Author: Philomin Juliana

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

Genotypic data for the South Asian panel with 184 lines

Xinyao He Philomin Juliana arun joshi Ravi Singh Pawan Singh (2021)

Genotypic data for the South Asian panel with 184 lines intended for multiple diseases resistance analysis



Genome-wide mapping and allelic fingerprinting provide insights into the genetics of resistance to wheat stripe rust in India, Kenya and Mexico

Philomin Juliana Ravi Singh JULIO HUERTA-ESPINO sridhar bhavani Mandeep Randhawa UTTAM KUMAR arun joshi (2020)

Stripe or yellow rust (YR) caused by Puccinia striiformis Westend. f. sp. tritici Erikss. is a persistent biotic-stress threatening global wheat production. To broaden our understanding of the shared genetic basis of YR resistance across multi-site and multi-year evaluations, we performed a large genome-wide association study using 43,706 YR observations on 23,346 wheat lines from the International Maize and Wheat Improvement Center evaluated between 2013 and 2019 at sites in India, Kenya and Mexico, against predominant races prevalent in the countries.We identified 114 repeatable markers tagging 20 quantitative trait loci (QTL) associated with YR on ten chromosomes including 1D, 2A, 2B, 2D, 3A, 4A, 4D, 5A, 5B and 6B, among which four QTL, QYr.cim-2DL.2, QYr.cim-2AS.1, QYr.cim-2BS.2 and QYr.cim-2BS.3 were significant in more than ten datasets.Furthermore, we report YR-associated allelic fingerprints for the largest panel of wheat breeding lines (52,067 lines) till date, creating substantial opportunities for YR favorable allele enrichment using molecular markers. Overall, the markers and fingerprints reported in this study provide excellent insights into the genetic architecture of YR resistance in different geographical regions, time-periods and wheat germplasm and are a huge resource to the global wheat breeding community for accelerating YR resistance breeding efforts.



Replication Data for: Allocation of wheat lines in sparse testing for genome-based multi-environment prediction

Leonardo Abdiel Crespo Herrera Ravi Singh Suchismita Mondal Philomin Juliana DIEGO JARQUIN Jose Crossa (2021)

Sparse testing can be used in plant breeding and genome-based prediction. In sparse testing not all of the lines are sown in all environments. The phenotypic and genotypic data files provided in this dataset were used to execute an analysis of three general cases of the composition of the sparse testing allocation design for wheat breeding.



An R package for multitrait and multienvironment data with the Item-based collaborative filtering algorithm

Osval Antonio Montesinos-Lopez Francisco Javier Luna Vázquez Philomin Juliana Ravi Singh Jose Crossa (2018)

The Item-Based Collaborative Filtering for Multitrait and Multienvironment Data (IBCF.MTME) package was developed to implement the item-based collaborative filtering (IBCF) algorithm for continuous phenotypic data in the context of plant breeding where data are collected for various traits and environments. The main difference between this package and the other available packages that can implement IBCF is that this one was developed for continuous phenotypic data, which cannot be implemented in the current packages because they can implement IBCF only for binary and ordinary phenotypes. In the following article, we will show how to both install the package and use it for studying the prediction accuracy of multitrait and multienvironment data under phenotypic and genomic selection. We illustrate its use with seven examples (with information from two datasets, Wheat_IBCF and Year_IBCF, which are included in the package) comprising multienvironment data, multitrait data, and both multitrait and multienvironment data that cover scenarios in which breeding scientists are interested. The package offers many advantages for studying the genomic-enabled prediction accuracy of multitrait and multienvironment data, ultimately helping plant breeders make better decisions.



Genomic prediction models for count data

Osval Antonio Montesinos-Lopez Paulino Pérez-Rodríguez Philomin Juliana Jose Crossa (2015)

Whole genome prediction models are useful tools for breeders when selecting candidate individuals early in life for rapid genetic gains. However, most prediction models developed so far assume that the response variable is continuous and that its empirical distribution can be approximated by a gaussian model. A few models have been developed for ordered categorical phenotypes, but there is a lack of genomic prediction models for count data. There are well-established regression models for count data that cannot be used for genomic-enabled prediction because they were developed for a large sample size (n) and a small number of parameters (p); however, the rule in genomic-enabled prediction is that p is much larger than the sample size n. Here we propose a bayesian mixed negative binomial (bmnb) regression model for counts, and we present the conditional distributions necessary to efficiently implement a gibbs sampler. The proposed bayesian inference can be implemented routinely. We evaluated the proposed bmnb model together with a poisson model, a normal model with untransformed response, and a normal model with transformed response using a logarithm, and applied them to two real wheat datasets from the international maize and wheat improvement center. Based on the criteria used for assessing genomic prediction accuracy, results indicated that the bmnb model is a viable alternative for analyzing count data.


Bayesian Analysis Gibbs Sampler Count Data Genomic Prediction Data Augmentation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Deep kernel and deep learning for genomic-based prediction

Juan Burgueño Ravi Singh Philomin Juliana Osval Antonio Montesinos-Lopez Jaime Cuevas (2019)

Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel, AK) method, Gaussian kernel (GK) method and the conventional kernel method (Genomic Best Linear Unbiased Predictor, GBLUP, GB). We used two real wheat data sets for the benchmarking of these methods. We found that the GK and deep kernel AK methods outperformed the DL and the conventional GB methods, although the gain in terms of prediction performance of AK and GK was not very large but they have the advantage that no tuning parameters are required. Furthermore, although AK and GK had similar genomic-based performance, deep kernel AK is easier to implement than the GK. For this reason, our results suggest that AK is an alternative to DL models with the advantage that no tuning process is required.