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Items: 1 to 50 of 127

1.

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

Howard R, Gianola D, Montesinos-López O, Juliana P, Singh R, Poland J, Shrestha S, Pérez-Rodríguez P, Crossa J, Jarquín D.

G3 (Bethesda). 2019 Jul 12. pii: g3.400508.2019. doi: 10.1534/g3.119.400508. [Epub ahead of print]

2.

Deep Kernel for Genomic and Near Infrared Predictions in Multi-Environment Breeding Trials.

Cuevas J, Montesinos-López O, Juliana P, Guzmán C, Pérez-Rodríguez P, González-Bucio J, Burgueño J, Montesinos-López A, Crossa J.

G3 (Bethesda). 2019 Jul 9. pii: g3.400493.2019. doi: 10.1534/g3.119.400493. [Epub ahead of print]

3.

isqg: A Binary Framework for in Silico Quantitative Genetics.

Toledo FH, Pérez-Rodríguez P, Crossa J, Burgueño J.

G3 (Bethesda). 2019 Aug 8;9(8):2425-2428. doi: 10.1534/g3.119.400373.

4.

Maize responsiveness to Azospirillum brasilense: Insights into genetic control, heterosis and genomic prediction.

Vidotti MS, Matias FI, Alves FC, Pérez-Rodríguez P, Beltran GA, Burgueño J, Crossa J, Fritsche-Neto R.

PLoS One. 2019 Jun 7;14(6):e0217571. doi: 10.1371/journal.pone.0217571. eCollection 2019.

5.

Genetic architecture of maize chlorotic mottle virus and maize lethal necrosis through GWAS, linkage analysis and genomic prediction in tropical maize germplasm.

Sitonik C, Suresh LM, Beyene Y, Olsen MS, Makumbi D, Oliver K, Das B, Bright JM, Mugo S, Crossa J, Tarekegne A, Prasanna BM, Gowda M.

Theor Appl Genet. 2019 Aug;132(8):2381-2399. doi: 10.1007/s00122-019-03360-x. Epub 2019 May 16.

6.

Resequencing of 429 chickpea accessions from 45 countries provides insights into genome diversity, domestication and agronomic traits.

Varshney RK, Thudi M, Roorkiwal M, He W, Upadhyaya HD, Yang W, Bajaj P, Cubry P, Rathore A, Jian J, Doddamani D, Khan AW, Garg V, Chitikineni A, Xu D, Gaur PM, Singh NP, Chaturvedi SK, Nadigatla GVPR, Krishnamurthy L, Dixit GP, Fikre A, Kimurto PK, Sreeman SM, Bharadwaj C, Tripathi S, Wang J, Lee SH, Edwards D, Polavarapu KKB, Penmetsa RV, Crossa J, Nguyen HT, Siddique KHM, Colmer TD, Sutton T, von Wettberg E, Vigouroux Y, Xu X, Liu X.

Nat Genet. 2019 May;51(5):857-864. doi: 10.1038/s41588-019-0401-3. Epub 2019 Apr 29.

PMID:
31036963
7.

Hybrid Wheat Prediction Using Genomic, Pedigree, and Environmental Covariables Interaction Models.

Basnet BR, Crossa J, Dreisigacker S, Pérez-Rodríguez P, Manes Y, Singh RP, Rosyara UR, Camarillo-Castillo F, Murua M.

Plant Genome. 2019 Mar;12(1). doi: 10.3835/plantgenome2018.07.0051.

8.

New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.

Montesinos-López OA, Martín-Vallejo J, Crossa J, Gianola D, Hernández-Suárez CM, Montesinos-López A, Juliana P, Singh R.

G3 (Bethesda). 2019 May 7;9(5):1545-1556. doi: 10.1534/g3.119.300585.

9.

An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction.

Montesinos-López OA, Montesinos-López A, Luna-Vázquez FJ, Toledo FH, Pérez-Rodríguez P, Lillemo M, Crossa J.

G3 (Bethesda). 2019 May 7;9(5):1355-1369. doi: 10.1534/g3.119.400126.

10.

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.

Krause MR, González-Pérez L, Crossa J, Pérez-Rodríguez P, Montesinos-López O, Singh RP, Dreisigacker S, Poland J, Rutkoski J, Sorrells M, Gore MA, Mondal S.

G3 (Bethesda). 2019 Apr 9;9(4):1231-1247. doi: 10.1534/g3.118.200856.

11.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage.

Sun J, Poland JA, Mondal S, Crossa J, Juliana P, Singh RP, Rutkoski JE, Jannink JL, Crespo-Herrera L, Velu G, Huerta-Espino J, Sorrells ME.

Theor Appl Genet. 2019 Jun;132(6):1705-1720. doi: 10.1007/s00122-019-03309-0. Epub 2019 Feb 18.

PMID:
30778634
12.

Provitamin A Carotenoids in Grain Reduce Aflatoxin Contamination of Maize While Combating Vitamin A Deficiency.

Suwarno WB, Hannok P, Palacios-Rojas N, Windham G, Crossa J, Pixley KV.

Front Plant Sci. 2019 Jan 29;10:30. doi: 10.3389/fpls.2019.00030. eCollection 2019.

13.

A robust Bayesian genome-based median regression model.

Montesinos-López A, Montesinos-López OA, Villa-Diharce ER, Gianola D, Crossa J.

Theor Appl Genet. 2019 May;132(5):1587-1606. doi: 10.1007/s00122-019-03303-6. Epub 2019 Feb 12.

PMID:
30747261
14.

A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding.

Montesinos-López OA, Martín-Vallejo J, Crossa J, Gianola D, Hernández-Suárez CM, Montesinos-López A, Juliana P, Singh R.

G3 (Bethesda). 2019 Feb 7;9(2):601-618. doi: 10.1534/g3.118.200998.

15.

Prospects and Challenges of Applied Genomic Selection-A New Paradigm in Breeding for Grain Yield in Bread Wheat.

Juliana P, Singh RP, Poland J, Mondal S, Crossa J, Montesinos-López OA, Dreisigacker S, Pérez-Rodríguez P, Huerta-Espino J, Crespo-Herrera L, Govindan V.

Plant Genome. 2018 Nov;11(3). doi: 10.3835/plantgenome2018.03.0017.

16.

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

Montesinos-López OA, Luna-Vázquez FJ, Montesinos-López A, Juliana P, Singh R, Crossa J.

Plant Genome. 2018 Nov;11(3). doi: 10.3835/plantgenome2018.02.0013.

17.

Modeling copy number variation in the genomic prediction of maize hybrids.

Lyra DH, Galli G, Alves FC, Granato ÍSC, Vidotti MS, Bandeira E Sousa M, Morosini JS, Crossa J, Fritsche-Neto R.

Theor Appl Genet. 2019 Jan;132(1):273-288. doi: 10.1007/s00122-018-3215-2. Epub 2018 Oct 31.

PMID:
30382311
18.

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

Juliana P, Montesinos-López OA, Crossa J, Mondal S, González Pérez L, Poland J, Huerta-Espino J, Crespo-Herrera L, Govindan V, Dreisigacker S, Shrestha S, Pérez-Rodríguez P, Pinto Espinosa F, Singh RP.

Theor Appl Genet. 2019 Jan;132(1):177-194. doi: 10.1007/s00122-018-3206-3. Epub 2018 Oct 19.

19.

Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.

Montesinos-López OA, Montesinos-López A, Crossa J, Gianola D, Hernández-Suárez CM, Martín-Vallejo J.

G3 (Bethesda). 2018 Dec 10;8(12):3829-3840. doi: 10.1534/g3.118.200728.

20.

Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture.

Montesinos-López A, Montesinos-López OA, Gianola D, Crossa J, Hernández-Suárez CM.

G3 (Bethesda). 2018 Dec 10;8(12):3813-3828. doi: 10.1534/g3.118.200740.

21.

Harnessing genetic potential of wheat germplasm banks through impact-oriented-prebreeding for future food and nutritional security.

Singh S, Vikram P, Sehgal D, Burgueño J, Sharma A, Singh SK, Sansaloni CP, Joynson R, Brabbs T, Ortiz C, Solis-Moya E, Govindan V, Gupta N, Sidhu HS, Basandrai AK, Basandrai D, Ledesma-Ramires L, Suaste-Franco MP, Fuentes-Dávila G, Moreno JI, Sonder K, Singh VK, Singh S, Shokat S, Arif MAR, Laghari KA, Srivastava P, Bhavani S, Kumar S, Pal D, Jaiswal JP, Kumar U, Chaudhary HK, Crossa J, Payne TS, Imtiaz M, Sohu VS, Singh GP, Bains NS, Hall A, Pixley KV.

Sci Rep. 2018 Aug 21;8(1):12527. doi: 10.1038/s41598-018-30667-4.

22.

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

Montesinos-López OA, Montesinos-López A, Crossa J, Kismiantini, Ramírez-Alcaraz JM, Singh R, Mondal S, Juliana P.

Heredity (Edinb). 2019 Apr;122(4):381-401. doi: 10.1038/s41437-018-0109-7. Epub 2018 Aug 17.

23.

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

Roorkiwal M, Jarquin D, Singh MK, Gaur PM, Bharadwaj C, Rathore A, Howard R, Srinivasan S, Jain A, Garg V, Kale S, Chitikineni A, Tripathi S, Jones E, Robbins KR, Crossa J, Varshney RK.

Sci Rep. 2018 Aug 3;8(1):11701. doi: 10.1038/s41598-018-30027-2.

24.

BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models.

Granato I, Cuevas J, Luna-Vázquez F, Crossa J, Montesinos-López O, Burgueño J, Fritsche-Neto R.

G3 (Bethesda). 2018 Aug 30;8(9):3039-3047. doi: 10.1534/g3.118.200435.

25.

Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.

González-Camacho JM, Ornella L, Pérez-Rodríguez P, Gianola D, Dreisigacker S, Crossa J.

Plant Genome. 2018 Jul;11(2). doi: 10.3835/plantgenome2017.11.0104.

26.

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

Sukumaran S, Jarquin D, Crossa J, Reynolds M.

Plant Genome. 2018 Jul;11(2). doi: 10.3835/plantgenome2017.12.0112.

27.

A Bayesian Decision Theory Approach for Genomic Selection.

Villar-Hernández BJ, Pérez-Elizalde S, Crossa J, Pérez-Rodríguez P, Toledo FH, Burgueño J.

G3 (Bethesda). 2018 Aug 30;8(9):3019-3037. doi: 10.1534/g3.118.200430.

28.

Correction to: Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture.

Montesinos-López A, Montesinos-López OA, de Los Campos G, Crossa J, Burgueño J, Luna-Vazquez FJ.

Plant Methods. 2018 Jul 9;14:57. doi: 10.1186/s13007-018-0321-8. eCollection 2018.

29.

Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture.

Montesinos-López A, Montesinos-López OA, de Los Campos G, Crossa J, Burgueño J, Luna-Vazquez FJ.

Plant Methods. 2018 Jun 11;14:46. doi: 10.1186/s13007-018-0314-7. eCollection 2018. Erratum in: Plant Methods. 2018 Jul 9;14:57.

30.

When less can be better: How can we make genomic selection more cost-effective and accurate in barley?

Abed A, Pérez-Rodríguez P, Crossa J, Belzile F.

Theor Appl Genet. 2018 Sep;131(9):1873-1890. doi: 10.1007/s00122-018-3120-8. Epub 2018 Jun 1.

PMID:
29858950
31.

Genome-wide association mapping for resistance to leaf rust, stripe rust and tan spot in wheat reveals potential candidate genes.

Juliana P, Singh RP, Singh PK, Poland JA, Bergstrom GC, Huerta-Espino J, Bhavani S, Crossa J, Sorrells ME.

Theor Appl Genet. 2018 Jul;131(7):1405-1422. doi: 10.1007/s00122-018-3086-6. Epub 2018 Mar 27.

32.

A Bayesian Genomic Regression Model with Skew Normal Random Errors.

Pérez-Rodríguez P, Acosta-Pech R, Pérez-Elizalde S, Cruz CV, Espinosa JS, Crossa J.

G3 (Bethesda). 2018 May 4;8(5):1771-1785. doi: 10.1534/g3.117.300406.

33.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.

Cuevas J, Granato I, Fritsche-Neto R, Montesinos-Lopez OA, Burgueño J, Bandeira E Sousa M, Crossa J.

G3 (Bethesda). 2018 Mar 28;8(4):1347-1365. doi: 10.1534/g3.117.300454.

34.

Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations.

Zhang A, Wang H, Beyene Y, Semagn K, Liu Y, Cao S, Cui Z, Ruan Y, Burgueño J, San Vicente F, Olsen M, Prasanna BM, Crossa J, Yu H, Zhang X.

Front Plant Sci. 2017 Nov 8;8:1916. doi: 10.3389/fpls.2017.01916. eCollection 2017.

35.

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems.

Montesinos-López OA, Montesinos-López A, Crossa J, Montesinos-López JC, Mota-Sanchez D, Estrada-González F, Gillberg J, Singh R, Mondal S, Juliana P.

G3 (Bethesda). 2018 Jan 4;8(1):131-147. doi: 10.1534/g3.117.300309.

36.

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

Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de Los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK.

Trends Plant Sci. 2017 Nov;22(11):961-975. doi: 10.1016/j.tplants.2017.08.011. Epub 2017 Sep 28. Review.

PMID:
28965742
37.

Applications of Genomic Selection in Breeding Wheat for Rust Resistance.

Ornella L, González-Camacho JM, Dreisigacker S, Crossa J.

Methods Mol Biol. 2017;1659:173-182. doi: 10.1007/978-1-4939-7249-4_15.

PMID:
28856650
38.

Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data.

Montesinos-López A, Montesinos-López OA, Cuevas J, Mata-López WA, Burgueño J, Mondal S, Huerta J, Singh R, Autrique E, González-Pérez L, Crossa J.

Plant Methods. 2017 Jul 27;13:62. doi: 10.1186/s13007-017-0212-4. eCollection 2017.

39.

Variability in iron, zinc and phytic acid content in a worldwide collection of commercial durum wheat cultivars and the effect of reduced irrigation on these traits.

Magallanes-López AM, Hernandez-Espinosa N, Velu G, Posadas-Romano G, Ordoñez-Villegas VMG, Crossa J, Ammar K, Guzmán C.

Food Chem. 2017 Dec 15;237:499-505. doi: 10.1016/j.foodchem.2017.05.110. Epub 2017 May 22.

40.

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.

Juliana P, Singh RP, Singh PK, Crossa J, Rutkoski JE, Poland JA, Bergstrom GC, Sorrells ME.

Plant Genome. 2017 Jul;10(2). doi: 10.3835/plantgenome2016.08.0082.

41.

Single-Step Genomic and Pedigree Genotype × Environment Interaction Models for Predicting Wheat Lines in International Environments.

Pérez-Rodríguez P, Crossa J, Rutkoski J, Poland J, Singh R, Legarra A, Autrique E, Campos GL, Burgueño J, Dreisigacker S.

Plant Genome. 2017 Jul;10(2). doi: 10.3835/plantgenome2016.09.0089.

42.

Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.

Sun J, Rutkoski JE, Poland JA, Crossa J, Jannink JL, Sorrells ME.

Plant Genome. 2017 Jul;10(2). doi: 10.3835/plantgenome2016.11.0111.

43.

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

Jarquín D, Lemes da Silva C, Gaynor RC, Poland J, Fritz A, Howard R, Battenfield S, Crossa J.

Plant Genome. 2017 Jul;10(2). doi: 10.3835/plantgenome2016.12.0130.

44.

Genetic Gains in Grain Yield of a Maize Population Improved through Marker Assisted Recurrent Selection under Stress and Non-stress Conditions in West Africa.

Abdulmalik RO, Menkir A, Meseka SK, Unachukwu N, Ado SG, Olarewaju JD, Aba DA, Hearne S, Crossa J, Gedil M.

Front Plant Sci. 2017 May 22;8:841. doi: 10.3389/fpls.2017.00841. eCollection 2017.

45.

Genetic Gains in Yield and Yield Related Traits under Drought Stress and Favorable Environments in a Maize Population Improved Using Marker Assisted Recurrent Selection.

Bankole F, Menkir A, Olaoye G, Crossa J, Hearne S, Unachukwu N, Gedil M.

Front Plant Sci. 2017 May 17;8:808. doi: 10.3389/fpls.2017.00808. eCollection 2017.

46.

Rapid Cycling Genomic Selection in a Multiparental Tropical Maize Population.

Zhang X, Pérez-Rodríguez P, Burgueño J, Olsen M, Buckler E, Atlin G, Prasanna BM, Vargas M, San Vicente F, Crossa J.

G3 (Bethesda). 2017 Jul 5;7(7):2315-2326. doi: 10.1534/g3.117.043141.

47.

Use of Genomic Estimated Breeding Values Results in Rapid Genetic Gains for Drought Tolerance in Maize.

Vivek BS, Krishna GK, Vengadessan V, Babu R, Zaidi PH, Kha LQ, Mandal SS, Grudloyma P, Takalkar S, Krothapalli K, Singh IS, Ocampo ETM, Xingming F, Burgueño J, Azrai M, Singh RP, Crossa J.

Plant Genome. 2017 Mar;10(1). doi: 10.3835/plantgenome2016.07.0070.

48.

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

Bandeira E Sousa M, Cuevas J, de Oliveira Couto EG, Pérez-Rodríguez P, Jarquín D, Fritsche-Neto R, Burgueño J, Crossa J.

G3 (Bethesda). 2017 Jun 7;7(6):1995-2014. doi: 10.1534/g3.117.042341.

49.

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids.

Acosta-Pech R, Crossa J, de Los Campos G, Teyssèdre S, Claustres B, Pérez-Elizalde S, Pérez-Rodríguez P.

Theor Appl Genet. 2017 Jul;130(7):1431-1440. doi: 10.1007/s00122-017-2898-0. Epub 2017 Apr 11.

PMID:
28401254
50.

Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat.

Juliana P, Singh RP, Singh PK, Crossa J, Huerta-Espino J, Lan C, Bhavani S, Rutkoski JE, Poland JA, Bergstrom GC, Sorrells ME.

Theor Appl Genet. 2017 Jul;130(7):1415-1430. doi: 10.1007/s00122-017-2897-1. Epub 2017 Apr 9.

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