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Items: 1 to 50 of 165

1.

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

Schrag TA, Schipprack W, Melchinger AE.

Theor Appl Genet. 2018 Nov 29. doi: 10.1007/s00122-018-3249-5. [Epub ahead of print]

PMID:
30498894
2.

Marker-Assisted Breeding of Improved Maternal Haploid Inducers in Maize for the Tropical/Subtropical Regions.

Chaikam V, Nair SK, Martinez L, Lopez LA, Utz HF, Melchinger AE, Boddupalli PM.

Front Plant Sci. 2018 Oct 18;9:1527. doi: 10.3389/fpls.2018.01527. eCollection 2018.

3.

Genomic Prediction Within and Among Doubled-Haploid Libraries from Maize Landraces.

Brauner PC, Müller D, Schopp P, Böhm J, Bauer E, Schön CC, Melchinger AE.

Genetics. 2018 Dec;210(4):1185-1196. doi: 10.1534/genetics.118.301286. Epub 2018 Sep 26.

PMID:
30257934
4.

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.

5.

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.

6.

Selection on Expected Maximum Haploid Breeding Values Can Increase Genetic Gain in Recurrent Genomic Selection.

Müller D, Schopp P, Melchinger AE.

G3 (Bethesda). 2018 Mar 28;8(4):1173-1181. doi: 10.1534/g3.118.200091.

7.

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.

PMID:
29363551
8.

Genomic Prediction Within and Across Biparental Families: Means and Variances of Prediction Accuracy and Usefulness of Deterministic Equations.

Schopp P, Müller D, Wientjes YCJ, Melchinger AE.

G3 (Bethesda). 2017 Nov 6;7(11):3571-3586. doi: 10.1534/g3.117.300076.

9.

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
10.

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.

11.

Dissection of a major QTL qhir1 conferring maternal haploid induction ability in maize.

Nair SK, Molenaar W, Melchinger AE, Boddupalli PM, Martinez L, Lopez LA, Chaikam V.

Theor Appl Genet. 2017 Jun;130(6):1113-1122. doi: 10.1007/s00122-017-2873-9. Epub 2017 Mar 18.

12.

Tapping the genetic diversity of landraces in allogamous crops with doubled haploid lines: a case study from European flint maize.

Böhm J, Schipprack W, Utz HF, Melchinger AE.

Theor Appl Genet. 2017 May;130(5):861-873. doi: 10.1007/s00122-017-2856-x. Epub 2017 Feb 13.

PMID:
28194473
13.

Metabolic robustness in young roots underpins a predictive model of maize hybrid performance in the field.

de Abreu E Lima F, Westhues M, Cuadros-Inostroza Á, Willmitzer L, Melchinger AE, Nikoloski Z.

Plant J. 2017 Apr;90(2):319-329. doi: 10.1111/tpj.13495. Epub 2017 Mar 14.

14.

Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection.

Müller D, Schopp P, Melchinger AE.

G3 (Bethesda). 2017 Mar 10;7(3):801-811. doi: 10.1534/g3.116.036582.

15.

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.

16.

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
17.

Domestication and Breeding of Jatropha curcas L.

Montes JM, Melchinger AE.

Trends Plant Sci. 2016 Dec;21(12):1045-1057. doi: 10.1016/j.tplants.2016.08.008. Epub 2016 Sep 14. Review.

PMID:
27639951
18.

Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale.

Marulanda JJ, Mi X, Melchinger AE, Xu JL, Würschum T, Longin CF.

Theor Appl Genet. 2016 Oct;129(10):1901-13. doi: 10.1007/s00122-016-2748-5. Epub 2016 Jul 7.

PMID:
27389871
19.

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.

20.

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.

21.

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.

22.

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
23.

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
24.

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
25.

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
26.

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.

27.

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
28.

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.

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.

30.

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
31.

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
32.

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.

33.

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.

34.

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.

35.

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.

36.

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
37.

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.

38.

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.

39.

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
40.

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.

41.

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.

PMID:
23737073
42.

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.

PMID:
23539086
43.

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.

44.

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.

45.

Unlocking the genetic diversity of maize landraces with doubled haploids opens new avenues for breeding.

Strigens A, Schipprack W, Reif JC, Melchinger AE.

PLoS One. 2013;8(2):e57234. doi: 10.1371/journal.pone.0057234. Epub 2013 Feb 22.

46.

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.

47.

Genomic prediction of dichotomous traits with Bayesian logistic models.

Technow F, Melchinger AE.

Theor Appl Genet. 2013 Apr;126(4):1133-43. doi: 10.1007/s00122-013-2041-9. Epub 2013 Feb 6.

PMID:
23385660
48.

Gametophytic and zygotic selection leads to segregation distortion through in vivo induction of a maternal haploid in maize.

Xu X, Li L, Dong X, Jin W, Melchinger AE, Chen S.

J Exp Bot. 2013 Feb;64(4):1083-96. doi: 10.1093/jxb/ers393. Epub 2013 Jan 23.

49.

Mapping quantitative trait loci for freezing tolerance in a recombinant inbred line population of Arabidopsis thaliana accessions Tenela and C24 reveals REVEILLE1 as negative regulator of cold acclimation.

Meissner M, Orsini E, Ruschhaupt M, Melchinger AE, Hincha DK, Heyer AG.

Plant Cell Environ. 2013 Jul;36(7):1256-67. doi: 10.1111/pce.12054. Epub 2013 Jan 17.

50.

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.

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