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Items: 1 to 20 of 25

1.

Across-years prediction of hybrid performance in maize using genomics.

Schrag TA, Schipprack W, Melchinger AE.

Theor Appl Genet. 2019 Apr;132(4):933-946. doi: 10.1007/s00122-018-3249-5. Epub 2018 Nov 29.

PMID:
30498894
2.

Small RNA-based prediction of hybrid performance in maize.

Seifert F, Thiemann A, Schrag TA, Rybka D, Melchinger AE, Frisch M, Scholten S.

BMC Genomics. 2018 May 21;19(1):371. doi: 10.1186/s12864-018-4708-8.

3.

Parental Expression Variation of Small RNAs Is Negatively Correlated with Grain Yield Heterosis in a Maize Breeding Population.

Seifert F, Thiemann A, Grant-Downton R, Edelmann S, Rybka D, Schrag TA, Frisch M, Dickinson HG, Melchinger AE, Scholten S.

Front Plant Sci. 2018 Jan 30;9:13. doi: 10.3389/fpls.2018.00013. eCollection 2018.

4.

Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.

Schrag TA, Westhues M, Schipprack W, Seifert F, Thiemann A, Scholten S, Melchinger AE.

Genetics. 2018 Apr;208(4):1373-1385. doi: 10.1534/genetics.117.300374. Epub 2018 Jan 23.

5.

Omics-based hybrid prediction in maize.

Westhues M, Schrag TA, Heuer C, Thaller G, Utz HF, Schipprack W, Thiemann A, Seifert F, Ehret A, Schlereth A, Stitt M, Nikoloski Z, Willmitzer L, Schön CC, Scholten S, Melchinger AE.

Theor Appl Genet. 2017 Sep;130(9):1927-1939. doi: 10.1007/s00122-017-2934-0. Epub 2017 Jun 24.

PMID:
28647896
6.

Safeguarding Our Genetic Resources with Libraries of Doubled-Haploid Lines.

Melchinger AE, Schopp P, Müller D, Schrag TA, Bauer E, Unterseer S, Homann L, Schipprack W, Schön CC.

Genetics. 2017 Jul;206(3):1611-1619. doi: 10.1534/genetics.115.186205. Epub 2017 May 3.

7.

Low validation rate of quantitative trait loci for Gibberella ear rot resistance in European maize.

Brauner PC, Melchinger AE, Schrag TA, Utz HF, Schipprack W, Kessel B, Ouzunova M, Miedaner T.

Theor Appl Genet. 2017 Jan;130(1):175-186. doi: 10.1007/s00122-016-2802-3. Epub 2016 Oct 5.

PMID:
27709251
8.

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;17:262. doi: 10.1186/s12864-016-2580-y.

9.

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.

10.

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
11.

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.

12.

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.

13.

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.

PMID:
23860723
14.

Association mapping for chilling tolerance in elite flint and dent maize inbred lines evaluated in growth chamber and field experiments.

Strigens A, Freitag NM, Gilbert X, Grieder C, Riedelsheimer C, Schrag TA, Messmer R, Melchinger AE.

Plant Cell Environ. 2013 Oct;36(10):1871-87. doi: 10.1111/pce.12096. Epub 2013 May 13.

15.

Genetic diversity analysis of elite European maize (Zea mays L.) inbred lines using AFLP, SSR, and SNP markers reveals ascertainment bias for a subset of SNPs.

Frascaroli E, Schrag TA, Melchinger AE.

Theor Appl Genet. 2013 Jan;126(1):133-41. doi: 10.1007/s00122-012-1968-6. Epub 2012 Sep 4.

PMID:
22945268
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.

Partial least squares regression, support vector machine regression, and transcriptome-based distances for prediction of maize hybrid performance with gene expression data.

Fu J, Falke KC, Thiemann A, Schrag TA, Melchinger AE, Scholten S, Frisch M.

Theor Appl Genet. 2012 Mar;124(5):825-33. doi: 10.1007/s00122-011-1747-9. Epub 2011 Nov 19.

PMID:
22101908
18.

Dissecting grain yield pathways and their interactions with grain dry matter content by a two-step correlation approach with maize seedling transcriptome.

Fu J, Thiemann A, Schrag TA, Melchinger AE, Scholten S, Frisch M.

BMC Plant Biol. 2010 Apr 12;10:63. doi: 10.1186/1471-2229-10-63.

19.

Prediction of hybrid performance in maize using molecular markers and joint analyses of hybrids and parental inbreds.

Schrag TA, Möhring J, Melchinger AE, Kusterer B, Dhillon BS, Piepho HP, Frisch M.

Theor Appl Genet. 2010 Jan;120(2):451-61. doi: 10.1007/s00122-009-1208-x. Epub 2009 Nov 15.

PMID:
19916002
20.

Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize.

Frisch M, Thiemann A, Fu J, Schrag TA, Scholten S, Melchinger AE.

Theor Appl Genet. 2010 Jan;120(2):441-50. doi: 10.1007/s00122-009-1204-1. Epub 2009 Nov 13.

PMID:
19911157

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