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Results: 1 to 20 of 142


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 Aug 1. [Epub ahead of print]


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


High-density genotyping: an overkill for QTL mapping? Lessons learned from a case study in maize and simulations.

Stange M, Utz HF, Schrag TA, Melchinger AE, Würschum T.

Theor Appl Genet. 2013 Oct;126(10):2563-74. doi: 10.1007/s00122-013-2155-0. Epub 2013 Jul 17.


Rapid and accurate identification of in vivo-induced haploid seeds based on oil content in maize.

Melchinger AE, Schipprack W, Würschum T, Chen S, Technow F.

Sci Rep. 2013;3:2129. doi: 10.1038/srep02129.


QTL mapping of stalk bending strength in a recombinant inbred line maize population.

Hu H, Liu W, Fu Z, Homann L, Technow F, Wang H, Song C, Li S, Melchinger AE, Chen S.

Theor Appl Genet. 2013 Sep;126(9):2257-66. doi: 10.1007/s00122-013-2132-7. Epub 2013 Jun 5.


Fine mapping of qhir1 influencing in vivo haploid induction in maize.

Dong X, Xu X, Miao J, Li L, Zhang D, Mi X, Liu C, Tian X, Melchinger AE, Chen S.

Theor Appl Genet. 2013 Jul;126(7):1713-20. doi: 10.1007/s00122-013-2086-9. Epub 2013 Mar 29.


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.

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