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Items: 1 to 20 of 118

1.

Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups.

Technow F, Bürger A, Melchinger AE.

G3 (Bethesda). 2013 Feb;3(2):197-203. doi: 10.1534/g3.112.004630. Epub 2013 Feb 1.

2.

Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize.

Technow F, Schrag TA, Schipprack W, Bauer E, Simianer H, Melchinger AE.

Genetics. 2014 Aug;197(4):1343-55. doi: 10.1534/genetics.114.165860. Epub 2014 May 21.

3.

Genome-wide association mapping reveals novel sources of resistance to northern corn leaf blight in maize.

Ding J, Ali F, Chen G, Li H, Mahuku G, Yang N, Narro L, Magorokosho C, Makumbi D, Yan J.

BMC Plant Biol. 2015 Aug 20;15:206. doi: 10.1186/s12870-015-0589-z.

4.

Genome-wide association mapping of flowering time and northern corn leaf blight (Setosphaeria turcica) resistance in a vast commercial maize germplasm set.

Van Inghelandt D, Melchinger AE, Martinant JP, Stich B.

BMC Plant Biol. 2012 Apr 30;12:56. doi: 10.1186/1471-2229-12-56.

5.

Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction.

Lehermeier C, Krämer N, Bauer E, Bauland C, Camisan C, Campo L, Flament P, Melchinger AE, Menz M, Meyer N, Moreau L, Moreno-González J, Ouzunova M, Pausch H, Ranc N, Schipprack W, Schönleben M, Walter H, Charcosset A, Schön CC.

Genetics. 2014 Sep;198(1):3-16. doi: 10.1534/genetics.114.161943.

6.

Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize.

Giraud H, Lehermeier C, Bauer E, Falque M, Segura V, Bauland C, Camisan C, Campo L, Meyer N, Ranc N, Schipprack W, Flament P, Melchinger AE, Menz M, Moreno-González J, Ouzunova M, Charcosset A, Schön CC, Moreau L.

Genetics. 2014 Dec;198(4):1717-34. doi: 10.1534/genetics.114.169367. Epub 2014 Sep 29.

7.

Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production.

Rincent R, Nicolas S, Bouchet S, Altmann T, Brunel D, Revilla P, Malvar RA, Moreno-Gonzalez J, Campo L, Melchinger AE, Schipprack W, Bauer E, Schoen CC, Meyer N, Ouzunova M, Dubreuil P, Giauffret C, Madur D, Combes V, Dumas F, Bauland C, Jamin P, Laborde J, Flament P, Moreau L, Charcosset A.

Theor Appl Genet. 2014 Nov;127(11):2313-31. doi: 10.1007/s00122-014-2379-7. Epub 2014 Oct 10.

PMID:
25301321
8.
9.

A comprehensive study of the genomic differentiation between temperate Dent and Flint maize.

Unterseer S, Pophaly SD, Peis R, Westermeier P, Mayer M, Seidel MA, Haberer G, Mayer KF, Ordas B, Pausch H, Tellier A, Bauer E, Schön CC.

Genome Biol. 2016 Jul 8;17(1):137. doi: 10.1186/s13059-016-1009-x.

10.

Validation of consensus quantitative trait loci associated with resistance to multiple foliar pathogens of maize.

Asea G, Vivek BS, Bigirwa G, Lipps PE, Pratt RC.

Phytopathology. 2009 May;99(5):540-7. doi: 10.1094/PHYTO-99-5-0540.

11.

Genomic BLUP decoded: a look into the black box of genomic prediction.

Habier D, Fernando RL, Garrick DJ.

Genetics. 2013 Jul;194(3):597-607. doi: 10.1534/genetics.113.152207. Epub 2013 May 2.

12.

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.

13.

Accuracy of Genomic Prediction in Synthetic Populations Depending on the Number of Parents, Relatedness, and Ancestral Linkage Disequilibrium.

Schopp P, Müller D, Technow F, Melchinger AE.

Genetics. 2017 Jan;205(1):441-454. doi: 10.1534/genetics.116.193243. Epub 2016 Nov 9.

PMID:
28049710
14.

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
15.

Using Bayesian Multilevel Whole Genome Regression Models for Partial Pooling of Training Sets in Genomic Prediction.

Technow F, Totir LR.

G3 (Bethesda). 2015 May 29;5(8):1603-12. doi: 10.1534/g3.115.019299.

16.

Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects.

Technow F, Riedelsheimer C, Schrag TA, Melchinger AE.

Theor Appl Genet. 2012 Oct;125(6):1181-94. doi: 10.1007/s00122-012-1905-8. Epub 2012 Jun 26.

PMID:
22733443
17.

Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms.

Lu Y, Yan J, Guimarães CT, Taba S, Hao Z, Gao S, Chen S, Li J, Zhang S, Vivek BS, Magorokosho C, Mugo S, Makumbi D, Parentoni SN, Shah T, Rong T, Crouch JH, Xu Y.

Theor Appl Genet. 2009 Dec;120(1):93-115. doi: 10.1007/s00122-009-1162-7. Epub 2009 Oct 11.

PMID:
19823800
18.

Threshold models for genome-enabled prediction of ordinal categorical traits in plant breeding.

Montesinos-López OA, Montesinos-López A, Pérez-Rodríguez P, de Los Campos G, Eskridge K, Crossa J.

G3 (Bethesda). 2014 Dec 23;5(2):291-300. doi: 10.1534/g3.114.016188.

19.

Trends in genetic diversity among European maize cultivars and their parental components during the past 50 years.

Reif JC, Hamrit S, Heckenberger M, Schipprack W, Maurer HP, Bohn M, Melchinger AE.

Theor Appl Genet. 2005 Sep;111(5):838-45. Epub 2005 Oct 18.

PMID:
16034585
20.

Genome-based prediction of testcross values in maize.

Albrecht T, Wimmer V, Auinger HJ, Erbe M, Knaak C, Ouzunova M, Simianer H, Schön CC.

Theor Appl Genet. 2011 Jul;123(2):339-50. doi: 10.1007/s00122-011-1587-7. Epub 2011 Apr 20.

PMID:
21505832

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