Author: Jessica Rutkoski

Genome-wide association mapping for leaf tip necrosis and pseudo-black chaff in relation to durable rust resistance in wheat

Philomin Juliana Jessica Rutkoski Jesse Poland Ravi Singh Mark Sorrells (2015)

The partial rust resistance genes lr34 and sr2 have been used extensively in wheat (triticum aestivum l.) improvement, as they confer exceptional durability. Interestingly, the resistance of lr34 is associated with the expression of leaf tip necrosis (ltn) and sr2 with pseudo-black chaff (pbc). Genome-wide association mapping using cimmyt’s stem rust resistance screening nursery (srrsn) wheat lines was done to identify genotyping-by-sequencing (gbs) markers linked to ltn and pbc. Phenotyping for these traits was done in ithaca, new york (fall 2011); njoro, kenya (main and off-seasons, 2012), and wellington, india (winter, 2013). Using the mixed linear model (mlm), 18 gbs markers were significantly associated with ltn. While some markers were linked to loci where the durable leaf rust resistance genes lr34 (7ds), lr46 (1bl), and lr68 (7bl) were mapped, significant associations were also detected with other loci on 2bl, 5b, 3bs, 4bs, and 7bs. Twelve gbs markers linked to the sr2 locus (3bs) and loci on 2ds, 4al, and 7ds were significantly associated with pbc. This study provides insight into the complex genetic control of ltn and pbc. Further efforts to validate and study these loci might aid in determining the nature of their association with durable resistance.


Partial Rust Resistance Genes Genome Wide Association Mapping Leaf Tip Necrosis Wheat CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

A genomic bayesian multi-trait and multi-environment model

Osval Antonio Montesinos-Lopez Jessica Rutkoski Jose Crossa (2016)

When information on multiple genotypes evaluated in multiple environments is recorded, a multi-environment single trait model for assessing genotype · environment interaction (G · E) is usually employed. Comprehensive models that simultaneously take into account the correlated traits and trait · genotype · environment interaction (T · G · E) are lacking. In this research, we propose a Bayesian model for analyzing multiple traits and multiple environments for whole-genome prediction (WGP) model. For this model, we used Half-t priors on each standard deviation term and uniform priors on each correlation of the covariance matrix. These priors were not informative and led to posterior inferences that were insensitive to the choice of hyper-parameters. We also developed a computationally efficient Markov Chain Monte Carlo (MCMC) under the above priors, which allowed us to obtain all required full conditional distributions of the parameters leading to an exact Gibbs sampling for the posterior distribution. We used two real data sets to implement and evaluate the proposed Bayesian method and found that when the correlation between traits was high (.0.5), the proposed model (with unstructured variance–covariance) improved prediction accuracy compared to the model with diagonal and standard variance–covariance structures. The R-software package Bayesian Multi-Trait and Multi-Environment (BMTME) offers optimized C++ routines to efficiently perform the analyses.


Bayesian theory Statistical methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding

Jared Crain Suchismita Mondal Jessica Rutkoski Ravi Singh Jesse Poland (2018)

Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called ‘Phenocart’ was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.


Wheats Phenotypes Genomics Wheat Breeding High Throughput Phenotyping Genomic Selection Yield Prediction Modeling WHEAT GENOMICS YIELD FORECASTING CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Single-step genomic and pedigree genotype x environment interaction models for predicting wheat lines in international environments

Paulino Pérez-Rodríguez Jose Crossa Jessica Rutkoski Ravi Singh Gustavo de los Campos Juan Burgueño Susanne Dreisigacker (2017)

Genomic prediction models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individuals. However, pedigree information for an entire breeding population is frequently available, as are historical data on the performance of a large number of selection candidates. The single-step method extends the genomic relationship information from genotyped individuals to pedigree information from a larger number of phenotyped individuals in order to combine relationship information on all members of the breeding population. Furthermore, genomic prediction models that incorporate genotype × environment interactions (G × E) have produced substantial increases in prediction accuracy compared with single-environment genomic prediction models. Our main objective was to show how to use single-step genomic and pedigree models to assess the prediction accuracy of 58,798 CIMMYT wheat (Triticum aestivum L.) lines evaluated in several simulated environments in Ciudad Obregon, Mexico, and to predict the grain yield performance of some of them in several sites in South Asia (India, Pakistan, and Bangladesh) using a reaction norm model that incorporated G × E. Another objective was to describe the statistical and computational challenges encountered when developing the pedigree and single-step models in such large datasets. Results indicate that the genomic prediction accuracy achieved by models using pedigree only, markers only, or both pedigree and markers to predict various environments in India, Pakistan, and Bangladesh is higher (0.25–0.38) than prediction accuracy of models that use only phenotypic prediction (0.20) or do not include the G × E term.


Genomics Breeding methods Genetic improvement Wheats CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Role of modelling in international crop research: overview and some case studies

Matthew Paul Reynolds Martinus Kropff Jose Crossa Jawoo Koo Gideon Kruseman Jessica Rutkoski Urs Schulthess Kai Sonder Vincent Vadez (2018)

Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers.


Crop modelling Agricultural research systems Crop management Food security International Agricultural Research Agri-Food-Systems Global Phenotyping Networks Data Sharing Big Data Foresight CROP MODELLING AGRICULTURAL RESEARCH SYSTEMS CROP MANAGEMENT FOOD SECURITY CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

A genomic selection index applied to simulated and real data

Jose Crossa Vivi Arief Kaye Basford Jessica Rutkoski DIEGO JARQUIN Gregorio Alvarado Yoseph Beyene Kassa Semagn Ian Delacy (2015)

A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors have proposed a GSI; however, they have not used simulated or real data to validate the GSI theory and have not explained how to estimate the GSI selection response and the GSI expected genetic gain per selection cycle for the unobserved traits after the first selection cycle to obtain information about the genetic gains in each subsequent selection cycle. In this paper, we develop the theory of a GSI and apply it to two simulated and four real data sets with four traits. Also, we numerically compare its efficiency with that of the phenotypic selection index (PSI) by using the ratio of the GSI response over the PSI response, and the PSI and GSI expected genetic gain per selection cycle for observed and unobserved traits, respectively. In addition, we used the Technow inequality to compare GSI vs. PSI efficiency. Results from the simulated data were confirmed by the real data, indicating that GSI was more efficient than PSI per unit of time.


Genomics Index Real data Simulated data CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Harnessing diversity in wheat to enhance grain yield, climate resilience, disease and insect pest resistance and nutrition through conventional and modern breeding approaches

Suchismita Mondal Jessica Rutkoski Govindan Velu Leonardo Abdiel Crespo Herrera Carlos Guzman sridhar bhavani CAIXIA LAN Xinyao He Ravi Singh (2016)

Current trends in population growth and consumption patterns continue to increase the demand for wheat, a key cereal for global food security. Further, multiple abiotic challenges due to climate change and evolving pathogen and pests pose a major concern for increasing wheat production globally. Triticeae species comprising of primary, secondary, and tertiary gene pools represent a rich source of genetic diversity in wheat. The conventional breeding strategies of direct hybridization, backcrossing and selection have successfully introgressed a number of desirable traits associated with grain yield, adaptation to abiotic stresses, disease resistance, and bio-fortification of wheat varieties. However, it is time consuming to incorporate genes conferring tolerance/resistance to multiple stresses in a single wheat variety by conventional approaches due to limitations in screening methods and the lower probabilities of combining desirable alleles. Efforts on developing innovative breeding strategies, novel tools and utilizing genetic diversity for new genes/alleles are essential to improve productivity, reduce vulnerability to diseases and pests and enhance nutritional quality. New technologies of high-throughput phenotyping, genome sequencing and genomic selection are promising approaches to maximize progeny screening and selection to accelerate the genetic gains in breeding more productive varieties. Use of cisgenic techniques to transfer beneficial alleles and their combinations within related species also offer great promise especially to achieve durable rust resistance.


Wheats Genetic variation Disease resistance CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat

Jessica Rutkoski Jesse Poland Suchismita Mondal Lorena González Pérez Jose Crossa Matthew Paul Reynolds Ravi Gopal Singh (2017)

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.