Philomin Juliana

Genome-Wide Association Mapping for Leaf Tip Necrosis and Pseudo-black Chaff in Relation to Durable Rust Resistance in Wheat

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 CIMMYTs 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.

A Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction

When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G × E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data. The proposed model was developed under the Bayesian paradigm for which we developed a Markov Chain Monte Carlo (MCMC) with noninformative priors. This allows obtaining all required full conditional distributions of the parameters leading to an exact Gibbs sampler for the posterior distribution. Our model was tested with simulated data and a real data set. Results show that the proposed multi-trait, multi-environment model is an attractive alternative for modeling multiple count traits measured in multiple environments.

Comparison of models and whole-genome profiling approaches for genomic-enabled prediction of Septoria Tritici Blotch, Stagonospora Nodorum Blotch, and tan spot resistance in wheat

The leaf spotting diseases in wheat that include Septoria tritici blotch (STB) caused by Zymoseptoria tritici, Stagonospora nodorum blotch (SNB) caused by Parastagonospora nodorum, and tan spot (TS) caused by Pyrenophora tritici‐repentis pose challenges to breeding programs in selecting for resistance. A promising approach that could enable selection prior to phenotyping is genomic selection that uses genome‐wide markers to estimate breeding values (BVs) for quantitative traits. To evaluate this approach for seedling and/or adult plant resistance (APR) to STB, SNB, and TS, we compared the predictive ability of least‐squares (LS) approach with genomic‐enabled prediction models including genomic best linear unbiased predictor (GBLUP), Bayesian ridge regression (BRR), Bayes A (BA), Bayes B (BB), Bayes Cπ (BC), Bayesian least absolute shrinkage and selection operator (BL), and reproducing kernel Hilbert spaces markers (RKHS‐M), a pedigree‐based model (RKHS‐P) and RKHS markers and pedigree (RKHS‐MP). We observed that LS gave the lowest prediction accuracies and RKHS‐MP, the highest. The genomic‐enabled prediction models and RKHS‐P gave similar accuracies. The increase in accuracy using genomic prediction models over LS was 48%. The mean genomic prediction accuracies were 0.45 for STB (APR), 0.55 for SNB (seedling), 0.66 for TS (seedling) and 0.48 for TS (APR). We also compared markers from two whole‐genome profiling approaches: genotyping by sequencing (GBS) and diversity arrays technology sequencing (DArTseq) for prediction. While, GBS markers performed slightly better than DArTseq, combining markers from the two approaches did not improve accuracies. We conclude that implementing GS in breeding for these diseases would help to achieve higher accuracies and rapid gains from selection.

Prediction of multiple-trait and multiple-environment genomic data using recommender systems

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center’s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress–resilience within years.

An R Package for Multitrait and Multienvironment Data with the Item-Based Collaborative Filtering Algorithm

We provide software for a recommender system. This software will assist plant breeders to make predictions of unobserved primary traits from other observed secondary traits. The software could be useful in conventional phenotypic selections or in genomic selection.

Genetic dissection of grain zinc concentration in spring wheat for mainstreaming biofortification in CIMMYT wheat breeding

Wheat is an important staple that acts as a primary source of dietary energy, protein, and essential micronutrients such as iron (Fe) and zinc (Zn) for the world’s population. Approximately two billion people suffer from micronutrient deficiency, thus breeders have crossed high Zn progenitors such as synthetic hexaploid wheat, T. dicoccum, T. spelta, and landraces to generate wheat varieties with competitive yield and enhanced grain Zn that are being adopted by farmers in South Asia. Here we report a genome-wide association study (GWAS) using the wheat Illumina iSelect 90 K Infinitum SNP array to characterize grain Zn concentrations in 330 bread wheat lines. Grain Zn phenotype of this HarvestPlus Association Mapping (HPAM) panel was evaluated across a range of environments in India and Mexico. GWAS analysis revealed 39 marker-trait associations for grain Zn. Two larger effect QTL regions were found on chromosomes 2 and 7. Candidate genes (among them zinc finger motif of transcription-factors and metal-ion binding genes) were associated with the QTL. The linked markers and associated candidate genes identified in this study are being validated in new biparental mapping populations for marker-assisted breeding.

A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model

Today, breeders perform genomic-assisted breeding to improve more than one trait. However, frequently there are several traits under study at one time, and the implementation of current genomic multiple-trait and multiple-environment models is challenging. Consequently, we propose a four-stage analysis for multiple-trait data in this paper. In the first stage, we perform singular value decomposition (SVD) on the resulting matrix of trait responses; in the second stage, we perform multiple trait analysis on transformed responses. In stages three and four, we collect and transform the traits back to their original state and obtain the parameter estimates and the predictions on these scale variables prior to transformation. The results of the proposed method are compared, in terms of parameter estimation and prediction accuracy, with the results of the Bayesian multiple-trait and multiple-environment model (BMTME) previously described in the literature. We found that the proposed method based on SVD produced similar results, in terms of parameter estimation and prediction accuracy, to those obtained with the BMTME model. Moreover, the proposed multiple-trait method is atractive because it can be implemented using current single-trait genomic prediction software, which yields a more efficient algorithm in terms of computation.