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Items: 1 to 50 of 67

1.

Correction to: Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs.

Fritsche-Neto R, Akdemir D, Jannink JL.

Theor Appl Genet. 2018 Jul;131(7):1603. doi: 10.1007/s00122-018-3118-2.

PMID:
29796770
2.

Resistance to Multiple Temperate and Tropical Stem and Sheath Diseases of Rice.

Rosas JE, Martínez S, Blanco P, Pérez de Vida F, Bonnecarrère V, Mosquera G, Cruz M, Garaycochea S, Monteverde E, McCouch S, Germán S, Jannink JL, Gutiérrez L.

Plant Genome. 2018 Mar;11(1). doi: 10.3835/plantgenome2017.03.0029.

3.

Regional Heritability Mapping Provides Insights into Dry Matter Content in African White and Yellow Cassava Populations.

Okeke UG, Akdemir D, Rabbi I, Kulakow P, Jannink JL.

Plant Genome. 2018 Mar;11(1). doi: 10.3835/plantgenome2017.06.0050.

4.

Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs.

Fritsche-Neto R, Akdemir D, Jannink JL.

Theor Appl Genet. 2018 May;131(5):1153-1162. doi: 10.1007/s00122-018-3068-8. Epub 2018 Feb 14. Erratum in: Theor Appl Genet. 2018 Jul;131(7):1603. Fristche-Neto, Roberto [corrected to Fritsche-Neto, Roberto].

PMID:
29445844
5.

Genome-wide association mapping and genomic prediction for CBSD resistance in Manihot esculenta.

Kayondo SI, Pino Del Carpio D, Lozano R, Ozimati A, Wolfe M, Baguma Y, Gracen V, Offei S, Ferguson M, Kawuki R, Jannink JL.

Sci Rep. 2018 Jan 24;8(1):1549. doi: 10.1038/s41598-018-19696-1.

6.

Improving Genomic Prediction in Cassava Field Experiments by Accounting for Interplot Competition.

Elias AA, Rabbi I, Kulakow P, Jannink JL.

G3 (Bethesda). 2018 Mar 2;8(3):933-944. doi: 10.1534/g3.117.300354.

7.

Genome-Wide Association Mapping of Correlated Traits in Cassava: Dry Matter and Total Carotenoid Content.

Rabbi IY, Udoh LI, Wolfe M, Parkes EY, Gedil MA, Dixon A, Ramu P, Jannink JL, Kulakow P.

Plant Genome. 2017 Nov;10(3). doi: 10.3835/plantgenome2016.09.0094.

8.

Prospects for Genomic Selection in Cassava Breeding.

Wolfe MD, Del Carpio DP, Alabi O, Ezenwaka LC, Ikeogu UN, Kayondo IS, Lozano R, Okeke UG, Ozimati AA, Williams E, Egesi C, Kawuki RS, Kulakow P, Rabbi IY, Jannink JL.

Plant Genome. 2017 Nov;10(3). doi: 10.3835/plantgenome2017.03.0015.

9.

Rapid analyses of dry matter content and carotenoids in fresh cassava roots using a portable visible and near infrared spectrometer (Vis/NIRS).

Ikeogu UN, Davrieux F, Dufour D, Ceballos H, Egesi CN, Jannink JL.

PLoS One. 2017 Dec 11;12(12):e0188918. doi: 10.1371/journal.pone.0188918. eCollection 2017.

10.

Accuracies of univariate and multivariate genomic prediction models in African cassava.

Okeke UG, Akdemir D, Rabbi I, Kulakow P, Jannink JL.

Genet Sel Evol. 2017 Dec 4;49(1):88. doi: 10.1186/s12711-017-0361-y.

11.

Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis.

Elias AA, Rabbi I, Kulakow P, Jannink JL.

G3 (Bethesda). 2018 Jan 4;8(1):53-62. doi: 10.1534/g3.117.300323.

12.

Locally epistatic models for genome-wide prediction and association by importance sampling.

Akdemir D, Jannink JL, Isidro-Sánchez J.

Genet Sel Evol. 2017 Oct 17;49(1):74. doi: 10.1186/s12711-017-0348-8.

13.

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.

14.

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.

15.

Population Genomics Related to Adaptation in Elite Oat Germplasm.

Esvelt Klos K, Huang YF, Bekele WA, Obert DE, Babiker E, Beattie AD, Bjørnstad Å, Bonman JM, Carson ML, Chao S, Gnanesh BN, Griffiths I, Harrison SA, Howarth CJ, Hu G, Ibrahim A, Islamovic E, Jackson EW, Jannink JL, Kolb FL, McMullen MS, Mitchell Fetch J, Murphy JP, Ohm HW, Rines HW, Rossnagel BG, Schlueter JA, Sorrells ME, Wight CP, Yan W, Tinker NA.

Plant Genome. 2016 Jul;9(2). doi: 10.3835/plantgenome2015.10.0103.

16.

The Triticeae Toolbox: Combining Phenotype and Genotype Data to Advance Small-Grains Breeding.

Blake VC, Birkett C, Matthews DE, Hane DL, Bradbury P, Jannink JL.

Plant Genome. 2016 Jul;9(2). doi: 10.3835/plantgenome2014.12.0099.

17.

Genome-Wide Association and Prediction Reveals Genetic Architecture of Cassava Mosaic Disease Resistance and Prospects for Rapid Genetic Improvement.

Wolfe MD, Rabbi IY, Egesi C, Hamblin M, Kawuki R, Kulakow P, Lozano R, Carpio DP, Ramu P, Jannink JL.

Plant Genome. 2016 Jul;9(2). doi: 10.3835/plantgenome2015.11.0118.

18.

Breeding Value of Primary Synthetic Wheat Genotypes for Grain Yield.

Jafarzadeh J, Bonnett D, Jannink JL, Akdemir D, Dreisigacker S, Sorrells ME.

PLoS One. 2016 Sep 22;11(9):e0162860. doi: 10.1371/journal.pone.0162860. eCollection 2016.

19.
20.

Evaluating Imputation Algorithms for Low-Depth Genotyping-By-Sequencing (GBS) Data.

Chan AW, Hamblin MT, Jannink JL.

PLoS One. 2016 Aug 18;11(8):e0160733. doi: 10.1371/journal.pone.0160733. eCollection 2016.

21.

Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement.

Spindel JE, Begum H, Akdemir D, Collard B, Redoña E, Jannink JL, McCouch S.

Heredity (Edinb). 2016 Apr;116(4):395-408. doi: 10.1038/hdy.2015.113. Epub 2016 Feb 10.

22.

An alternative covariance estimator to investigate genetic heterogeneity in populations.

Heslot N, Jannink JL.

Genet Sel Evol. 2015 Nov 26;47:93. doi: 10.1186/s12711-015-0171-z.

23.

Correction: Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines.

Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, Atlin G, Jannink JL, McCouch SR.

PLoS Genet. 2015 Jun 30;11(6):e1005350. doi: 10.1371/journal.pgen.1005350. eCollection 2015 Jun. No abstract available.

24.

Identification and distribution of the NBS-LRR gene family in the Cassava genome.

Lozano R, Hamblin MT, Prochnik S, Jannink JL.

BMC Genomics. 2015 May 7;16:360. doi: 10.1186/s12864-015-1554-9.

25.

Optimization of genomic selection training populations with a genetic algorithm.

Akdemir D, Sanchez JI, Jannink JL.

Genet Sel Evol. 2015 May 6;47:38. doi: 10.1186/s12711-015-0116-6.

26.

Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, Atlin G, Jannink JL, McCouch SR.

PLoS Genet. 2015 Feb 17;11(2):e1004982. doi: 10.1371/journal.pgen.1004982. eCollection 2015 Feb. Erratum in: PLoS Genet. 2015 Jun;11(6):e1005350.

27.

Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.

Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink JL, Singh RP, Autrique E, de los Campos G.

G3 (Bethesda). 2015 Feb 6;5(4):569-82. doi: 10.1534/g3.114.016097.

28.

Locally epistatic genomic relationship matrices for genomic association and prediction.

Akdemir D, Jannink JL.

Genetics. 2015 Mar;199(3):857-71. doi: 10.1534/genetics.114.173658. Epub 2015 Jan 22.

29.

solGS: a web-based tool for genomic selection.

Tecle IY, Edwards JD, Menda N, Egesi C, Rabbi IY, Kulakow P, Kawuki R, Jannink JL, Mueller LA.

BMC Bioinformatics. 2014 Dec 14;15:398. doi: 10.1186/s12859-014-0398-7.

30.

Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs.

Zhang X, Pérez-Rodríguez P, Semagn K, Beyene Y, Babu R, López-Cruz MA, San Vicente F, Olsen M, Buckler E, Jannink JL, Prasanna BM, Crossa J.

Heredity (Edinb). 2015 Mar;114(3):291-9. doi: 10.1038/hdy.2014.99. Epub 2014 Nov 19.

31.

Training set optimization under population structure in genomic selection.

Isidro J, Jannink JL, Akdemir D, Poland J, Heslot N, Sorrells ME.

Theor Appl Genet. 2015 Jan;128(1):145-58. doi: 10.1007/s00122-014-2418-4. Epub 2014 Nov 1.

32.

Genomic selection in plant breeding.

Newell MA, Jannink JL.

Methods Mol Biol. 2014;1145:117-30. doi: 10.1007/978-1-4939-0446-4_10.

PMID:
24816664
33.

High-resolution mapping of resistance to cassava mosaic geminiviruses in cassava using genotyping-by-sequencing and its implications for breeding.

Rabbi IY, Hamblin MT, Kumar PL, Gedil MA, Ikpan AS, Jannink JL, Kulakow PA.

Virus Res. 2014 Jun 24;186:87-96. doi: 10.1016/j.virusres.2013.12.028. Epub 2013 Dec 31.

34.

Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions.

Heslot N, Akdemir D, Sorrells ME, Jannink JL.

Theor Appl Genet. 2014 Feb;127(2):463-80. doi: 10.1007/s00122-013-2231-5. Epub 2013 Nov 22.

PMID:
24264761
35.

Impact of marker ascertainment bias on genomic selection accuracy and estimates of genetic diversity.

Heslot N, Rutkoski J, Poland J, Jannink JL, Sorrells ME.

PLoS One. 2013 Sep 5;8(9):e74612. doi: 10.1371/journal.pone.0074612. eCollection 2013.

36.

Genomic prediction in maize breeding populations with genotyping-by-sequencing.

Crossa J, Beyene Y, Kassa S, Pérez P, Hickey JM, Chen C, de los Campos G, Burgueño J, Windhausen VS, Buckler E, Jannink JL, Lopez Cruz MA, Babu R.

G3 (Bethesda). 2013 Nov 6;3(11):1903-26. doi: 10.1534/g3.113.008227.

37.

Genomic predictability of interconnected biparental maize populations.

Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink JL, Melchinger AE.

Genetics. 2013 Jun;194(2):493-503. doi: 10.1534/genetics.113.150227. Epub 2013 Mar 27.

38.

SNP discovery and chromosome anchoring provide the first physically-anchored hexaploid oat map and reveal synteny with model species.

Oliver RE, Tinker NA, Lazo GR, Chao S, Jellen EN, Carson ML, Rines HW, Obert DE, Lutz JD, Shackelford I, Korol AB, Wight CP, Gardner KM, Hattori J, Beattie AD, Bjørnstad Å, Bonman JM, Jannink JL, Sorrells ME, Brown-Guedira GL, Mitchell Fetch JW, Harrison SA, Howarth CJ, Ibrahim A, Kolb FL, McMullen MS, Murphy JP, Ohm HW, Rossnagel BG, Yan W, Miclaus KJ, Hiller J, Maughan PJ, Redman Hulse RR, Anderson JM, Islamovic E, Jackson EW.

PLoS One. 2013;8(3):e58068. doi: 10.1371/journal.pone.0058068. Epub 2013 Mar 22. Erratum in: PLoS One. 2013;8(10). doi:10.1371/annotation/9b2ca31c-0aca-44b1-84a1-8bdf8ded7439.

39.

Imputation of unordered markers and the impact on genomic selection accuracy.

Rutkoski JE, Poland J, Jannink JL, Sorrells ME.

G3 (Bethesda). 2013 Mar;3(3):427-39. doi: 10.1534/g3.112.005363. Epub 2013 Mar 1.

40.

Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.

Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink JL, Sorrells ME, Raman B, Cairns JE, Tarekegne A, Semagn K, Beyene Y, Grudloyma P, Technow F, Riedelsheimer C, Melchinger AE.

G3 (Bethesda). 2012 Nov;2(11):1427-36. doi: 10.1534/g3.112.003699. Epub 2012 Nov 1.

41.

Shrinkage estimation of the realized relationship matrix.

Endelman JB, Jannink JL.

G3 (Bethesda). 2012 Nov;2(11):1405-13. doi: 10.1534/g3.112.004259. Epub 2012 Nov 1.

42.

Multiple-trait genomic selection methods increase genetic value prediction accuracy.

Jia Y, Jannink JL.

Genetics. 2012 Dec;192(4):1513-22. doi: 10.1534/genetics.112.144246. Epub 2012 Oct 19.

43.

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin.

Newell MA, Asoro FG, Scott MP, White PJ, Beavis WD, Jannink JL.

Theor Appl Genet. 2012 Dec;125(8):1687-96. doi: 10.1007/s00122-012-1945-0. Epub 2012 Aug 3.

PMID:
22865125
44.

Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach.

Poland JA, Brown PJ, Sorrells ME, Jannink JL.

PLoS One. 2012;7(2):e32253. doi: 10.1371/journal.pone.0032253. Epub 2012 Feb 28.

45.

Population genetics of genomics-based crop improvement methods.

Hamblin MT, Buckler ES, Jannink JL.

Trends Genet. 2011 Mar;27(3):98-106. doi: 10.1016/j.tig.2010.12.003. Epub 2011 Jan 10. Review.

PMID:
21227531
46.

Performance of single nucleotide polymorphisms versus haplotypes for genome-wide association analysis in barley.

Lorenz AJ, Hamblin MT, Jannink JL.

PLoS One. 2010 Nov 22;5(11):e14079. doi: 10.1371/journal.pone.0014079.

47.

Population structure and linkage disequilibrium in oat (Avena sativa L.): implications for genome-wide association studies.

Newell MA, Cook D, Tinker NA, Jannink JL.

Theor Appl Genet. 2011 Feb;122(3):623-32. doi: 10.1007/s00122-010-1474-7. Epub 2010 Nov 2.

PMID:
21042793
48.

Dynamics of long-term genomic selection.

Jannink JL.

Genet Sel Evol. 2010 Aug 16;42:35. doi: 10.1186/1297-9686-42-35.

49.

Genomic selection in plant breeding: from theory to practice.

Jannink JL, Lorenz AJ, Iwata H.

Brief Funct Genomics. 2010 Mar;9(2):166-77. doi: 10.1093/bfgp/elq001. Epub 2010 Feb 15. Review.

PMID:
20156985
50.

Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study.

Zhong S, Dekkers JC, Fernando RL, Jannink JL.

Genetics. 2009 May;182(1):355-64. doi: 10.1534/genetics.108.098277. Epub 2009 Mar 18.

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