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Items: 1 to 20 of 147

1.

Association mapping for cold tolerance in two large maize inbred panels.

Revilla P, Rodríguez VM, Ordás A, Rincent R, Charcosset A, Giauffret C, Melchinger AE, Schön CC, Bauer E, Altmann T, Brunel D, Moreno-González J, Campo L, Ouzunova M, Álvarez Á, Ruíz de Galarreta JI, Laborde J, Malvar RA.

BMC Plant Biol. 2016 Jun 6;16(1):127. doi: 10.1186/s12870-016-0816-2.

2.

Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles.

Zenke-Philippi C, Thiemann A, Seifert F, Schrag T, Melchinger AE, Scholten S, Frisch M.

BMC Genomics. 2016 Mar 29;17(1):262. doi: 10.1186/s12864-016-2580-y.

3.

The Genetic Basis of Haploid Induction in Maize Identified with a Novel Genome-Wide Association Method.

Hu H, Schrag TA, Peis R, Unterseer S, Schipprack W, Chen S, Lai J, Yan J, Prasanna BM, Nair SK, Chaikam V, Rotarenco V, Shatskaya OA, Zavalishina A, Scholten S, Schön CC, Melchinger AE.

Genetics. 2016 Apr;202(4):1267-76. doi: 10.1534/genetics.115.184234. Epub 2016 Feb 19.

PMID:
26896330
4.

Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize.

Han S, Utz HF, Liu W, Schrag TA, Stange M, Würschum T, Miedaner T, Bauer E, Schön CC, Melchinger AE.

Theor Appl Genet. 2016 Feb;129(2):431-44. doi: 10.1007/s00122-015-2637-3. Epub 2015 Dec 10.

PMID:
26660464
5.

Fine mapping of qhir8 affecting in vivo haploid induction in maize.

Liu C, Li W, Zhong Y, Dong X, Hu H, Tian X, Wang L, Chen B, Chen C, Melchinger AE, Chen S.

Theor Appl Genet. 2015 Dec;128(12):2507-15. doi: 10.1007/s00122-015-2605-y. Epub 2015 Oct 6.

PMID:
26440799
6.

Forecasting the accuracy of genomic prediction with different selection targets in the training and prediction set as well as truncation selection.

Schopp P, Riedelsheimer C, Utz HF, Schön CC, Melchinger AE.

Theor Appl Genet. 2015 Nov;128(11):2189-201. doi: 10.1007/s00122-015-2577-y. Epub 2015 Aug 1.

PMID:
26231985
7.

Shrinkage estimation of the genomic relationship matrix can improve genomic estimated breeding values in the training set.

Müller D, Technow F, Melchinger AE.

Theor Appl Genet. 2015 Apr;128(4):693-703. doi: 10.1007/s00122-015-2464-6. Epub 2015 Mar 4.

PMID:
25735232
8.

Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems.

Junker A, Muraya MM, Weigelt-Fischer K, Arana-Ceballos F, Klukas C, Melchinger AE, Meyer RC, Riewe D, Altmann T.

Front Plant Sci. 2015 Jan 20;5:770. doi: 10.3389/fpls.2014.00770. eCollection 2014.

9.

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
10.

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.

11.

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.

12.

Identification of key ancestors of modern germplasm in a breeding program of maize.

Technow F, Schrag TA, Schipprack W, Melchinger AE.

Theor Appl Genet. 2014 Dec;127(12):2545-53. doi: 10.1007/s00122-014-2396-6. Epub 2014 Sep 11.

PMID:
25208647
13.

Optimum allocation of test resources and comparison of breeding strategies for hybrid wheat.

Longin CF, Mi X, Melchinger AE, Reif JC, Würschum T.

Theor Appl Genet. 2014 Oct;127(10):2117-26. doi: 10.1007/s00122-014-2365-0. Epub 2014 Aug 8.

PMID:
25104327
14.

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.

15.

Genome-wide meta-analysis of maize heterosis reveals the potential role of additive gene expression at pericentromeric loci.

Thiemann A, Fu J, Seifert F, Grant-Downton RT, Schrag TA, Pospisil H, Frisch M, Melchinger AE, Scholten S.

BMC Plant Biol. 2014 Apr 2;14:88. doi: 10.1186/1471-2229-14-88.

16.

Recovering power in association mapping panels with variable levels of linkage disequilibrium.

Rincent R, Moreau L, Monod H, Kuhn E, Melchinger AE, Malvar RA, Moreno-Gonzalez J, Nicolas S, Madur D, Combes V, Dumas F, Altmann T, Brunel D, Ouzunova M, Flament P, Dubreuil P, Charcosset A, Mary-Huard T.

Genetics. 2014 May;197(1):375-87. doi: 10.1534/genetics.113.159731. Epub 2014 Feb 14.

17.

Intraspecific variation of recombination rate in maize.

Bauer E, Falque M, Walter H, Bauland C, Camisan C, Campo L, Meyer N, Ranc N, Rincent R, Schipprack W, Altmann T, Flament P, Melchinger AE, Menz M, Moreno-González J, Ouzunova M, Revilla P, Charcosset A, Martin OC, Schön CC.

Genome Biol. 2013;14(9):R103.

18.

Optimizing the allocation of resources for genomic selection in one breeding cycle.

Riedelsheimer C, Melchinger AE.

Theor Appl Genet. 2013 Nov;126(11):2835-48. doi: 10.1007/s00122-013-2175-9. Epub 2013 Aug 27.

PMID:
23982591
19.

The maize leaf lipidome shows multilevel genetic control and high predictive value for agronomic traits.

Riedelsheimer C, Brotman Y, Méret M, Melchinger AE, Willmitzer L.

Sci Rep. 2013;3:2479. doi: 10.1038/srep02479.

20.

Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation.

Busemeyer L, Ruckelshausen A, Möller K, Melchinger AE, Alheit KV, Maurer HP, Hahn V, Weissmann EA, Reif JC, Würschum T.

Sci Rep. 2013;3:2442. doi: 10.1038/srep02442.

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