aus and indica , 2014, Genome Biology.

[111]  Emily J. Warschefsky,et al.  Back to the wilds: tapping evolutionary adaptations for resilient crops through systematic hybridization with crop wild relatives. , 2014, American journal of botany.

[112]  Ruiqiang Li,et al.  De novo assembly of soybean wild relatives for pan-genome analysis of diversity and agronomic traits , 2014, Nature Biotechnology.

[113]  Corinne Da Silva,et al.  Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome , 2014, Science.

[114]  J. Batley,et al.  A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome , 2014, Science.

[115]  Y. Saranga,et al.  Plant domestication versus crop evolution: a conceptual framework for cereals and grain legumes. , 2014, Trends in plant science.

[116]  R. Terauchi,et al.  Harvesting the Promising Fruits of Genomics: Applying Genome Sequencing Technologies to Crop Breeding , 2014, PLoS biology.

[117]  R. Visser,et al.  Beyond genomic variation - comparison and functional annotation of three Brassica rapa genomes: a turnip, a rapid cycling and a Chinese cabbage , 2014, BMC Genomics.

[118]  Xiangfeng Wang,et al.  Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis[W] , 2014, Plant Cell.

[119]  Jingyin Yu,et al.  Genome-wide comparative analysis of NBS-encoding genes between Brassica species and Arabidopsis thaliana , 2014, BMC Genomics.

[120]  M. A. Pedraza,et al.  Insights into the Maize Pan-Genome and Pan-Transcriptome[W][OPEN] , 2014, Plant Cell.

[121]  M. Muthamilarasan,et al.  Development of 5123 Intron-Length Polymorphic Markers for Large-Scale Genotyping Applications in Foxtail Millet , 2013, DNA research : an international journal for rapid publication of reports on genes and genomes.

[122]  K. Shinozaki,et al.  Genome-Wide Analysis of ZmDREB Genes and Their Association with Natural Variation in Drought Tolerance at Seedling Stage of Zea mays L , 2013, PLoS genetics.

[123]  J. Foley,et al.  Yield Trends Are Insufficient to Double Global Crop Production by 2050 , 2013, PloS one.

[124]  W. Cowling Sustainable plant breeding , 2013 .

[125]  Le Cong,et al.  Multiplex Genome Engineering Using CRISPR/Cas Systems , 2013, Science.

[126]  Mihaela M. Martis,et al.  A physical, genetic and functional sequence assembly of the barley genome. , 2022 .

[127]  P. Pesaresi,et al.  The protein kinase Pstol1 from traditional rice confers tolerance of phosphorus deficiency , 2012, Nature.

[128]  J. Doudna,et al.  A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity , 2012, Science.

[129]  B. Segerman The genetic integrity of bacterial species: the core genome and the accessory genome, two different stories , 2012, Front. Cell. Inf. Microbio..

[130]  E. T. Lammerts van Bueren,et al.  The need to breed crop varieties suitable for organic farming, using wheat, tomato and broccoli as examples: A review , 2011 .

[131]  David M. A. Martin,et al.  Genome sequence and analysis of the tuber crop potato , 2011, Nature.

[132]  A. Crabb The Hybrid Corn Makers: Prophets Of Plenty , 2011 .

[133]  P. Gupta,et al.  Marker‐Assisted Selection as a Component of Conventional Plant Breeding , 2010 .

[134]  Yunbi Xu,et al.  Molecular Plant Breeding , 2010 .

[135]  T. Sakurai,et al.  Genome sequence of the palaeopolyploid soybean , 2010, Nature.

[136]  Patrick S. Schnable,et al.  Maize Inbreds Exhibit High Levels of Copy Number Variation (CNV) and Presence/Absence Variation (PAV) in Genome Content , 2009, PLoS genetics.

[137]  Kenneth L. McNally,et al.  Genomewide SNP variation reveals relationships among landraces and modern varieties of rice , 2009, Proceedings of the National Academy of Sciences.

[138]  Dorian Q. Fuller,et al.  The nature of selection during plant domestication , 2009, Nature.

[139]  Mihaela M. Martis,et al.  The Sorghum bicolor genome and the diversification of grasses , 2009, Nature.

[140]  R. Twyman,et al.  Precision breeding for novel starch variants in potato. , 2008, Plant biotechnology journal.

[141]  M. R. Vishnupriya,et al.  Marker assisted introgression of bacterial blight resistance in Samba Mahsuri, an elite indica rice variety , 2008, Euphytica.

[142]  A. Fehér,et al.  The effect of drought and heat stress on reproductive processes in cereals. , 2007, Plant, cell & environment.

[143]  W. Pfeiffer,et al.  Simulation Modeling in Plant Breeding: Principles and Applications , 2007 .

[144]  Robbie Waugh,et al.  Applying plant genomics to crop improvement , 2007, Genome Biology.

[145]  D. Conrad,et al.  Global variation in copy number in the human genome , 2006, Nature.

[146]  R. Varshney,et al.  Genomics-assisted breeding for crop improvement. , 2005, Trends in plant science.

[147]  Jaideep P. Sundaram,et al.  Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pan-genome". , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[148]  T. Mitchell-Olds,et al.  A Multilocus Sequence Survey in Arabidopsis thaliana Reveals a Genome-Wide Departure From a Neutral Model of DNA Sequence Polymorphism , 2005, Genetics.

[149]  Bingru Huang,et al.  Physiological Recovery of Kentucky Bluegrass from Simultaneous Drought and Heat Stress , 2004 .

[150]  W. G. Hill,et al.  D. S. Falconer and Introduction to quantitative genetics. , 2004, Genetics.

[151]  G. Hewitt,et al.  Nuclear DNA analyses in genetic studies of populations: practice, problems and prospects , 2003, Molecular ecology.

[152]  D. Zamir Improving plant breeding with exotic genetic libraries , 2001, Nature Reviews Genetics.

[153]  M. Wilkinson Broadening the Genetic Base of Crop Production , 2001, Heredity.

[154]  The Arabidopsis Genome Initiative Analysis of the genome sequence of the flowering plant Arabidopsis thaliana , 2000, Nature.

[155]  J. Ali,et al.  Doubled Haploids in Rice Improvement: Approaches, Applications, and Future Prospects , 2021, Rice Improvement.

[156]  Kareem A. Mosa,et al.  Omics and System Biology Approaches in Plant Stress Research , 2017 .

[157]  J. Batley,et al.  Speed breeding: a powerful tool to accelerate crop research and breeding , 2017, bioRxiv.

[158]  Hikmet Budak,et al.  CRISPR/Cas9 genome editing in wheat , 2017, Functional & Integrative Genomics.

[159]  Kareem A. Mosa,et al.  Plant Stress Tolerance , 2017, SpringerBriefs in Systems Biology.

[160]  José Crossa,et al.  Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). , 2016, Plant science : an international journal of experimental plant biology.

[161]  C. K. Chan,et al.  Identification and characterization of more than 4 million intervarietal SNPs across the group 7 chromosomes of bread wheat. , 2015, Plant biotechnology journal.

[162]  Nigel Maxted,et al.  Predictive characterization of crop wild relatives and landraces: technical guidelines version 1 , 2014 .

[163]  J. Cairns,et al.  Genomic Tools and Strategies for Breeding Climate Resilient Cereals , 2013 .

[164]  W. Martin,et al.  Genetic Diversity, Evolution and Domestication of Wheat and Barley in the Fertile Crescent , 2010 .

[165]  B. Gill,et al.  Development of a PCR Assay and Marker-Assisted Transfer of Leaf Rust and Stripe Rust Resistance Genes Lr57 and Yr40 into Hard Red Winter Wheats , 2009 .

[166]  Anil Kumar Singh,et al.  Adaptation and quality traits of a germplasm-derived commercial seed parent of pearl millet , 2008 .

[167]  J. Bennetzen,et al.  Transposable element contributions to plant gene and genome evolution , 2004, Plant Molecular Biology.

[168]  D. Zilberman,et al.  Is marker-assisted selection cost-effective compared with conventional plant breeding methods? The case of quality protein Maize. , 2002 .

[169]  J. Welsh,et al.  Fingerprinting genomes using PCR with arbitrary primers. , 1990, Nucleic acids research.

[170]  L. Pauling,et al.  Evolutionary Divergence and Convergence in Proteins , 1965 .

[171]  B. Mcclintock,et al.  Controlling elements and the gene. , 1956, Cold Spring Harbor symposia on quantitative biology.

[172]  L. Stadler,et al.  Genetic Effects of X-Rays in Maize. , 1928, Proceedings of the National Academy of Sciences of the United States of America.