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Items: 1 to 50 of 112

1.

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.

2.

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.

3.

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. 2018 Oct 31. doi: 10.1007/s00122-018-3215-2. [Epub ahead of print]

PMID:
30382311
4.

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. 2018 Oct 19. doi: 10.1007/s00122-018-3206-3. [Epub ahead of print]

PMID:
30341493
5.

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.

6.

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.

7.

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.

8.

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). 2018 Aug 17. doi: 10.1038/s41437-018-0109-7. [Epub ahead of print]

PMID:
30120367
9.

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.

10.

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.

PMID:
30049744
Free PMC Article
11.

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.

12.

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.

13.

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.

PMID:
30021830
Free PMC Article
14.

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.

15.

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.

16.

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
17.

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.

18.

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.

19.

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.

20.

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.

21.

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.

22.

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
23.

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
24.

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.

25.

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.

26.

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.

27.

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.

28.

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.

29.

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.

30.

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.

31.

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.

32.

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.

33.

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.

34.

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.

35.

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
36.

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.

37.

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction.

Montesinos-López OA, Montesinos-López A, Crossa J, Montesinos-López JC, Luna-Vázquez FJ, Salinas-Ruiz J, Herrera-Morales JR, Buenrostro-Mariscal R.

G3 (Bethesda). 2017 Jun 7;7(6):1833-1853. doi: 10.1534/g3.117.041202.

38.

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

Montesinos-López OA, Montesinos-López A, Crossa J, Toledo FH, Montesinos-López JC, Singh P, Juliana P, Salinas-Ruiz J.

G3 (Bethesda). 2017 May 5;7(5):1595-1606. doi: 10.1534/g3.117.039974.

39.

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data.

Montesinos-López OA, Montesinos-López A, Crossa J, de Los Campos G, Alvarado G, Suchismita M, Rutkoski J, González-Pérez L, Burgueño J.

Plant Methods. 2017 Jan 3;13:4. doi: 10.1186/s13007-016-0154-2. eCollection 2017.

40.

Wheat quality improvement at CIMMYT and the use of genomic selection on it.

Guzman C, Peña RJ, Singh R, Autrique E, Dreisigacker S, Crossa J, Rutkoski J, Poland J, Battenfield S.

Appl Transl Genom. 2016 Oct 29;11:3-8. doi: 10.1016/j.atg.2016.10.004. eCollection 2016 Dec. Review.

41.

Genome-Enabled Prediction Models for Yield Related Traits in Chickpea.

Roorkiwal M, Rathore A, Das RR, Singh MK, Jain A, Srinivasan S, Gaur PM, Chellapilla B, Tripathi S, Li Y, Hickey JM, Lorenz A, Sutton T, Crossa J, Jannink JL, Varshney RK.

Front Plant Sci. 2016 Nov 22;7:1666. eCollection 2016.

42.

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

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

G3 (Bethesda). 2017 Feb 9;7(2):481-495. doi: 10.1534/g3.116.036251.

43.

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

Cuevas J, Crossa J, Soberanis V, Pérez-Elizalde S, Pérez-Rodríguez P, Campos GL, Montesinos-López OA, Burgueño J.

Plant Genome. 2016 Nov;9(3). doi: 10.3835/plantgenome2016.03.0024.

44.

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

Cuevas J, Crossa J, Montesinos-López OA, Burgueño J, Pérez-Rodríguez P, de Los Campos G.

G3 (Bethesda). 2017 Jan 5;7(1):41-53. doi: 10.1534/g3.116.035584.

45.

Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat.

Rutkoski J, Poland J, Mondal S, Autrique E, Pérez LG, Crossa J, Reynolds M, Singh R.

G3 (Bethesda). 2016 Sep 8;6(9):2799-808. doi: 10.1534/g3.116.032888.

46.

Performance and grain yield stability of maize populations developed using marker-assisted recurrent selection and pedigree selection procedures.

Beyene Y, Semagn K, Mugo S, Prasanna BM, Tarekegne A, Gakunga J, Sehabiague P, Meisel B, Oikeh SO, Olsen M, Crossa J.

Euphytica. 2016;208:285-297. Epub 2015 Nov 9.

47.

A Genomic Bayesian Multi-trait and Multi-environment Model.

Montesinos-López OA, Montesinos-López A, Crossa J, Toledo FH, Pérez-Hernández O, Eskridge KM, Rutkoski J.

G3 (Bethesda). 2016 Sep 8;6(9):2725-44. doi: 10.1534/g3.116.032359.

48.

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

Saint Pierre C, Burgueño J, Crossa J, Fuentes Dávila G, Figueroa López P, Solís Moya E, Ireta Moreno J, Hernández Muela VM, Zamora Villa VM, Vikram P, Mathews K, Sansaloni C, Sehgal D, Jarquin D, Wenzl P, Singh S.

Sci Rep. 2016 Jun 17;6:27312. doi: 10.1038/srep27312.

49.

Corrigendum: Unlocking the genetic diversity of Creole wheats.

Vikram P, Franco J, Burgueño-Ferreira J, Li H, Sehgal D, Pierre CS, Ortiz C, Sneller C, Tattaris M, Guzman C, Sansaloni CP, Ellis M, Fuentes-Davila G, Reynolds M, Sonder K, Singh P, Payne T, Wenzl P, Sharma A, Bains NS, Singh GP, Crossa J, Singh S.

Sci Rep. 2016 May 20;6:26216. doi: 10.1038/srep26216. No abstract available.

50.

Genomic Prediction of Gene Bank Wheat Landraces.

Crossa J, Jarquín D, Franco J, Pérez-Rodríguez P, Burgueño J, Saint-Pierre C, Vikram P, Sansaloni C, Petroli C, Akdemir D, Sneller C, Reynolds M, Tattaris M, Payne T, Guzman C, Peña RJ, Wenzl P, Singh S.

G3 (Bethesda). 2016 Jul 7;6(7):1819-34. doi: 10.1534/g3.116.029637.

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