Format
Sort by
Items per page

Send to

Choose Destination

Search results

Items: 12

1.

Genetic Control and Geo-Climate Adaptation of Pod Dehiscence Provide Novel Insights into Soybean Domestication.

Zhang J, Singh AK.

G3 (Bethesda). 2019 Dec 13. pii: g3.400876.2019. doi: 10.1534/g3.119.400876. [Epub ahead of print]

2.

Identification and Genetic Characterization of Soybean Accessions Exhibiting Antibiosis and Antixenosis Resistance to Aphis glycines (Hemiptera: Aphididae).

Natukunda MI, Parmley KA, Hohenstein JD, Assefa T, Zhang J, MacIntosh GC, Singh AK.

J Econ Entomol. 2019 May 22;112(3):1428-1438. doi: 10.1093/jee/toz017.

PMID:
30768167
3.

Genome-wide Scan for Seed Composition Provides Insights into Soybean Quality Improvement and the Impacts of Domestication and Breeding.

Zhang J, Wang X, Lu Y, Bhusal SJ, Song Q, Cregan PB, Yen Y, Brown M, Jiang GL.

Mol Plant. 2018 Mar 5;11(3):460-472. doi: 10.1016/j.molp.2017.12.016. Epub 2018 Jan 2.

4.

Genetic Architecture of Charcoal Rot (Macrophomina phaseolina) Resistance in Soybean Revealed Using a Diverse Panel.

Coser SM, Chowda Reddy RV, Zhang J, Mueller DS, Mengistu A, Wise KA, Allen TW, Singh A, Singh AK.

Front Plant Sci. 2017 Sep 21;8:1626. doi: 10.3389/fpls.2017.01626. eCollection 2017.

5.

Main and epistatic loci studies in soybean for Sclerotinia sclerotiorum resistance reveal multiple modes of resistance in multi-environments.

Moellers TC, Singh A, Zhang J, Brungardt J, Kabbage M, Mueller DS, Grau CR, Ranjan A, Smith DL, Chowda-Reddy RV, Singh AK.

Sci Rep. 2017 Jun 15;7(1):3554. doi: 10.1038/s41598-017-03695-9.

6.

Leveraging genomic prediction to scan germplasm collection for crop improvement.

de Azevedo Peixoto L, Moellers TC, Zhang J, Lorenz AJ, Bhering LL, Beavis WD, Singh AK.

PLoS One. 2017 Jun 9;12(6):e0179191. doi: 10.1371/journal.pone.0179191. eCollection 2017.

7.

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean.

Naik HS, Zhang J, Lofquist A, Assefa T, Sarkar S, Ackerman D, Singh A, Singh AK, Ganapathysubramanian B.

Plant Methods. 2017 Apr 8;13:23. doi: 10.1186/s13007-017-0173-7. eCollection 2017.

8.

Computer vision and machine learning for robust phenotyping in genome-wide studies.

Zhang J, Naik HS, Assefa T, Sarkar S, Reddy RV, Singh A, Ganapathysubramanian B, Singh AK.

Sci Rep. 2017 Mar 8;7:44048. doi: 10.1038/srep44048.

9.

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity.

Jubery TZ, Shook J, Parmley K, Zhang J, Naik HS, Higgins R, Sarkar S, Singh A, Singh AK, Ganapathysubramanian B.

Front Plant Sci. 2017 Jan 19;7:2066. doi: 10.3389/fpls.2016.02066. eCollection 2016.

10.

Genome-wide association and epistasis studies unravel the genetic architecture of sudden death syndrome resistance in soybean.

Zhang J, Singh A, Mueller DS, Singh AK.

Plant J. 2015 Dec;84(6):1124-36. doi: 10.1111/tpj.13069.

11.

Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max).

Zhang J, Song Q, Cregan PB, Jiang GL.

Theor Appl Genet. 2016 Jan;129(1):117-30. doi: 10.1007/s00122-015-2614-x. Epub 2015 Oct 30.

12.

Genome-wide association study for flowering time, maturity dates and plant height in early maturing soybean (Glycine max) germplasm.

Zhang J, Song Q, Cregan PB, Nelson RL, Wang X, Wu J, Jiang GL.

BMC Genomics. 2015 Mar 20;16:217. doi: 10.1186/s12864-015-1441-4.

Supplemental Content

Loading ...
Support Center