High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth1[OPEN]

Combining high-throughput phenotyping and large-scale QTL mapping dissects the dynamic genetic architecture of maize development by using a RIL population. With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize (Zea mays) recombinant inbred line population (n = 167) across 16 developmental stages using the automatic phenotyping platform. Quantitative trait locus (QTL) mapping with a high-density genetic linkage map, including 2,496 recombinant bins, was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots. Biomass accumulation and final yield were predicted using a combination of dissected traits in the early growth stage. These results reveal the dynamic genetic architecture of maize plant growth and enhance ideotype-based maize breeding and prediction.

[1]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[2]  Z. Zeng.  Precision mapping of quantitative trait loci. , 1994, Genetics.

[3]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[4]  Jianbing Yan,et al.  Dynamic analysis of QTL for plant height at different developmental stages in maize (Zea mays L.) , 2003 .

[5]  S. Chander,et al.  Using molecular markers to identify two major loci controlling carotenoid contents in maize grain , 2007, Theoretical and Applied Genetics.

[6]  D. Mackill,et al.  Marker-assisted selection: an approach for precision plant breeding in the twenty-first century , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[7]  S. Chander,et al.  Genetic dissection of tocopherol content and composition in maize grain using quantitative trait loci analysis and the candidate gene approach , 2008, Molecular Breeding.

[8]  Peter J. Bradbury,et al.  The Genetic Architecture of Maize Flowering Time , 2009, Science.

[9]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Broome,et al.  Literature cited , 1924, A Guide to the Carnivores of Central America.

[11]  Y. Barrière,et al.  QTL for Yield, Earliness, and Cell Wall Quality Traits in Topcross Experiments of the F838 × F286 Early Maize RIL Progeny , 2010 .

[12]  Falk Schreiber,et al.  HTPheno: An image analysis pipeline for high-throughput plant phenotyping , 2011, BMC Bioinformatics.

[13]  P. Langridge,et al.  Breeding Technologies to Increase Crop Production in a Changing World , 2010, Science.

[14]  W. Broekaert,et al.  TraitMill: a Discovery Engine for Identifying Yield-enhancement Genes in Cereals , 2010 .

[15]  Xiaohong Yang,et al.  Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize , 2011, Molecular Breeding.

[16]  S. Omholt,et al.  Phenomics: the next challenge , 2010, Nature Reviews Genetics.

[17]  Peter J. Bradbury,et al.  Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population , 2011, Nature Genetics.

[18]  Jianbing Yan,et al.  Association Mapping for Enhancing Maize (Zea mays L.) Genetic Improvement , 2011 .

[19]  O. Martin,et al.  A Large Maize (Zea mays L.) SNP Genotyping Array: Development and Germplasm Genotyping, and Genetic Mapping to Compare with the B73 Reference Genome , 2011, PloS one.

[20]  Peter J. Bradbury,et al.  Genome-wide association study of leaf architecture in the maize nested association mapping population , 2011, Nature Genetics.

[21]  Zhao-Bang Zeng,et al.  Windows QTL Cartographer 2·5 , 2011 .

[22]  M. Tester,et al.  Phenomics--technologies to relieve the phenotyping bottleneck. , 2011, Trends in plant science.

[23]  C. Haley.  A cornucopia of maize genes , 2011, Nature Genetics.

[24]  M. Stitt,et al.  Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize , 2012, Proceedings of the National Academy of Sciences.

[25]  H. Scharr,et al.  GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. , 2012, Functional plant biology : FPB.

[26]  L. Xiong,et al.  Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. , 2013, Current opinion in plant biology.

[27]  Xiaohong Yang,et al.  Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels , 2012, Nature Genetics.

[28]  J. Reif,et al.  Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation , 2013, Scientific Reports.

[29]  Jianbing Yan,et al.  Dynamic QTL Analysis and Candidate Gene Mapping for Waterlogging Tolerance at Maize Seedling Stage , 2013, PloS one.

[30]  H. Seo,et al.  Seed size: a priority trait in cereal crops. , 2013, Physiologia plantarum.

[31]  Bin Wang,et al.  Genetic Analysis of Grain Filling Rate Using Conditional QTL Mapping in Maize , 2013, PloS one.

[32]  Jeffrey W. White,et al.  Development and evaluation of a field-based high-throughput phenotyping platform. , 2013, Functional plant biology : FPB.

[33]  Jianbing Yan,et al.  Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights , 2014, Nature Communications.

[34]  Jianru Zuo,et al.  Molecular dissection of complex agronomic traits of rice: a team effort by Chinese scientists in recent years , 2014 .

[35]  L. Xiong,et al.  Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice , 2014, Nature Communications.

[36]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[37]  Fine mapping of quantitative trait loci for seed size traits in soybean , 2014, Molecular Breeding.

[38]  C. Klukas,et al.  Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] , 2014, Plant Cell.

[39]  Christian Klukas,et al.  Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping1[C][W][OPEN] , 2014, Plant Physiology.

[40]  A. Melchinger,et al.  Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production , 2014, Theoretical and Applied Genetics.

[41]  M. Tester,et al.  High-Throughput Phenotyping to Detect Drought Tolerance QTL in Wild Barley Introgression Lines , 2014, PloS one.

[42]  A. Fernie,et al.  Genetic Determinants of the Network of Primary Metabolism and Their Relationships to Plant Performance in a Maize Recombinant Inbred Line Population[OPEN] , 2015, Plant Cell.

[43]  Astrid Junker,et al.  Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems , 2015, Front. Plant Sci..

[44]  Joshua S Yuan,et al.  Redesigning photosynthesis to sustainably meet global food and bioenergy demand , 2015, Proceedings of the National Academy of Sciences.

[45]  S. Salvi,et al.  Yield QTLome distribution correlates with gene density in maize. , 2016, Plant science : an international journal of experimental plant biology.

[46]  Shizhong Xu,et al.  Genome-wide dissection of the maize ear genetic architecture using multiple populations. , 2016, The New phytologist.

[47]  Jianbing Yan,et al.  Genome-wide recombination dynamics are associated with phenotypic variation in maize. , 2016, The New phytologist.

[48]  A. Junker,et al.  Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non‐invasive phenotyping , 2017, The Plant journal : for cell and molecular biology.

[49]  Jianbing Yan,et al.  Distant eQTLs and Non-coding Sequences Play Critical Roles in Regulating Gene Expression and Quantitative Trait Variation in Maize. , 2017, Molecular plant.