D. Jarquín

发表

Coupling day length data and genomic prediction tools for predicting time-related traits under complex scenarios

M. Yamasaki, D. Jarquín, H. Kajiya-Kanegae, 2019, Scientific Reports.

Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials

D. Gianola, P. Pérez-Rodríguez, D. Jarquín, 2019, Front. Genet..

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

G. de los Campos, Y. Beyene, R. Varshney, 2017, Trends in plant science.

Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population

Shizhong Xu, P. Cregan, Q. Song, 2017, G3: Genes, Genomes, Genetics.

Genome-Wide Association Mapping and Genomic Prediction of Anther Extrusion in CIMMYT Hybrid Wheat Breeding Program via Modeling Pedigree, Genomic Relationship, and Interaction With the Environment

D. Jarquín, S. Dreisigacker, J. Crossa, 2020, Frontiers in Genetics.

A Hierarchical Bayesian Estimation Model for Multienvironment Plant Breeding Trials in Successive Years

D. Jarquín, J. Crossa, J. Burgueño, 2016 .

Increasing Genomic‐Enabled Prediction Accuracy by Modeling Genotype × Environment Interactions in Kansas Wheat

Jose Crossa, Jesse Poland, Reka Howard, 2017, The plant genome.

Genomic Predictions for Common Bunt, FHB, Stripe Rust, Leaf Rust, and Leaf Spotting Resistance in Spring Wheat

A. N’Diaye, C. Pozniak, D. Jarquín, 2022, Genes.

Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments

D. Gianola, J. Poland, P. Pérez-Rodríguez, 2019, G3: Genes, Genomes, Genetics.

Genomic‐enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat

M. Reynolds, D. Jarquín, J. Crossa, 2018, The plant genome.

Genomic Prediction Enhanced Sparse Testing for Multi-environment Trials

Y. Beyene, D. Jarquín, Réka Howard, 2020, G3.

Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems

A. N’Diaye, C. Pozniak, D. Jarquín, 2021, Theoretical and Applied Genetics.

Enhancing Genomic Prediction Models for Forecasting Days to Maturity in Soybean Genotypes Using Site-Specific and Cumulative Photoperiod Data

J. Specht, G. Graef, D. Jarquín, 2022, Agriculture.

Interaction between FTO rs9939609 and the Native American-origin ABCA1 rs9282541 affects BMI in the admixed Mexican population

D. Jarquín, S. Canizales-Quinteros, S. Romero-Hidalgo, 2017, BMC Medical Genetics.

The effect of artificial selection on phenotypic plasticity in maize

Joseph L. Gage, Darwin A. Campbell, James C. Schnable, 2017, Nature Communications.

Pedigree‐Based Prediction Models with Genotype × Environment Interaction in Multienvironment Trials of CIMMYT Wheat

M. Reynolds, D. Jarquín, J. Crossa, 2017 .

Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico

M. Reynolds, D. Jarquín, J. Crossa, 2016, G3: Genes, Genomes, Genetics.

Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions

Malachy T. Campbell, G. Morota, D. Jarquín, 2019, bioRxiv.

Use of family structure information in interaction with environments for leveraging genomic prediction models

D. Jarquín, Reyna Persa, H. Iwata, 2020, The Crop Journal.

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.

P. Pérez-Rodríguez, M. Lillemo, D. Jarquín, 2022, Methods in molecular biology.

Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean

G. Graef, D. Jarquín, A. Lorenz, 2022, bioRxiv.

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

P. Pérez-Rodríguez, D. Jarquín, J. Crossa, 2017, G3: Genes, Genomes, Genetics.

Evaluating dimensionality reduction for genomic prediction

R. Varshney, D. Jarquín, Réka Howard, 2022, Frontiers in Genetics.

Genome-based trait prediction in multi- environment breeding trials in groundnut

R. Varshney, D. Jarquín, J. Crossa, 2020, Theoretical and Applied Genetics.

Genome-Wide Association and Gene Co-expression Network Analyses Reveal Complex Genetics of Resistance to Goss’s Wilt of Maize

Alex B. Brohammer, D. Jarquín, Amritpal S. Singh, 2019, G3: Genes, Genomes, Genetics.

Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices

D. Jarquín, Réka Howard, J. Crossa, 2022, Plants.

Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding

Pengyin Chen, D. Jarquín, Reyna Persa, 2022, Frontiers in Genetics.

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project

James C. Schnable, Carolyn J. Lawrence-Dill, Nathan M. Springer, 2021, Frontiers in Genetics.

Branch angle and leaflet shape are associated with canopy coverage in soybean

Allison M. Haaning, R. Stupar, D. Jarquín, 2021, The plant genome.

Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds

Darwin A. Campbell, Carolyn J. Lawrence-Dill, Nathan D. Miller, 2020, Crop Science.

Optimizing predictions in IRRI’s rice drought breeding program by leveraging 17 years of historical data and pedigree information

D. Jarquín, Mahender Anumalla, W. Hussain, 2022, Frontiers in Plant Science.

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction

A. N’Diaye, C. Pozniak, D. Jarquín, 2022, Plants.

Comparing artificial‐intelligence techniques with state‐of‐the‐art parametric prediction models for predicting soybean traits

D. Jarquín, Réka Howard, Susweta Ray, 2022, The plant genome.

Identification of genomic regions associated with soybean responses to off-target dicamba exposure

H. Nguyen, Jing Zhou, Jianfeng Zhou, 2022, Frontiers in Plant Science.

Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems

A. N’Diaye, C. Pozniak, D. Jarquín, 2021, Theoretical and Applied Genetics.

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

M. Calus, G. de los Campos, D. Jarquín, 2013, Theoretical and Applied Genetics.

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

José Crossa, Gustavo de los Campos, Juan Burgueño, 2013, Theoretical and Applied Genetics.

Differentiate Soybean Response to Off-Target Dicamba Damage Based on UAV Imagery and Machine Learning

H. Nguyen, Jing Zhou, Jianfeng Zhou, 2022, Remote. Sens..

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea

Jose Crossa, Abhishek Rathore, Elizabeth Jones, 2018, Scientific Reports.

The use of high-throughput phenotyping in genomic selection context

D. Jarquín, Reyna Persa, P. C. Ribeiro, 2021, Crop Breeding and Applied Biotechnology.

Genotyping by sequencing for genomic prediction in a soybean breeding population

George Graef, Josef E. Jedlicka, Aaron Lorenz, 2014, BMC Genomics.

A chickpea genetic variation map based on the sequencing of 3,366 genomes

M. S. Pithia, Huanming Yang, Jian Wang, 2021, Nature.

Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds

D. Jarquín, Réka Howard, Zhikai Liang, 2020, Frontiers in Genetics.

Comparison of array‐ and sequencing‐based markers for genome‐wide association mapping and genomic prediction in spring wheat

C. Sansaloni, D. Jarquín, Matthew Reynolds, 2020 .

Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program

J. Poland, D. Jarquín, M. Guttieri, 2018, G3: Genes, Genomes, Genetics.

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones

Ky L. Mathews, J. Burgueño, J. Crossa, 2016, Scientific Reports.

Identification of Disease Resistance Parents and Genome-Wide Association Mapping of Resistance in Spring Wheat

A. N’Diaye, C. Pozniak, D. Jarquín, 2022, Plants.

Simulations of rate of genetic gain in dry bean breeding programs

D. Jarquín, J. Osorno, V. Hoyos-Villegas, 2023, Theoretical and Applied Genetics.

Prospects of Genomic Prediction in the USDA Soybean Germplasm Collection: Historical Data Creates Robust Models for Enhancing Selection of Accessions

J. Specht, D. Jarquín, A. Lorenz, 2016, G3: Genes, Genomes, Genetics.

Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification

D. Jarquín, Réka Howard, V. Manthena, 2023, Frontiers in Genetics.

A Population Structure and Genome‐Wide Association Analysis on the USDA Soybean Germplasm Collection

P. Cregan, Q. Song, J. Specht, 2015, The plant genome.

Genomic Prediction of Gene Bank Wheat Landraces

D. Akdemir, C. Sneller, C. Sansaloni, 2016, G3: Genes, Genomes, Genetics.

A General Bayesian Estimation Method of Linear–Bilinear Models Applied to Plant Breeding Trials With Genotype × Environment Interaction

D. Jarquín, J. Crossa, S. Pérez-Elizalde, 2012 .

Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data.

Gota Morota, Diego Jarquin, Hiroyoshi Iwata, 2019, Methods in molecular biology.

Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials

H. Piepho, P. Pérez-Rodríguez, D. Jarquín, 2021, The plant genome.

Genome‐wide Association Mapping of Qualitatively Inherited Traits in a Germplasm Collection

D. Hyten, J. Specht, R. Nelson, 2017, The plant genome.

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

Alencar Xavier, D. Jarquín, Réka Howard, 2018 .

Bayesian estimation of the additive main effects and multiplicative interaction model

P. Cornelius, D. Jarquín, J. Crossa, 2011 .

CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for Maize phenotype predictability in the U.S. and Canada

Natalia de Leon Gatti, D. Jarquín, F. Muñoz-Arriola, 2023 .

Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability.

D. Jarquín, F. Muñoz-Arriola, Parisa Sarzaeim, 2022, Journal of Experimental Botany.

Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction

Diego Jarquin, Reyna Persa, Martin Grondona, 2021, Agriculture.

Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets

Patrick S. Schnable, Seth C. Murray, Joseph L. Gage, 2020, BMC Research Notes.

Analytics for climate-uncertainty estimation and propagation in maize-phenotype predictions

Parisa Sarzaeim, Francisco Munoz-Arriola, Diego Jarquin, 2020 .

Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics

D. Jarquín, J. A. R. Nunes, E. Rios, 2023, The plant genome.

Combining phenotypic and genomic data to improve prediction of binary traits

S. Ghosal, D. Jarquín, S. Ghosal, 2022, bioRxiv.

Modeling spatial trends and enhancing genetic selection: An approach to soybean seed composition breeding

D. Jarquín, A. Borém, R. S. Alves, 2020 .

A Population Structure and Genome‐Wide Association Analysis on the USDA Soybean Germplasm Collection

P. Cregan, Q. Song, J. Specht, 2015, The plant genome.

An Assessment of the Factors Influencing the Prediction Accuracy of Genomic Prediction Models Across Multiple Environments

G. Graef, A. Lipka, D. Jarquín, 2021, Frontiers in Genetics.

Increasing Genomic‐Enabled Prediction Accuracy by Modeling Genotype × Environment Interactions in Kansas Wheat

J. Poland, D. Jarquín, Réka Howard, 2017, The plant genome.

Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi-Trait Approaches in Soybeans

D. Jarquín, Reyna Persa, Arthur Bernardeli, 2020, Agriculture.

Response Surface Analysis of Genomic Prediction Accuracy Values Using Quality Control Covariates in Soybean

G. Graef, D. Jarquín, Réka Howard, 2019, Evolutionary bioinformatics online.