Dr Seema Yadav
Dr

Seema Yadav

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Background

Seema Yadav is Postdoctoral Fellow with Centre for Animal Science at Queensland Alliance for Agriculture and Food Innovation. Her Ph.D. project was focused on implementing genomic selection to accelerate genetic gains in Australian sugarcane breeding programs. Before joining the UQ, she was working as an international consultant with the Quantitative Genetics cluster at the International rice research institute, Philippines. She has double master's degrees in Mathematics and Statistics. Her research interests include developing novel genomic prediction methods, specifically their ability to capture G x E interaction effects. She had deep interest in machine learning models and optimization techniques within this domain.

Availability

Dr Seema Yadav is:
Available for supervision

Qualifications

  • Doctor of Philosophy of Quantitative Genetics, The University of Queensland

Research interests

  • Quantitative genetics

  • Genomic prediction

  • Machine learning models

  • Operational research

Search Professor Seema Yadav’s works on UQ eSpace

8 works between 2020 and 2023
All (8) Journal Article (7) Other Outputs (1)

1 - 8 of 8 works

2023

Journal Article

Use of continuous genotypes for genomic prediction in sugarcane

Yadav, Seema, Ross, Elizabeth M., Wei, Xianming, Liu, Shouye, Nguyen, Loan To, Powell, Owen, Hickey, Lee T., Deomano, Emily, Atkin, Felicity, Voss‐Fels, Kai P. and Hayes, Ben J. (2023). Use of continuous genotypes for genomic prediction in sugarcane. The Plant Genome, 17 (1) e20417, e20417. doi: 10.1002/tpg2.20417

Use of continuous genotypes for genomic prediction in sugarcane

2023

Journal Article

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies

Yadav, Seema, Ross, Elizabeth M., Wei, Xianming, Powell, Owen, Hivert, Valentin, Hickey, Lee T., Atkin, Felicity, Deomano, Emily, Aitken, Karen S., Voss-Fels, Kai P. and Hayes, Ben J. (2023). Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies. Frontiers in Plant Science, 14 1260517, 1260517. doi: 10.3389/fpls.2023.1260517

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies

2023

Journal Article

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits

Chen, Chensong, Powell, Owen, Dinglasan, Eric, Ross, Elizabeth M., Yadav, Seema, Wei, Xianming, Atkin, Felicity, Deomano, Emily and Hayes, Ben J. (2023). Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits. The Plant Genome, 16 (4) e20390, 1-13. doi: 10.1002/tpg2.20390

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits

2023

Other Outputs

Optimising genomic selection for sugarcane

Yadav, Seema (2023). Optimising genomic selection for sugarcane. PhD Thesis, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland. doi: 10.14264/6d283df

Optimising genomic selection for sugarcane

2022

Journal Article

Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane

Yadav, Seema, Ross, Elizabeth, Wei, Xianming, Powell, Owen, Hivert, Valentin, Hickey, Lee T., Atkin, Felicity, Deomano, Emily, Aitken, Karen S., Voss-Fels, Kai P. and Hayes, Ben J. (2022). Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane.

Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane

2021

Journal Article

A linkage disequilibrium-based approach to position unmapped SNPs in crop species

Yadav, Seema, Ross, Elizabeth M., Aitken, Karen S., Hickey, Lee T., Powell, Owen, Wei, Xianming, Voss-Fels, Kai P. and Hayes, Ben J. (2021). A linkage disequilibrium-based approach to position unmapped SNPs in crop species. BMC Genomics, 22 (1) 773, 1-9. doi: 10.1186/s12864-021-08116-w

A linkage disequilibrium-based approach to position unmapped SNPs in crop species

2021

Journal Article

Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

Yadav, Seema, Wei, Xianming, Joyce, Priya, Atkin, Felicity, Deomano, Emily, Sun, Yue, Nguyen, Loan T., Ross, Elizabeth M., Cavallaro, Tony, Aitken, Karen S., Hayes, Ben J. and Voss-Fels, Kai P. (2021). Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects. Theoretical and Applied Genetics, 134 (7), 2235-2252. doi: 10.1007/s00122-021-03822-1

Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

2020

Journal Article

Accelerating genetic gain in sugarcane breeding using genomic selection

Yadav, Seema, Jackson, Phillip, Wei, Xianming, Ross, Elizabeth M., Aitken, Karen, Deomano, Emily, Atkin, Felicity, Hayes, Ben J. and Voss-Fels, Kai P. (2020). Accelerating genetic gain in sugarcane breeding using genomic selection. Agronomy, 10 (4) 585, 1-21. doi: 10.3390/agronomy10040585

Accelerating genetic gain in sugarcane breeding using genomic selection

Availability

Dr Seema Yadav is:
Available for supervision

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Supervision history

Current supervision

Enquiries

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