Dr. Jianming Yu

People

About

Professor

Pioneer Distinguished Chair in Maize Breeding

Jianming Yu is Professor and Pioneer Distinguished Chair in Maize Breeding in the Department of Agronomy, Iowa State University. The focus of Yu’s program is to address significant questions in quantitative genetics by combining cutting-edge genomic technologies and maize breeding. All members of this research program conduct empirical experiments in genetics and breeding and contribute to summer and winter nursery work, and many of them also carry out computer simulations or bioinformatics research to generalize their specific findings to a broad context. Yu teaches a graduate student class, AGRON 621 - Advanced Plant Breeding, each Spring semester. Guiding questions addressed by this program include:

How can we leverage design thinking, genetic design, and experimental design to plan out our research?
How can we efficiently identify genes underlying quantitative traits so that the resulting empirical findings can help answer fundamental questions?
How should we design the current plant breeding methods to make better use of genetic resources and high throughput genotyping and phenotyping technologies?

Yu's research integrates knowledge in quantitative genetics, genomics, plant breeding, molecular genetics, and statistics, and has the ultimate goal of developing and implementing new strategies and methods in complex trait dissection and crop improvement. Current research includes Genome-Wide Association Studies (GWAS) with diverse germplasm or multiple designed mapping populations (such as Nested Association Mapping, NAM; or meta-QTL analysis), Genomic Selection (GS) to efficiently integrate high throughput genotyping into various breeding processes, Gene Cloning for traits with agronomic and domestication importance, Genotype-by-Environment Interaction (GEI) and Epistasis dissection to causal polymorphic sites, Genome and Chromosome Size Evolution across taxonomic groups, and Genome-Wide Base Composition changes and underlying principles.

Yu was elected as a Fellow of the American Association for the Advancement of Science (AAAS) in 2018.

(YouTube) Integrati ng Design, Analytics, and Genomics in Crop Improvement, or (UNLMeidaHub)
(YouTube) Towards a Better Understanding of Genes, Organisms, and Environments
(YouTube) Pattern Discovery, Predictive Modeling, and Design in Plant Breeding and Genetics
(YouTube) Genomic Selection: Historical Context, Technical Details, Empirical Findings, and Perspectives
(YouTube) Quantitative Genetics in the Era of High Throughput Genotyping and Phenotyping, or (UNLMediaHub)

News Releases:

Sensing what plants sense: Integrated framework helps scientists explain biology and predict crop performance (2021)
Making sense of a universe of corn genetics (2020)
Patterns in crop data reveal new insight about plants and their environments (2020)
Sorghum study illuminates relationship between humans, crops and the environment in domestication (2019)
News and Views article from Nature Plants: When bitter is better (2019)
Research sheds light on genomic features that make plants good candidates for domestication (2019)
Make crops climate ready (2019)
Data mining brings new clarity to plant breeding (2019)
Genomic hotspots control maize leaf angle (2019)
AAAS honors seven Iowa State researchers for distinguished work (2018)
Scientists find 'patterns in the noise' that could help make more accurate crop performance predictions (2018)
Raymond and Mary Baker Agronomic Excellence Award (2018)
New strategy to accelerate plant breeding by turbocharging gene banks (2016)
News and Views article from Nature Plants, Plant breeding: Effective use of genetic diversity, Editorial from Nature Plants, Technologies to boost breeding
Iowa State University agronomist explores the genetics that allow hybrid plants to perform better than parents (2015)
Sorghum height research offers insight for wider crop improvement (2015)
Researchers find patterns in evolving genomes of thousands of species (2015)
ISU Plant Science Institute's Faculty Scholars Program award announcement (2015)
2014 University of Minnesota Emerging Leader in Plant Sciences Award (2014)
Unlocking new possibilities for plants in each new generation (2013)
Agronomist joins Iowa State University’s Corn-breeding Program (2013)
Prioritizing rather than canvassing entire plant genome may lead to improved crops (2012)
Tannins in sorghum and benefits focus of genetic research (2012)
Genes underlying the key domestication process in sorghum and other cereals (2012)
News and Views article from Nature Genetics, One gene's shattering effects
Harnessing the power of plants: University team studies sorghum genetics to fuel green energy research (2011)
Chromosomes' big picture: Similarities found in genomes across multiple species; Platypus still out of place (2011)
ASA-CSSA-SSSA Early Career Professional Award Recipient (2010)
Cornell geneticists improve methods for identifying what controls complex traits, from disease to crop yields (2006)

Contact

jmyu@iastate.edu

Lab Website

Google Scholar

(515) 294-2757

1569 Agronomy 716 Farm House Ln

Ames

50011-1051

Interdepartmental Programs:

Genetics and Genomics

Plant Biology

Teaching:

Agron 621: Advanced Plant Breeding

CV:

cv_jianmingyu_01062021.pdf

Tags:

Quantitative Genetics

plant breeding

genetics

Statistical Genetics

GWAS

Genomic Selection

Genotype-by-Environment Interaction

Phenotypic Plasticity

Genetic Architecture

corn

corn breeding

Projects

RESEARCH-PGR: A Genome-level Approach to Balancing the Vitamin Content of Maize Grain

This collaborative research project is directed at identifying a subset of the ~40,000 genes in the corn genome that work together to determine the levels of five essential and limiting dietary vitamins in kernels: vitamin E and the four B vitamins, B1 (thiamin), B2 (riboflavin), B3 (niacin) and B6 (pyridoxine). By combining approaches similar to those used in the Human Genome project, the researchers will identify alleles, special variations in these "vitamin" genes, and learn how to put them together to generate high amounts of vitamins in corn kernels. An important outcome of this research will be the knowledge by which to enhance these micronutrient levels in corn kernels such that diets in which maize is a major component provide a balanced nutritional content. Such direct translation of these findings will be the eventual incorporation and fixation of identified alleles in maize breeding programs that are favorable for the increased levels of vitamins E and B to enhance the food and feed supply chain. In addition, this research will provide guiding principles for parallel efforts in other agricultural crops and thus enable predictive breeding and metabolic engineering of more nutritious crops worldwide. Finally, integration of research with education within the project will permit training of the next generation of plant scientists with knowledge of plant genetics, breeding, genomics, biochemistry, and bioinformatics.

This project seeks to leverage the tremendous genetic and genomic tool sets developed in maize the past decade to advance and accelerate our fundamental understanding of the genes, alleles and genetic mechanisms controlling synthesis and accumulation of vitamins that are limiting in maize grain and hence result in vitamin deficiencies in maize-based diets: four B vitamins (B1, thiamine; B2, riboflavin; B3, niacin; B6, pyridoxine) and vitamin E. This project brings together a team of scientists with divergent but complementary knowledge and skills that together will allow the genes, alleles and underlying mechanisms controlling these nutritional traits to be elucidated and the knowledge deployed on a global scale. Specific objectives are to (i) perform genome-wide association studies with the maize Ames inbred line panel (n~2,000) to identify and resolve quantitative trait loci (QTL) controlling accumulation of these micronutrients; (ii) assess the role of rare alleles by constructing and analyzing segregating F2 populations derived from Ames lines that are extreme outliers for traits; (iii) determine the contribution of expression QTL and presence-absence variants (PAVs) to vitamin composition using whole transcriptome sequencing data obtained from grain 24 days after pollination in 500 inbred lines that represents the phenotypic variation of the Ames panel; and, (iv) perform genomic prediction with the Ames panel to accelerate the efficiency of breeding improved grain micronutrient composition in developing countries. The broader impacts of this project to the broader scientific community and public will be ensured through a set of coordinated activities that engage students, postdoctoral associates, scientists and the public. Data and biological resources generated in this project will be made accessible to the community. Data will be disseminated through publications, project websites and long-term repositories such as the NCBI's SRA and MaizeGDB.

Funding Organization:

National Science Foundation

Duration:

08/09/2016 to 07/31/2021

Principal Investigator(s):

Tags:

Genetic networks regulating structure and function of the maize shoot apical meristem

The shoot apical meristem (SAM) is responsible for development of all above ground organs in the plant. SAM structure and function correlates with agronomically-important adult traits in the maize plant, and is also affected by planting density and shade stresses induced by agricultural environments. The ultimate goal of this project is to increase understanding of the regulatory networks controlling SAM structure and function and the responses of these networks to environmental stresses. The specific objectives are to: 1) describe the SAM allometric space in maize and its relatives using nanoscale computer tomographic scanning to provide 3-dimensional images of the phenotypic diversity of SAM structure and identify adult plant traits correlated with SAM structure; 2) identify differentially expressed genes in SAM size/shape outliers and mutants with abnormal SAM structures and generate a co-expression network of key genes implicated during SAM structure and function; 3) perform quantitative genetic analyses to identify specific variations within genes that correlate with variations in SAM structure/function and adult plant traits, and test functions of 40 key genes using reverse genetic aaproaches; 4) analyze the shade avoidance response and its effects on SAM structure and function; and 5) investigate epigenetic changes of SAM functional domains in response to shade avoidance using novel protocols that distinguish the stem cell organizing regions from the organogenic domains in the maize SAM.

These studies will provide the framework for scientific training and the public release of original data. Undergraduates at Truman State University, a small liberal arts institution, will be trained in morphological and LM-RNAseq analyses of maize mutants. REU students and undergraduates enrolled in Plant Physiology courses at Cornell University will participate in physiological experiments. This project will generate extensive transcriptomic data and vector constructs for tissue-specific epigenetic analyses which will be available to the scientific research community. Molecular markers and phenotypic data for diverse maize lines will be supplied to Panzea (http://www.panzea.org/). Genetic mapping associations, physiological shade-avoidance response data, transcriptomic and phenotypic data will be curated at MaizeGDB (http://www.maizegdb.org/), and seed stocks for maize shoot mutants and SAM size variants will be released through the Maize Genetics Cooperation Stock Center (http://maizecoop.cropsci.uiuc.edu/).

Funding Organization:

National Science Foundation

Duration:

02/01/2013

Principal Investigator(s):

Dr. Patrick S Schnable

Tags:

Root Genetics in the Field to Understand Drought Adaptation and Carbon Sequestration

Critical Need: Plants capture atmospheric carbon dioxide (CO₂) using photosynthesis, and transfer the carbon to the soil through their roots. Soil organic matter, which is primarily composed of carbon, is a key determinant of soil's overall quality. Even though crop productivity has increased significantly over the past century, soil quality and levels of topsoil have declined during this period. Low levels of soil organic matter affect a plant's productivity, leading to increased fertilizer and water use. Automated tools and methods to accelerate the process of measuring root and soil characteristics and the creation of advanced algorithms for analyzing data can accelerate the development of field crops with deeper and more extensive root systems. Crops with these root systems could increase the amount of carbon stored in soils, leading to improved soil structure, fertilizer use efficiency, water productivity, and crop yield, as well as reduced topsoil erosion. If deployed at scale, these improved crops could passively sequester significant quantities of CO₂ from the atmosphere that otherwise cannot be economically captured.

Project Innovation + Advantages: Colorado State University (CSU) will develop a high-throughput ground-based robotic platform that will characterize a plant's root system and the surrounding soil chemistry to better understand how plants cycle carbon and nitrogen in soil. CSU's robotic platform will use a suite of sensor technologies to investigate crop genetic-environment interaction and generate data to improve models of chemical cycling of soil carbon and nitrogen in agricultural environments. The platform will collect information on root structure and depth, and deploy a novel spectroscopic technology to quantify levels of carbon and other key elements in the soil. The technology proposed by the Colorado State team aims to speed the application of genetic and genomic tools for the discovery and deployment of root traits that control plant growth and soil carbon cycling. Crops will be studied at two field sites in Colorado and Arizona with diverse advantages and challenges to crop productivity, and the data collected will be used to develop a sophisticated carbon flux model. The sensing platform will allow characterization of the root systems in the ground and lead to improved quantification of soil health. The collected data will be managed and analyzed through the CyVerse "big data" computational analytics platform, enabling public access to data connecting aboveground plant traits with belowground soil carbon accumulation.

Potential Impact: If successful, developments made under the ROOTS program will produce crops that will greatly increase carbon uptake in soil, helping to remove CO₂ from the atmosphere, decrease nitrous oxide (N₂O) emissions, and improve agricultural productivity.

Security: America's soils are a strategic asset critical to national food and energy security. Improving the quality of soil in America's cropland will enable increased and more efficient production of feedstocks for food, feed, and fuel.
Environment: Increased organic matter in soil will help reduce fertilizer use, increase water productivity, reduce emissions of nitrous oxide, and passively sequester carbon dioxide from the atmosphere.
Economy: Healthy soil is foundational to the American economy and global trade. Increasing crop productivity will make American farmers more competitive and contribute to U.S. leadership in an emerging bio-economy.

Funding Organization:

Advanced Research Projects Agency-Energy (ARPA-E)

Duration:

07/03/2017 to 07/02/2020

Principal Investigator(s):

Dr. Patrick S Schnable

Tags:

People

People:

Mahule-Elyse-Boris Alladassi

Publications

Publications:

Jianming Yu on Google Scholar

[Breeding Strategy]

Yu, X., S. Leiboff, X. Li, T. Guo, N. Ronning, X. Zhang, G.J. Muehlbauer, M.C.P. Timmermans, P.S. Schnable, M.J. Scanlon, and J. Yu*. 2020. Genomic prediction of maize micro-phenotypes provides insights for optimizing selection and mining diversity. Plant Biotechnology Journal 18:2456-2465.
Guo, T., X. Yu, X. Li, H. Zhang, C. Zhu, S. Flint-Garcia, M.D. McMullen, J.B. Holland, S.J. Szalman, R.J. Wisser, and J. Yu*. 2019. Optimal designs for genomic selection in hybrid crops. Molecular Plant 12:390-401.
Yu, X., X. Li, T. Guo, C. Zhu, Y. Wu, S.E. Mitchell, K.L. Roozeboom, D. Wang, M.L. Wang, G.A. Pederson, T.T. Tesso, P.S. Schnable, R. Bernardo, and J. Yu*. 2016. Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nature Plants 2:16150.
Yu, J.* 2009. Realizing the potential of ultrahigh throughput genomic technologies in plant breeding. Plant Genome 2:2.
Bernardo, R.*, and J. Yu. 2007. Prospects for genomewide selection for quantitative traits in maize. Crop Science 47:1082-1090.

[Complex Trait Dissection]

Li, X., T. Guo, J. Wang, W.A. Bekele, S. Sukumaran, A.E. Vanous, J.P. McNellie, L. Tibbs Cortes, M.S. Lopes, K. Lamkey, M.E. Westgate, J. McKay, S.V. Archontoulis, M.P. Reynolds, N.A. Tinker, P.S. Schnable, and J. Yu*. 2021. An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops. Molecular Plant 14:874-887.
Tibbs Cortes, L., Z. Zhang, and J. Yu*. 2021. Status and prospects of genome-wide association studies in plants. Plant Genome 14:e20077.
Guo, T., Q. Mu, J. Wang, A.E. Vanous, A. Onogi, H. Iwata, X. Li*, and J. Yu*. 2020. Dynamic effects of interacting genes underlying rice flowering-time phenotypic plasticity and global adaptation. Genome Research 30:673-683.
Wu, Yuye, T. Guo, Q. Mu, J. Wang, Xin. Li, Yun Wu, B. Tian, M.L. Wang, G. Bai, R. Perumal, H.N. Trick, S.R. Bean, I.M. Dweikat, M.R. Tuinstra, G. Morris, T.T. Tesso, J. Yu*, and Xianran Li*. 2019. Allelochemicals targeted to balance competing selections in African agroecosystems. Nature Plants 5:1229-1236.
Dzievit, M.J., X. Li, and J. Yu*. 2019. Dissection of leaf angle variation in maize through genetic mapping and meta-analysis. Plant Genome 12:180024.
McNellie, J.P., J. Chen*, X. Li, and J. Yu*. 2018. Genetic mapping of foliar and tassel heat stress tolerance in maize. Crop Science 58:2484-2493.
Li. Xin, T. Guo, Q. Mu, Xianran Li*, and J. Yu*. 2018. Genomic and environmental determinants and their interplay underlying phenotypic plasticity. PNAS 115:6679-6684.
Sukumaran, S., Xin Li, Xianran Li, C. Zhu, G. Bai, R. Perumal, M.R. Tuinstra, P.V.V. Prasad, S.E. Mitchell, T.T. Tesso, and J. Yu*. 2016. QTL mapping for grain yield, flowering time, and stay-green traits in sorghum using genotyping-by-sequencing markers. Crop Science 56:1429-1442.
Li, Xin, Xianran Li, E. Fridman, T.T. Tesso, and J. Yu*. 2015. Dissecting repulsion linkage in the dwarfing gene Dw3 region for sorghum plant height provides insights into heterosis. PNAS 112:11823-11828.
Sukumaran, S., W. Xiang, S.R. Bean, J.F. Pedersen, S. Kresovich, M.R. Tuinstra, T.T. Tesso, M.T. Hamblin, and J. Yu*. 2012. Association mapping for grain quality in a diverse sorghum collection. Plant Genome 5:126-135.
Li, X., C. Zhu, C.-T. Yeh, W. Wu, K. Petsch, E. Takacs, F. Tian, G. Bai, E.S. Buckler, G.J. Muehlbauer, M.C.P. Timmermans, M.J. Scanlon, P.S. Schnable* and J. Yu*. 2012. Genic and non-genic contributions to natural variation of quantitative traits in maize. Genome Research 22:2436-2444.
Zhu, C., X. Li, and J. Yu* 2011. Integrating rare-variant testing, function prediction, and gene network in composite resequencing-based genome-wide association studies (CR-GWAS). G3 1:233-243.
Sun, G., C. Zhu, S. Yang, W. Song, M. H. Kramer, H.-P. Piepho, and J. Yu*. 2010. Variation explained in mixed model association mapping. Heredity 105:333-340.
Zhang, Z.*, E. Ersoz, C.-Q. Lai, R.J. Todhunter, H.K. Tiwari, M.A. Gore, P.J. Bradbury, J. Yu, D.K. Arnett, J.M. Ordovas, and E.S. Buckler. 2010. Mixed linear model approach adapted for genome-wide association studies. Nature Genetics 42:355-360.
Zhu, C., and J. Yu*. 2009. Nonmetric multidimensional scaling corrects for populationstructure in association mapping with different sample types. Genetics 182:875-888.
Yu, J.*, Z. Zhang, C. Zhu, D. Tabanao, G. Pressoir, M.R. Tuinstra, S. Kresovich, R.J. Todhunter, and E.S. Buckler. 2009. Simulation appraisal of the adequacy of number of background markers for relationship estimation in association mapping. Plant Genome 2:63-77.
Zhu, C., M. Gore, E.S. Buckler, and J. Yu*. 2008. Status and prospects of association mapping in plants. Plant Genome 1:5-20.
Yu, J., J.B. Holland, M.D. McMullen, and E.S. Buckler*. 2008. Genetic design and statistical power of nested association mapping in maize. Genetics 138:539-551.
Yu, J., G. Pressoir, W.H. Briggs, I. Vroh Bi, M. Yamasaki, J.F. Doebley, M.D. McMullen, B.S. Gaut, D. Nielsen, J.B. Holland, S. Kresovich, and E.S. Buckler*. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics 38:203-208.
Yu, J., and E.S. Buckler*. 2006. Genetic association mapping and genome organization of maize. Current Opinion in Biotechnology 17:155-160.

[Genes and Genetics]

Char, S.N., J. Wei, Q. Mu, X. Li, Z. Zhang, J. Yu, and B. Yang*. 2020. An Agrobacterium-delivered CRISPR/Cas9 system for targeted mutagenesis in sorghum. Plant Biotechnology Journal 18:319-321.
Su, Z., A. Bernardo, B. Tian, H. Chen, S. Wang, H. Ma, S. Cai, D. Liu, D. Zhang, T. Li, H. Trick, P. St. Amand, J. Yu, Z. Zhang, and G. Bai*. 2019. A deletion mutation in TaHRC confers Fhb1 resistance to Fusarium head blight in wheat. Nature Genetics 51:1099–1105.
Leiboff, S., X. Li, H. Alvis, N. Todt, J. Yang, X. Li, X. Yu, G.J. Muehlbauer, M.C.P. Timmermans, J. Yu, P.S. Schnable, and M.J. Scanlon*. 2015. Genetic control of morphometric diversity in the maize shoot apical meristem. Nature Communications 6:8974.
Lin, Z., X. Li, L.M. Shannon, C.-T. Yeh, M.L. Wang, G. Bai, Z. Peng, J. Li, H.N. Trick, T.E. Clemente, J. Doebley, P.S. Schnable, M.R. Tuinstra, T.T. Tesso, F. White, and J. Yu*. 2012. Parallel domestication of the Shattering1 genes in cereals. Nature Genetics 44:720–724.
Wu, Y., X. Li, W. Xiang, C. Zhu, Z. Lin, Y. Wu, J. Li, S. Pandravada, D.D. Ridder, G. Bai, M.L. Wang, H.N. Trick, S.R. Bean, M.R. Tuinstra, T.T. Tesso, and J. Yu*. 2012. Presence of tannins in sorghum grains is conditioned by different natural alleles of Tannin1. PNAS 109:10281-10286.
Wisser, R.J.*, J.M. Kolkman, M.E. Patzoldt, J.B. Holland, J. Yu, M. Krakowskyc, R.J. Nelson, and P.J. Balint-Kurti. 2011. Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a glutathione S-transferase gene. PNAS 108:7339-7344.
Tian, Z., Q. Qian, Q. Liu, M. Yan, X. Liu, C. Yan, G. Liu, Z. Gao, S. Tang, D. Zeng, Y. Wang, J. Yu*, M. Gu*, and J. Li*. 2009. Allelic diversities in rice starch biosynthesis lead to a diverse array of rice eating and cooking qualities. PNAS 106:21760-21765.
Buckler, E.S.*, J.B. Holland*, ..., J. Yu, Z. Zhang, S. Kresovich*, and M.M. Mullen* 2009. The genetic architecture of maize flowering time. Science 325:714-718.

[Genomes and Chromosomes]

Wang, J., X. Li*, K.D. Kim, M.J. Scanlon, S.A. Jackson, N.M. Springer, and J. Yu*. 2019. Genome-wide nucleotide patterns and potential mechanisms of genome divergence following domestication in maize and soybean. Genome Biology 20:74.
Li, X., M.J. Scanlon, and J. Yu*. 2015. Evolutionary patterns of DNA base composition and correlation to polymorphisms in DNA repair systems. Nucleic Acids Research 43:3614-3625.
Li, X., C. Zhu, Z. Lin, Y. Wu, D. Zhang, G. Bai, W. Song, J. Ma, G.J. Muehlbauer, M.J. Scanlon, M. Zhang*, and J. Yu*. 2011. Chromosome size in diploid eukaryotic species centers on the average length with a conserved boundary. Molecular Biology and Evolution 28:1901–1911.

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Dr. Jianming Yu

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Jianming Yu on Google Scholar