发布人:     发布日期: 2021-03-22    浏览次数:

教育及工作经历

11/2020 – 至今            副教授，ylg8099官方网站

01/2018 – 08/2020      Associate Scientist, 爱荷华州立大学，美国

12/2014 – 01/2018      博后, 爱荷华州立大学，美国

09/2010 – 12/2014      植物学-遗传育种，博士，南达科他州立大学，美国

09/2006 – 05/2009      科研助理，香港大学

09/2003 – 07/2006      微生物学，硕士，厦门大学

09/1999 – 07/2003      水产养殖，学士，集美大学

研究方向

从事大豆基因组学和遗传育种的相关研究，主要包括：1）挖掘和利用大豆自然群体中优异的等位变异改良大豆品种。 2）利用新兴技术，如高通量表型鉴定技术、全基因组预测、快速育种技术、基因编辑等技术加快实现大豆育种目标；3）大豆驯化的遗传基础。野生大豆的驯化过程是其适应早期农业生产的过程，其植株形态，生理特征都发生了巨大的变化。理解这些变化背后的遗传基础有助于我们更好的利用野生大豆种质资源。（常年招博后，欢迎加盟！Currently, we do NOT have open positions for internatioanl applicants. 联系方式：zjp@njau.edu.cn）

教学

CROP6201  数量遗传学（国际研究生）

CROP7219  作物遗传和育种导论 参与（研究生）

CROP3213  生物统计和实验设计 （本科生）

科研项目

1. 崖州湾水稻和大豆高通量表型监测与精准育种决策支持系统研发。海南崖州湾种子实验室揭榜挂帅项目，2021-2024，参与（共340万）

2. 大豆产量相关性状精准鉴定。科技部国家重点研发计划，2021-2024，参与（100万）

3. 科研启动经费。ylg8099官方网站高层次人才引进项目，2020-11，主持（50万）

4. 我国西南大豆重要品质性状的全基因组关联分析及关键候选基因鉴定。国家自然科学基金委员会地区科学基金项目，2019-2022，参与（共40万）

5. Using engineering tools to identify and quantify biotic and abiotic stresses in soybean for customizable agriculture production. 2015-2018, Iowa Soybean Association. Co-PI (total amount $142,824).

育成大豆品种

1. Contributed to the development of IAS19C3, IAS25C1, and IAS31C1, commercialized in 2020 at ISU。

2. Jiang G.L., Scott R.A., Green M., Wang X., Bhusal S. and Zhang J. (2014) Registration of ‘Brookings’ Soybean with High Yield and Good Quality. Journal of Plant Registrations. 8:2:139-144.

3. Jiang G.L., Scott R.A., Green M., Wang X., Bhusal S., Zhang J. and Hall N. (2014) ‘Codington’, a High-Yielding, High-Quality, Large-Seeded Soybean. Journal of Plant Registrations 8:1:13-17.

4. Jiang G.L., Scott R.A., Green M., Wang X., Bhusal S., Zhang J., Hall N., Agoub M. and Tirumalaraju S. (2014) Registration of ‘Roberts’ Soybean with High Yield and Good Quality. Journal of Plant Registrations 8:1:18-21.

期刊论文

(Google Scholar: https://scholar.google.com/citations?hl=en&user=8nBMTpMAAAAJ)

1. Shook J., Zhang J., Jones S., Singh Arti, Diers B., Singh Asheesh, (2020) Meta-GWAS for quantitative trait loci identification in soybean. (Under review)

2. Assefa T., Zhang J., Chowda-Reddy R.V., Singh A., O’Rourke J. A., Graham M.A., and Singh A.K. (2020) Deconstructing the genetic architecture of iron deficiency chlorosis in soybea  n using genome-wide approaches. BMC Plant Biology 20 (1), 42.

3. Zhang J.* and Singh A.K*. (2020) Genetic control and geo-climate adaption of pod dehiscence provide novel insights into soybean domestication. G3, 10(2), 545-554 https://doi.org/10.1534/g3.119.400876 (*Co-corresponding author)

4. Natukunda M.I., Parmley K.A., Hohenstein J.D., Mamo T., Zhang J., MacIntosh G.C. and Singh A.K. (2019) Identification and Genetic Characterization of Soybean Accessions Exhibiting Antibiosis and Antixenosis Resistance to Soybean Aphids. Journal of Economic Entomology, 112(3):1428-1438.

5. Jiang G.L., Chen P., Zhang J., Florez-Palacios L., Zeng A., Wang X., Bowen R.A., Miller A., and Berry H. (2018) Genetic Analysis of Sugar Composition and Its Relationship with Protein, Oil and Fiber in Soybean. Crop Science, 58(6), 2413-2421.

6. Gao T., Emadi H., Saha H., Zhang J. Lofquist A., Singh A., Ganapathysubramanian B., Sarkar S., Singh A.K., and Bhattacharya S. (2018) A Novel Multirobot System for Plant Phenotyping. Robotics, 7(4), 61.

7. Zhang J.*, Wang X.*, Lu Y., Bhussal S., Song Q., Cregan P.B., Yen Y., Brown M. and Jiang G.L. (2018) Genome-wide scan for seed composition provides insights into the improvement of soybean quality and the impacts of domestication and modern breeding. Molecular Plant, 2018, 11(3):460-472. (*Co-first author)

8. Coser S., Chowda R.R.V., Zhang J., Mueller D.S., Mengistu A., Wise K.A., Allen T.W., Singh A. and Singh A.K. (2017) Genetic architecture of Charcoal Rot (Macrophomina phaseolina) Resistance in Soybean revealed using a diverse panel. Front. Plant Sci. 10.3389/fpls.2017.01626.

9. Moellers T.C., Singh A., Zhang J., Brungardt J., Kabbage M., Mueller D.S., Grau C.R., Ranjan A., Smith D.L., Chowda-Reddy R.V., Singh A.K. (2017) Main and epistatic loci studies in soybean for Sclerotinia sclerotiorum resistance reveal multiple modes of resistance in multi-environments. Scientific Reports 7, 3554.

10. De Azevedo Peixoto L., Moellers T.C., Zhang J., Lorenz A.J., Bhering L.L., Beavis W.D. and Singh A.K. (2017) Application of Genomic Prediction to Identify Useful Genetic Sources of Resistance in a Soybean Germplasm Collection. PLOS ONE 12(6):e0179191.

11. Naik H.S., Zhang J., Lofquist A., Assefa T., Sarkar S., Ackerman D., Singh A., Singh A.K. and Ganapathysubramanian B. (2017) A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant Methods, 13:23.

12. Zhang J., Naik H.S., Assefa T., Sarkar S., Chowda-Reddy R.V., Singh A., Ganapathysubramanian B. and Singh A.K. (2017) Computer vision and machine learning for robust phenotyping in genome-wide studies. Scientific Reports 7, 44048.

13. Jubery T., Shook J., Parmley K., Zhang J., Naik H., Higgins R., Sarkar S., Singh A., Singh A. and Ganapathysubramanian B. (2017) Deploying Fourier coefficients to unravel soybean canopy diversity. Front. Plant Sci. 7:2066.

14. Zhang J., Song Q., Cregan P.B., and Jiang G.L. (2016) Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theor. Appl. Genet. 129.1: 117-130.

15. Zhang J., Singh A., Mueller D.S., and Singh A.K. (2015) Genome-wide association and epistasis studies unravel the genetic architecture of sudden death syndrome resistance in soybean. Plant J. 84: 1124–1136.

16. Zhang J., Song Q., Cregan P.B., Nelson R.L., Wang X., Wu J., and Jiang G.L. (2015) Genome-wide association study for flowering time, maturity dates and plant height in early mature soybean (Glycine max) germplasm. BMC Genomics 16: 217-227.

17. Wang X., Jiang G.L., Song Q., Cregan P.B., Scott R.A., Zhang J., Yang Y., and Brown M. (2014) Quantitative trait locus analysis of seed sulfur-containing amino acids in two recombinant inbred line populations of soybean. Euphytica 1-13.

18. Xiao S., Li H.Y., Zhang J., Chan S.W. and Chye M.L. (2008) Arabidopsis acyl-CoA-binding proteins ACBP4 and ACBP5 are subcellularly localized to the cytosol and ACBP4 depletion affects membrane lipid composition. Plant Mol. Biol. 68:571-583.

19. 张焦平，江良荣，张凯，黄建勋，黄育民 (2006) 水稻 (Oryza sativa L.)开花期QTL上位效应和环境互作分析. 分子植物育种 4(3): 351-357.

20. 张焦平，张子平，邹志华，王艺磊 (2004) 日本对虾（Penaeus japonics）性别差异基因GD13 cDNA 5‘ 端的克隆 . 厦门大学学报（自然科学版）43(6), 847-851.